Texture in Food: Solid Foods v. 2

Texture in food Volume 2: Solid foods

Related titles from Woodhead’s food science, technology and nutrition list: Texture in food Volume 1: Semi-solid foods (ISBN 1 85573 673 X) Understanding and controlling the texture of semi-solid foods such as yoghurt and ice cream is a complex process. With a distinguished international team of contributors, this important collection summarises some of the most significant research in this area. The first part of the book looks at the behaviour of gels and emulsions, how they can be measured and their textural properties improved. The second part of the collection discusses the control of texture in particular foods such as yoghurt, ice cream, spreads and sauces. Understanding and measuring the shelf-life of food (ISBN 1 85573 732 9) The shelf-life of a product is critical in determining both its quality and profitability. This important collection reviews the key factors in determining shelf-life and how they can be measured. Taints and off-flavours in foods (ISBN 1 85573 449 4) Taints and off-flavours are a major problem for the food industry. The first part of this important collection reviews the major causes of taints and off-flavours, from oxidative rancidity and microbiologically-derived off-flavours, to packaging materials as a source of taints. The second part of the book discusses the range of techniques for detecting taints and off-flavours, from sensory analysis to instrumental techniques, including the development of new rapid, on-line sensors. Details of these books and a complete list of Woodhead’s food science, technology and nutrition titles can be obtained by: • visiting our web site at www.woodhead-publishing.com • contacting Customer services (e-mail: [email protected]; fax: +44 (0) 1223 893694; tel.: +44 (0) 1223 891358 ext.30; address: Woodhead Publishing Ltd, Abington Hall, Abington, Cambridge CB1 6AH, England) Selected food science and technology titles are also available in electronic form. Visit our web site (www.woodhead-publishing.com) to find out more. If you would like to receive information on forthcoming titles in this area, please send your address details to: Francis Dodds (address, tel. and fax as above; e-mail: [email protected]). Please confirm which subject areas you are interested in.

Texture in food Volume 2: Solid foods Edited by David Kilcast

CRC Press Boca Raton Boston New York Washington, DC

WOODHEAD

PUBLISHING LIMITED Cambridge England

Published by Woodhead Publishing Limited, Abington Hall, Abington Cambridge CB1 6AH, England www.woodhead-publishing.com Published in North America by CRC Press LLC, 2000 Corporate Blvd, NW Boca Raton FL 33431, USA First published 2004, Woodhead Publishing Ltd and CRC Press LLC © 2004, Woodhead Publishing Ltd The authors have asserted their moral rights. This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. Reasonable efforts have been made to publish reliable data and information, but the authors and the publishers cannot assume responsibility for the validity of all materials. Neither the authors nor the publishers, nor anyone else associated with this publication, shall be liable for any loss, damage or liability directly or indirectly caused or alleged to be caused by this book. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming and recording, or by any information storage or retrieval system, without permission in writing from the publishers. The consent of Woodhead Publishing and CRC Press does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from Woodhead Publishing or CRC Press for such copying. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. Library of Congress Cataloging in Publication Data A catalog record for this book is available from the Library of Congress. Woodhead Publishing ISBN 1 85573 724 8 (book) 1 85573 836 8 (e-book) CRC Press ISBN 0-8493-2537-4 CRC Press order number: WP2537 The publisher’s policy is to use permanent paper from mills that operate a sustainable forestry policy, and which have been manufactured from pulp which is processed using acid-free and elementary chlorine-free practices. Furthermore, the publisher ensures that the text paper and cover board used have met acceptable environmental accreditation standards. Typeset by Replika Press Pvt Ltd, India. Printed by TJ International Ltd, Padstow, Cornwall, England.

Contents

Contributor contact details ................................................................... List of abbreviations ..............................................................................

xiii xix

Consumers, texture and food quality ..................................

1

Part I

1 Measuring consumer perceptions of texture: an overview .......................................................................................... D. Kilcast, Leatherhead Food International, UK 1.1 Introduction: texture and food quality ................................. 1.2 Perception and sensory assessment of food texture .................................................................................... 1.3 Tests and test procedures ...................................................... 1.4 Instrumental measurement of texture .................................. 1.5 In vivo texture measurement ................................................ 1.6 Future developments ............................................................. 1.7 Conclusions ........................................................................... 1.8 References ............................................................................. 2 Consumers and texture: understanding their perceptions and preferences .............................................................................. J-F. Meullenet, University of Arkansas, USA 2.1 Introduction: problems with consumer descriptions of texture .................................................................................... 2.2 Investigating consumer descriptions of texture ................... 2.3 Tests and test procedures ...................................................... 2.4 Understanding consumer preferences .................................. 2.5 Challenges to understanding consumer preferences ............................................................................ 2.6 Future trends ......................................................................... 2.7 Conclusions ........................................................................... 2.8 References .............................................................................

3 3 6 8 13 20 23 26 28

33

33 34 36 39 44 48 50 51

vi

Contents

3 Texture and mastication ............................................................... A. C. Smith, Institute of Food Research, UK 3.1 Introduction ........................................................................... 3.2 The mastication process ....................................................... 3.3 Measuring mastication .......................................................... 3.4 Chewing, swallowing, salivation and bolus formation ....... 3.5 Future trends ......................................................................... 3.6 Mastication and particular foods .......................................... 3.7 Reviews ................................................................................. 3.8 Acknowledgement ................................................................ 3.9 References .............................................................................

53 53 55 56 66 71 75 76 76 77

4 Understanding and measuring consumer perceptions of crispness ..................................................................................... P. Mallikarjunan, Virginia Polytechnic Institute and State University, USA 4.1 Introduction ........................................................................... 4.2 Characterization and determination of crispness ................ 4.3 Methods of data correlation, evaluation and analysis ......... 4.4 Case-study: breaded chicken nuggets .................................. 4.5 Future trends ......................................................................... 4.6 References .............................................................................

82 85 91 94 103 103

Instrumental techniques for analysing texture ................

107

5 Force/deformation techniques for measuring texture .............. R. Lu and J. A. Abbott, USDA Agricultural Research Service, USA 5.1 Introduction ........................................................................... 5.2 Mechanical characterization of solid foods ......................... 5.3 Destructive measurements .................................................... 5.4 Non-destructive measurements ............................................ 5.5 Conclusions ........................................................................... 5.6 References .............................................................................

109

6 Sound input techniques for measuring texture ......................... L. M. Duizer, Massey University, New Zealand 6.1 Introduction ........................................................................... 6.2 Sound and its detection: what is sound? ............................. 6.3 Destructive testing ................................................................ 6.4 Non-destructive testing ......................................................... 6.5 Application of sound measurement techniques ................... 6.6 Future trends ......................................................................... 6.7 Sources of further information and advice .......................... 6.8 References .............................................................................

146

Part II

82

109 110 118 128 138 139

146 147 148 155 158 162 162 163

Contents vii

7 Near infrared (NIR) diffuse reflectance in texture measurement .................................................................................. S. Millar, Campden and Chorleywood Food Research Association, UK 7.1 Introduction ........................................................................... 7.2 Application of NIR to cereals and their products ............... 7.3 Application of NIR to fruit and vegetables ......................... 7.4 Application of NIR to meat ................................................. 7.5 Application of NIR to other foods ....................................... 7.6 Conclusions and future trends .............................................. 7.7 Sources of further information ............................................. 7.8 References ............................................................................. 8 Nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) in texture measurement .................. A. K. Thybo, A. H. Karlsson, H. C. Bertram and H. J. Andersen, Danish Institute of Agricultural Sciences, P. M. Szczypinski, Technical University of Lodz, Poland and S. Donstrup, Aarhus University Hospital, Denmark 8.1 Introduction ........................................................................... 8.2 Methods and analysis ........................................................... 8.3 Application of NMR: texture determination of solid foods ...................................................................................... 8.4 Application of MRI: texture determination of solid foods ...................................................................................... 8.5 Future trends ......................................................................... 8.6 References ............................................................................. 9 Modelling food texture ................................................................. L. M. M. Tijskens and H. Luyten, Wageningen University and Research Centre, The Netherlands 9.1 Introduction ........................................................................... 9.2 Factors affecting texture ....................................................... 9.3 Effects of enzymes on texture .............................................. 9.4 Applying models to predict texture ..................................... 9.5 Future trends ......................................................................... 9.6 Notation ................................................................................. 9.7 References ............................................................................. Part III Understanding and improving the texture of particular foods .................................................................................... 10 Plant structure and fruit and vegetable texture ........................ K. W. Waldron, Institute of Food Research, UK 10.1 Introduction ...........................................................................

167

167 170 173 176 178 179 180 180

184

184 187 189 195 199 200 205

205 211 218 222 233 234 235

239 241 241

viii Contents

10.2 Measurement of texture ........................................................ 10.3 Plant structure ....................................................................... 10.4 Cellular basis of crispness, juiciness and mealiness in fruit tissue ............................................................................. 10.5 Cellular stability during processing ..................................... 10.6 Improving cell adhesion ....................................................... 10.7 Future trends ......................................................................... 10.8 Acknowledgements ............................................................... 10.9 References ............................................................................. 11 Plant compounds and fruit texture: the case of pear .............. T. Kojima, S. Fujita and M. Tanaka, Saga University, Japan and P. Sirisomboon, King Mongkut’s Institute of Technology Ladkrabang, Thailand 11.1 Introduction: variations in pear texture ............................... 11.2 Measuring and modelling fruit firmness ............................. 11.3 Chemical compounds affecting firmness: the example of Japanese pear .................................................................... 11.4 The effect of constituents on fruit texture .................................................................................... 11.5 Use of near infrared spectroscopy (NIR) to evaluate textural properties ................................................................. 11.6 Future trends ......................................................................... 11.7 Sources of further information and advice .......................... 11.8 Acknowledgement ................................................................ 11.9 References ............................................................................. 12 Controlling the texture of fruit and vegetables: the role of oxidising enzymes ..................................................................... H. J. Wichers and C. Boeriu, Agrotechnology and Food Innovations, The Netherlands 12.1 Introduction: distribution of polyphenoloxidases (PPOs) and peroxidases (PODs) in plants and plant cells .............................................................................. 12.2 Biochemical and physiological role of PPOs and PODs ..................................................................................... 12.3 PPOs and PODs: structure and mechanisms of action ..................................................................................... 12.4 PPOs, PODs and texture development ................................ 12.5 Controlling PPO and POD activity ...................................... 12.6 PPOs and PODs: implications for food texture .................. 12.7 Future trends ......................................................................... 12.8 Sources of further information ............................................. 12.9 References .............................................................................

242 244 244 249 251 254 255 255 259

259 262 270 275 279 283 288 289 290

295

295 296 300 304 307 311 311 312 312

Contents

13 Improving fruit and vegetable texture by genetic transformation ............................................................................... G. Tucker, University of Nottingham, UK 13.1 Introduction ........................................................................... 13.2 Tools of genetic modification .............................................. 13.3 Approaches to the manipulation of texture: the tomato .................................................................................... 13.4 Other approaches to the manipulation of texture ................ 13.5 Future trends ......................................................................... 13.6 References ............................................................................. 14 Raw materials quality and the texture of processed vegetables ........................................................................................ J. B. Adams, formerly of CCFRA, UK 14.1 Introduction ........................................................................... 14.2 Vegetable texture determined by starch ............................... 14.3 Vegetable texture determined by cell wall polysaccharides ..................................................................... 14.4 Vegetable texture affected by phenolic reactions ................ 14.5 Future trends ......................................................................... 14.6 Sources of further information and advice .......................... 14.7 References ............................................................................. 15 Improving the texture of processed vegetables by vacuum infusion ............................................................................ R. Saurel, University of Lyon, France 15.1 Introduction ........................................................................... 15.2 Vacuum infusion technology ................................................ 15.3 Applications to improve texture ........................................... 15.4 Future trends ......................................................................... 15.5 Sources of further information and advice .......................... 15.6 References ............................................................................. 16 Improving the texture of frozen fruit: the case of berries ............................................................................................. M. Suutarinen and K. Autio, VTT Biotechnology, Finland 16.1 Introduction: the effects of freezing and thawing on berry texture .................................................................... 16.2 Maintaining texture: conventional pre-freezing treatments .............................................................................. 16.3 Maintaining texture: alternative pre-freezing treatments .............................................................................. 16.4 Application: frozen berries and jams ................................... 16.5 Future trends ......................................................................... 16.6 References .............................................................................

ix

321 321 323 327 333 335 336

342 342 342 349 354 358 359 360

364 364 365 373 383 383 384

388

388 390 393 400 405 405

x

Contents

17 Improving the texture of processed fruit: the case of olives ........................................................................................... I. Mafra, University of Porto and M. A. Coimbra, University of Aveiro, Portugal 17.1 Introduction: the texture of table olives............................... 17.2 Factors affecting the texture quality of raw olives .............. 17.3 Influence of processing on table olives ............................... 17.4 Improving texture .................................................................. 17.5 Future trends ......................................................................... 17.6 Sources of further information and advice .......................... 17.7 References .............................................................................

410

410 412 418 425 426 428 430

18 Improving the texture of bread ................................................... S. P. Cauvain, CCFRA, UK 18.1 Introduction ........................................................................... 18.2 Textural characteristics of bread and other cereal-based foods ................................................................ 18.3 Definitions of texture ........................................................... 18.4 Measuring texture ................................................................. 18.5 Influence of processing and storage .................................... 18.6 Improving texture .................................................................. 18.7 Future trends ......................................................................... 18.8 Sources of further information and advice .......................... 18.9 References .............................................................................

432

19 Analysing and improving the texture of cooked rice ............... S. K. Kim, Dankook University and C. O. Rhee, Chonnam National University, Korea 19.1 Introduction ........................................................................... 19.2 Criteria for evaluating rice quality ....................................... 19.3 Hydration of rice ................................................................... 19.4 Factors affecting cooking quality ......................................... 19.5 Testing texture quality .......................................................... 19.6 Problems and challenges ...................................................... 19.7 Sources of further information and advice .......................... 19.8 References .............................................................................

451

20 Improving the texture of pasta .................................................... B. A. Marchylo and J. E. Dexter, Canadian Grain Commission and L. J. Malcolmson, Canadian International Grains Institute 20.1 Introduction ........................................................................... 20.2 Measuring the texture of cooked pasta ................................ 20.3 Influence of raw materials .................................................... 20.4 Influence of processing ........................................................ 20.5 Trends in consumer preference ............................................ 20.6 References .............................................................................

475

432 434 436 437 442 445 447 448 449

451 452 455 459 464 469 470 470

475 478 484 490 492 494

Contents

xi

21 Improving the texture of fried food ............................................ C-J. Shieh and C-Y. Chang, Da-Yeh University and C-S. Chen, Chao-Yang University of Technology, Taiwan 21.1 Introduction ........................................................................... 21.2 Measuring texture ................................................................. 21.3 Factors influencing texture ................................................... 21.4 The use of response surface methodology (RSM) .............. 21.5 A case study: fried gluten balls ........................................... 21.6 Conclusions ........................................................................... 21.7 References .............................................................................

501

Index ................................................................................................

525

501 501 504 508 514 521 522

Contributor contact details

(* = main point of contact)

Chapter 1

Chapter 3

Dr D. Kilcast Leatherhead Food International Randalls Road Leatherhead Surrey KT22 7RY UK

Dr A. C. Smith Food Quality & Materials Science Division Institute of Food Research Norwich Research Park, Colney Norwich NR4 7UA UK

Tel: +44 (0) 1372 822321 Fax: +44 (0) 1372 386228 E-mail: [email protected]

Tel: +44 (0) 1603 255286 Fax: +44 (0) 1603 507723 E-mail: [email protected]

Chapter 2

Chapter 4

Professor J-F. Meullenet Department of Food Science University of Arkansas 2650 N. Young Avenue Fayetteville, AR 72704 USA

Dr P. Mallikarjunan Biological Systems Engineering Department 312 Seitz Hall Virginia Polytechnic Institute and State University Blacksburg VA 24060 USA

Tel: +1 (479) 236 1926 Fax: +1 (479) 575 6936 E-mail: [email protected]

Tel: +1 (540) 231 7937 Fax: +1 (540) 231 3199 E-mail: [email protected]

xiv

Contributor contact details

Chapter 5 Dr Renfu Lu* USDA ARS Sugarbeet and Bean Research Unit 224 Farrall Hall Michigan State University East Lansing, MI 48824 USA Tel: +1 (517) 432-8062 Fax: +1 (517) 337-6782 E-mail: [email protected] Dr Judith A. Abbot USDA ARS Produce Quality & Safety Laboratory Building 002, BARC-W 10300 Baltimore Avenue Beltsville, MD 20705-2350 USA Tel: +1 (301) 504 6128 Fax: +1 (301) 504 5107 E-mail: [email protected]

Chapter 6 Dr L. M. Duizer Institute of Food, Nutrition and Human Health Massey University Private Bag 102 904 NSMC Albany, Auckland New Zealand Tel: +64 09 443 9753 Fax: +64 09 443 9640 E-mail: [email protected]

Chapter 7 Dr S. Millar

Baking and Cereals Processing Department Campden & Chorleywood Food Research Association Chipping Campden Gloucestershire GL55 6LD UK Tel: +44 (0) 1386 842157 Fax: +44 (0) 1386 842150 E-mail: [email protected]

Chapter 8 Dr A. K. Thybo* Department of Food Science Danish Institute of Agricultural Sciences DK-5792 Aarslev Denmark Tel: +045 63 90 43 05 Fax: +045 63 90 43 95 E-mail: [email protected] Dr A. H. Karlsson, Dr H. C. Bertram and Dr H. J. Andersen Department of Food Science Danish Institute of Agricultural Sciences DK-8830 Tjele Denmark Tel: +045 89 99 19 00 Fax: +045 89 99 15 64 E-mail: [email protected] Dr S. Donstrup Aarhus University Hospital Department of Biomedical Engineering DK-8200 Aarhus N Denmark

Contributor contact details

Dr P. M. Szczypinski Institute of Electronics Technical University of Lodz 90-924 Lodz Poland Tel: +48 42 636 2238 Fax: +48 42 631 2638 E-mail: [email protected]

Chapter 9 L. M. M. Tijskens* and Dr H. Luyten Wageningen University and Research Centre Agrotechnology & Food Innovations PO Box 17 6700 AA Wageningen The Netherlands Tel: +31 317 475 303 Fax: +31 317 475 347 E-mail: [email protected] [email protected]

Chapter 10 Dr K. W. Waldron Institute of Food Research Norwich Research Park Colney Norwich NR4 7UA UK Tel: +44 1603 255385 Fax: +44 1603 507723 E-mail: [email protected]

xv

Chapter 11 Dr T. Kojima, Dr S. Fujita and Dr M. Tanaka Saga University Japan 840-8502 Tel: +81 952 288 750 Fax: +81 952 288 768 E-mail: [email protected] Dr P. Sirisomboon* Department of Agricultural Engineering Faculty of Engineering King Mongkut’s Institute of Technology Ladkrabang Bangkok 10520 Thailand Tel: +66 2737 300 ext 5120 Fax: +66 2326 4178 E-mail: [email protected]

Chapter 12 Dr H. J. Wichers and Dr C. Boeriu Agrotechnology & Food Innovations Bornsesteeg 59 6708 PD Wageningen The Netherlands Tel: +31 317 475228 Fax: +31 317 475347 E-mail: [email protected] [email protected]

Chapter 13 Professor G. Tucker University of Nottingham School of Biosciences

xvi Contributor contact details

Sutton Bonington Campus Loughborough Leicestershire LE12 5RD UK Tel: +44 (0) 1159 516 126 Fax: +44 (0) 1159 516 122 E-mail: [email protected]

Tel: +358 9 4565175 Fax: +358 9 4552103 E-mail: [email protected]

Chapter 17 Professor M. A. Coimbra* Department of Chemistry University of Aveiro PT 3810-193 Aveiro Portugal

Chapter 14 Dr J. B. Adams 5 Orchard View Draycott Moreton-in-Marsh Gloucestershire GL56 9LW UK Tel: +44 (01386) 700374 E-mail: [email protected]

Tel: +1 351 234 370 706 Fax: +1 351 234 370 084 E-mail: [email protected] Dr I. Mafra REQUIMTE Laboratory of Bromatology Faculty of Pharmacy University of Porto R Anibal Cunha, 164 PT 4050-047 Porto Portugal

Chapter 15 Dr R. Saurel University of Lyon Rue Henri de Boissieu 01 060 Bourg-en-Bresse Cedex 09 France

Tel: +351 22 2078902 Fax: +351 22 2003977 E-mail: [email protected]

Tel: +33 (0)4 74 45 52 52 Fax: +33 (0)4 74 45 52 53 E-mail:[email protected]

Chapter 16

Dr S. P. Cauvain Campden & Chorleywood Food Research Association Chipping Campden Gloucestershire GL55 6LD UK

Dr M. Suutarinen and Dr K. Autio* VTT Biotechnology PO Box 1500, 02044 VTT Finland

Tel: +44 (0) 1386 842 000 Fax: +44 (0) 1386 842 150 E-mail: [email protected]

Chapter 18

Contributor contact details

Chapter 19 Dr S. K. Kim* Dankook University Korea Tel: +82 2 709 2426 Fax: +82 2 790 2447 E-mail: [email protected] Dr C. O. Rhee Chonnam National University Korea

xvii

Dr L. J. Malcolmson Canadian International Grains Institute 1000-303 Main Strect Winnipeg MB R3C 3G7 Canada Tel: +1 (204) 983 8584 Fax: +1 (204) 983 2642 E-mail: [email protected]

Chapter 21

Tel: +82 62 530 2142 Fax: +82 530 2149 E-mail: [email protected]

Dr C-J. Shieh and Dr C-Y. Chang Department of Food Engineering Da-Yeh University Cheng-Hwa Taiwan 515

Chapter 20

E-mail: [email protected] [email protected]

Dr B. A. Marchylo* and Dr J. E. Dexter Grain Research Laboratory Canadian Grain Commission 1404-303 Main Street Winnipeg MB R3C 3G8 Canada Tel: +1 (204) 983 3320 Fax: +1 (204) 983 0724 E-mail: [email protected] [email protected]

Dr C-S. Chen* Department of Applied Chemistry Chao-Yang University of Technology 168 Gifeng E. Rd, Wufeng Taichung County Taiwan Fax: +886 4 2374 2341 E-mail: [email protected]

Abbreviations

AACC ADC AF AFC AIS AMG AOAC APX ASAE

American Association of Cereal Chemists Analogue to digital converter aqueous freezant alternative forced choice alcohol insoluble solids acoustic myography Association of Analytical Communities ascorbate peroxidase American Society of Agricultural Engineers

bpm

beats per minute

CA CCFRA CcP CDTA CVA CWC

controlled atmosphere Campden and Chorleywood Food Research Assn cytochrome C peroxidase 1,2-cyclohexanediaminetetraacetic acid canonical variate analysis Chinese water chestnut

DAC DATI DFD DHF DIS DMTA DOPA DP DSP

dynamic axial compression dual-attribute time-intensity dark, firm, dry dihydroxyfumaric acid dewatering impregnation soaking dynamic mechanical thermal analysis dihydroxyphenylananine degree of polymerisation deformation at skin puncture

E EMC

enzyme equilibrium moisture content

xx Abbreviations

EMG Endo-PG EPG Eth

electromyography endo-polygalacturonase electropalatography ethylene

FA F/D FES FFT FITC FMBRA FT-NIR FW

ferulic acid force/deformation functional electrical stimulation fast Fourier transform fluorescein isothiocyanate Flour Milling and Baking Research Association Fourier transform NIR fresh weight

GF GPIB GRL GS

general foods general purpose interface board Grain Research Laboratory glutein subunits

HDM HM HPLC HRP HT HTC HVK

hydrodynamic mechanism high methylated high performance liquid chromatography horseradish peroxidase high temperature hard-to-cook hard vitreous kernel

IAA ICF IDT IFR IQF ISO

indole-3-acetic acid immersion chilling and freezing iso-dityrosine impact force response individually quick frozen International Standards Organisation

KI

kinesiograph

LAM LiP LM LMW LT LVDT

labelled affective magnitude lignin peroxidase low methylated low molecular weight low temperature linear voltage displacement transducer

MA MARS

modified atmosphere multivariate adaptive regression splines

Abbreviations

MassL, MassR MLR MRI MT

masseter left, right multiple linear regression magnetic resonance imaging Magness-Taylor

NIR NIRS NMR NSP

near-infrared near-infrared reflectance spectroscopy nuclear magnetic resonance non-soluble pectin

OD OLS ORF OSP

osmotic dehydration ordinary least squares open reading frame oxalate soluble pectin

P PCA PEL PG PIHMI PLS PME POD POM PPO PSE PSP PVOD

product principal component analysis pectate lyase polygalacturonas paired increasing-height multiple-impacting partial least squares pectin methylesterase peroxidase proportional odds modelling polyphenoloxidase pale, soft, exudative polysterene pack pulsed vacuum osmotic dehydration

QC QDA QTL

quality control quantitatived descriptive analysis quantitative trait loci

Re RH RMS RMSEP RSM RSREG RVA

respiration relative humidity root-mean-square root mean square error of prediction response surface methodology response surface regression rapid visco analyser

S SAS SDP SE

substrate statistical analysis system symmetrised dot pattern softness equivalent

xxi

xxii

Abbreviations

SEM SEP SS

scanning electron microscope standard error of prediction soluble solids

TEM TempL, TempR T-I TOF TOM TP TPA

transmission electron microscopy temporalis left, temporalis right time-intensity time-of-flight total organic matter total pectin texture profile analysis

UHT UV

ultra high temperature ultraviolet

VI VMG VOD

vacuum infusion vibromyography vacuum osmotic dehydration

WB WSP WVP

Warner-Bratzler water-soluble pectin water vapour permeability

XET

xyloglucan endotransglycosylase

Part I Consumers, texture and food quality

1 Measuring consumer perceptions of texture: an overview D. Kilcast, Leatherhead Food International, UK

1.1

Introduction: texture and food quality

In prosperous societies, we have available an enormous and ever-increasing range of foods, and manufacturers find themselves in an intensely competitive situation. In less well-developed societies, hunger will be the constant driving force, and our diet will be determined by availability of any food that satisfies our basic nutritional needs. It is increasingly clear that if we are to understand what drives consumers’ choice of food, no single factor can be considered in isolation from others. For some years, psychology researchers have been developing models to understand consumer behaviour (e.g. Shepherd and Sparks, 1994). Although there are many possible circumstances under which non-sensory factors such as price and nutritional image can have dominant effects, the sensory characteristics of foods are central to their continued purchase. The importance of a holistic approach is also becoming more clear when the components of sensory perception are considered. During the sequences of actions that constitute food consumption, we perceive a whole range of different characteristics relating to the appearance, flavour and texture of the food. Numerous tools are available for investigating the sensory properties of foods, and the information required must be carefully defined if appropriate tools are to be selected. Systematic development of new products will inevitably depend on the use of different tools at different stages of the development cycle. 1.1.1 The human senses It is generally accepted that human beings have five senses in operation, namely sight, smell, taste, touch and hearing, although warmth, cold, movement

4

Texture in food

and pain may also be considered as senses of importance in a food context (Fig. 1.1). Foods are complex mixtures of chemical compounds, arranged into structural units. The perception of the sensory characteristics of foods results from the stimulation of all our senses to some extent by the physicochemical properties of the foods. The sensory characteristics of food are generally grouped into three categories, namely appearance, flavour and texture. These categories are, however, not independent of one another. For example, colour, which is obviously an important appearance characteristic, can be shown to have an influence on flavour perception; consumers will assign higher scores for flavour intensity to darker foods than to lighter foods. The interaction between appearance and flavour is referred to as ‘visual flavour’. Similarly, textural characteristics such as viscosity can influence the perception of flavour, and some flavour characteristics, e.g. acidity, can affect textural characteristics. One means of defining flavour, texture and appearance is by taking into account the fact that each can be attributed to the stimulation of one or possibly two of the senses. On this basis the International Standards Organisation (ISO, 1992) has proposed working definitions for flavour, texture and appearance, as given below. • Appearance: sensory characteristics of foods perceived largely by way of the visual sense. Input from other senses, especially smell, may contribute. • Flavour: the combination of taste and odour. Pain, heat, cold, tactile and visual sensations may also contribute. • Texture: sensory characteristics perceived largely by way of the senses of movement and touch. Input from other senses, especially vision and taste, may sometimes contribute. The above definitions give little information on how the senses are used in the perception of quality attributes. Appearance is sometimes, mistakenly, equated only with colour, and yet many other visual aspects of form, shape, translucency, etc., may influence our use of the visual senses. Taste (gustation)

Vision

Gustation

Olfaction

Trigeminal

Taste

Odour

Irritant

Touch

Hearing

FLAVOUR SOUND

APPEARANCE

TEXTURE

Fig. 1.1 Schematic diagram of the human senses and their operation in the perception of food quality.

Measuring consumer perceptions of texture: an overview

5

is strictly defined as the response by the tongue to soluble, involatile materials. These have classically been defined as four primary basic taste sensations: salt, sweet, sour and bitter, although umami, the sensation associated with monosodium glutamate, is now widely recognised as a basic taste. This list is frequently extended further to include sensations such as metallic and astringency. The taste receptors are organised groups of cells, known as taste buds, located within specialised structures called papillae. These are located mainly on the tip, sides and rear upper surface of the tongue. Taste stimuli are characterised by the relatively narrow range between the weakest and the strongest stimulants (ca 104), and are strongly influenced by factors such as temperature and pH (Meilgaard et al., 1999). The odour response is much more complex, and odours are detected as volatiles entering the nasal passage, either directly via the nose or indirectly through the retronasal path via the mouth. The odorants are sensed by the olfactory epithelium, which is located in the roof of the nasal cavity. Some 150–200 odour qualities have been recognised, and there is a very wide range (ca 1012) between the weakest and the strongest stimulants (Meilgaard et al., 1999). The odour receptors are easily saturated, and specific anosmia (blindness to specific odours) is common. It is thought that the wide range of possible odour responses contributes to variety in flavour perception. Both taste and odour stimuli can be detected only if they are released effectively from the food matrix during the course of mastication. The chemical sense corresponds to a pain response through stimulation of the trigeminal nerve. This is produced by chemical irritants such as ginger and capsaicin (from chilli), both of which give a heat response, and chemicals such as menthol and sorbitol, which give a cooling response. With the exception of capsaicin, these stimulants are characterised by high thresholds. The combined effect of the taste, odour and chemical responses gives rise to the sensation generally perceived as flavour, although these terms are often used loosely. Texture is perceived by the sense of touch, and comprises two components: somesthesis, a tactile, surface response from skin, and kinesthesis (or proprioception), which is a deep response from muscles and tendons. For many foods, visual stimuli will generate an expectation of textural properties. The touch stimuli themselves can arise from tactile manipulation of the food with the hands and fingers, either directly or through the intermediary of utensils such as a knife or spoon. Oral contact with food can occur through the lips, tongue, palate and teeth, all of which provide textural information.

1.1.2 Texture and food enjoyment Most studies which have investigated the importance of different sensory modalities on consumer acceptability conclude that flavour is the most important modality, followed by texture and then appearance (e.g. Moskowitz and Krieger, 1995). Such conclusions do not reflect the enormous efforts

6

Texture in food

that the food industry devotes to designing appealing textural characteristics, and to maintaining those characteristics to long-term production. Research with consumers in the USA carried out by Szczesniak and Kahn (1971) showed that awareness of texture lies at a subconscious level, and that textural properties are taken for granted. If the expectations of texture are violated, however, awareness of textural defects is accentuated, and texture becomes a focal point for criticism and rejection of the food. Expectations are being increasingly recognised as important factors in food choice by consumers (e.g. Vickers, 1991; Cardello, 1994).

1.1.3 The interactive role of texture In addition to its direct contribution to consumer acceptance, texture has a vitally important secondary effect, through modulation of flavour release. If flavour components are to be perceived, they must be released from the food matrix in order to reach the appropriate receptors. This release of flavour is intimately related to the way in which the food structure breaks down in the mouth, and consequently to both the initial texture of the food and the change in texture throughout mastication (Section 1.2). In addition, the structural factors that deliver a specific texture can also influence appearance characteristics, for example the glossy surface of chocolate confectionery.

1.1.4 Texture and product design Texture and food structure are inextricably linked; the micro- and macrostructural composition of foods will determine the sensory perception, and any change in structure carries the risk of changing perceived texture and violating consumer expectations. Industry therefore needs to take great care to ensure that products with key textural characteristics, such as snack foods and confectionery products, are manufactured to consistently high quality. This can present an enormous challenge for foods relying on primary components such as meat and vegetables that are naturally subject to high variability, and for any processed food manufactured on high-volume production lines. Product modifications, for example to produce low-fat variants, can introduce structural changes that can generate substantial textural modifications. Industry therefore needs methods to measure textural characteristics. However, designing suitable measurement systems requires an understanding of the mechanisms by which texture is perceived.

1.2 Perception and sensory assessment of food texture 1.2.1 Oral breakdown processes The importance of the interaction between the texture of foods and their perceived flavour can be clearly seen if the sequence of events during food

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consumption is considered. A strong expectation of the flavour and texture characteristics can be generated before the food is introduced into the mouth. As food enters the mouth, and is either bitten or manipulated between tongue and palate, catastrophic changes occur to the structure of the food that strongly influence the way in which tastants and odorants are released from it. Of particular importance are temperature increase (cold foods) or decrease (hot foods) and dilution by saliva. Salivary introduction also serves to lubricate the food bolus. The factors that influence such release are under active study (e.g. Overbosch et al., 1991), and include: • • • • •

rate and mode of production of new surface rate of production of saliva dissolution and dispersion of the food release of involatile tastants and volatile odorants transport of volatiles to the nasal cavity

1.2.2 Oral food management Even the complex picture of food breakdown described previously is an over-simplification of actual oral processes (Heath and Prinz, 1999), which show substantial differences for different foods and between individuals. The first bite by the incisors is an important stage which generates an early textural response that can influence subsequent chewing actions. The food is then transported between the cheek teeth, the jaw closes and main-sequence mastication starts. Hard foods are comminuted into particles, which are then formed into a soft bolus with saliva. Before this bolus is swallowed, the tongue is used to clear any remaining particles. Hutchings and Lillford (1988) have described two thresholds that need to be satisfied before swallowing is initiated: a food particle size threshold and a lubrication threshold. Finally, debris can be left in the mouth after swallowing, and further clearance and swallows may be necessary. 1.2.3 Mechanisms of texture perception Either of the two mechanisms described in Section 1.1.1 (proprioception and somesthesis) can operate during the mastication process, depending on the nature and texture of the food. The texture of solid foods is perceived primarily through proprioception, as the food is chopped by the incisors and ground by the molars. As the physical state of the food changes dramatically during mastication, both mechanisms can be operative. In particular, during the mastication of solid foods somesthesis becomes important as the bolus is formed and manipulated. However, even at the early stages of mastication (and prior to mastication, through use of fingers and lips), somesthesis can give important textural sensations. The texture of semi-solid and liquid foods is perceived primarily through somesthesis, from the action of the tongue and the soft palate, and is usually expressed by the term mouthfeel.

8

Texture in food

1.2.4 Sensory assessment of texture A sensory stimulus to a human subject produces a set of physiological sensations that are interpreted by the brain as perceptions. The sensations will vary considerably between subjects, reflecting the natural physiological differences in the population, and the interpretation by the brain will be modified by psychological differences. Perceptions are recorded as actions by the subject, or as verbal responses. This may be the selection of a certain sample from a group (as in a difference test), words to describe the nature of a perception, and numbers to measure the size of a perception. To ensure that the subjects’ responses relate as closely to their perception as possible, it is necessary to use carefully controlled environmental conditions and test procedures to carry out experiments. In particular, it is important to minimise the many sources of psychological bias that can produce unwanted influences on responses, and it is frequently necessary to minimise the spread of physiological responses characteristic of biological systems through careful panel selection and training procedures.

1.3 Tests and test procedures 1.3.1 Procedures A basic classification of the main sensory test procedures is shown in Fig. 1.2. The primary classification is into analytical tests and hedonic tests. Analytical tests use trained panels as a form of analytical instrument to generate information on the sensory properties of the food, whereas hedonic tests measure the response of untrained consumers to the sensory properties in terms of liking or acceptability. Different psychological procedures form

Sensory test methods

Analytical Difference tests

Descriptive tests

Paired comparison

Qualitative

Duo-trio

Profile

Hedonic Preference tests Paired preference

Acceptability tests Acceptability scaling

Triangle

Instrumental measures of perceived quality

Response of consumers to perceived quality

Fig. 1.2 Classification of the main sensory testing procedures.

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the basis for this test distinction, and the information produced is distinct but complementary. Discrimination tests are commonly used to establish, for example, if a formulation modification has changed the sensory quality. Although the tests can be a sensitive measure of change, and more recently have been generalised from their traditional use as difference tests to permit testing for similarity (Meilgaard et al., 1999), they generate relatively little information. The most informative analytical procedures identify the sensory properties that are characteristic of that food, and quantify the individual characteristics; these are termed sensory profile methods. Numerous sensory profile methods have been developed for foods, but the most important in practical use are the Texture Profile Method, Quantitative Descriptive Analysis and the Spectrum Method (reviewed in Kilcast, 1999).

1.3.2 Difference tests Paired comparison test In the most common form of the test, two coded samples are presented either sequentially or simultaneously in a balanced presentation order (i.e. AB and BA). There are two variations on the test. In the directional difference variant, the panellists are asked to choose the sample with the greater or lesser amount of a specified characteristic. The panellists are usually instructed to make a choice (forced-choice procedure), even if they have to guess, or they may be allowed to record a ‘no-difference’ response. Duo-trio test In the most common variant of the duo-trio test, the panellists are presented with a sample that is identified as a reference, followed by two coded samples, one of which is the same as the reference and the other different. These coded samples are presented in a balanced presentation order, i.e. A (reference) A (reference)

A B

B A

The panellists are asked to identify which sample is the same as the reference. The duo-trio test is particularly useful when testing foods that are difficult to prepare in identical portions. Testing such heterogeneous foods using the triangle test, which relies on identical portions, can give rise to practical difficulties, but in the duo-trio test there are no major difficulties in asking the question: Which sample is most similar to the reference? Triangle test Three coded samples are presented to the panellists, two of which are identical, using all possible sample permutations, i.e.

10 Texture in food

ABB BAB BBA

AAB ABA BAA

The panellists are asked to select the odd sample in a fixed-choice procedure. The increased number of samples compared with a paired comparison test can result in problems with flavour carry-over when using strongly flavoured samples, making identification of the odd sample more difficult. Difficulties can also be encountered in ensuring presentation of identical samples of some foods (see above). 3-AFC (Alternative Forced Choice) test This less common procedure uses one-half of the same sample permutations from the triangle test in a triad format, but either the difference of interest between the samples is revealed to the panellists in advance, or the panellists identify the nature of any difference in advance. In the test itself, the panellists are then asked to identify the sample (or samples) with the specified characteristic. For example, a typical instruction might be: One of these samples is more bitter than the others; please identify this sample. O’Mahony (1995) has identified the reasons why this test can be more sensitive than the triangle test, but the test suffers from the need to identify the nature of the difference positively in advance. R-index test This short-cut signal-detection method (O’Mahony, 1979; 1986) is less well used but has found applications in industry. The test samples are compared against a previously presented standard, and rated in one of four categories. For difference testing, these categories are standard, perhaps standard, perhaps not standard and not standard. The test can also be carried out as a recognition test, in which case the categories are standard recognised, perhaps standard recognised, perhaps standard not recognised and standard not recognised. The results are expressed in terms of R-indices, which represent probability values of correct discrimination or correct identification. The method is claimed to give some quantification of magnitude of difference, but its use has not been widely reported in the literature. One important limitation is that a relatively high number of judgements is needed in this form of test, leading to the risk of severe panellist fatigue. Difference from control test The test is of particular value when a control is available; the panellists are presented with an identified control and a range of test samples. They are asked to rate the samples on suitable scales anchored by the points ‘not different from control’ to ‘very different from control’. The test results are usually analysed as scaled data.

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1.3.3 Quantitative descriptive tests The Texture Profile Method The Texture Profile Method was developed by the General Foods Company specifically to define and measure the textural parameters of foods. Panellists are selected on the basis of ability to discriminate known textural differences in the specific product application for which the panel is to be trained (solid foods, beverages, semi-solid foods, skin care products, fabrics and paper goods). Panellists selected for training are exposed to a wide range of products from the category under investigation, to provide a wide frame of references. The characteristics of the product, the order of appearance and the degree to which each is present are determined. Attributes are usually evaluated in the following order: 1 2 3 4

surface characteristics (can be visual); initial compression (perceived on first bite); masticatory phase (perceived during chewing); residual phase (changes made during mastication and often perceived after swallowing).

In addition to the mechanical (e.g. firmness, adhesiveness, viscosity, springiness, cohesiveness) and geometrical (e.g. flakiness, grittiness, beady, crystalline) characteristics evaluated during the initial compression and masticatory stages, auditory characteristics such as crunchiness, crackliness or crispness might be evaluated. The panel verdicts are derived by group consensus or by statistical analysis of the data. Results are displayed in tabular or graphic form. Quantitative Descriptive Analysis (QDA) QDA is a total system covering sample selection, panellist screening, vocabulary development, testing and data analysis (Meilgaard et al., 1999). Variants of the original QDA procedures are probably used more than any other profiling procedure. The QDA technique uses small numbers of highly trained panellists. Typically, 6 to 12 people are screened for sensory acuity and trained to perform the descriptive task to evaluate the product. Three major steps are required: development of a standardised vocabulary, quantification of selected sensory characteristics and analysis of the results using parametric statistics. Development of the vocabulary is a group process for creating a complete list of descriptors for the products under study. Panellists freely describe the flavour, appearance, odour, mouthfeel, texture and aftertaste characteristics of different samples. No hedonic (good or balanced ), general (full or typical ) or intensity-based (strong or weak) terms are permitted. Terminology should be consistent from product to product and tied to reference materials. The references decrease panellist variability, reduce the amount of time necessary to train sensory panellists, and allow calibration of the panel in the use of intensity scales. References should be simple, reproducible and clear to the

12 Texture in food

panellists, and illustrate only a single sensory descriptor. They can be single chemical substances or finished products, and are made available during both the training and the testing phase, at various concentrations or intensity. The attributes are collected and compiled into a master list. This individual preliminary evaluation of the samples may be revised during an open discussion to eliminate any redundant or synonymous descriptors. New terms might be added and physical references proposed. The panel leader condenses and formats the information into a proposal for standardised vocabulary. This vocabulary is then modified and improved in several interactive sessions. Multivariate statistical methods (e.g. factor analysis) are sometimes used to reduce the number of descriptors. Finally, definitions for the attributes are agreed. When the panellists have agreed a vocabulary, further training is performed. The number of training sessions is dependent on the subject’s performance, product and attribute difficulties and the time allowed for QDA testing. Panel training increases panellist sensitivity and memory and helps panellists to make valid, reliable judgements independent of personal preferences. Once the training sessions have established satisfactory panel performance, and after removal of ambiguities and misunderstandings, the test samples can be evaluated. This is usually carried out in replicated (commonly three) sessions, using experimental designs that minimise biases. In each session, the mean is calculated for group and individual judgements of each attribute. The results are then subjected to univariate statistics (e.g. analysis of variance) or multivariate statistics (e.g. principal component analysis). Test results may also be visualised via bar charts or line graphs. The Spectrum Method This more recent method provides a tool with which to design a descriptive procedure for a given product category. The method resembles QDA in many respects; for example, the panel must be trained to fully define all product sensory attributes, to rate the intensity of each and to include other relevant characterising aspects such as change over time, difference in the order of appearance of attributes, and integrated total aroma and/or flavour impact. Panellists develop their lists of descriptors by first evaluating a broad array of products that define the product category. The process includes using references to determine the best choice of term and to best define that term so that it is understood in the same way by all panellists. Words such as vanilla, chocolate or orange must describe an authentic vanilla, chocolate and orange character, for which clear references are supplied. All terms from all panellists are then compiled into a list that is comprehensive but not overlapping. The Spectrum Method is based on an extensive use of reference points. The choice of scaling technique may depend on the available facilities for

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computer manipulation of data and on the need for sophisticated data analysis. Whatever the scale chosen, it must have at least two, preferably three or five, reference points distributed across the range.

1.3.4 Time-intensity methods Sensory attributes are not perceived instantaneously and can change in intensity with time in the mouth. Time-intensity methods are used to measure intensity of a specific attribute as a function of time in the mouth. They have been used to investigate the temporal behaviour of tastants, such as sweet and bitter molecules, and to investigate the release of volatile flavour materials from foods (Overbosch et al., 1991; Shamil et al., 1992) during mastication. Such studies are particularly important in the reformulation of foods that results in structural modifications, and in changes that can occur on storage. These structural modifications are often accompanied by textural changes, and these often result in complex perceptual phenomena that are direct consequences of the changes in texture with time producing different flavour release phenomena. The use of time-intensity for flavour measurement is well established, and there have also been studies to measure textural changes using the method (Burger, 1992; Duizer et al., 1993). A major limitation of time-intensity methods is that only a single attribute can be tracked with time, and, if a number of important attributes are thought to be time-dependent, separate sessions are needed for each attribute. Difficulties encountered in time-intensity profiling prompted the development of a hybrid technique, progressive profiling (Jack et al., 1994). In this technique, assessors carried out a profile on a set of texture descriptors at each chew stroke over the mastication period. Such a method has a number of potential advantages: several attributes can be assessed in one session; scaling is reduced to a unidimensional process; and the most important aspects of the shape of a time-intensity curve are retained.

1.4 Instrumental measurement of texture Sensory methods are, for the foreseeable future, the primary means of measuring the range of textural characteristics of food that are important to consumer acceptance. The highly labour-intensive nature of sensory analysis has inevitably led to the development of instrumental methods designed to measure food properties that relate to relevant sensory characteristics. These methods have been classified in various ways, according to the type of measurement and the type of food.

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1.4.1 Empirical, imitative and fundamental measurement Instrumental methods have been classified into three main categories: empirical, imitative and fundamental. Empirical methods Empirical tests often measure ill-defined variables that are indicated by practical experience to be related to some aspect of textural quality. Devices have been developed within different sectors of the industry that are appropriate to specific product types. Even for the same product type, different food manufacturers have developed their own in-house devices. Fuller details of the devices described in this section are given in Bourne (2002). • Puncture or penetration devices measure either the force needed to push a probe into the food to a specified depth or the penetration distance achieved by application of a specified force. Examples include MagnessTaylor testers (for fruit), the Bloom Gelometer and the FIRA Jelly Tester (for gels), the cone penetrometer (for fats) and the Christel Texture Meter (for peas). • Shearing devices measure the force needed for one or more blades to shear through the food. The maximum force is often assumed to measure toughness, firmness or fibrousness. Instruments include the Warner-Bratzler Shear (for meat), the Kramer Shear Cell (general-purpose) and the FMC Pea Tenderometer. • Compression devices measure the force needed to achieve a given compression or the compression achieved at a given force. Examples include the Baker Compressimeter (for bread) and the ball indenter (for fats). In extrusion tests, the food is forced through one or more orifices and the maximum force, average force or work done over a specified period is measured. The measured values are assumed to relate to firmness, toughness, consistency or spreadability. Examples include the FIRA-NIRD Extruder (for fats) and various cells used in conjunction with generalpurpose instruments. • Cutting devices use wires or blades (sometimes rotating) to cut through the food and measure the maximum force developed or the time needed to cut through a standard size of sample. Measurements are assumed to relate to fibrousness, firmness or hardness. The FMBRA Biscuit Texture Meter is a rotating blade device used to measure biscuit hardness. • Flow and mixing devices are used to give a measure of viscosity or consistency of liquid and semi-liquid foods. They often measure the extent to which samples flow or spread under specific geometric conditions, e.g. the Bostwick Consistometer and the Lyons Gel Flow Meter. Although such empirical devices are often simple, inexpensive and portable, precision and reproducibility are generally poor, and the measured parameters are poor measures of perceived texture. Extensive use is still made of them in industry, however, mainly for quality control purposes.

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Imitative methods Imitative methods of measurement mimic the conditions to which the material is subjected in practice during eating. The Volodkevich bite tenderometer attempted to mimic the action of teeth on food. It recorded the force of biting on a piece of food as a function of the deformation incurred. Two wedges with rounded points were substituted for teeth, the lower being fixed to a frame. The upper wedge moved with a linear motion through the arc of a circle by a lever, squeezing a sample between the wedges. A device using human dentures served as the prototype for the General Foods (GF) Texturometer (Friedman et al., 1963), in which the dentures are replaced by a plunger. The location of the sensing element was moved from the articular arm to the sample area to eliminate gravity forces, and the oscilloscope was replaced by a chart recorder, enabling easy and permanent recording of any chosen number of consecutive chews. In this device, the driving mechanism no longer imparts a combined lateral and forward motion to the lower jaw, although it still drives the plunger through the arc of a circle. Although the GF Texturometer remains in use to a small extent in North America and in Japan, the general-purpose testing machines designed for use with foods, exemplified by those made by Stable Micro Systems, Stevens, Lloyd and Instron, are commonly used in the food industry in most countries. The instruments differ in their mechanical construction and in their data acquisition and data analysis capabilities, but they have a number of important features in common. All have a crosshead containing a load cell, which is driven vertically at a range of constant speeds, and which can cycle over a fixed distance or load range. Probes can be attached to the crosshead for penetrating, shearing or crushing food, which can be held in a variety of cells. The load is recorded relative to time or to penetration/deformation distance, and displayed on a suitable recorder. Computer control of the instrument and sophisticated and rapid computer analysis of the data are increasingly common. A major advantage of such instruments is that flexibility of design allows them to be used for a wide range of foods. This is particularly useful for companies that are handling or manufacturing a varied product range. Load cells can be changed to give a high level of accuracy for relatively soft foods through to very hard foods. Probes and sample holders can easily be changed to accommodate measurements on different product types. An additional advantage is that such instruments can often be adapted for fundamental texture measurement. Fundamental methods Fundamental methods involve measuring well-defined physical properties of food, which, if measured properly, are independent of the method of measurement. The most common fundamental parameters are Young’s modulus, shear modulus, bulk modulus and Poisson’s ratio (for solids) and viscosity

16 Texture in food

(for liquids). Fundamental parameters for solids can be measured on generalpurpose testing machines, but such measurements require a carefully designed experimental set-up and are consequently slow. In addition, foods are generally heterogeneous and do not exhibit simple elastic behaviour. Fundamental parameters can be measured on liquids using suitable instrumentation, for example the Weissenberg Rheogoniometer and the Carri-Med Rheometer. Again, however, liquid foods rarely exhibit simple viscosity behaviour. Such fundamental measurements are valuable in investigating the physical properties of food, but are too complex for routine use and, in common with other instrumental measurements, rarely correlate well with perceived texture. Some reasons for this can be identified on examining some of the physiological factors associated with chewing.

1.4.2 Application to solid foods Development of measurement methods For most solid foods, key sensory attributes can be defined that are known to be highly important in defining consumer acceptability, for example crispness in salad vegetables and snack foods, tenderness in meat, and snap in chocolate. The evolution of measurement methods has followed the need both to control these attributes in routine production and to understand how they can be designed into new products. The computerised modern instruments that utilise force-deformation principles are used almost universally in research functions and, with particular success, in quality control (QC) functions. A good example of this is the use by the French company Isigny Sainte-Mère of Stable Micro Systems TAXT2 Texture Analysers for on-line measurement of the firmness of Camembert cheeses to sort for different maturing conditions (Toursel, 1996). The reason for the successful applications in QC is easy to see. In such applications, it is more important to be able to detect changes in measurable parameters than to measure precisely specific textural parameters. A change in any measured parameter outside set control limits can act as a signal that some aspect of the food production cycle has drifted. Of course, this introduces the risk that actions could be taken on the basis of changes in parameters that have little importance to consumer acceptability, but the measurements would normally trigger sensory tests that would minimise any production holds. A more serious risk is that the measurement system would not identify textural changes that were unmeasurable by that system, and, unless routine sensory tests were also carried out, defective material could be supplied to consumers. For example, a standard texture instrument capable of measuring the characteristic snap of chocolate might not detect the dryness that characterises stale chocolate. The deficiencies of texture measurement instruments become particularly apparent when they are used for R&D purposes. One practical problem is that no single instrument is likely to be able to measure the food properties

Measuring consumer perceptions of texture: an overview

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that are detected by both the tactile and the deep senses. One route to address this problem is to investigate more closely those physical processes that give rise to the texture sensations. In a study of stickiness in confectionery products, Kilcast and Roberts (1998) explained the need to understand both the meaning of the word ‘stickiness’ by consumers, and in what context the phenomenon is perceived, as well as the physical processes that contribute to the various phenomena commonly described as stickiness. They showed that the perception of stickiness can occur through combinations of both adhesive and cohesive failure (Fig. 1.3). Most industrial problems are associated with cohesive failure, which leaves unwanted material behind on surfaces. Both product rheology and the surface energy of the surfaces can contribute to the observed sticking phenomena, and under critical conditions it is possible to minimise sticking by either changing the surfaces involved or changing product composition or operating conditions. The research led to a test procedure adapted from that developed by Chen and Hoseney (1995) for measuring the stickiness of dough that can be used to study the stickiness of caramels over a wide range of conditions. Figure 1.4 shows the cell, which consists of a Probe movement

Probe movement

Adhesive failure

Cohesive failure a2

a1

start

c1

c2

Fig. 1.3 Adhesive and cohesive failure mechanisms during the force–deformation testing of food. The sequence to the left (start, a1, a2) illustrates adhesive failure; the sequence to the right (start, c1, c2) illustrates cohesive failure. Grid

Perforated cap

Sample reservoir

Water jacket

Piston

Fig. 1.4 Modified Chen-Hoseney cell for measuring the stickiness of caramel.

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water-jacketed cylinder holding the caramel sample at the required temperature. The screw piston is used to extrude the caramel through a perforated stainless steel cap to give a fresh surface. The instrumental measurements are carried out by lowering a cylindrical probe onto the caramel surface, and then withdrawing it and measuring the force-deformation characteristics. For solid foods giving the proprioceptive (deep) response, the principles of fracture mechanics have been applied widely, and are outlined in the following section. Fracture mechanics in food texture measurements The science of fracture mechanics was originally developed to explain the fracture of brittle materials such as glass. The breakage of a material is influenced by the relationship between the applied force and the bond holding the material together. Bonds will break more readily under the high local stress concentration around the tip of a sharp blade, for example. The basic premise underlying fracture mechanics is that all solids contain inhomogeneities, which exist in the form of flaws, or cracks. The magnitude and distribution of these defects govern the strength of the material. Fracture occurs when these defects grow and traverse the solid, creating new fracture surfaces. Early studies showed the potential of wedge fracture testing to brittle and semi-brittle foods such as apples and cheeses (Vincent et al., 1991). One difficulty in applying the principles of fracture mechanics to food lies in the great complexity of food structure, and in the complex viscoelastic behaviour of most foods, although recent research has demonstrated the feasibility of these techniques (Dolores Alvarez et al., 2000). However, deformation tests applied to carefully prepared food samples have been shown to give improved correlations between instrumentally measured properties and sensory measures. The principles of fracture mechanics have been extended to the measurement of fracture toughness of complex foods, such as the pastry casing of spring rolls (Sim et al., 1993). Lillford (2001) has reviewed the work relating to fracture mechanisms in food, and Lucas et al., (2002) have proposed physiological models relating to the fragmentation and swallowing of food particles.

1.4.3 Analysis and validation of instrumental measurements Statistical methods A physical measurement of textural characteristics can be of practical value only if it is shown to relate to some relevant sensory texture measure. The relationship should take the form of a statistic that represents the fit between the instrumental measurement and the sensory attribute, or an equation that relates the instrumental measure (or set of measures) to the required sensory attribute. Two basic procedures are used.

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• Pearson product moment correlation coefficients (r), where a perfect positive correlation gives r = +1, a perfect negative correlation gives r = –1, and no correlation gives r = 0. The square of the correlation coefficient (r2) gives a measure of the data variance accounted for by the linear correlation, and is a measure of the value of the correlation. • Multiple linear regression (MLR), in which the variable of interest (e.g. sensory attribute) is expressed as a linear combination of other variables (e.g. instrumental parameters). The variable combination is usually found using stepwise selection procedures. The method is of greatest value when the number of data points exceeds the number of attributes of interest. The complexity of both instrumental and sensory data, however, increasingly demands the use of multivariate statistical procedures. Many techniques are available, but, when examining for structure and relationships in data sets, the most common technique is principal component analysis (PCA). PCA is a data reduction technique that replaces a large number of original variables by a smaller number of linear combinations, whilst still explaining a substantial proportion of the original variation in the data. Essentially, PCA projects an n-dimensional space onto a 2-dimensional plot. Other multivariate analyses, such as partial least squares (PLS) analysis are increasingly being used for combined data sets. For example, in a study of the sensory and instrumental characteristics of Reggiano grating cheeses, PLS was used to show that sensory texture correlated best with strain at breaking point (Hough et al., 1996). Novel methods for analysing force–deformation data Analytical software included in the modern force-deformation test systems is capable of parameterising the curve shapes generated by many foods. The most extreme deviation from the idealised force–deformation curves is commonly found in testing brittle foods, which are characterised by very jagged curves. In a series of papers (e.g. Barrett et al., 1992), Peleg and coworkers have described different mathematical approaches to analysing these highly irregular curve shapes, which can give rise to difficulties in parameterisation. One approach has been to carry out a Fast Fourier Transform on the force–deformation curve, giving a power spectrum of underlying frequencies. This procedure gives a qualitative representation of the jaggedness of the original curve, but cannot give a quantitative representation. Such quantification can be carried out through fractal analysis. The fractal concept is based on the geometry of self-similar objects expressed in terms of noninteger dimensions. The fractal dimension is determined from the slope, in logarithmic co-ordinates between the length of the force–deformation contour and the corresponding measurement scale. The latter measurement was found to be convenient in giving a measure of overall jaggedness in terms of a single number, but the power spectrum gave more information on the location of the fracture, and its shape could be related more directly to structure and

20 Texture in food

texture. The use of the power spectrum was subsequently described in relation to the measurement of crunchiness in friable baked products (Rohde et al., 1993). A further innovative attempt to analyse jagged force–deformation curves has been to use a technique used in speech analysis and medical diagnosis, symmetrised dot-pattern (SDP) displays (Peleg and Normand, 1992). This concept is based on the premise that, whereas humans find it difficult to analyse the visual appearance of highly irregular shapes, they are highly sensitive to changes in symmetric patterns. In the method, the recorded data are transformed into several symmetrically arranged sets of points, each reflected by a mirror plane, in an analogous way to the production of symmetric visual images by a kaleidoscope. The technique was used in the hope that the displays could be used to identify crunchiness in the same way that they can be used to identify vowels, but, in practice, the SDP displays were so sensitive to minor details that every signature appeared unique (Peleg, 1998).

1.5 In vivo texture measurement The limitations in trying to mimic the events occurring during mastication using relatively simple instruments have long been appreciated. An alternative approach that has gained credence in recent years has been to attempt to record signals generated by or within the human subject that may relate to the texture of the food being masticated. 1.5.1 Electromyography (EMG) and associated techniques EMG involves the use of a polygraph to measure electrical signals generated in muscles that are active during mastication. For certain muscles that lie close to the surface of the skin, for example the masseter muscle, which is active during the chewing of solid foods, this activity can be related to a specific muscle. Other oral activity, for example tongue movement, is controlled by groups of muscles that are deeper-lying. Monitoring of signals from this latter type of musculature ideally requires implanted electrodes, whereas signals from the masseter muscle can be readily recorded using surface electrodes. Early attempts to use EMG in the study of food texture were limited by difficulty in interpreting the complex data patterns produced. In the absence of suitable computerised acquisition and analysis equipment, visual inspection of the raw data was carried out. For example, motor pauses (or silent periods) were more frequent with hard foods than with soft foods (Boyar and Kilcast, 1986). The development of more sophisticated EMG equipment and computer systems, however, has permitted much deeper analysis of EMG data and their relationship to food texture. An added potential advantage of this technique is studying changes in food texture in the mouth throughout the whole chewing

Measuring consumer perceptions of texture: an overview

21

cycle. Research projects carried out at the Leatherhead Food Research Association (e.g. Eves, 1990; Kilcast and Eves, 1991) were carried out to investigate the potential use for EMG as a means of characterising food texture, and the way in which texture changes during mastication. Following this work, a number of papers have appeared in the literature in which EMG has been used to investigate food texture. Further studies on the use of EMG in the confectionery sector were reported by Smalls (1992), and indicated good correlations between EMG and Instron measurements. Applications to cheese texture were described by Jack et al. (1993). In this work, EMG was used in conjunction with sensory and instrumental measurements of the texture of a range of Cheddar cheeses, but inconsistency between subjects resulted in difficulties in correlating EMG and Instron measurements. In a review of texture measurements for use in product development, Jack et al. (1995) described the use of EMG in conjunction with other methods. Duizer et al. (1996) used a combination of EMG, timeintensity measurement and instrumental measurement to investigate beef tenderness. The results indicated that the effects of early mastication should be compared with the effects of late mastication. More fundamental aspects of the use of EMG in understanding the oral breakdown process have been reported by Brown and co-workers (e.g. Brown et al., 1994; Brown, 1995; Brown et al., 1998). These studies focused primarily on understanding the chewing behaviour of consumers rather than on texture measurement. EMG was used either as the sole technique, or in combination with synchronous measurement of jaw movement (kinesthesiology) by a set of transducers mounted on a head-frame to track the movement of a small magnet attached to the lower front incisors. Several oral techniques, including EMG, have also been used by Mioche and co-workers to study the mastication process (e.g. Mathevon et al., 1995; Peyron et al., 1996; Mioche and Martin, 1998). A more recent study within the EU HealthSense project has seen the use of EMG to investigate differences in chewing patterns between young and elderly populations (Kohyama et al., 2002). An unusual related technique for studying mastication behaviour has been reported by Jack and Gibbon (1995). The technique, electropalatography (EPG), is used to measure tongue movement during eating and swallowing, and comprises an artificial plate, moulded to the individual’s hard palate, embedded in which are 62 electrodes covering the entire palate surface. Tongue contact with the electrodes generates a signal that can be used to monitor the movement of the tongue. Experiments were carried out with liquid, semi-solid and gelled foods. The authors concluded that the technique could be used for liquid and semi-solid foods, but that bulky or sticky foods prevented the tongue making contact with the palate. 1.5.2 Sound emission Early studies on food-crushing sounds (Drake, 1963) showed that sounds from crisp foods differed from those of non-crisp foods, primarily in terms

22 Texture in food

of amplitude. Frequency and duration played a less important role. A subsequent paper (Vickers and Bourne, 1976) presented studies of the acoustical properties of tape-recorded biting sounds of wet and dry crisp foods. Amplitude–time plots indicated that both sound amplitude and the number of sounds produced in a given bite distance discriminated between different levels of crispness. A series of papers on crispness appeared subsequently (reviewed in Vickers, 1988). In one of this series (Vickers, 1985), it was shown that pitch was a useful parameter in distinguishing between crispness and crunchiness; crisp sounds tended to be higher in pitch than crunchy sounds. These extensive studies gave considerable improvements to the understanding of crispness, and associated work has continued in other laboratories. Dacremont et al. (1992) investigated the contribution of both air-transmitted and bone-conducted sound to the perception of crispness, crackliness and crunchiness in foods. In a further paper, Dacremont and Colas (1993) investigated whether a person’s perception of duration, loudness and pitch of biting sounds might be biased by the sight of food samples. The results suggest that the visual perception of foods has no influence on the loudness or pitch judgement; however, it does influence the judgement of sound duration during eating. This paper pointed out that sound propagation in air is different between an incisor bite, with lips open, and a molar chew, with lips closed. The contact area between food and teeth is different, and the amount of vibration through the bones is different. Although the bite sounds are different for the three textural types, the differences are not sufficient to distinguish them clearly. Roudaut et al. (1998) used acoustic emission in conjunction with sensory analysis, compression tests and Dynamic Mechanical Thermal Analysis (DMTA) to investigate the effect of water on the texture of crispy breads. One difficulty in appraising the practical relevance of the research into sound emission and the texture of foods lies in the difficulty in the use of descriptive terminology. The words crispy and crunchy are, in particular, commonly used without agreed definitions. Work carried out at Leatherhead Food International on the measurement of the texture of fruit and vegetables has taken a consumer-led approach, and has investigated the understanding of textural terms by consumers. Repertory Grid interviewing methods were used to elicit textural descriptions of fruit and vegetables by consumers, and to relate them to attributes developed by trained sensory panels (Kilcast and Fillion, 2001). The trained panel developed a sensory profile that included different attributes associated with the sounds emitted during mastication. Table 1.1 shows a comparison of term usage between consumers and panellists. Of the ten consumers interviewed, nine used the term ‘crunchy’ to describe the texture of the products, but only five mentioned the term ‘crispy’, while one did not mention ‘crispy’ or ‘crunchy’ at all. Those who mentioned both ‘crispy’ and ‘crunchy’ were always using the two terms with different meanings and usually found it easier to describe one against the other. There was generally a good agreement on the meaning of the term ‘crunchy’, with

Measuring consumer perceptions of texture: an overview

23

Table 1.1 Definitions of crispy and crunchy attributes from consumer and panellist interviews

Number of subjects mentioning the attribute Assessment on front teeth only Assessment on back teeth only Number of times a descriptor was given: Hard, dense Sound Loud Low pitch High pitch Repetition of sound Brittle Snap, clean break Light Fresh Moist

Consumers*

Panellists**

Crunchy

Crispy

Crunchy

Crispy

9

5 1

11

10 6 2

1 8 8 2

6 3

8 10 4 8

9 4 8

1 3 2 3 1

5 3 8 6 1

* n = 11 ** n = 10

most definitions mentioning hardness and sound. Definitions for the ‘crispy’ attributes were more diverse, and were a combination of a snap clean break, a light texture and a sound, and were also associated with freshness, moistness and brittleness for some consumers. These studies have assisted in developing improved methods for texture measurement. Measurement of sound can be used to show the close relationship between the release of sound energy and failure events that result in detectable changes in force. Figure 1.5 shows a comparison of force–deformation and emitted sounds for celery subjected to an incisor probe test in a Stable Micro Systems TA-HD instrument (Kilcast, 2001). The upper curve shows the sound emitted during the test, and the lower curve shows the differential of the force curve, measured from the signal taken directly from the load cell; the close correspondence is clear. However, substantial practical difficulties remain in the measurement of sound during the test, primarily from interferences by the noise emitted from the motor drive of the test machine, and noise from extraneous sources.

1.6 Future developments Although it is increasingly being recognised that only sensory methods can give a complete picture of food texture, many needs can be identified for the

24 Texture in food

Sound level

Differential of the force curve

Fig. 1.5 Comparison of the emitted sound level with the differential of the force changes from cutting celery using an incisor probe.

instrumental measurement of key textural characteristics, and considerable research is being carried out to find new methods. There is particular interest in finding and validating non-destructive methods that can be used for quality control purposes, and for portable instruments that can be used directly in the field.

1.6.1 Selection procedures for sensory panellists Methods for the selection of panellists to ensure that they are capable of carrying out the required sensory tests are described in an ISO Standard (ISO, 1993). This recommends tests for acuity of visual perception, taste and odour perception, but not tests for texture perception ability, other than the generation of verbal descriptions. As part of a European Union-funded project on Healthy Eating for the Elderly, Fillion and Kilcast (2001) have developed tests designed to investigate texture perception and oral dexterity in the elderly. These tests are potentially of great value in screening panellists for use in food texture assessment, and are currently being adapted for this purpose.

1.6.2 Dynamic force/deformation methods The most common material parameters linked to firmness are the elastic properties of the product: hard materials have high values of the elastic modulus, while soft materials have lower values (Pitts et al., 1994). Abbott and Massie (1995) believed that approximate values of the elastic properties of produce were adequate for sorting, and that a dynamic force/deformation (F/D) test might be relatively simple to implement on a kiwifruit packing line. They compared various parameters from a low-frequency F/D test with

Measuring consumer perceptions of texture: an overview

25

data from the Magness-Taylor puncture test, and found that a relatively simple firmness tester could be devised on the basis of 60-Hz vibrations. 1.6.3 Sound input methods Sonic resonance testing is a potential method for non-destructive measurement of firmness. Resonant frequencies of intact fruits and vegetables decrease with ripening and are directly related to their rigidity, firmness and ripeness (Abbott et al., 1968). Abbott et al. (1995) showed that sonic stiffness coefficients were satisfactory predictors of the firmness of Golden Delicious apples, when compared with the maximum force in compression or puncture tests. Mealiness in apples has been studied using acoustic impulse methods (Tu and de Baerdemaeker, 1996). Chen et al. (1996) and Armstrong et al. (1997) also used the resonant frequency of the vibrational modes to evaluate firmness of melons and peaches, respectively. Muramatsu et al. (1997) used pulsed sound transmission to measure the firmness of kiwifruit during ripening, and found good correlations with penetration testing. Low-frequency ultrasonics have been used (Nielsen and Martens, 1997) to evaluate the texture of carrots during cooking. Ultrasonic methods have also been used in the study of the texture of beef (Park et al., 1994), wafers (Juodeikiene et al., 1990) and crackers (Juodeikiene et al., 1994). 1.6.4 Spectroscopic and related methods A number of both direct and indirect methods for assessing texture have been reported. Near-infrared spectroscopy was investigated as a method for the quality control of asparagus (Garrido Varo et al., 2000). Laser scattering properties of apples were investigated as a means of measuring firmness (Cho and Han, 1999). Scatter parameters showed good correlations with compression testing, but strong varietal differences were found. Laser Doppler techniques have been used to measure firmness of fruit and to detect fruit disorders (Muramatsu et al., 1999). The Doppler method gave good correlations with conventional instrumental testing on peaches, pears and citrus fruit, and is claimed to have potential as a remote-sensing device. Textural investigations using nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) have also been reported. Schmidt (1999) has reviewed general uses of NMR methods, and Kim et al. (1998) have studied possible uses of both NMR and MRI for textural investigations. 1.6.5 Image analysis Image analysis techniques have been reported for measuring different aspects of food texture (Affeldt et al., 1994). The paper reviews methods for the measurement of surface texture, dimension and shape and surface cracks. Coles and Wang (1997), described the use of a Bread Quality Imaging System to measure the fineness of bread texture by detecting bubbles above a minimum

26 Texture in food

size. Measurement of the texture of corn puffs by image analysis has been described (Gao et al., 1999). Visually determined sensory scores were effectively predicted by the intensity band image features. A further aspect of visualisation was demonstrated by Kilcast et al. (1984) and Boyar et al. (1984), in which the photoelastic properties of gelatin were used to visualise stress distributions in gelatin during the course of penetration testing. Figure 1.6 shows an example of the interference fringes produced on testing a gelatin gel with a hemispherically ended probe viewed under sodium light between crossed polarisers. Whilst such imaging is restricted to a small number of transparent gels, computation techniques are now available to calculate stress distributions in such systems, but they do not appear to have been used in this context.

1.7 Conclusions Given the complex nature of food texture, developments in measurement techniques are likely to be evolutionary rather than revolutionary. Can the ideal instrumental test, that incorporates the essential elements of fundamental tests, imitative tests and empirical tests, be devised? Table 1.2 summarises

Fig. 1.6 Interference fringes produced on testing a gelatin gel with a hemispherically-ended probe, and viewed under sodium light between crossed polarisers.

Mainly solids

Mainly solids

Physiological5

Spectroscopic High

High/moderate

Moderate/high

Moderate/high

High

Moderate

Low

High

Initial costs

Moderate/high

Moderate

Low

Low/moderate

Low

Low

Low

Moderate/high

Running costs

2

Trained profile panels e.g. hand-held penetrometers 3 e.g. instrumental test rigs, General Foods Texturometer 4 Force–deformation devices under strictly defined operational conditions 5 Including EMG 6 Consumer relevance depends on ability to correlate test with subjective textural responses

1

Brittle solids

Solids

Sound emission

Sound input

Solids

All

All

Empirical2

Fundamental4

All

Sensory1

Imitative3

Food types

Class

Table 1.2 Summary of the main classes of texture measurement

Laboratory

Laboratory

Laboratory

Laboratory

Laboratory

Laboratory/QC

QC/production

Laboratory/QC/ production

Operating environment

Continuing

Continuing

Continuing

Continuing

Mature

Continuing

Mature/ continuing

Mature/ continuing

Development status

Moderate

Moderate/high

Low/moderate

Moderate/high

Low

Low/moderate

Low

High

Consumer relevance6

Measuring consumer perceptions of texture: an overview 27

28 Texture in food

the status of texture measurement methods at the time of writing. In recent years, an increasing number of novel techniques have been investigated, and more are being used in production/QC environments. A number of factors have contributed to this situation. Firstly, conventional force–deformation instruments have undergone considerable enhancement through the extensive use of computerised data acquisition and analysis software, and through the availability of a wider range of test rigs for specific purposes. Secondly, the increased interest in the mechanics of mastication has enhanced our understanding of oral processes and of their relationship to texture perception. Thirdly, the importance of texture as an important attribute driving consumer acceptance has stimulated further research, particularly in the search for smaller but reliable measuring devices that can be used outside the QC laboratory. However, in recognition of the importance of texture to the consumer, and in keeping with a food characteristic that is defined by the human senses, texture assessment is increasingly being carried out using trained sensory panels alongside the assessment of other organoleptic characteristics such as appearance, odour and taste.

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and KRIEGER B (1995) The contributions of sensory liking to overall liking: An analysis of 6 food categories, Food Quality and Preference, 6(2), 83–90. MURAMATSU N, SAKURAI N, YAMAMOTO D, NEVINS D J, TAKAHARA T and OGATA T (1997) Comparison of a non-destructive acoustic method with an intrusive method for firmness measurement of kiwi fruit, Postharvest Biology and Technology, 12(3), 221–8. MURAMATSU N, SAKURAI N, WADA N, YAMAMOTO D, TAKAHARA T and OGATA T (1999) Evaluation of fruit tissue texture and internal disorders by laser Doppler detection, Postharvest Biology and Technology, 15(1), 83–8. NIELSON M and MARTENS H J (1997) Low-frequency ultrasonics for texture measurement in cooked carrots (Daucus carota L), Journal of Food Science, 62(6), 1167–70+1175. O’MAHONY M (1979) Short-cut signal detection measures for sensory analysis, Journal of Food Science, 44(1), 302–3. O’MAHONY M (1986) Sensory Evaluation of Food. Statistical Methods and Procedures, New York, Marcel Dekker Inc. O’MAHONY M (1995) Who told you the triangle test was simple? Food Quality and Preference, 6(4), 227–38. OVERBOSCH P, AFTEROF W G M and HARING P G M (1991) Flavour release in the mouth, Food Reviews International, 7(2), 137–84. PARK B, WHITTAKER A D, MILLER R K and HALE D S (1994) Ultrasonic spectral analysis for beef sensory attributes, Journal of Food Science, 59(4), 697–701+724. PELEG M (1998) Extracting useful information from irregular and irreproducible mechanical and other signatures. In New Techniques in the Analysis of Foods. Ed. M H Tunick, New York, Kluwer, 37–52. PELEG M and NORMAND M D (1992) Symmetrized dot-patterns (SDP) of irregular compressive stress-strain relationships, Journal of Texture Studies, 23(4), 427–38. PEYRON M-A, MIOCHE L, RENON P and ABOUELKARAM S (1996) Masticatory jaw movement recordings: a new method to investigate food texture, Food Quality and Preference, 7(3-4), 229–37. PITTS M J, ABBOTT J A, ARMSTRONG P R, BROWN G K, BRUSEWITZ G H, DAVIS D C, DELWICHE M J, GALILI N, GAN-MOR S, HAUGH C G, MASSIE D R, MIZRACH A, NAHIR D, PELEG K, ROHRBACH R P, SARIG Y, SCHAARE P N, SCHNILOVITCH Z, SHMULEVICH I, STONE M L, STROSHINE R L and YOUNCE F L (1994) Sensing fruit and vegetable firmness. In Nondestructive Technologies for Quality Evaluation of Fruits and Vegetables: Proceedings of the International Workshop, Spokane, Washington, June 1993. Michigan, ASAE, 31–43. ROHDE F, nORMAND M D and PELEG M (1993) Characterization of the power spectrum of force-deformation relationships of crunchy foods, Journal of Texture Studies, 24(1), 45–62. ROUDAUT G, DACREMONT C and LE MESTE M (1998) Influence of water on the crispness of cereal-based foods: acoustic, mechanical and sensory studies, Journal of Texture Studies, 29(2), 199–213. SCHMIDT S J (1999). Probing the physical and sensory properties of food systems using NMR spectroscopy. In Advances in Magnetic Resonance in Food Science: Proceedings of the Fourth International Conference on Applications of Magnetic Resonance in Food Science, Norwich, September 1998. Ed. P S Belton, Cambridge, RSC, 79–94. SHAMIL S H, WYETH L J and KILCAST D (1992) Flavour release and perception in reduced-fat foods, Food Quality and Preference, 3(1), 51–60. SHEPHERD R and SPARKS P (1994) Modelling food choice. In Measurement of Food Preferences. Eds H J H MacFie and D M H Thomson, London, Blackie 202–26. SIM B J, LUCAS P W, PEREIRA B P and OATES C G (1993) Mechanical and sensory assessment of the texture of refrigerator-stored spring roll pastry, Journal of Texture Studies, 24(1), 27–44. SMALLS I (1992) Electromyography versus Instron texture measurement of confectionery products, Proceedings of the 46th Annual Production Conference, Hershey, April 1992. Medford, Pennsylvania Manufacturing Confectioners Association, 56–61. MOSKOWITZ H R

32 Texture in food and KAHN E L (1971) Consumer attitudes to and awareness of food texture 1: adults, Journal of Texture Studies, 2, 280–95. TOURSEL P (1996) Equipment for texture analysis, Process, 1122, 48–50. TU K and DE BAERDEMAEKER J (1996) Investigation of apple quality using instrumental methods, Agri-food Quality: an Interdisciplinary Approach; Proceedings of a Conference, Norwich, June 1995, Cambridge, RSC, 204–7. VICKERS Z M (1985) The relationships of pitch, loudness and eating technique to judgements of the crispness and crunchiness of food sounds, Journal of Texture Studies, 16(1), 85– 95. VICKERS Z M (1988) Evaluation of crispness In Food Structure – its Creation and Evaluation. Eds J M V Blanshard and J R Mitchell, Oxford, Butterworths, 433–48. VICKERS Z M (1991) Sound perceptions and food quality, Journal of Food Quality, 14(1), 87–96. VICKERS Z M and BOURNE M C (1976) A psychoacoustical theory of crispness, Journal of Food Science, 41(5), 1158–64. VINCENT J F V, JERONIMIDIS G, KHAN A A and LUYTEN H (1991) The wedge fracture test. A new method for the measurement of food texture, Journal of Texture Studies, 22(1), 45–57. SZCZESNIAK A S

2 Consumers and texture: understanding their perceptions and preferences J-F. Meullenet, University of Arkansas, USA

2.1 Introduction: problems with consumer descriptions of texture The British Standards Institution defined texture as an attribute of a substance resulting from a combination of physical properties and perceived by the senses of touch, sight and hearing (Brennan, 1980). From a consumer point of view, this definition probably makes little sense. In fact, the obscureness of the various definitions proposed and their disconnection with what consumers define as texture is a major challenge for product developers in the food industry today. This will be the focus of this chapter. We shall start with a simple example. In the recent past, our lab was involved in a project dealing with a processor of poultry meat. The project had to do with meat tenderness and how long you have to leave the breast meat on the carcass to achieve maximum tenderness. Various treatments were evaluated using a trained descriptive panel and the resulting profiles presented to the client. On the profiles were obscure attributes such as cohesiveness of mass, toothpack and toothpull in addition to more commonly known attributes such as hardness. The client pondered for a few minutes and finally asked: “So, where are the results for tenderness?” I had to explain that tenderness is an integrated term and that a trained panel uses several attributes that, all put together, contribute to a consumer’s perception of tenderness. This illustrates the fact that descriptive analysis is sometimes not a very actionable tool for food processors, if the relationship between the sensory attributes defined by a descriptive panel and the consumer’s perception of the texture in the product is not well understood. This is a common problem in sensory evaluation, and stems from the fact that consumers use

34 Texture in food

loosely defined integrated terms that sometimes do not have the same meaning to the whole population. This seems, at first, like an insurmountable problem. However, several techniques are available to elucidate the complex nature of the acceptance of food from a hedonic standpoint by consumers. This chapter will discuss the issue of bridging the gap between science (rheology or sensory) and the perception and acceptance of texture by consumers.

2.2 Investigating consumer descriptions of texture 2.2.1 Establishing the importance of texture to consumers The effects of appearance and flavor on consumer acceptance of foods have been extensively studied. However, there are fewer studies dealing with the effect of texture attributes on food acceptability. In 1971, Szczesniak assessed the apparent relative and a priori importance of texture and flavour on the acceptability of foods via a survey of 150 consumers. Surprisingly, the texture/ flavour index, a measure of the relative importance of texture in comparison to flavour was 0.89 for the group. This demonstrated that texture was an important attribute of food acceptance and that more attention should be given to it. Texture has also been reported to be especially important in foods that are bland in flavour, such as rice, or those that are crispy or crunchy, such as puffed cereals (Szczesniak,1990). One of the major issues with attempting to understand consumer responses is the fact that the language used by consumers is very different from words used by experts, such as sensory scientists, practising descriptive analysis to describe the texture of food products. This is well illustrated by the classification of texture characteristics proposed by Szczesniak (1963; see Table 2.1). As seen in Table 2.1, consumers (popular terms) use different words to refer to increasing intensities on a specific continuum. For example, hardness, which is the term used in descriptive analysis, is referred to by consumers as soft, firm or hard, depending on the intensity of the stimulus. The main problem is that those terms are mutually exclusive of each other and that consumers will associate specific foods with specific descriptors. For example, soft and crumbly is often more appropriate to consumers for describing the texture of foods (for example, some cheeses such as Feta) of very low hardness and cohesiveness (i.e. by the sensory and not engineering definition) while hard and brittle are more applicable to describing confectionary products such as candies. Although these examples are trivial, it implies that those involved with consumer testing have to carefully design questionnaires in order to fit the language of consumers. 2.2.2 One model fits all? There is today a body of knowledge developed on how specific texture attributes are governing the acceptability of particular food products. Textural

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35

Table 2.1 Relations between textural parameters and popular nomenclature (Szczesniak, 1963) Mechanical characteristics Primary parameters Hardness Cohesiveness

Secondary parameters Brittleness Chewiness Gumminess

Viscosity Elasticity Adhesiveness

Popular terms Soft → Firm → Hard Crumbly → Crunchy → Brittle Tender → Chewy → Tough Short → Mealy → Pasty → Gummy Thin → Viscous Plastic → Elastic Sticky → Tacky → Gooey

Geometrical characteristics Class Particle size and shape Particle shape and orientation

Examples Gritty, Grainy, Coarse, etc. Fibrous, Cellular, Crystalline, etc.

Other characteristics Primary parameters Moisture content Fat content

Secondary parameters Oiliness Greasiness

Popular terms Dry → Moist → Wet → Watery Oily Greasy

attributes have been found to have both positive and negative impacts on consumer acceptance of foods (Szczesniak and Kahn, 1971). Universally liked texture attributes include crispness, crunchiness, juiciness and tenderness, while attributes such as toughness or sogginess are for the most part disliked. However, although these general rules provide useful information, they do not address specific products or various consumer groups. A simple example of this is cooked rice. Rice is a staple food in a large portion of the world and Asian cultures are very discriminating about the rice they eat. It is known that texture is a very important attribute of rice. Its two main characteristics are hardness/firmness and stickiness. Okabe (1979) studied the acceptability of rice by Japanese consumers. He reported that there were narrow regions of acceptability for cooked Japanese rice for both hardness and stickiness, and that consumers favoured sticky and tender rice. However, this observation needs to be confined to Japan as other Asian cultures have different expectations. For example, a study by Meullenet et al. (2000) reported on the acceptance of various rices by consumers primarily from South-east Asia and concluded that rice texture was not a very important driver of overall acceptance when compared to rice appearance and aroma. However, the same general rules (i.e. preference for tender and sticky rice) were confirmed. These results were further confirmed by work published by Suwansri et al. (2002) on the acceptability of Jasmine rice by US Asian consumers.

36 Texture in food

This shows that, unfortunately, the importance of texture attributes to the acceptance of food is both product and consumer group dependent. The conclusion is that texture should be considered in every product development process.

2.3 Tests and test procedures Before considering methods designed to develop an understanding of consumers’ perception and acceptance of texture, we should briefly consider the methodologies best suited for this type of endeavour. Consumer testing methods described in the literature vary greatly. However, one common denominator of all methods is the use of some type of rating scale, at least when the hedonic response to a product is assessed. The primary goal in designing scales for use with consumers has usually been to keep them easy to use and easily understandable by all. The type of information gathered from consumers is either related to the acceptance/preference for a food product or to the perception of sensory attribute intensity or appropriateness. The main three types of scales are category, line and magnitude estimation scales. Category scales are probably overall most broadly used followed by line and magnitude estimation scales, respectively. Category scales are those using a fixed number of possible responses. For use with consumers, verbal anchors are associated with some or all of the categories. The most common examples of these scales are the nine-point hedonic scale and the relative to ideal or just right scales. Line scales have been used as hedonic scales (Pangborn et al., 1989) or as just right scales (Shepherd et al., 1988), but they can also be used to assess the intensity perceived by consumers for a specific attribute. Line scales described in the literature have varied between 100 and 150 mm, and it is not clear whether the scale length has true impacts on the results.

2.3.1 Hedonic scales The most commonly used hedonic scale, at least in English speaking countries, is the nine-point hedonic scale (Jones et al. 1955). The main characteristics of the scale are that each category is associated with a verbal descriptor from “dislike extremely” to “like extremely” and that the scale has a neutral category “neither like nor dislike”. Although it has been widely used for almost 50 years, the scale has been equally criticised. The nine-point hedonic scale has been popular because of its simplicity, accuracy and precision while being criticised mostly for end effects (i.e. avoidance of extreme categories) and the lack of equal hedonic intervals between categories. The hedonic scale has been accepted by sensory professionals to infer consumer acceptance from “liking”, despite its flaws, because it provides internal validity (accurate and precise results of consumer liking) at the expense of external validity (relevance to the marketplace) as described by van Trijp and Schifferstein (1995). In

Consumers and texture

37

addition, it has been shown not to translate well into Spanish (Curia et al., 2001) and several Asian languages (Yeh et al., 1998). The most reported complaint is the different meaning of the word “extreme” in these languages. Some illustration of results obtained from hedonic ratings is given in Fig. 2.1, which is a frequency plot of responses given in each category for various products. These results point out several of the challenges associated not only with the nine-point hedonic scale but also with most other hedonic scales. First is the type of distribution yielded by hedonic scales. With scales featuring a neutral category, it is often seen that the “neither like nor dislike” category is grossly under-represented. An obvious observation is that the distributions do not closely follow a normal distribution, rendering the use of analysis of variance questionable. Figure 2.1 also points out that product C was not perceived uniformly by the consumer group studied. Under these conditions, the mean hedonic scores for products B and C would be very similar although the distributions of the scores are quite dissimilar, leading to potentially erroneous conclusions. These results point out the need, when dealing with consumer data, to assess the distribution of the responses and to ensure that the distributions are unimodal (i.e. that a relative consensus exists among the group about the hedonic status of the products).

2.3.2 Just-about-right scales Another type of scale used for the optimisation of attribute levels in food products is the just-about-right (JAR) scale. Individual samples can be rated 35

Number of responses

30

A B C

25 20 15 10 5 0 Dislike Dislike very Dislike Dislike extremely much moderately slightly

Neither Like Like Like very dislike nor slightly moderately much like

Like extremely

9-point hedonic scale

Fig. 2.1 Distribution of consumer acceptance scores for texture of three candy bars. The question to consumers was worded as follows: Considering only the texture of this product, which of these statements best describes your impression?

38 Texture in food

on a JAR scale as “too weak,” “too strong” or “just-about-right” along a particular continuum such as tenderness or crispness. Data that are normally distributed around the center of the scale (i.e. the “just-about-right” point) are indicative of an optimized level of the continuum or attribute. It is important in this technique to examine the distribution of the raw data. For example, a consumer panel might contain one segment that prefers products with one set of sensory characteristics and another segment that prefers the same product with a completely different set of sensory characteristics. This is illustrated in Fig. 2.2 which is a distribution of scores on a JAR scale for the crispness of cheese sticks. In this example, the appropriateness of the level of crispness in a fried cheese stick appetizer was assessed with a panel comprising 180 individuals. The data clearly shows that it is not distributed around the just right score, although the mean value of scores would be close to the JAR point. Instead, the distribution of the data is bimodal with roughly equal numbers of consumers who found the product to be too soggy or too crispy. The appropriateness of the cheese texture for the same product was evaluated by the same consumer panel and the distribution of scores is given in Fig. 2.3. For cheese texture, there was a much greater consensus among consumers as to the appropriateness of the level of cheese firmness. This is in sharp contrast to results from Fig. 2.2. These two examples illustrate the importance of data distribution assessment of consumer data before more advanced analyses are performed. This is especially true when means are to be used for further analyses such as special cases of preference mapping. These methods will be discussed later in this chapter.

60

Number of responses

50 40 30 20 10 0 Much too soggy

Too soggy

Just about right Too crisp Crispness JAR scale

Much too crisp

Fig. 2.2 Distribution of the JAR scores for crispness of a fried cheese stick product.

Consumers and texture

39

140

Number of responses

120 100 80 60 40 20 0 Much too soft

Too soft

Just about right

Too firm

Much too firm

Cheese texture JAR scale

Fig. 2.3 Distribution of JAR scores for the cheese texture of a fried cheese stick product.

2.4 Understanding consumer preferences Moskowitz and Jacobs (1987) described several methods for developing an understanding of the importance of texture in determining the relative importance of food texture to the product acceptance. The simplest method they described is the direct rating of importance. With this method, consumers are asked to rate the importance of various product attributes, including texture, on a scale from 0 to 100. By comparison of mean values for the various attributes, one can evaluate if texture plays an important role in determining overall acceptance. However, this method has limitations in that it does not involve product testing and relies on the a priori judgement of the subject. It is similar to that employed by Szczesniak and Kahn (1971). A more popular approach has been to correlate attribute ratings to acceptance of foods. Several approaches have been described. Researchers often look at correlations between acceptance scores and intensity ratings in an attempt to assess the relative importance of specific texture attributes in consumer acceptance. Although many have used correlations to understand consumer response, there are some limitations to this approach. This is due to the fact that correlations evaluate linear relationships, and it is well known that attribute intensity and acceptance are not necessarily linearly related. Although it is probably true that, in cultures such as that of the USA, the sweeter the cola beverage the more acceptable it is, this is not necessarily true for most food products.

40 Texture in food

2.4.1 Linear relationships Example: Poultry breast meat One example of linear relationship between perceived intensity and acceptance of specific attributes is given in Fig. 2.4. In this consumer study, 80 panellists were presented with ten treatments of poultry breast meat (i.e. deboned at various times post mortem) representing a vast array of tenderness intensities. Among other questions, consumers were asked to rate the acceptance of the product’s texture on a nine-point hedonic scale (1 = dislike extremely and 9 = like extremely) and their perceived intensity of the various treatments on a nine-point category scale anchored at the extreme categories with extremely tough and extremely tender. Results show that the relationship between perceived intensity and acceptability was linear and that the more tender the product, the more acceptable it was to this group of consumers. This is not to say that in a different context, the results could not have been different. In this study, no common additives designed to enhance the tenderness of poultry meat were added. One can imagine that a product so tender that it became “mushy” could be produced and that its acceptance would be lower than for products of optimal tenderness. However, in many practical cases related to texture attributes of food the relationship between acceptance and perceived intensity will be linear.

2.4.2 Non-linear relationships When the relationship is known to be non-linear, it is often parabolic where the liking for a product will first increase with increasing levels of the

8.0

R 2 = 0.88

Perceived tenderness intensity

7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

Acceptance of poultry breast meat tenderness

Fig. 2.4 Relation between the acceptance of poultry breast meat tenderness and its perceived tenderness intensity.

Consumers and texture

41

attribute, peak and then decrease as the intensity of the attribute continues to increase. The peak has been described as the bliss point, and the shape of the curve, known as the inverted U or L function, relating hedonic response to stimulus intensity was first proposed by Wundt (Moskowitz and Jacobs, 1987). It should be noted that the relationships between the sensory experience or the hedonic response and the physical stimulus are governed by very different laws as the relationship between perceived intensity and stimulus intensity can usually be modelled by a power law (Stevens, 1975).

2.4.3 Single attribute analysis A common model described by Moskowitz (1980) is of the quadratic form: liking = A + B(intensity) + C(intensity)2

[2.1]

The main advantage of the equation is that it allows liking to follow a parabolic response, but does not force it if the relationship is not of that form. The relative importance of the attribute can be calculated as the absolute value of the partial derivative of Eq. [2.1] and is defined as: importance = |B + C(intensity)|

[2.2]

This equation implies that the importance of a single attribute for liking is dependent on the level of that attribute, which is a far superior approach to that of the simple correlations. One limitation of this approach is that liking is related to a single sensory attribute, disregarding the fact that multiple sensory texture attributes may be present in the product and that they may all influence liking. Even if multiple attributes are used to model liking, the correlation among the predictors is not being considered.

2.4.4 Multivariate analysis techniques Other researchers have considered multivariate analysis techniques to assess the relationship between liking and texture attributes ratings. Although it is not the primary purpose of this chapter to discuss these techniques in detail, some of the basics should be covered as they often help to elucidate the likes or dislikes of consumers for specific food products. Preference mapping techniques have been described in detail in publications such as that of MacFie and Thomson (1999). Their development came from the inability of sensory scientists to fully understand consumer reactions (acceptability) to specific food products. This comes from the simple fact that consumers have a limited vocabulary when it comes to describing perceived sensory attributes of food. As a general rule, they confine themselves to commenting about very general terms or to making vague statements such as “I disliked the texture of this product”. It is obvious that this type of data is not very actionable. To remedy these problems, researchers will usually try to ask more pointed questions about specific product attributes. For example, for

42 Texture in food

dairy beverages or nutritional supplements, one may consider asking about two main texture attributes, thickness/consistency and chalky or smooth. If the attribute ratings are that of perceived intensities or appropriateness level and a sufficient number of products (six or more) are evaluated by consumers, it is then possible to predict the acceptance of a product (i.e. overall acceptance or acceptance of texture) from attribute ratings. Figure 2.5 is an illustration of such a concept. In this study, 10 chocolate nutritional beverages were evaluated by 150 consumers. The objective of the study was to identify the drivers of product acceptance, including important texture attributes. The nine-point hedonic scale was used to assess the overall degree of liking by consumers while the appropriateness of the level of color, chocolate flavour, sweetness, tartness, smoothness and thickness was assessed using JAR scales. Anchors used for the JAR scales are given in Table 2.2. PC2 (20 %, 17 %) 0.8 C Thickness

0.6 H 0.4 B 0.2

I

G 0 –1.2

–1

–0.8

–0.6

–0.4

–0.2 0 –0.2 –0.4

F J

0.2

0.4

Color

Acceptance A Smoothness

0.6 0.8 Sweetness

PC1 (57 %, 59 %)

1

Chocolate

–0.6 Tartness E

–0.8

D

–1 –1.2

Fig. 2.5 Partial least squares regression of acceptability versus diagnostic ratings for 10 chocolate nutritional beverages. Table 2.2 Diagnostic attribute scales used to assess the appropriateness of various attributes of nutritional beverages Color Chocolate Sweetness Tartness Thickness Smoothness

Much too light Much too weak Not nearly sweet enough Not nearly tart/ sour enough Much too thin

Too light

Not sweet enough Not tart/ sour enough Too thin

Much too coarse/chalky

Too coarse/ chalky

Too weak

Just about right Just about right Just about right Just about right Just about right Just about right

Too dark Too strong Too sweet Too tart/ sour Too thick Too smooth

Much too dark Much too strong Much too sweet Much too tart/sour Much too thick Much too smooth

Consumers and texture

43

Consumer data was then analyzed by partial least squares (PLS) regression to predict consumer acceptance scores from attribute ratings. Results of this analysis are given in Figs 2.5 and 2.6. Figure 2.5 shows the product map, which is a spatial representation of products, acceptance and diagnostic attribute ratings. This map allows the interpretation of several pieces of information. First it allows the examination of the relationship between the acceptance dimensions and the diagnostic ratings. Figure 2.5 is a twodimensional scatter plot of X (attribute ratings) loading weights and Y loadings (acceptance) for two specified components from PLS. It shows the importance of the different variables for the two components selected and can thus be used to detect important predictors and understand the relationships between X (i.e. attribute ratings) and the Y-variable (i.e. acceptance). To interpret the relationships between attribute ratings and the product acceptance, start by looking at your response (Y) variable. Predictors (attribute ratings), projected in roughly the same direction from the center as a response, are positively linked to that response. Predictors projected in the opposite direction have a negative link. Predictors projected close to the center are not well represented in that plot and cannot be interpreted. In our example, the projection of the diagnostic attributes onto the acceptance vector allows the determination of the most important drivers of liking. Attributes which load most highly on the acceptance vector, either positively or negatively, are the most important drivers of acceptance. In our example, the most important drivers seemed to be smoothness and sweetness as positive drivers (i.e. those for which high attribute ratings resulted in greater acceptance) and tartness and thickness as negative drivers (i.e. those for which high attribute ratings resulted in lower acceptance).

Weighted regression coefficients

1.50E-01

1.00E-01

5.00E-02

0.00E+00

–5.00E-02

–1.00E-01 Color

Chocolate

Sweetness

Tartness

Thickness

Smoothness

Fig. 2.6 Weighted regression coefficients for the PLS model predicting overall acceptance from diagnostic attribute ratings for 10 nutritional beverages.

44 Texture in food

This interpretation is confirmed by Fig. 2.6 which is a plot of the corresponding weighted regression coefficients for a PLS model with two factors. Weighted regression coefficients rather than raw are useful when the relative importance of a specific predictive variable needs to be determined. In Fig. 2.6, it is clear that smoothness and sweetness are the most important positive determinants of product acceptance while thickness and tartness are the only negative drivers of acceptance. In addition, predictive attribute variables close to each other in the loading plot (Fig. 2.5) will have a high positive correlation if the two components explain a large portion of the variance of X. The same is true for variables in the same quadrant lying close to a straight line through the origin. Variables in diagonally-opposed quadrants will have a tendency to be negatively correlated. In our example, sweetness and smoothness are highly correlated while chocolate and thickness were negatively correlated. One should be reminded that correlations do not imply causality and that they are often a function of the product set chosen for the experiment. The overlay of product loadings in the space also allows for the interpretation of the product’s sensory dimensions using a multivariate approach. The plot can be used to interpret the products’ sensory properties. Look for variables projected far away from the center. Samples lying in an extreme position in the same direction as a given variable have large values for that variable; samples lying in the opposite direction have low values. For example, product A has the highest smoothness while product C has the highest thickness.

2.5 Challenges to understanding consumer preferences Although this simple example seems to provide the necessary elements for a product developer to optimize this type of product, it is rarely the case in practice. This is due to the fact that, to gain valuable information and a complete picture of the drivers of liking, one has to get several things right in the design of the experiment. First, the right questions need to be asked of consumers, and that is where the greatest challenge lies. The major limitation is that consumers have a very limited vocabulary and that words within that vocabulary do not necessarily have the same meaning for all. The example previously described is a good illustration of this phenomenon. The attribute smoothness and the JAR scale developed to establish its level of appropriateness in the products tested was the result of long debates with the industrial client for which the test was performed. The debate centred around the fact that smoothness and chalkiness are not necessarily mutually exclusive of each other. This seems especially true since the temporal perception of both attributes is not the same. While smoothness is perceived while manipulating the product in the oral cavity, chalkiness is often perceived most intensely after swallowing. In addition, the meaning of chalkiness to consumers can vary greatly as part of the population, at least in the USA, associates chalkiness as a texture

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attribute while others think most readily of chalkiness as a visual attribute such as that of chalky rice or halibut. It is obviously of utmost importance for consumers to understand the scales they are asked to use, and confusion in terminology is often a difficult problem to overcome. In the previous example, it was decided that the scale anchors should state coarse/chalky instead of simply chalky although one could argue the use of the word coarse rather than gritty. Because of the challenges associated with understanding consumer responses and communicating effectively with them, many have shied away from trying to gather detailed information about the quality of the products tested from consumers. Many studies with consumers are now strictly designed to assess the overall degree of liking of food products. This leaves sensory scientists with little information to understand the underlying reason for the liking or disliking of product sensory qualities by consumers. The researcher is then left with explaining consumer liking from other sources of data.

2.5.1 External preference mapping External preference mapping is a technique designed to explain consumer results from data not generated by consumers (i.e. external data). The most common source of external data is sensory profiling data generated by trained descriptive panels. However, for texture, rheological data can also be used. We will give here several examples of such methods. Example 1: Jasmine rice In Suwansri et al. (2002), external preference mapping principles were applied to understanding the acceptance of Jasmine rice by Asian consumers living in the USA. Since the Asian population in the USA is very discriminating about the quality of the rice they consume, there is a need to assess the likes and dislikes of this population segment and to evaluate the key sensory differences between domestic and imported aromatic rice. The objectives of this study were to assess the acceptability of both domestic and imported Jasmine rice by US Asian consumers and to correlate the US Asian consumers’ acceptance of Jasmine rice to descriptive sensory data so that acceptance drivers could be identified. Fifteen commercial “Jasmine” rices consisting of three domestic varieties and 12 imported products were evaluated by a trained sensory panel and by 105 Asian families. An in-home-use test was used to gather consumer data of the 15 products, including acceptance data of rice texture which is usually a major driver of acceptance. A nine member panel, well trained in descriptive analysis of rice, evaluated five visual, 16 flavour and 11 texture attributes for all 15 Jasmine rice samples. Visual, flavour and texture lexicons were developed by the trained panellists during three orientation sessions (Meullenet et al., 1998). Results showed that this consumer group preferred imported over domestic

46 Texture in food

Jasmine rice. However, the univariate analysis of the descriptive data did not clearly point out the weaknesses of the domestic Jasmine products. The use of external preference mapping clearly identified the differences between domestic and imported products. As can be seen on Fig. 2.7, a bar chart of weighted regression coefficients for the descriptive attributes used to predict texture acceptance scores, there are two main constructs, hardness and stickiness, describing the texture of cooked rice. Manual stickiness, initial cohesion, adhesion to lips, cohesiveness, cohesiveness of mass and toothpull were identified as positive drivers of acceptance. From the texture lexicon, it is clear that these attributes are all related to the stickiness of rice. Hardness and roughness of mass, attributes that are usually highly correlated to each other, were found to be negative drivers of texture acceptance by consumers. These two attributes are indicative of firmness in rice. From this data, it could be implied that Asian consumers of Jasmine rice like it to be sticky and less firm. Although this is true in this particular context, one would have to be cautious about generalizing this type of result. This is definitely true in this instance as Jasmine rice is classified as a long grain rice not particularly known for being sticky. A previous study which was performed with a similar consumer group on a much wider range of rice products indeed drew different conclusions (Meullenet et al., 2000) as both hardness and stickiness (or attributes related to these two constructs) were negative drivers of consumer acceptance. This shows that the results obtained from preference maps are highly dependent on the samples selected for testing. In the case of Suwansri et al. (2002), the samples were carefully selected to represent products from a niche market. This allowed the identification of the sensory weaknesses of Jasmine rice samples produced in the USA and provided insight for rice breeders for improving the existing cultivars.

Weighted regression coefficients

0.3

0.2

R 2 = 0.61 RMSEP = 0.39 Manual Initial Adhesion stickiness cohesion to lips

Cohesiveness Cohesiveness of mass Toothpull

0.1

0

–0.1 Hardness

Roughness of mass

–0.2 Texture attributes

Fig. 2.7 Weighted regression coefficients for the PLS predicting the acceptance of cooked white rice texture from texture attributes evaluated by a descriptive panel.

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Example 2: White corn tortilla chips Another example of external mapping applied to understanding the drivers of acceptance for the texture of white corn tortilla chips is that published by Meullenet et al., 2002. In that study, 11 white corn tortilla chip products were evaluated by a group of heavy users of the product. In addition, a descriptive panel profiled the products for appearance, flavor and texture attributes. Figure 2.8 gives the weighted regression coefficients for the prediction of the acceptance of texture in the 11 products by consumers from the texture profiles generated by the panel. A total of 13 attributes were assessed by the descriptive panel. However, it is unlikely that all of these attributes are contributors to liking by consumers. Therefore, for multivariate regression, statistical techniques need to be employed to reduce the number of predictors to those that are significant contributors. The approximate uncertainty variance of the regression coefficients can be estimated and a t-test performed for each element relative to its estimated uncertainty variance, giving the significance level for each parameter. All parameters with p < 0.05 were retained in the model. This allows for removal of independent variables either not influencing the prediction or creating noise in the model, a procedure which reduces “the uncertainty in the prediction models” and, in most cases, improves the validation statistics. In this study, only important variables selected by the Jack-knife optimisation method (i.e. hardness, crispness, loose particles and oily/greasy film) were included in the predictive models to predict consumer acceptance. R2 and RMSEP (root mean square of prediction) were 0.96 and 0.34, respectively, for the optimal PLS model predicting consumer texture acceptance from descriptive texture attributes. The weighted regression coefficients (Fig. 2.8)

Weighted regression coefficients

0.6 0.4 0.2 0 –0.2 –0.4 –0.6

Hardness

Crispness

Loose particles

Oily/greasy film

Attributes

Fig. 2.8 Weighted regression coefficients for the PLS predicting the acceptance of white corn tortilla chips texture from texture attributes evaluated by a descriptive panel.

48 Texture in food

show that the first bite attributes of hardness and crispness, surface characteristic of loose particles and residual characteristic of oily/greasy film were important contributors to the consumer acceptance of tortilla chip texture. Crispness and loose particles contributed to increased consumer acceptance of texture while hardness and oily/greasy film decreased consumer acceptance. Crispness is one of the most important texture attributes for potato chips (Smith, 1975; Segnini and Dejmek, 1999). Since tortilla chips and potato chips are similar salty snacks, it was expected that crispness of tortilla chips would positively contribute to overall acceptance.

2.6 Future trends Although it is difficult to foresee the future of this type of research, one certain fact is that consumers will remain difficult to understand. This implies that progress toward understanding consumer perception and acceptance of food texture will rely on the development of novel methods of data analysis and of improved methods for probing consumers. Many have documented the limitations of the internationally popular ninepoint hedonic scale developed by Peryam and Girardot (1952). These include the inequality of intervals between the semantic labels (Jones et al., 1955; Moskowitz and Sidel, 1971; Moskowitz, 1980), the presence of the neutral category (neither like nor dislike) and shortcomings of bipolar attitude scales (Olsen, 1999), and the central tendency (i.e. avoidance of end categories) issues (Stevens and Galanter, 1957). In the recent past, several researchers (Schutz and Cardello, 2001; Henderson and Shewfelt, 2002; Villanueva et al., 2002) have proposed alternatives to the scale widely used now for over 50 years. The most significant contribution is probably that of Schutz and Cardello (2001) who used modulus free magnitude estimation to develop a labelled affective magnitude (LAM) scale using the original labels from the nine-point hedonic scale. The results showed that the problems associated with the nine-point hedonic scale were minimised and that the data was shown to closely follow a normal distribution. This recent development provides an effective alternative to the problematic nine-point hedonic scale and the difficult to implement magnitude estimation scales. Another recent development is the application of alternative statistical methodologies for performing preference mapping. Because preference mapping is critical to understanding consumer acceptance of food texture, these methods have the potential to make a significant contribution. The methods described to date mostly rely upon deterministic modelling where the data dealt with is assumed to be normal. Several examples given in this chapter have demonstrated that this assumption is often violated. Consumer acceptance responses are usually ordinal categories. Ordinary least square (OLS) regression can be used for preference modeling when the ordinal response (Y) is treated as a continuous variable. As a result, with OLS

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regression models, the mean scores of the ordinal response and the information on the structure (frequencies) of the ordinal response are lost. From a research point of view, the information from the structure of the ordinal response is often more meaningful and useful than that from the mean scores of the response. OLS regression requires that the distribution of error be normal. However, for the limited number of response categories used in consumer testing, or if the nine response categories (i.e. nine-point hedonic scale) are collapsed to five categories (i.e. end of scale avoidance), the normal distribution assumption will not be satisfied. In addition, OLS regression may produce extreme predictions that may be out of the categorical range. These drawbacks have limited the use of OLS regression in preference modeling. Proportional odds models (POM) do not have these limitations and could be an alternative for preference modeling. They are widely used in categorical data analysis in health science (Agresti, 2002) and have recently been applied to sensory data from a study on consumer acceptance of canola oil (VaiseyGenser et al., 1994), qualitative studies of food choice (Tepper et al., 1997) and consumer acceptance of oca cultivars (Sangketkit et al., 2000). The major advantages of the proportional odds model analysis are that it can apply to ordinal categorical responses, model the structure (frequencies) of the categorical responses and estimate the mean scores of the responses. Its invariance to choice of response categories is also an advantage (Agresti 2002). However, there exists little information on the application of proportional odds models for internal and external preference modeling besides that published by Meullenet et al. (2003), Malundo et al. (2001) and Jones and Wang (2000). Although PLS regression and POM are two commonly used methods in preference mapping, if the predictor variables are “JAR” scores, these linear models seem not to be appropriate, at least in most cases, because the relationship between the response and the JAR predictors is no longer linear. Multivariate adaptive regression splines (MARS), developed in 1991 by world-renowned Stanford physicist and statistician Jerome Friedman (Friedman, 1991), can automate variable selection as well as model selection. It has been proven effective in a variety of learning problems and is competitive with neural networks and non-parametric regressions (Dwinnell, 2000). MARS can be used to uncover underlying non-linear relationships between response and predictors in a piecewise linear regression function, which could be useful for modeling the JAR data. It is only in very recent years that MARS has become widely known in the data mining and business intelligence communities due to the availability of the commercial MARS software program (MARS, 2001). Recently, MARS was applied to the prediction of consumer acceptance of fried cheese stick texture from JAR data. One of the main advantages of MARS is its ability to determine the effect that JAR scores on both sides of the just right level have on the response variable (i.e. acceptance of texture). Figure 2.9 shows that for crispness, the drop rate was faster over the region

50 Texture in food 3

Texture acceptance

Texture acceptance

4 3 2 1

2

1

0

0 0

1

2 3 Crispness score (a)

4

5

0

1 2 3 4 Cheese texture score (b)

5

Fig. 2.9 Contributions to texture acceptance from crispness (a) and cheese texture (b) JAR scores.

of 3 to 5 than over the region of 3 to 1, suggesting that “too crispy” was more harmful to the acceptance of texture than “not crispy enough”. For cheese texture (i.e. 1 = much too soft/melted, 5 = much too firm/not melted), the drop rate was slower over the region of 3 to 5 than over the region of 3 to 1, implying that “too soft/melted” had more detrimental effects on texture acceptance than “too firm/not melted”. MARS, similarly to PLS regression, has the ability to determine the relative importance of variables toward the prediction of the response variable (100 and 96.73 % for crispness and cheese texture, respectively). Crispness had slightly more contribution to determining texture acceptance than did cheese texture. The R2 between the observed and predicted mean scores of texture acceptance was 0.95, indicating that the fitted MARS model could accurately predict the mean score for texture acceptance.

2.7 Conclusions Although a considerable amount of research has been done on food texture, most of this work has focused on the development of sensory and rheological methods designed to assess the effects of parameters such as processing conditions or formulation on food texture. The number of studies dealing with consumers and their expectation of texture in various food products has been limited. In a sense, texture remains the overlooked attribute (Szczesniak, 1990). However, the techniques used for understanding consumers have vastly improved and, with methods such as preference mapping and the like, there is an opportunity to expand studies focusing on consumers’ perception, expectation and acceptance of and preference in food texture.

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2.8 References AGRESTI A

(2002) Categorical data analysis (Second Edition). New York, Wiley.

BRENNAN J G (1980) Food texture measurement. In Developments in Food Analysis Techniques-

2. Ed. R D King, London, Applied Science Publishers, 1–78. and MARGALEF M I (2001) How Argentine consumers understand the Spanish translation of the 9-point hedonic scale, Food Qual. Pref. 12, 217–21. DWINNELL W (2000) Exploring MARS: an alternative to neural networks, PC AI, 14(1), 21. FRIEDMAN J (1991) Multivariate adaptive regression splines, The Annals of Statistics, 19, 1–141. HENDERSON J D and SHEWFELT R L (2002) Evaluation of scales to measure consumer acceptability, 2002 IFT Annual Meeting Book of Abstract, Anaheim, CA. JONES B and WANG J (2000) The analysis of repeated measurements in sensory and consumer studies, Food Qual Pref, 11, 35–41. JONES L V, PERYAM D R and THURSTONE L L (1955) Development of a scale for measuring soldiers’ food preferences, Food Res, 20, 512–20. MACFIE H J H and THOMSON D M H (1999) Measurement of Food Preferences, Gaithersburg, MD Aspen. MALUNDO T M M, SHEWFELT R L, WARE G O and BALDWIN E A (2001) An alternative method for relating consumer and descriptive data used to identify critical flavor properties of mango (mangifera indica L.), J Sens Stud, 16, 199–214. MARS (2001) MARS User’s Guide, Salford Systems. MEULLENET J F, GROSS J, MARKS B P and DANIELS M (1998) Sensory profiling of cooked rice and its correlation to instrumental parameters using an extrusion cell, Cereal Chem, 75, 714–20. MEULLENET J-F, GRIFFIN V K, CARSON K, DAVIS G, DAVIS S, GROSS J, HANKINS J A, SAILER E, SITAKALIN C, SUWANSRI S and VASQUEZ CAICEDO A L (2000) External rice preference mapping for Asian consumers living in the United States, J Sens Stud, 16, 73–93. MEULLENET J-F, XIONG R, MONSOOR M, BELLMAN-HORNER T, ZIVANOVIC S, DIAS P, FROMM H and LIU Z (2002) Preference mapping of commercial toasted white corn tortilla chips, J Food Sci, 67, 1950–57. MEULLENET J-F, XIONG R, HANKINS J A, DIAS P, ZIVANOVIC S, MONSOOR M A, BELLMAN-HORNER T, LIU Z and FROMM H (2003) Modeling preference of commercial toasted white corn tortilla chips using proportional odds models, Food Qual Pref, 14, 603–14. MOSKOWITZ H R (1980) Psychometric evaluation of food preferences, J Foodservice Systems, 1, 149–67. MOSKOWITZ H R and JACOBS B E (1987) Consumer evaluation and optimizaton of food texture. In Food Texture: Instrumental and Sensory Measurement. Ed. H R Moskowitz, New York, Marcel Dekker Inc, 293–328. MOSKOWITZ H R and SIDEL J L (1971) Magnitude and hedonic scales of food acceptability, J Food Sci, 36, 677–80. OKABE M (1979) Texture measurement of cooked rice and its relationship to eating quality, J Text Stud, 10, 131. OLSEN S O (1999) Strength and conflicting valance in the measurement of food attitudes and preferences, Food Qual Pref, 10, 483–94. PANGBORN R M, GUINARD J X and MEISELMAN H L (1989) Evaluation of bitterness of caffeine in hot chocolate drink by category, graphic and ratio scaling, J Sens Stud, 4, 31–53. PERYAM D R and GIRARDOT N F (1952) Advanced taste-test method. Food Eng., 24, 58–61. SANGKETKIT C, SAVAGE G P, MARTIN R J, SEARLE B P and MASON S L (2000) Sensory evaluation of new lines of oca (Oxalis tuberosa) grown in New Zealand, Food Qual Pref, 11, 189–99. SCHUTZ H G and CARDELLO A V (2001) A labeled affective magnitude (LAM) scale for assessing food liking/disliking, J. Sens. Stud, 16, 117–59. CURIA A V, HOUGH G, MARTINEZ M C

52 Texture in food and DEJMEK P (1999) Relationship between instrumental and sensory analysis of texture and color of potato chips, J Text Stud, 30, 677–90. SHEPHERD R, GRIFFITHS N M and SMITH K (1988) The relationship between consumer preferences and trained panel responses, J Sens Stud, 3, 19–35. SMITH O (1975) Potato chips. In Potato Processing. Eds W F Talbut and O Smith, Westport, CT, Avi Publishing Co, 305–402. STEVENS S S (1975) Psychophysics: an Introduction to its Perceptual, Neural, and Social Aspects, New York, John Wiley. STEVENS S S and GALANTER E H (1957) Ratio scales and category scales for a dozen perceptual continua, J Exp Psych 54, 377–411. SUWANSRI S, MEULLENET J-F, HANKINS J A and GRIFFIN K (2002) Preference mapping of domestic/ imported jasmine rice for US Asian consumers, J Food Sci, 67, 2420–31. SZCZESNIAK A S (1963) Classification of textural characteristics, J Food Sci 28, 385. SZCZESNIAK A S (1971) Consumer awareness of texture and of other food attributes, J Text Stud, 2, 196. SZCZESNIAK A S (1990) Texture: is it still an overlooked attribute? Food Tech, 9, 86–95. SZCZESNIAK AS AND KAHN E L (1971) Consumer awareness of and attitudes to food texture, J Text Stud, 2, 280–950. TEPPER B J, YOUNG S C and NAYGA R M (1997) Understanding food choice in adult men: influence of nutrition knowledge, food beliefs and dietary restraint, Food Qual Pref, 8(4), 307–17. VAISEY-GENSER M, MALCOLMSON L J, RYLAND D, PRZYBYLSKI R, ESKIN NAM and ARMSTRONG L (1994) Consumer acceptance of canola oils during temperature-accelerated storage, Food Qual Pref, 5(4), 237–43. VILLANUEVA N D M, DA SILVA M A A P and PETENATE A J (2002) Performance of the selfadjusting and hybrid hedonic scales in the generation of internal preference maps, 2002 IFT Annual Meeting Book of Abstract, Anaheim, CA. VAN TRIJP H C M and SCHIFFERSTEIN H N J (1995) Sensory analysis in marketing practice: comparison and integration, J Sen Stud, 10, 127–47. YEH L L, KIM K O, CHOMPREEDA P, RIMKEEREE H, YAU N J N and LUNDAHL D S (1998) Comparison in use of the 9-point hedonic scale between Americans, Chinese, Koreans and Thai, Food Qual and Pref, 9, 413–19. SEGNINI S

3 Texture and mastication A. C. Smith, Institute of Food Research, UK

3.1 Introduction Texture perception is an important factor in consumer sensory appreciation (Wilkinson et al., 2000). Bourne (2002) defined the textural properties of a food as the group of physical characteristics that: 1 arise from the structural elements of the food; 2 are sensed by the feeling of touch; 3 are related to the deformation, disintegration and flow of food under force; 4 are measured objectively by functions of mass, time and distance. Brennan (1989) defines perceived texture as the attribute of a substance resulting from a combination of physical properties and perceived by the senses of touch, sight and hearing. Figure 3.1 shows the involvement of the various senses in eating. This chapter considers the sensory and instrumental definitions of texture before describing the different facets of mastication. Aspects of the mastication process are described and various in vivo techniques are described which join, albeit tenuously, sensory and instrumental texture. Muscle activity, acoustic emission, force and displacement have been measured. Subjective tasks such as event recording and time-intensity may be added as part of multi-tasking combined with objective monitoring of the subject while eating. Having discussed the wherewithal of the data acquisition, the next section stands back and considers the principal aspects of chewing, swallowing, salivation and bolus formation. The wider context of texture and mastication is considered, drawing on some current emphases such as flavour and nutrient release, the

54 Texture in food

Observation

Vision

Handling

Consumption – biting – chewing – manipulation – swallowing

Vision, hearing, somesthesis, kinesthesis

Hearing, somesthesis, kinesthesis

Fig. 3.1 The involvement of the senses in texture perception during the process of food consumption. (Reprinted from ‘From food structure to texture’, Wilkinson C, Dijksterhuis G B and Minekus M (2000), Trends in Food Science and Technology, 11, 442–50, with permission from Elsevier).

possibilities for modelling and the advances now possible with modern data analysis. A brief summary of research by commodity and related reviews conclude the chapter. Texture is a key factor in influencing consumer acceptability. Texture comprises visual, followed by tactile and in the mouth senses (Peleg, 1980). Szczesniak and Kahn (1971) described perception in the mouth as a mixture of conscious and unconscious processes, the awareness being accentuated when visual expectations are violated.

3.1.1 Sensory and instrumental texture Instrumental measurements are only as relevant as their predictive power with regard to sensory attributes. Szczesniak (1963) linked texture to sensory, structural and physical parameters.The subtlety is that instrumental measurements are not always focussed on texture but on maturity, in the case of fresh fruits and vegetables, or susceptibility to damage in transport or processing. Sherman (1979) expressed texture as the composite of those properties arising from the structural elements and the manner in which they register with the physiological senses. Jowitt (1974) stated that the appreciation of texture involves the interaction between both motor and sensory components of the masticatory and central nervous systems. Kilcast and Eves (1993) summarise this as ‘the complex reactions occurring during the chewing of food are all integrated by the brain into the sensation perceived as texture’. Peleg (1983) points out that sensory terms can be used interchangeably and cites crunchy, crisp and brittle as overlapping as do firm, tough and hard. Instrumental tests can be fundamental, empirical and imitative (Bourne, 2002). In the context of this chapter on texture and mastication imitative tests are most worthy of emphasis. In selecting fruit ripeness the tactile assessment by squeezing is often used in sorting and grading. The use of dentures or opposing single teeth with a universal test machine are probably the most memorable imitative tests. However, there is often failure of a

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single instrumental measurement as a reliable texture descriptor. Mohsenin and Mittal (1977) commented that sensory tests generally correlated better with large strain failure instrumental tests than with small strain tests.

3.2 The mastication process Mastication needs to be seen as part of the wider oral processing of food, which can be divided into motility and secretory contributions. Oral processes affect breakdown of food in the mouth, and thence sensory perception, and depend on the properties of the food and their time dependence. The motility effect operates through lower jaw, tongue, cheeks and lips. Motility is needed to transport food to the pharynx for swallowing and to reduce particle size and to form a bolus for swallowing. If it is just placed in the mouth, this involves the tongue itself and pushing it against the palate. The tongue, cheeks and lips are able to push the food between particular teeth. Three stages have been identified (Hiiemae et al., 1996; Lund, 1991): Stage I involves transport from the front of the mouth to the premolars and is characterised by low amplitude jaw movements; Stage II reduces particle size; Stage III is pre-swallowing and the food moves to the back of the tongue by tongue–palate interaction. Food clearance uses tongue and jaw movements to remove food at the end of mastication and involves swallowing (Wilkinson et al., 2000). Swallowing occurs intermittently in a chewing sequence. Oral secretion of saliva is by the salivary glands located under the tongue, between the jaw bones, at the lower jaw and beneath the ear. Saliva is mixed with food during mastication to form a bolus for swallowing. Mastication is a process combining simultaneous food comminution and lubrication, although the formation of a cohesive bolus is seen as important (Prinz and Lucas, 1997). When food is introduced into the mouth, it is moved by the tongue and then pressed against the palate which serves to indicate surface morphology. Moistening with saliva and minor deformation give way to incision and chewing and the food is deformed and may be fractured (Lillford, 2001). The subject of oral motility in relation to food structure makes use of methods from oral health and speech therapy. Hence some studies have compared speech and mastication.

3.2.1 Physiology Oral processing of foods involves initial ingestion, incision and repetitive chewing and swallowing. The incision and chewing constitutes mastication. Mastication involves teeth, gums, palate, cheeks, tongue and lips and the movements of lower (mandible) and upper (maxilla) jaws together with the secretion from the salivary glands. The tongue has an important role in deciding whether particle comminution is sufficient and moist enough to swallow (Wilkinson et al., 2000).

56 Texture in food

Oral sensitivity varies with position in the body and after the finger tips, the tongue, peridontal membrane, lips and palate are particularly acute. Perception of texture and mouthfeel involves three distinct groups (Guinard and Mazzucchelli, 1996) of mechanoreceptors: 1 in the palate, tongue and gums; 2 in the peridonytal membrane surrounding the roots of the teeth; 3 of the muscles and tendons involved in mastication. All mechanoreceptors have characteristic nerve endings. The importance of physiology is at the scale of the receptor behaviour and also at the larger scale of oral processes and motility.

3.2.2 Jaw and teeth movement The teeth play an important part at different stages of oral processing. The first bite with the incisors is the part of the eating process which has been best emulated by texture measuring devices since it is closest in action to that of a unidirectional single deformation test in a universal test machine. The main chewing stage or mastication involves jaw movement and action of the teeth to break down foods. In some stages of jaw movement, such as moving the food into the mouth and manipulation to the molars, the teeth may not reach occlusion. Speeds are variable over a large range, but the teeth may move vertically and horizontally or with a myriad of combinations leading to compressive, tensile and shear forces. Food is bitten often at the extremes rapidly with low forces or slowly with high force. The lower jaw is attached to the head by the temporomandibular joint which enables a wide range of movements. Depression (mouth opening), elevation (mouth closing), protrusion (jaw moving forward) and retrusion (jaw moving backward) and lateral movements are possible (Boyar and Kilcast, 1986).

3.2.3 Muscle activity The muscle actions corresponding to the jaw movements described above are described in detail by Boyar and Kilcast (1986) and involve the masseter, temporal, pterygoid, mylohyoid, digastric and geniohyoid muscles (Fig. 3.2). Muscle fatigue is a phenomenon that is well charted, although it is rarely described in the context of the masticatory muscles.

3.3 Measuring mastication The complexity of eating or even the chewing phase makes high demands on a texture measuring instrument. Here there is a gulf with universal test machines which operate at one rate and in one direction or even instrumental tests such as texture profile analysis (TPA). One would ideally want to make measurements

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Temporalis

Masseter

Temporalis

Lateral pterygoid

Mylohyoid

Medialpterygoid

Temporalis

Geniogossus Geniohyoid Digastric

Fig. 3.2 Lateral view of the skull showing the muscle attachments. Inset: muscle attachments to the medial surface of the mandible. (Adapted from Romanes G J (1967), Cunningham’s Manual of Practical Anatomy, vol. 3: head and neck and brain, with permission from Oxford University Press).

of structure, displacement and force on the subject. A logical argument is to make objective measurements on people while they are chewing or eating. However, the act of measurement may affect the observations and hence should be as unintrusive as possible. Various textural techniques used to monitor mastication have been reviewed by Boyar and Kilcast (1986). Among these techniques, electromyography (EMG), the measurement of the electrical activity of muscles, has found wide application. EMG is a potential link between purely instrumental measures of food mechanical properties and sensory evaluations of consumers. It is complementary to recording sounds emitted during consumption (Section

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3.3.6) and tracking displacement using kinesiology (Section 3.3.5). Although forces between teeth and dentures have been measured, the local displacement information necessary to produce the same type of force-displacement data as generated by a universal test machine are not usually available simultaneously.

3.3.1 Electromyography (EMG) Electromyography is a technique to characterise the chewing patterns of foods which differ in texture (Agrawal et al., 1997; Brown et al., 1994; Kilcast and Eves, 1991). It forms something of a bridge between instrumental tests and sensory assessments since it is an objective measurement carried out on human subjects. Brown (1994) has shown the use of EMG to characterise consistent differences in chewing patterns between individuals, and reviewed its use in the literature. EMG characteristics depend strongly on muscle fibre length, position of the electrodes relative to the muscle fibres, electrode area and inter-electrode distance and fat thickness between skin and muscles (Lateva et al., 1993; Dimitrova et al., 2001). When the muscle lies close to the surface of the skin the EMG activity can be related to specific muscles using surface electrodes. Other electrodes need to be implanted for deeperlying muscles. In many studies the temporal and masseter muscles are used (Brown, 1994; Kilcast and Eves, 1993), although Plesh et al. (1987) and Hiiemae et al. (1996) describe the monitoring of the digastric muscle. In the study of surface muscles it is non-invasive, but Hiiemae et al. (1996) used unipolar needle electrodes to record from geniohyoid/genioglossus muscles. The EMG signal is amplified and recorded and then analysed as shown in Fig. 3.3. Filtering of the signal in various ways can take place to avoid mains Time intensity (T-I) slider

Data collection computer

Event marker button

ADC interface

Polygraph (4-channel amplifier)

EMG electrodes (left & right temporal and masseter)

Fig. 3.3 EMG, time intensity and swallow button block circuit diagram. (ADC: analogue to digital converter.)

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electricity spikes and other undulations. A large amount of data is possible with monitoring of four muscles and other time-dependent signals. Kilcast and Eves (1993) describe the integration of the signal of each muscle and the extraction of various gradients, heights and areas. Brown (1994) used software to pool and rectify the signals from four muscles. This aided identification of chews and swallow events. Subsequent data analysis extracted parameters: chew time, number of chews, chew rate and chew work. In combination with kinesiology (Section 3.3.5), the chew work was divided into that occurring during jaw closing in the vertical direction and subsequent horizontal movement at the end of the chew stroke. Swallows were also identified from EMG traces by the shape of the EMG chew bursts. Jack et al. (1993) defined the following from their integrated EMG response: number of chews, duration of muscle activity up to first swallow, duration of muscle activity after first swallow, height of first peak and also mean peak amplitude, total of peak amplitudes and also maximum peak amplitude, and chew frequency. Plesh et al. (1987) defined cycle duration, burst duration and interburst duration. Edlund and Lamm (1980) evaluated ‘masticatory efficiency’ as integrated maximum values of EMG activity in volts for the temporal and masseter muscles of five subjects. The mean values ranged from 384 µV for apple to 690 µV for white bread, with 612 µV obtained for silicone-based dental test material Optosil. Horio and Kawamura (1989) found that the mean EMG amplitude from the masseter muscles of 29 subjects decreased from about 818 µV to 187 µV as the TPA (see Bourne 2002) shear force fell from 6.3 kg to 0.1 kg. The TPA ‘gumminess’, defined as the chewing energy to bite through a cross-section of the sample, fell non-uniformly from 0.74 to 0.22 (no units). Plesh et al. (1993) defined a ‘kinematic index of stiffness’ for jaw movements as the three-dimensional speed divided by the three-dimensional path length, and found it decreased with increasing chew rate.

3.3.2 Other-myographys This section completes the picture for the related muscle measurement methods, although these are used in the context of muscles more commonly studied by occupational therapists. EMG signals are affected by muscle fatigue. The frequency spectrum becomes narrower with fatigue, losing parts of the highfrequency content (Herzog et al., 1994). Other ‘-myographys’ behave differently. Acoustic myography (AMG) in which muscle sounds are recorded from the vibrations of contracting muscle applied to quadricep muscles. Vibromyography (VMG) (Matheson et al., 1997) uses an accelerometer to measure lateral oscillations during contraction. The mean power frequency does not change, with fatigue, leading to the claim that it is a measure of intrinsic mechanical activity (Zwarts and Keidel, 1991). Interestingly VMG was found to discriminate better the absolute forces between subjects than EMG. In comparison with EMG, where the median frequency decreased continuously,

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an abrupt drop in the median frequency of the VMG signal occurred after fatigue. This change in signal was readily observed in the time domain for the raw data leading to identification of the onset of fatigue (Herzog et al. 1994).

3.3.3 Time-intensity and multi-tasking Time-intensity (T-I) techniques involve the recording of specific sensory attributes as a function of time (Lee and Pangborn 1986). Cliff and Heymann (1993) have reviewed the use of T-I techniques and their use for sensory flavour and texture. Wilson and Brown (1997) used combined EMG and TI in a study of mastication and flavour release from gels of differing mechanical properties, where they observed that the act of swallowing was often associated with a marked increase in flavour perception. Studies by Sprunt et al. (2002) and Wright et al. (2003) considered mastication and flavour release from gels incorporating different concentrations of flavour. The dual-attribute time-intensity (DATI) method has been developed recently (Duizer et al. 1996a; Duizer et al., 1997) for the collection of the perception of two attributes simultaneously. These authors suggested that the use of DATI should improve evaluation of interactions, allowing foods to be assessed in a more realistic manner. Duizer et al. (1996b) have studied the relationship between T-I, EMG and instrumental texture measurements of beef tenderness. More recently, Brown and Braxton (2000) have used combined EMG and T-I together with recording of jaw movement patterns in a study on biscuits, relating dynamics of food breakdown to perceptions of texture and preference. In this case T-I was used to monitor the wetness of the biscuit sample. Brown et al. (1996) studied the tenderness of meat and trained panellists recorded it as a function of time using a sliding potentiometer in conjunction with EMG. Significant correlations were obtained between perceived tenderness and the masticatory muscle chewing work rather than chewing time or number. Brown et al. (1996) comment that flavour perception starts at zero for a given stimulus, follows a smooth increase as the concentration of the stimulus is released and falls smoothly back to zero with increasing time. In contrast, texture perception may increase sharply on ingestion and may be little changed up to swallowing. Some researchers have investigated the concept of multi-tasking (asking panellists to perform at least two sensory assessment procedures at once). Larson-Powers and Pangborn (1978) asked panellists to use a foot-pedal to initiate chart recording when carrying out time-intensity measurements on beverages and gelatins. In evaluating tactile properties of skincare products, trained panellists can be asked to evaluate absorbency while rubbing at a rate marked to a metronome (Civille and Dus, 1991). A swallow button was used by Hiiemae et al. (1996) who also used videotape recording. Jack et al. (1994) used the event marker of their time-intensity

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computer module to mark chewing strokes in combination with EMG. Brown et al. (1996) used a microphone strapped over the larynx at the same time as EMG and T-I studies. EMG and T-I can be combined with a swallow indicator button for panellists consuming confectionery gels of differing flavour levels. There are opportunities for the subject to make subjective measurements as well, such as T-I curves and swallowing events (Fig. 3.4) (Sprunt et al., 2002). This non-invasive combination allows synchronous collection of chewing activity, swallowing events and T-I data. The extracted parameters from each recording session (with units) are as follows: T-I area, T-I duration, T-I maximum intensity, number of chews, time of last chew, chewing rate, number of swallows, time of first swallow, time of last swallow and swallow rate (Sprunt et al., 2002). 3.3.4 Force measurement Force transducers have been used in dental prostheses to measure bite forces directly. Yeh et al. (2000) used a cross-arch transducer and found that it was directly correlated with salivary flow. Van Eijden (1991) and Waltimo and Kononen (1994, 1995) have made bite force measurements, including the contribution from single and multiple teeth. Mioche et al. (1993) and Peyron et al. (1994) used an intra-oral load cell. Kilcast and Eves (1993) compared EMG integrated peak height with force for a subject biting on a force transducer using the molar teeth, which yielded a good linear relationship (Fig. 3.5). Tornberg et al. (1985) used 10 strain gauges in a dental prosthesis which enabled measurement of deformation rates in the order of 2000–4000 mm min–1 and the maximum strain for different samples.

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a magnet attached to the lower incisors, termed kinesiography. Bellisle et al. (2000) described ‘edograms’ which use a strain gauge resting on the subject’s cheek via a light headset to detect jaw movements, corresponding to chewing. Simultaneously they measured swallowing (deglutition), using a water-filled ballon mounted against the patient’s throat with a collar, the volume of which reduced and the resulting increased pressure was detected with a pressure transducer. The authors also describe the use of videoing subjects eating to deduce eating parameters for foods of different physical form, described in their case as traditional, sandwich and semi-liquid. Horio and Kawamura (1989) used a Kinesiograph (KI) to measure vertical movements of the lower jaw at the same time as making EMG measurements for the first five chewing strokes. They defined a tooth contact period and degree of jaw opening. Plesh et al. (1986, 1987, 1993) used EMG and a KI to study the jaw movements of subjects chewing gum, at individuals’ preferred rates, and also at rates prescribed using a metronome. The Myotronics Kinesiograph measured three-dimensional movement of a magnet on the lower central incisors using three magnetometers mounted on a light frame. They allowed subjects to chew at their preferred rate and also in time with a metronome at faster rates. Various parameters were identified from the KI traces, such as cycle duration, duration of opening, duration of closing and duration of occlusal. Brown et al. (1998) and Brown and Braxton (2000) also used EMG and KI and synchronised the two techniques for various foods. They were then able to apportion which phase of jaw movement corresponded to which part of the muscle work (Fig. 3.6) (see Section 3.3.1).

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Hiiemae et al. (1996) used a Sirognathograph with a magnet glued to the lower central incisors and detected movement in three directions, with one set of subjects depressing a swallow button and another set being recorded on videotape. Togashi et al. (2000) used a magnet ‘pasted’ to the gum of a lower molar. Three mutually perpendicular Hall probes resulted in the threedimensional tracking of tooth movement. They divided mastication movements into two parts, a rhythmical chewing period followed by an irregular period in preparation for swallowing. Subsequently Agrawal et al. (2000) made measurements of height and width of the chewing loop and closing angle of the jaw from Sirognathograph traces. Peyron et al. (1997) used an infrared tracking system using three cameras with four infrared emitting diodes mounted on a framework attached to the subject’s forehead and a fifth on the chin. From data analysis the movement of the chin relative to the skull was obtained. Ostry and Flanagan (1989) used a linear voltage displacement transducer (LVDT) with the transformer fixed relative to the skull with a modified hockey helmet and the core linked to the subject’s chin. The LVDT was

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oriented to capture the principal direction of motion of the jaw. These authors compared jaw movement in speech with mastication (of rubber tubes). They found amplitudes, velocities and times to be greater for mastication. They measured speaking and mastication rates at the subject’s preferred rate and at an imposed faster rate.

3.3.6 Acoustic emission and monitoring Mechanoreceptors are complemented with auditory cues. Vickers (1985, 1988) has reviewed the evaluation of crispness and the hypothesis that auditory sensations are involved in the perception of crispness (Vickers and Bourne, 1976). They concluded that the number of emitted sounds per unit biting distance and the loudness of the sounds changed with perceived crispness.One means of assessing sensory attributes such as crispness is to measure the sounds produced during compression of foods. This may occur with instrumentally or manually deformed samples or by holding a microphone against the outer ear (Vickers, 1985). The signal may be played back through a frequency analyser and the data can be presented as amplitude–time curves at different frequencies. Studies have indicated that vibratory stimuli can lead to the distinguishing of crisp and crunchy foods (Vickers, 1985). Although crispness and crunchiness were closely related sensations, crisper sounds were higher in pitch and louder than the crunchier sounds (Vickers, 1984). Crisp products are characterised by sudden, clean and total fractures. Loudness, crunchiness and crispness were judged to be very closely related. A complementary area of science is gnathosonics, the study of sounds from teeth occlusion. Occlusal disorders have been monitored using equipment from stethoscope to microphone, the permanent record being termed an occlusogram. Watt (1976) classified sounds into categories depending on the duration of the occlusal sound. Malocclusion was diagnosed from longer periods of sound emission during voluntary tooth contact. As described above (Section 3.3.2) AMG ‘listens’ to contracting muscles (Herzog et al., 1994).

3.3.7 Comparisons with physical properties The first bite is often used as the focus for relating food physical properties and sensory attributes. Peyron et al. (1997) emphasise that biting may be an isolated voluntary act or the first step of the masticatory process. A number of studies have compared in vivo mastication with aspects determined using laboratory equipment. Olthoff et al. (1986) used a pneumatic bite simulator with a crosshead speed of 30 mm s–1 and measured force for different angled cusp-shaped probes together with the particle size distribution of various foods and the model material Optosil. In the case of Optosil in vivo tests were carried out and the particle size distribution measured after different numbers of chewing strokes. Fragmentation into smaller particles was found with in vivo biting.

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Ostry and Flanagan (1989) compared thin and thick walled rubber tubes. The duration of the jaw movement and its amplitude were less and the maximum velocity higher for the thinner-walled tube which had a lower stiffness (greater compliance). Kemsley et al. (2003) have recorded the EMG response for opening and closing elastic elements held between the incisors. The root-mean-square (RMS) voltage across the concatenated signals from the masseter-right and masseter-left channels (Fig. 3.4) was calculated for all measurements. It was plotted (in beats per minute) versus chew rate in Fig. 3.7(a) and (b). Elements with stiffer springs (Peg L) resulted in signals with generally greater amplitude, although they can lead to muscle tiredness, and the authors elected not to use these in the subsequent multi-volunteer study. The RMS voltage for less stiff elements (Peg W) averaged across all volunteers and sessions is shown in Fig. 3.7(c). The trend of increasing EMG response with chew rate is clear. The average energy needed to compress the elements had been calculated from force–displacement data obtained with a universal test machine. Turning to the relationship between mastication and instrumental texture of foods, Agrawal et al. (1997, 1998, 2000) point to the importance of the

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engineering properties of toughness and modulus of elasticity in determining the rate of breakdown of foods (Section 3.4.4), pattern of mandibular movements and muscular activity measured by EMG in mastication. Engineering properties fall within the class of fundamental texture measurement (Section 3.1.1), and these studies are noteworthy in defining a fragmentation index, (toughness/modulus)0.5 from a fracture mechanics point of view.

3.3.8 Monitoring subjects Kilcast and Eves (1993) have described aspects of reproducibility in EMG experiments in a study of 30 subjects chewing fruit pastilles. They compared the integrated peak height as a function of time and divided the responses into three groups. Grouping of volunteers on the basis of chewing efficiency as judged by almond attrition and chewing gum weight loss was reported by Brown and Braxton (2000) (Section 3.4.1). Kilcast and Eves (1993) also compared the response of five subjects on three separate occasions, this time with toffee. Initial differences in integrated peak height gave way to similar results in terms of the rate of decrease of height and similar total chewing times.

3.4 Chewing, swallowing, salivation and bolus formation One aspect of chewing function is to quantify it, and a number of authors have reported on the definition of ‘masticatory efficiency’. One approach is to identify a test substance. Dahlberg (1942) listed a number of requirements of a test material: 1 it should resemble an ordinary food in that it is crushed by the alveolar cusps of the teeth or, at the other extreme, not be difficult for those with a poor dentition; 2 it should not swell or dissolve in water or saliva and should be such that its pulverisation can be established; 3 it should not break along cleavage surfaces or be tough or sticky; 4 it should be capable of standardisation, non-perishable and of good or indifferent taste. Dahlberg (1942) concluded that a hardened gelatine was suitable. Peanuts were favoured from the 35 foods tested by Yurkstas and Manly (1950). Heath (1982) observed that chewing gum bolus has different shapes that relate to the chewer’s dentition. Liedberg and Owall (1995) described a masticatory test using two-coloured chewing gum. Colour mixing and shape indices were found to depend on dental status. Interestingly they compared comminution of foods with kneading and shaping in determining a swallowing threshold. Horio and Kawamura (1989) concluded that number of chewing strokes could stimulate the swallowing process, regardless of comminution.

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3.4.1 Chewing Chewing activity appears as a burst in the EMG output from each of the monitored muscles. Horio and Kawamura (1989) found that the chewing force and movements were strongly influenced by the texture of food, highlighting its ‘hardness’. They measured the number of chewing strokes and chewing time until the final swallow and classified their subjects into two groups from these data. As a precursor to EMG studies, chewing efficiency was measured and used to divide subjects into groups by Braxton et al. (1996) and Brown and Braxton (2000). They used two techniques: 1 chewing gum, in which the weight loss from a stick of gum was measured after 100 chews as used earlier by Heath (1982) 2 chewing almonds, in which the median size for a whole, blanched nut after 10 chews was measured using the diameters from a scanned image. The EMG work for gum was also used to normalise EMG work obtained for other foods (Brown et al. 1994). Edlund and Lamm (1980) evaluated ‘masticatory efficiency’ as integrated maximum values of EMG activity in volts for the temporal and masseter muscles of five subjects. After the main chewing activity has finished some lower-level EMG activity may continue, due to muscle movements associated with mouth clearance. Swallowing (see Section 3.4.2) may be seen as longer EMG bursts due to teeth clenches during the chewing sequence as reported by Brown et al. (1994). As described in Section 3.3.1 above, a number of time and number parameters are used to define chewing. Hiiemae et al. (1996) compared the number of chewing cycles to the first swallow and the masticatory sequence duration for banana, peeled and unpeeled apple and ginger nut biscuits and concluded that they were determined by initial food consistency.

3.4.2 Swallowing In an exploration of EMG in assessing chewing behaviour for different foodstuffs, Brown et al. (1994) described the measurement of the timing and number of swallows, identifying a swallowing event as a short pause in the chewing sequence associated with a burst of activity from the masticatory muscles as the teeth are clenched. However, they were unable to measure these events for individuals who swallow with little involvement of the masticatory muscles (termed ‘visceral’ swallowing). Hiiemae et al. (1996) compared video evidence, a swallow button and inspection of the EMG output. In a later combined EMG and T-I study, Brown et al. (1996) used a miniature microphone placed over the larynx to measure swallowing, but only reported the final swallow at the end of mastication. A number of other techniques for measuring human swallowing events have appeared in the literature. Piezoelectric sensors have been used to measure laryngeal movements during swallowing of liquids (Pehlivan et al., 1996), and have also been used in combination with EMG activity of submental and cricopharyngeal muscles

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(Ertekin et al., 1996). Commercial devices exist for measuring the ear’s tympanic membrane displacement during swallowing (Marchbanks, 1984). Sonotubometry is another method, whereby changes in sound pressure level due to eustachian tube opening are measured in the external auditory meatus, while a constant sound source is applied via the nostril (Munro et al., 1999). However, none of these methods has been shown to be effective for swallowing measurement during mastication of solid foods, where both a large amount of laryngeal and tympanic membrane displacement and inner-ear noise can be associated with mandibular movements and chewing activity. As a combined technique to couple with T-I, it is also desirable that any swallow measurement device should be as non-invasive as possible, to avoid distraction of the panellist. Some of the techniques are described in Section 3.3.3 above. At the end of the chewing sequence mouth clearance occurs, typified by irregular patterns of jaw movement (Hiiemae et al., 1996). They comment that there is little masseter muscle activity compared to that of the geniohyoid and genioglossus.

3.4.3 Salivation Saliva has a lubrication effect and also has a role in taste perception as well as containing enzymes which digest lipids and starches, counter microorganisms and buffer acids in the mouth. Saliva mixed with food initiates digestion and facilitates swallowing through formation of a bolus. If a food is dry it is difficult to swallow, e.g. the party game of how many cream crackers can be eaten. A key factor in determining salivary rate is the gustatory stimulus. Salivary flow rate has been found to vary considerably depending on the type of foodstuff being consumed (Guinard et al., 1998), being strongly influenced by food texture and water content as well as flavour type and level (Prinz and Lucas, 1997; Watanabe and Dawes, 1988a,b). Total salivary flow rates as a result of eating have been published by a number of workers. Hoebler et al. (1998) found that a basal flow rate of 0.8–0.9 g/min rose to 1.1 g/min for cooked spaghetti and 1.3 g/min for white bread. In a study of oil/water emulsions containing vanillin and limonene, Mialon and Ebeler (1997) found flow rates ranging from 1.4 g/min to 5.61 g/min for their 14 volunteers. For wines of varying composition, Fischer et al. (1994) found flow rates equivalent to 1.8–5.98 g/min after 30 seconds in the mouth. Watanabe and Dawes (1988a) studied a variety of foods, and found mean salivary flow rates of 0.72 ml/min with no oral stimulation and 3.15 ml/min for boiled rice, rising to 4.94 ml/min for rhubarb pie and 7.07 ml/min for oral infusion with 260 mmmol/l citric acid.

3.4.4 Structural change during mastication Methods for expressing food breakdown in terms of comminution typically focus on measurement of particle size after a given number of chewing

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strokes. Multiple sieving techniques used to measure particle size distributions have been described by Lucas and Luke (1983) for chewed carrot samples, Edlund and Lamm (1980) for Optosil, and Olthoff et al. (1984) for both Optosil and peanuts. Mowlana et al. (1995) described a rapid optical scanning method to measure two-dimensional surface area of chewed almonds, Vanderbilt et al. (1993) used a similar device to compare different types of particle size distributions in foods and Agrawal et al. (1997) used image analysis to study fragmentation due to chewing for a variety of foodstuffs. The latter also related breakage of food particles to material properties and discussed a fragmentation index based on toughness and stiffness of the food (Section 3.3.7). More recently, Hoebler et al. (1998, 2000) have measured particle sizes of chewed and minced bread and pasta using laser light diffraction and image analysis. These authors also measured saliva content of the chewed foods, drawing attention to its importance in bolus formation. The resulting salivary impregnation for white bread to give an adequate water content for swallowing was found to be up to five times as great as that for pasta (Section 3.3.7). Some methods for measuring surface areas of foodstuffs have been reviewed by Mohsenin (1970). These include an airflow planimeter to measure flat surface area of leaves, peeling strips from the outer surfaces of fruits and vegetables for direct measurement of total outer layer, and calculations based on measured outer linear dimensions assuming idealised shapes (e.g. apples and eggs). Methods suitable for irregularly shaped soft samples such as chewed gels were not described. Adamson (1982) has reviewed surface area determination by adsorption of substances on solid surfaces, where the adsorbate forms a surface coating that may be one or more molecular layers thick. Among these are the well-known gas adsorption isotherm (BET) method, and adsorption of dyes on impermeable solids. The surface area is then determined by a consideration of quantity of adsorbed substance. However, gels may have surfaces permeable to water, so an adsorbate such as a dye used for coating may penetrate the outer surface layer. Sprunt and Smith (2002) presented a method to give total surface area of chewed gelatin gels by absorption of aqueous amaranth dye into the gel surface. This method was used to measure profiles of surface area, gel weight and saliva weight as a function of time for panellists chewing gels throughout the mastication sequence. These gels tended to congeal on mastication, so the fragments would not be easily separable for individual coating or particle size analysis. The method described should be suitable for any similar gels that can be coated by dye absorption, but since specific dye permeation quantity will be affected by the factors discussed above, a calibration graph of absorbed dye versus surface area would need to be constructed for each separate type and composition of gel. Figure 3.8 shows results for surface area with gel weight and saliva weight as a function of number of chews. Swallow positions were seen as sharp vertical drops in all three curves. Swallowing brings about an abrupt decrease in gel surface area and gel

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weight from removal of gel mass. Saliva weight also drops significantly on each swallow as would be expected. The original (no. of chews = 0) gel mean surface area and weight are shown on the upper two curves. A sample is shown taken approximately half way to the first swallow, in this case at 17 chews. This gives an indication of the build-up of surface area and any change in solid weight as the gel is initially chewed. Although this study obtained total salivary flow rates indirectly from weights by subtraction, other studies have measured saliva flow rate directly from particular ducts. For example Guinard et al. (1998) used a suction cap over the orifice of Stenson’s duct.

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3.4.5 Bolus formation The particle size of a friable food must be reduced to a size that can be accepted by the pharyngeal and esophageal passageways for comfortable swallowing. The end product of mastication is the bolus (Hutchings and Lillford, 1988), although authors comment that some foods form a bolus and some do not (Lucas and Luke 1983). Hutchings and Lillford (1988) report a classification into at least three types of bolus: 1 a subdivision model where the teeth subdivide the sample into smaller pieces for swallowing singly or gathered together in foods such as cheese and carrot which are the cited examples; 2 rolled bolus applicable to anisotropic foods such as meats; 3 shattered foods comprising many small pieces as in biscuits and snacks. Prinz and Lucas (1997) comment that formation of a bolus is instrumental in the initiation of swallowing.

3.5 Future trends 3.5.1 Texture related to flavour and nutrient release The organoleptic quality of fruits and vegetables is influenced not only by texture, but also by the taste and aroma (Boyar and Kilcast, 1986; Piggott, 2000). The taste of an ingested plant tissue will be dependent on the extent to which flavours and volatiles within the food are released into the mouth, enabling them to come into contact with saliva and then sensory cells. The most relevant cell-wall influence relates to the fracture or separation of plant cells, and therefore to texture. Cell rupture releases cell contents into the saliva as is the case in crisp, juicy apples. In some foods, cell rupture will also be instrumental in flavour creation. Where tissue fracture involves cell separation, as in thermallysoftened vegetables or over-ripe, mealy fruits, a degree of encapsulation will occur. This is why mealy apples are generally perceived to have a dry mouthfeel. In such circumstances, the opportunity for the juice (cell contents) to reach the sensory receptors responsible for taste will depend on the rate of diffusion across the intact cell wall into the surrounding saliva in addition to the ability of the saliva to transport them to the receptors. Flavour is the combination of taste and odour influenced by sensations of pain, heat and cold and by tactile sensation. Aroma, taste, texture and mouthfeel account for the major stimuli that make up flavour (Taylor, 1996). Chemicals from the food come into contact with sensors in the nose and mouth and interact with mucous membranes. Plant cell walls in tissues and organs affect the chewing process and interact with the mouth lining. Studies of chewing using EMG can be combined with volunteer’s direct recording of time-intensity signals with swallowing recorded indirectly with a throat microphone or directly with a swallow button on the volunteer’s console. The T-I device can be used to record sensory or quality attributes, including flavour intensity.

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Again, indirect measurement of volatile flavour can be carried out. Most analyses of volatile flavours have been carried out on whole foods by extracting all aroma compounds by distillation or extraction. However, physical changes occur in the mouth with regard to both texture and flavour. Surface area of food may increase initially and then decrease during bolus formation. Exhaledbreath sampling during eating using mass spectrometry techniques is an objective approach to quantify aroma release, often in combination with generation of T-I profiles by the subject (Taylor, 1996). Modelling of flavour release is outside the scope of this chapter, but the interdisciplinarity of the approach requires information from different areas of science, notably T-I studies of flavour intensity together with chewing data: number of chews and chew times, swallow times and surface area change with chewing (Harrison et al., 1998; Wright et al., 2003). The arguments about flavour release also carry over to release of nutrients and impinge on digestion. The structure and physical properties of the food following comminution and bolus formation, and then swallowing are expected to be important. In fact Prinz and Lucas (1997) argue that models of the digestive system are incomplete without specification of the food being swallowed. 3.5.2 Modelling Ledley (1971) reviewed the analysis of forces on teeth and design of denture occlusal surfaces. Starting with flat-surfaced teeth he progressed to real anatomical-shaped teeth with cusps. Numerical force analysis was carried out for different chew angles and occlusal shapes and then extended to stability of complete dentures. A very interesting current area of research is that being carried out by Professors MR Heath, M Hector and PS Wright (Queen Mary University of London, School of Medicine and Dentistry) and RH Crompton (University of Liverpool) who are collaborating on the use of finite element stress analysis to create a 3D model of food reduction, capable of predicting differences in food failure from tooth morphology. In combination with instrumented dental prostheses to measure in vivo bite forces in humans, they propose to develop and verify a general model of food comminution applicable to prediction of food texture perception and design of dental prostheses. Another modelling area is that of neural networks that produce motor patterns and receive sensory patterns. Otten (1988) describes the modelling of neurons with responses that depend on firing rates. A pattern generator was simulated in a neural network consisting of a matrix of 15 by 15 neurons. 3.5.3 Bringing together sensory and fundamental tests Attempts to correlate physical measures of food properties with sensory quality do not account for the mastication process. Instrumental measures do not imitate oral motion, rates of force application and the effects of temperature and saliva (Pierson and Le Magnen, 1970). The distinction between structure

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and texture is often ignored (Cardello, 1994) and the terms are used interchangeably. In other disciplines, texture is purely a structural, or topological term. The blurring of perceptional and instrumental terms has been highlighted and “may impede progress in either field”. This has been a recurring theme. Szczesniak (1963) separates textural attributes into three broad categories: mechanical, geometrical and compositional. The Scott-Blair approach divides instrumental methods of texture measurement into three types: empirical, imitative and fundamental (Brennan and Jowitt, 1977; Szczesniak, 1963). Empirical tests have been developed from practical experience and are often marked out as arbitrary, poorly defined, lacking an absolute standard and effective only for a limited number of foods (Bourne, 1994). Imitative tests are often seen as a subset of empirical tests that subject the food to a process that partially mimics the consumer. Empirical tests cannot easily be expressed in fundamental terms and are dependent on test geometry, friction and sample size (Peleg, 1983). Fundamental tests are more rigorously defined, usually in engineering units, whereas empirical tests are often more successful than their fundamental counterparts. Many fundamental tests use low stresses which do not cause the material to break or fail; they also use rectilinear motion, whereas the movement of the teeth is along an arc and much faster than speeds in the universal test machine. Physical tests often produce single values while the consumers may change rates and manipulate the food during mastication. How a food deforms during mastication depends on mechanical property contributions at the different levels of structure and their interaction. In line with this philosophy, the human mouth can be instrumented to make objective measurements during eating (Kilcast and Eves, 1991). Recent work studied EMG responses simplified by dispensing with the food stimulus and the associated phenomena of particle breakdown and saliva stimulation. Instead, volunteers simulated an aspect of chewing by making jaw movements to elastically deform calibrated springs held between their incisors. Moreover, the volunteers were asked to coordinate their movements with the sound produced by a metronome, operated at a number of predetermined rates (Kemsley et al., 2003). Advances in the understanding of texture depend on a multidisciplinary approach as claimed by several reviewers (Heath and Lucas, 1987; Wilkinson et al., 2000). Three main areas are sensory, physiology and, more broadly, physicochemical properties comprising rheology (including mechanical properties) and structure.

3.5.4 More data analysis The development of EMG took place in the early days of computing power. Much of the data was discarded or averaged or simplified in order to be analysed with what are now viewed as low-level PCs. The current surge in computing power and the advent of parallel computing has opened up new opportunities, demonstrated by the following example (Kemsley et al., 2002).

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Multivariate modelling (Kemsley et al., 2002) was used to analyse individual EMG recordings, which were Fourier-transformed to give a power spectrum in the frequency domain. Examination of the raw data, and of subsets of power spectra, was carried out using Matlab 6.1 (The Mathworks, Inc.) running on a desktop computer. Algorithms were written in-house to identify bursts of activity corresponding to chews, and to carry out distributional analysis of the intervals between chews. A real Fourier transform algorithm was used, provided in the IMSL Fortran 90 MP Library (Visual Numerics Inc.) supplied with Visual Fortran 6.1 (Compaq Computer Corp). Before performing principal component analysis (PCA), data points corresponding to the d.c. component were discarded, the required number of data points from each channel was concatenated, and the data area-normalised. The sum of the vector elements was set equal to unity, a transformation which has been demonstrated to emphasise band shape differences between spectra. Correlation matrix PCA was used throughout. Matrices of power spectra too large to be processed on a desktop computer were analysed on the SGI Origin2000 (Silicon Graphics Inc., Mountain View, CA, USA), at Computer Services for Academic Research (CSAR, Manchester, UK). Algorithms to perform PCA were written in Fortran 90, incorporating LINPACK library functions. The low-frequency (<10 Hz) region contained spectral differences that varied between volunteers and were consistent between sessions. Principal component analysis showed some clustering of scores from different volunteers. However, PCA of the whole frequency range showed more pronounced clustering and therefore the higher frequencies also contribute to the distinction between volunteers. The higher frequency components of the signal are neglected in conventional analysis of EMG data, which processes raw signals to obtain parameters such as chew intervals and chew rate. Volunteers can, for example, be distinguished by their high-frequency EMG spectra. The later study on the deformation of elastic elements (Kemsley et al., 2003) has examined masticatory muscle activity during constrained mouth movements, in which four different chew rates were prescribed.Within-volunteer variance, arising from the different chew rates, as well as between-session variance is concluded to be a lesser phenomenon than between-volunteer variance. The low-frequency (<10 Hz) region was found to contain spectral features related to the prescribed chew rate. Multivariate analysis of the power spectra revealed that readings from each volunteer clustered together, and the clusters could be largely separated. Such grouping was found irrespective of whether data from each chew rate were analysed separately or simultaneously. This suggests that high-frequency components of the EMG signal are largely independent of gross chewing behaviour, and arise from factors beyond the control of individuals: even when asked to make jaw movements in the same prescribed manner, volunteers can nevertheless be uniquely distinguished by their EMG frequency spectrum. These findings open up a number of possibilities for the future. Signals

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could be ratioed to a fundamental or background response collected from each individual, perhaps whilst chewing a model material, as in the studies of Brown et al. (1994, 1996, 1998). Alternatively, it may be possible to view the inherent subjectivity of EMG as an advantage: groups of individuals could be defined on the basis of their fundamental EMG responses, and correlations sought with groupings defined on a different basis, according, say, to food preferences. In combination with other information from a suite of tests, such as salivation and structure breakdown, more information on the origin of the differences might be sought to differentiate volunteers.

3.6 Mastication and particular foods Kilcast and Eves (1993) studied three commercial fruit pastilles and attempted to correlate EMG parameters (Section 3.3.1) with sensory panel terms. Given ambiguous relationships, they used canonical variate analysis (CVA) to describe EMG parameters. They were able to plot three non-overlapping groups. The method was applied to a further four confectionery materials of different structure and texture. One sample was separated, which was consistent with observations, although CVA of sensory profile data separated two of the four samples, which was attributed to some profile terms not being differentiated by the EMG response. Nonetheless the conclusion was that EMG combined with multivariate data analysis is a powerful approach to defining textures. Jack et al. (1993) describe a study on cheese in which EMG, sensory quantitative descriptive analysis (QDA) and instrumental tests were used. EMG muscle activity was subject-dependent. Amplitude of the integrated EMG peak increased with sample hardness. Brown et al. (1994) gave results of a study on various foods including cubes of raw carrot and showed the largest variations between subjects for carrot. Brown et al. (1998), using KI to complement EMG, assessed raw and cooked carrot. Brown et al. (1996) in a study of tenderness of roast meats found that the maximum tenderness intensity obtained using T-I correlated significantly with masticatory muscle activity for 18 out of 20 consumers. Tornberg et al. (1985) evaluated cooked meats and meat products using an instrumented prosthetic appliance and found that sensory evaluations correlated best with the product of loading rate and number of chewing cycles. Brown et al. (1998) compared meats, biscuit, apple and carrot using EMG and KI. Mastication of apple and carrot relied on vertical compression for each chew, whereas in biscuit early compression was replaced by shear with the progress of chewing. A consideration of the mechanical, structural and mechanical property changes accompanying apple mastication is given in Brown et al. (1999). A more detailed description of biscuits is given in Brown and Braxton (2000) where chewing work initially increased over the first 5 to 10 chews and then decreased. The chewing work and the assessment

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of sensory attributes of hardness, crunchiness and crumbliness varied between the four groups which were classified by chewing efficiency with chewing gum and almond attrition. Hiiemae et al. (1996) examined biscuit, apple and banana using EMG, sirognathography, swallowing by button and video. Peyron et al. (1997) considered cheese and carrot and found that sensory hardness, crispness and firmness were related to the first bite. Interestingly they found that thicker samples were perceived as harder. Simple flavoured confectionery gums were considered as part of a study for understanding and modelling of flavour release as part of a multi-site BBSRC LINK project where the mastication data were provided as described in Section 3.6.1 (Kemsley et al., 2002; Sprunt et al., 2002; Wright et al., 2003 and included references).

3.7 Reviews A number of other reviews deal with aspects of this chapter in much more detail, including: • Sensory, T-I: Dijksterhuis and Piggott, 2001; Guinard and Mazzucchelli, 1996; Kilcast, 1999; Piggott, 2000 • EMG, dental, physiological: Ahlgren, 1966; Boyar and Kilcast, 1986; Heath, 1988; Heath and Prinz, 1999 • In vivo texture: Bourne, 2002; Wilkinson et al., 2000 • Neural: Lund, 1991 Mastication papers are published in a diverse number of journals from food science and technology, biomechanics, theoretical biology, dental and medical journals. In addition, the International Society of Electrophysiology and Kinesiology (ISEK) is a multidisciplinary organisation composed of members from health related fields such as biomedical sciences as well as engineering, physical education, physical therapy and many other disciplines. Every two years, these scientists gather at a Congress of ISEK to share advances and knowledge in the broad field of Electrophysiological Kinesiology. The participants with affiliated institutes and universities bring experience in the fields of EMG, Functional Electrical Stimulation (FES), Motor Unit Control, Neuromuscular Diseases, Rehabilitation, Muscle Fatigue, Kinesiology, Motion Analysis, Ergonomics and many other areas of study.

3.8 Acknowledgement The author was funded from the BBSRC competitive strategic grant. I would like to thank my collaborators and IFR colleagues Drs Kate Kemsley, Marianne Defernez and John Sprunt.

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(1991) Three-dimensional analyses of human bite-force magnitude and moment, Arch Oral Biol, 36(7), 535–9. VICKERS Z M (1984) Crispness and crunchiness: a difference of pitch?, J Text Stud, 15(2), 157–63. VICKERS Z M (1985) The relationships of pitch, loudness and eating technique to judgments of the crispness and crunchiness of food sounds, J Text Stud, 16(1), 85–95. VICKERS Z M (1988) Evaluation of crispness. In Food Structure: its Creation and Evaluation. Eds J M V Blanshard and J R Mitchell, London, Butterworths, 433–48. VICKERS Z M and BOURNE M C (1976) A psychoacoustical theory of crispness, J Food Sci, 41(5), 1158–64. WALTIMO A and KONONEN M (1994) Bite force on single as opposed to all maxillary front teeth, Scand J Dent Res, 102(6), 372–5. WALTIMO A and KONONEN M (1995) Maximal bite force and its assocation with signs and symptoms of craniomandibular disorders in young Finnish non-patients, Acta Odontol Scand, 53(4), 254–8. WATANABE S and DAWES C (1988a) The effects of different foods and concentrations of citric acid on the flow rate of whole saliva in man, Arch Oral Biol, 33(1), 1–5. WATANABE S and DAWES C (1988b) A comparison of the effects of tasting and chewing foods on the flow rate of whole saliva in man, Arch Oral Biol, 33(10), 761–4. WATT D M (1976) Monitoring mastication, J Dent, 4(6), 271–8. WILKINSON C, DIJKSTERHUIS G B and MINEKUS M (2000) From food structure to texture, Trends Food Sci Tech, 11(12), 442–50. WILSON C E and BROWN W E (1997) Influence of food matrix structure and oral breakdown during mastication on temporal perception of flavor, J Sensory Stud, 21(1), 69–86. WRIGHT K M, SMITH A C, SPRUNT J C and HILLS B P (2003) Modelling flavour release from a chewed bolus in the mouth, Part 1: mastication, Int J Food Sci Tech, 38(3), 351–60. YEH C-K, JOHNSON D A, DODDS M W J, SAKAI S, RUGH J D and HATCH J P (2000) Association of salivary flow rates with maximal bite force, J Dent Res, 79(8), 1560–65. YURKSTAS A A and MANLY R S (1950) Value of different test foods in estimating masticatory ability, J Appl Physiol, 3(7), 45–53. ZWARTS M J and KEIDEL M (1991) Relationship between electrical and vibratory output of muscle during voluntary contraction and fatigue, Muscle Nerve, 14(8), 756–61. VAN EIJDEN T M G J

4 Understanding and measuring consumer perceptions of crispness P. Mallikarjunan, Virginia Polytechnic Institute and State University, USA

4.1 Introduction The consumer market for fresh and frozen-coated foods has been rapidly expanding in recent years. An increase of 16 % in breading and batter usage occurred from 1978 to 1988 (Kulp and Loewe, 1992), and the number of crispy food products on the market has increased tremendously in the decades since 1980 (Bouvier et al., 1997), especially snack food products. In addition to snack foods, consumers like fried foods such as breaded fried shrimp and breaded chicken nuggets because of the unique and desirable combination of crispness from the dried outer crust layer with the soft and pliable inner crust layer. The traditional fried products, such as French fries and hushpuppies (bite-sized, US southern style fried cornmeal nuggets) still prevail in the food service business. However, new breaded products have been developed by food researchers and have become available as choices for consumers; for instance, in late 1997, Cargill Company introduced “breaded beef steak” with an attractive golden brown coloring and seasoned flavor and a solid texture bite into the market (Weller, 1997). According to Restaurants and Institutions’ 1995 Menu Census, half of the top 20 appetizers considered to be “good sellers” are frozen convenience items, e.g. onion rings, chicken wings, mozzarella sticks, breaded calamari and breaded vegetables (Anon., 1998). From the above list, for both food service products and frozen food products, crispness appears to be the most versatile single textural characteristic determining the success of the product in the market place.

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4.1.1 Crispness vs crunchiness The definition of crispness is not yet completely understood with only the generalized concept having been established. In general, crispness is a highly valued and universally linked textural characteristic that has many positive connotations (Szczesniak and Kahn, 1971). Crispness in one commodity may not mean the same in terms of textural quality as crispness in another, e.g. crispness in nuts as compared with crispness in potato chips. Historically, crispness was defined as the quality of fracturing into many small pieces under compressive pressure and associated with brittleness. Amerine et al. (1965) defined crispness as a textural property characterized by a brittle, friable nature. Bourne (1975) indicated that a crisp or crunchy food is characterized by: • • • • • • • •

having a rigid, non-deformable, stiff structure that suddenly collapses with a brittle fracture and a rapid decay of the forces after fracture; having very low shear strength; breaking up under simple compression between the teeth with little or no grinding or tearing; rapidly breaking down into small pieces; requiring small number of chews per piece (not chewy); requiring low work for mastication; having sound effects associated with brittle fracture which is often a desirable feature; having a structure usually comprising cellular aggregates.

Eventually Vickers and Bourne (1976a, b) concluded that crispness has not been adequately defined. This conclusion corresponded to the work by Szczesniak (1988) who compiled the consumer descriptions of crispness and found it to be associated with brittleness, crackling, snapping, crunchiness and sound emission during eating. According to the American Heritage college dictionary (1997), crisp means (1) firm but easily broken or crumbled; brittle (2) pleasingly firm and fresh; crunch means to chew with a noisy crackling sound. The term crispness is more associated with mechanical failure, and crunchiness is more associated with sound burst during chewing. More often the use of crispness and crunchiness is interchangeable and users are unaware of their connotations. Primarily, it would be more likely that the distinction between crispness and crunchiness was conceived to be due to the physiological manipulation of specimens in the mouth. Vickers (1979) proposed that pronouncing the word “crunchy” makes use of the molars (back teeth) rather than the other parts of teeth; on the other hand, pronouncing “crispy” employs the incisors (front teeth) more intensely than the other parts. Perhaps it may be a consequence of this that crispness and crunchiness were evaluated by using the incisors and molars, respectively. The sound emission from a chew by the molars does not project much compared to that from a bite by the incisors. Therefore, Vickers (1984)

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speculated that the sensations of crispness and crunchiness may differ in pitch. She found that the more crisp foods (than crunchy) nearly always produced the higher pitch sounds than the more crunchy foods (than crisp). In addition, when the eating technique was altered from a bite to a chew, judgments of crispness were depressed. Bite sounds were assumed to be relatively higher in pitch due to a combination of airborne and bone-conducted sounds, in contrast to chewing sounds which were assumed to be relatively lower in pitch due to the dampening effect of the higher frequency sounds by the soft tissue in the mouth (Vickers, 1987). This research implied that the association of sound may clarify a distinction between crispness and crunchiness. Seymour and Hamann (1988) used trained panelists to evaluate crispness and crunchiness in crunch twist, saltine cracker and three different potato chip brands. The definitions of crispness and crunchiness were based upon physiological manipulation and sound; that is, crispness was evaluated by the placing of a sample between the incisors and sensorial detection of a level of high-pitched sound; meanwhile, crunchiness was evaluated by the placing of a sample between molars and sensorial detection of a degree of low-pitched sound. They found that the crunchiness in a product advertised as crunchy (crunch twist) tended to be at a lower frequency than that in a product advertised as crisp (potato chips).

4.1.2 Batter/breading and its significance to crispness The terms batter, breading and coating, are often misused. Suderman (1983) described the distinctions among them. Batter was defined as a liquid mixture comprising water, flour, starch, and seasonings into which food products are dipped prior to cooking. Breading was defined as a dry mixture of flour, starch, and seasonings, applied to moistened or battered food products prior to cooking. Coating was referred to as the batter and/or breading adhering to a food product after cooking. Batters and breadings serve many functions as food coatings, i.e. enhancing a food product’s appearance (Elston, 1975) and contributing to a crispy texture (Elston, 1975; Zwiercan, 1974). Coating material is a key to producing a desirable crispness in breaded fried shrimp. The coating should exhibit a structure that sufficiently resists the initial bite, then it should disappear with a quick meltaway in the mouth (Loewe, 1993). With regard to the relation of cooking and crispness, crispness can be adjusted by time and temperature of cooking (Donahoo, 1970); however, it was formerly reported that mixtures of thickening agents offer the greatest promise for achieving desirable crispness (Hanson and Fletcher, 1963). The coating system for fried shrimp can be classified into two types: a tempura coating, where the batter forms the coating, and interface/adhesion coatings, where batter serves to hold a coating onto the product (LaBell, 1994). Loewe (1993) commented, “Consumers will praise or condemn a

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battered or breaded food based on several general quality factors”. Furthermore, he also summarized the critical coating characteristics for breaded products to be appearance, color, crispness, adhesion and flavor. According to the author’s observations, the store brand breaded fried shrimp was less desirable in appearance and crispness quality than that obtained from a brand name company specializing in shrimp processing. The coating of the store brand product was mushy and easily absorbed moisture after the products were cooked and held for a short time. Low-quality batters can diminish the coating properties, e.g. minimizing moisture loss of the product (Suderman, 1983), thereby resulting in a decrease in shelf life and crispness. Another problem common to all breaded products is adhesion. It is especially important for both breading and batter to maintain uniform adhesion to the food substrate under the stresses involved (Loewe, 1993). The loss of breading may occur during processing, transportation, or handling to the consumers, as a result creating aesthetic and economic problems (Suderman, 1983). Loewe (1993) suggested that during reconstitution of a breaded product in the oven, the coating should not develop voids or pockets along the interface. These voids can create a “shelling” or coating separation indicating a lessthan-desirable product. However, adding appropriate starches can help control batter viscosity and yields a firmer coating that holds a shrimp better. Smooth, bubbly, puffy or crispy surfaces can be achieved, depending on the choice of starch (LaBell, 1994).

4.2 Characterization and determination of crispness Although many experiments have been dedicated to determining crispness, the best measurements are still inconclusive. However, the properties related to crispness were able to disclose the complexity of crispness and its association with other similar sensory attributes, e.g., brittleness, hardness, crackliness or crunchiness.

4.2.1 Structural and geometrical properties Many researchers agree that crispness should result from the structural properties of a food (Barrett and Peleg, 1992; Barrett et al., 1994; Bouvier et al., 1997; Gao and Tan, 1996a; Mohamed et al., 1982; Stanley and Tung, 1976; Vickers and Bourne, 1976 a,b). Matz (1962) and Coppock and Carnford (1960) proposed that crisp, dry foods, such as biscuit, break into many pieces when masticated and their eating quality is affected by the size of air cells and thickness of the cell walls. Crispness is conceived as being related to the cellular structure of foods. Perhaps the most direct method for its objective measurement is likely to be the investigation of the product’s structure and geometrical properties. A scanning electron microscope (SEM) is commonly employed to reveal the

86 Texture in food

internal structure of a product. Cell size and number of cells can then be measured and quantified from the projected image (Gao and Tan, 1996a). Barrett et al. (1994) investigated the structural properties determined from cell size distribution and bulk density of corn meal extrudates. They found that the mechanical strength, determined from a compressive stress, and fracturability, determined from fractal and Fourier analysis of the stress– strain function, increased with decreasing mean cell size or increasing bulk density. Furthermore, the sensory results indicated that cellularity strongly influences the pattern of mechanical failure. Gao and Tan (1996b) employed an image processing technique and hinted that some important sensory attributes could be predicted by processing the surface and cross-section images of the product. This may be another potential means of gaining further understanding of the relationship between crispness and structural properties.

4.2.2 Mechanical properties Perhaps the most prevalent objective measurement for crispness is determination via mechanical properties. Mechanical properties are believed to reveal the structural properties of materials by means of resistance to compression by a blade/probe and to a tensile fixture that pulls the structure of food material apart by using a universal testing machine or a texture analyzer. The measurement of mechanical properties is easier to conduct than that of structural properties, determined via the SEM. The mechanical variables are extracted from a force–deformation or stress–strain curve and shown in the chart recorder; force is used to determine brittleness and hardness (Szczesniak, 1963), and the area under the peak force is used to determine the energy required to bite or chew the products or toughness (Seymour and Hamann, 1988). Due to the various sensory attributes of foods, the mechanical properties measurement was developed in order to construct the texture profile analysis to obtain the correlation with sensory attributes (Bourne and Comstock, 1981). In order to authenticate the sensory assumptions, various modifications of jigs and tools were created for objective investigations, such as shear compression blade, puncture probe, Kramer shear test cell, and snap test cell. Nevertheless, there are no definite criteria for selection of an apparatus to measure the mechanical properties of foods. Also, the tests are dependent upon the nature of products; therefore, a variety of mechanical tests have been reported for different types of snacks even though the products are put into the same category as low-moisture foods, as shown in the following examples. • A puncture test has been used to measure crispness in extrudates (Bouvier, 1996) and cookies (Gaines et al., 1992). • A snap test or 3-point bending test, first proposed by Bourne (1975), was used to evaluate hardness and brittleness of cookies. It consists of two beams with a known distance apart supporting the product. Another beam

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is brought down to touch the product at a point equidistant from both support beams (Gaines et al., 1992). • A shear compression test using the Kramer cell was used to determine crispness in peanuts and found to be the best correlated among the shear and compression tests with sensory crispness (Hung and Chinnan, 1989). Ward et al. (1998) also applied this cell to snack chips containing masa cornmeal, wheat flour, cowpea meal, and cornstarch. • A shear compression test by a modified Warner-Bratzler blade was used to determined crispness in bacon (Voisey and Stanley, 1979). • A needle test was used to determine crispness in Chee-tos and ginger snap by pushing a needle against the food. The average slope of each peak was found to correlate well with sensory crispness (Vickers, 1979). Voisey (1976) suggested that the criteria for selection of apparatus for mechanical properties tests should be considered as follows: its readings must relate to sensory analysis, the test conditions should be simple to replicate and the sample size and shape must also be predictable. Peleg (1978) also commented that the geometry of the material to be tested can pose a crucial problem with regard to the interpretation of the results of mechanical properties. The mechanical tests were performed for measuring crispness in different foods, such as bacon (Voisey and Stanley, 1979), potato chips (Katz and Labuza, 1981), biscuits (Mohamed et al., 1982), breakfast cereals (Sauvageot and Blond, 1991), and breaded shrimps (Tahnpoonsuk, 1999). Mohamed et al. (1982) used a constant force rate texture testing instrument to study the crispness of the biscuits. Good correlations were found between sensory crispness and the ratio of work to fracture to total work. Seymour (1985) used a Kramer shear cell in an Instron to crush samples of several dry crisp foods altered in crispness by humidification. He found large negative correlations between crispness and the following mechanical parameters: maximum force at failure and work done to failure. Although mechanical tests are relatively quick and easy to perform, they have not produced a high enough degree of correlation with sensory crispness. Also, many crisp foods cannot be tested by them because they are too small, have irregular sizes and shapes or are part of a food that also consists of noncrisp parts. 4.2.3 Acoustical properties Perhaps the first impression of a crisp food is the sound burst during biting. Due to the fact that the crushing of crisp or crunchy foods results in fracture and fragmentation, it appears that fracture and sound emission are associated. Attenburrow et al. (1992) reported that the sounds emitted during the crushing of a dry product are due to a sudden release of stored elastic energy. The association between mechanical fracture and sound emission has been further elaborated by Chakra et al. (1996) via the first and second law of thermodynamic principles. In brief, the rupture of a brittle product, obtained

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as the applied stress reaches a critical value, induces an instantaneous liberation of the elastic energy from a binding of the inter-atomic bonds in the form of acoustical energy. Pioneering work on acoustical properties in foods was conducted by Drake (1963, 1965) who found that sounds from crisp foods differ from non-crisp foods, in the loudness. Later in the 1970s and 80s, acoustic measurement in food research gained more attention in the characterization of sound-related textural attributes, e.g. crispness, crunchiness. Mechanical properties were primarily used to determine and interpret crispness until Vickers (1987), Seymour and Hamann (1988), Mohamed et al. (1982) and Vickers and Bourne (1976a) found that the combination of sound and mechanical properties predicted sensory crispness better than mechanical properties alone. For example, crispness was found to be poorly correlated with an instrumental fracture force (r = 0.018) in a low-moisture food (Mohamed et al., 1982). This indicated that mechanical properties alone may not entirely explain crispness. Sensory crispness has been predicted using both mechanical and acoustical variables by the multiple linear regression technique summarized in Table 4.1. Studies have shown that auditory sensations are an important for evaluating crispness (Christensen and Vickers, 1981; Edmister and Vickers, 1985; Lee et al., 1988; Mohamed et al., 1982; Vickers and Bourne, 1976b). Vickers and Table 4.1 The regression equation for crispness prediction in different foods. (Adapted from Vickers, 1988) R2

Products

Prediction equation

Seymour and Hamann (1988) Potato chips (Pringles) Potato chips (O’Gradys) Potato chips (Rippled Pringles) Crunch twist Saltine crackers

Crispness Crispness Crispness Crispness Crispness

Vickers (1987) Potato chips

Crispness = –15.6 + 5.35 NP + 133 MHP

0.98

Vickers (1988) Breafast cereals

Crispness = 538 + 539 (log MHP) – 222 PF

0.74

F = peak force at failure (N) by Kramer shear cell Wy = work done to failure (mJ) SPL1= Sound pressure level (dBA) in 0.5–1.2 kHz ILT = Acoustic intensity (watts/m2) in 0.5–3.3 kHz NP = number of sound occurrences during bite MHP = mean heights taken from oscilloscope display of bite sounds

= = = = =

13.6 – 0.19Wx + 0.03 MP 6.1 – 0.08F + 0.21 SPL3 8.1 – 0.004Wy + 0.003 ILT 16.5 – 0.06F – 0.11 SPL1 9.5 – 0.12F + 0.017 MPL

0.96 0.88 0.89 0.95 0.91

Wx = work done to 1 cm deformation (mJ) MP = mean sound pressure (N/m2) in 2.6–3.3 kHz SPL3 = sound pressure level (dBA) in 1.9–2.6 kHz MPL = acoustic intensity (watts/m2) in 0.5–1.9 kHz PF = maximum force from a force– deformation curve

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Bourne (1976a) studied the acoustical properties of tape-recorded biting sounds of wet and dry crisp foods. They found that crisp foods consist of an uneven and irregular series of noises, and suggested that the repeated breaking or fracturing of food samples during biting and chewing produced these acoustical characteristics. Observing differences in amplitude–time plots between the samples, Vickers and Bourne (1976a) concluded that less crisp samples produced less noise. Christensen and Vickers (1981) evaluated separately the loudness and crispness of 16 different products during chewing and biting. They found high positive correlations between crispness and loudness, indicating that biting and chewing sounds were important for evaluating crispness. Mohamed et al. (1982) studied the sound produced by five varieties of dry crisp foods stored at different relative humidities. The sounds were recorded as the foods were fractured by compressing in a constant loading rate, texture testing instrument. The sound energy correlated significantly with sensory crispness. Edmister and Vickers (1985) investigated the relationships between several instrumental acoustical parameters and sensory crispness. They found that the best acoustical predictor of auditory crispness was the logarithm of the number of sound bursts and the mean amplitude of the bursts. Vickers (1988) noted that an inverse relationship between crispness and the force–deformation variables (a negative sign preceding the force and work term) is more unusual and could mean that sensations of hardness and/or toughness are detracting from the sensation of crispness. Another problem in acoustic analysis relates to the source of the sound generation. Dacremont (1995) asserted that sounds generated from a food fracturing through a mechanical apparatus are different from eating sounds and do not contain the relevant information for texture judgment. Nevertheless, eating sounds still comprise various frequency components which can be either airborne or conducted via the bone. Regarding this matter, previously Lee et al. (1988) investigated acoustic behavior during 10 consecutive chews of potato chips and tortilla chips. They found that as chews increased, sound intensity tended to decrease. Also, the higher frequency of chewing sound which is audible decreased as chews increased. The latter finding was somewhat supportive of the psycho-acoustical theory proposed by Vickers (1979) that crispness should be characterized by high-pitched sound. Therefore, she hypothesized that the assessment of crispness may be more dependent on the information obtained from initial mastication as opposed to later chews. Caution has been raised by Peleg (1997) concerning the measured acoustic variables used in the past. They specifically questioned the use of peak count as it does not account for the peak magnitude and shape. In addition, the count can also be affected by the selected resolution and sampling rate adopted by the researchers. Peleg (1997) recommended using the Fourier transform method to obtain more reliable information for determining crispness (or crunchiness) in foods.

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The development of acoustic measurement occurred in the late 1990s. Chakra et al. (1996) suggested that structural properties of pasta could be characterized based on the sound emission during its rupture. The mechanical and acoustical parameters changed in the same pattern over the water activity ranges, and good correlation was obtained. This ascertained their proposed assumption that both properties would have an existing link with each other. Nevertheless, characterization appeared to be more complicated since Tesch et al. (1996) found no relationships between mechanical and acoustical parameters. They were concerned that this complication may have been due to the effect of unequal frequency for the mechanical and acoustical measurements or because some parts of the crisp or crunchy information manifested in acoustical properties may not have been fully revealed in the mechanical properties, or vice versa. In response to the work of Chakra et al. (1996), Tesch et al. (1996) commented that changes in mechanical properties need not follow the same trend as those in acoustical properties, particularly if the changes occurred around the glass transition region. At the time of writing, acoustical measurements for crispness are undergoing study and are being used in investigations into the effect of glass transition on the tested materials. Hopefully, some textural changes in that range, particularly with regard to crispness, can be revealed and will elucidate the links between the material itself and the mechanical as well as the acoustical properties. Tahnpoonsuk (1999) conducted sensory and instrumental and acoustic measurements to evaluate crispness in breaded shrimps baked in an oven and held under a heat lamp. The 12 trained panelists rated the intensity of crispness on a 150 mm line scale with anchors at the ends. Crispness was defined as the ease of fracture in the mouth combined with the loudness of the sound produced. Panelists perceived significant differences in crispness among the tested samples. The change in sensory crispness was significantly dependent on holding time under a heat lamp and baking location in the oven. Although the objective analysis was significant, it did not produce satisfactory correlations with sensory crispness. A detailed description of various methods to collect texture-related acoustic properties using both destructive and non-destructive acoustic techniques can be found in Chapter 6. That chapter also provides a brief discussion of the types of most often used equipment for specific applications. In addition to using sound in the hearing range of humans, Povey and Harden (1981) measured crispness of biscuits using the ultrasonic pulse echo technique. They found good correlation between the crispness from sensory measurement and the velocity of longitudinal sound. The ultrasonic velocity correlated with crispness better than either the ultrasonically derived Young’s modulus, or the Instron universal testing machine derived modulus. Povey and Harden concluded that the ultrasonic technique offers promise as a method for the electromechanical measurement of crispness. Antonova et al. (2003) attempted to characterize crispness in breaded chicken nuggets using an ultrasonic technique and found that the ultrasonic

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velocity had a higher correlation with sensory crispness. Higher ultrasonic velocities correlated with higher crispness in the samples suggesting that ultrasound traveled faster in dry and crisp samples compared to moist and soggy samples. They also found very high correlation between moisture content of the samples with sensory crispness and ultrasonic velocity. The details of the study can be found under the case study part of this chapter.

4.3 Methods of data correlation, evaluation and analysis Crispness has been associated with crunchiness, crackling, freshness, brittleness, snapping and sound emission during eating (Szczesniak, 1988). Many investigators have tried to measure crispness in foods, using different instrumental methods. Studies related to crispness in high-moisture products are very limited. Any reliable objective method that can accurately measure and quantify crispness should correlate well to sensory crispness. Such a method will allow control of crispness for process evaluation and the production of foods with desirable attributes. In order to assess the validity of any objective instrumental method, trained sensory panel tests have to be performed. A well trained sensory panel will produce results as valid and reliable as those from an analytical instrument.

4.3.1 Product description Different products should be used to familiarize the panelists with definitions of crispness. Eight samples of low-moisture foods, viz. Granola bar, club cracker, Graham cracker, oat cereal, bran flakes, cheese crackers, corn flakes, and Melba toast, can be used as standard dry crisp samples with different levels of crispness during panel training. The purpose is to increase familiarity with the crispness attribute and practise describing it. In the case of breaded fried foods, four samples, including French fries, onion rings, chicken nuggets and chicken strips, should be used to assist panelists in relating crispness intensity in a product with a dry outer crust layer and a moist core. In addition, panelists could be presented with products stored under different conditions for crispness evaluation. A suitable storage condition to simulate a fast food restaurant setting is to store the samples uncovered under a heat lamp at 60 °C to maintain crispness. Samples should also be held under ambient conditions (45–55% RH) and high humid conditions (75% RH) to accelerate loss in crispness. Samples should be removed at 10-min intervals and tested for sensory crispness in order to validate a trained panel. 4.3.2 Panelist training Eight to twelve panelists can participate in 12 1-h training sessions over a period of six weeks during which training in identifying and rating the intensity

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of crispness should be completed. Sensory definitions and techniques for different texture attributes, including crispness, hardness, cohesiveness, and fracturability have to be relayed to the panelists during the training period (Table 4.2). In order to become familiar with crispness, the panel can spend three or four sessions evaluating samples of dry crisp foods with different intensities of crispness as reference standards. Standard crispness scale values of dry crisp foods can be used (Table 4.3). During the first session, the researcher should involve the panel in a group discussion of texture characteristics of presented samples, concentrating on crispness. For the following two sessions, panelists should rate crispness of dry crisp products on a nine-point intensity scale (1 = not crisp/soggy, 9 = very crisp) by checking the appropriate space on the scale. Crispness scale values obtained by panelists should then be compared to standard crispness scale values. Having established that the panelists can rate crispness intensity in dry crisp products, the training should then continue with discussion of crispness in breaded fried foods with a moist core. For the following four sessions, panelists should be presented with four samples of breaded fried foods such as onion rings, chicken nuggets, chicken strips, and breaded chicken breasts obtained from local fast food restaurants for crispness evaluation. Panelists will practise describing the texture characteristics of the presented products, focusing on crispness. A nine-point category scale can be used to indicate the intensity of crispness of tested breaded fried foods.

Table 4.2 Sensory definition and technique for crispness. (Source Meilgaard et al., 1999) Term

Definition

Technique

Crispness

The force and noise with which a product breaks or fractures (rather than deforms) when chewed with the molar teeth (first and second chew)

Place sample between molars and bite down evenly until the food breaks, crumbles, cracks or shatters

Hardness

The force to attain a given deformation, such as the force to compress between molars, the force to compress between tongue and palate, and the force to bite through with incisors

Place food between the molars and bite down evenly, evaluating the force required to compress the food

Cohesiveness

The degree to which sample deforms rather than crumbles, cracks, or breaks

Place sample between molars and compress fully (can be done with incisors)

Fracturability

The force with which the sample breaks

Place food between molars and bite down evenly until the food crumbles, cracks, or shatters

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Table 4.3 Standard intensity crispness values (0–9) for low-moisture foods. (Source Meilgaard et al., 1999) Scale value

Product

Brand/type/manufacturer

Sample size

1.0 3.0 3.0 4.0 5.0 6.0

Granola Bar Club Cracker Graham Cracker Oat Cereal Bran Flakes Cheese Crackers Goldfish Corn Flakes Melba Toast

Quaker Low Fat Chewy Chunk Keeblers Partner Club Cracker Honey Maid Cheerios Kellogg’s Bran Flakes Cereal Cheddar Cheese Crackers

1

/3 bar 2 cracker 1 in2 1 oz 1 oz 1 oz

Kellogg’s Corn Flakes Cereal Devonsheer Melba Toast

1

7.0 9.0

1/

1 oz /2 cracker

For the final training sessions, panelists should evaluate samples of commercially available fried products, stored under different conditions, for crispness intensity by using the same nine-point scale. 4.3.3 Evaluation of breaded fried products under experimental conditions For actual experiments, a trained panel should evaluate crispness intensity in breaded fried samples under different processing and storage conditions to get a range of crispness values. Samples could be finish cooked using the following procedures: (1) finish-fried in a deep-fat fryer; (2) baked in a convection oven; (3) heated in a microwave oven. The samples cooked in a microwave oven will have the least crispness, followed by the baked samples and the fried samples will have the highest crispness. The samples can also be placed uncovered under a heat lamp at 60 °C for up to 40 min to maintain crispness. Samples can also be held under ambient conditions (45–55% RH) to accelerate loss of crispness. Panelists can evaluate three samples at a time at 10-min intervals, using a nine-point category scale (1 = not crisp/soggy, 9 = very crisp). In addition to the category scale, panels could also use an unstructured 150 mm scale marked with specific crispness scores developed during training. Evaluations should be completed in individual testing booths with red lighting to mask appearance differences of the samples. 4.3.4 Statistical analysis A generalized randomized complete block design can be used to analyze the data from each training test and to determine if panelists could perceive significant differences in crispness among the products. A randomized block design is effective when the panelists are consistent in rating the samples but

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might use different parts of the scale to express their perceptions. Data in the form of ratings from a randomized block design can be analyzed by analysis of variance (ANOVA). Tukey’s Test can be used to determine which of the samples differ significantly.

4.4 Case-study: breaded chicken nuggets This study was undertaken with following objectives: to determine ultrasonic parameters and mechanical properties and to investigate the relationships between the instrumental parameters and sensory crispness obtained using a trained panel for chicken nuggets (Antanova et al., 2003). Breaded par-fried chicken nuggets were used in this study. Frozen breaded chicken nuggets were finish cooked using the following procedures: (1) finish-fried in a deep-fat fryer at 375 °F for 4–5 min; (2) baked in a convection oven at 400 °F for 10 min; (3) heated in a microwave oven at full power for 3 min. The finish-frying times were obtained by measuring the temperature in the product and frying the sample until the geometric center of the product reached 70 °C. Samples were then placed uncovered under a heat lamp (model SW-2430, Merco Inc., Lakewood, NJ, USA) at 60 °C for up to 40 min to maintain crispness. Samples also were held under ambient conditions (45–55% RH) to accelerate loss of crispness (by getting soggier). Samples were removed at 10-min intervals and tested for sensory and objective crispness. Five replications were conducted in the study. A sensory panel consisting of eight trained members was used to evaluate sensory crispness in breaded fried chicken nuggets. The panelists had previously participated in 11 1-h training sessions over a period of six weeks during which training in identifying and rating the intensity of crispness was completed. Crispness was defined as the force and noise with which a product breaks or fractures (rather than deforms) when chewed with the molar teeth (first and second chew) (Meilgaard et al., 1999). Panelists were instructed to evaluate each tested sample for crispness by placing it between molar teeth and biting down evenly until the food breaks, crumbles, cracks or shatters and rate the intensity of crispness on a nine-point category scale (1 = not crisp/soggy, 9 = very crisp) by checking the appropriate space on the scale. Panelists evaluated three samples at a time at 10-min intervals. The instructions to panelists were given in the appropriate scorecard. Evaluations were completed in individual testing booths with red lighting to mask appearance differences of the samples. Samples were served in plastic soufflé cups coded with three-digit random numbers. Samples were presented such that each panelist received all samples, three at a time, in a random order.

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4.4.1 Ultrasonic property measurements The ultrasonic non-destructive evaluation system developed at Virginia Tech (Antonova, 2001) was used to conduct ultrasonic measurements. The basic set-up of the ultrasonic non-destructive evaluation system included a highpower burst pulser (Model BP 9400A, Ultran Labs, Boalsburg, PA) a broadband receiver (Model BR 640A, Ultran Labs, Boalsburg, PA), a digital storage oscilloscope (Model 2232, Tektronix, Inc., Beaverton, OR), 250-kHz drycoupling ultrasonic transducers (Ultran Labs, Boalsburg, PA), and a microcomputer system for data acquisition and analysis. The driving voltage from the burst pulser for the transducers was 400 V with nominal output impedance of 4 ohms (Ω). The pulse width and separation time between pulses were adjusted for 250-kHz transducers. The broadband receiver, functioning as a signal amplifier and signal filter, had a maximum gain of 64 dB. The transmitted signal was measured and shown on the oscilloscope screen. A general purpose interface board (GPIB) installed in a microcomputer allowed the transfer of the digital data from the signal from the oscilloscope to the microcomputer for further analysis. The system set-up was in the through-transmission mode because breaded fried chicken nugget is highly attenuative material. Two transducers were placed on the opposite sides of a chicken nugget surface, one acting as a transmitter and the other as a receiver. For ultrasonic transducers at a frequency of 250 kHz, the transmitting transducer was connected directly to the burst pulser. The receiving transducer was wired directly to the receiver. A breaded fried chicken nugget to be tested was placed directly between the two transducers along the center-line of the sample and transducers to ensure the optimal propagation of the sound wave. A transducer holding device was used to apply a uniform pressure and a precise alignment of the transducers during each ultrasonic measurement, permitting the most efficient ultrasonic energy transmission through the sample. The burst pulser sent electrical energy bursts into the transmitting transducer to convert the electrical energy to ultrasonic energy in the form of ultrasonic pulses. The transducer then launched an ultrasonic pulse into the sample material. The ultrasound traveled through the sample material until it reached a boundary or discontinuity in the material. In such a case, some ultrasonic energy will be reflected. The transmitted ultrasound was received and converted to an electrical signal by the receiving transducer, amplified and displayed on the oscilloscope screen as real-time radio-frequency traces, from which the properties of the ultrasonic wave could be determined. The data were then transferred to the microcomputer for further analysis. From the transmitted ultrasonic signal, such ultrasonic properties as ultrasonic velocity (v) and transmission loss (Tloss) were determined in this study. Frequency analysis was performed to obtain the energy content distributed over the frequency bandwidth determined as a power spectrum of the signal. A fast Fourier transformation (FFT) algorithm was used to convert data at the time domain into the frequency domain, using MATLAB®. Peak

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signal amplitudes and corresponding frequency ranges were identified and analyzed. Ultrasonic velocity Ultrasonic velocity represents the average speed of ultrasound through the sample from one side to the other. The time-of-flight (TOF), known as the traveling time of the ultrasonic pulse from one side of the sample to the other, was derived from the plot of the time domain waveform. The predetermined sample thickness and the determined time-of-flight through the sample and time-of-flight of the transducer were used to calculate the propagation velocity of the ultrasonic wave through the sample. The ultrasonic velocity for each breaded fried chicken nugget was determined according to the following equation: vsample =

l TOF – TOF0

[4.1]

where vsample = l= TOF = TOF0 =

ultrasonic velocity for breaded fried chicken nugget (m/sec) path length of transmission (mm) time-of-flight with the sample (ms) calibrated time-of-flight without the sample (ms)

4.4.2 Mechanical property measurements An Instron universal testing machine (Model 1011, Instron Inc., Canton, MA) interfaced with a microcomputer for data acquisition was used for mechanical property measurements. The test sample was placed in the Kramer shear-compression cell and the crosshead speed was set at 100 mm/min. The mass and thickness of the samples were measured before analysis as described before. The maximum peak force and total energy were obtained from the measurements using the software program (Instron Inc., Canton, MA).

4.4.3 Sensory crispness evaluation of breaded fried chicken nuggets The cooking method of breaded chicken nuggets, storage conditions under either a heat lamp or ambient conditions, and holding time had a significant effect on sensory crispness. Breaded chicken nuggets fried in a deep-fat fryer were crispier (6–8) than nuggets baked in an oven (3–5) and nuggets cooked in a microwave oven (1–2). The panelists found that cooked samples held under a heat lamp were significantly crispier than samples stored under ambient conditions. Changes in crispness with holding time, under either a heat lamp or ambient conditions, were perceived by judges. The panel determined the increasing intensity of crispness among samples kept under a heat lamp. The effect of holding time under a heat lamp and under ambient

Understanding and measuring consumer perceptions of crispness

97

conditions on the crispness intensity ratings of breaded fried chicken nuggets for each cooking method is shown in Fig. 4.1. The panel indicated the decreasing intensity of crispness with holding time under ambient conditions. Figure 4.2 shows the normalized sensory scores (each score divided by the mean score for a particular cooking method) against crust moisture content to see the effect of crust moisture content on sensory crispness. Sensory crispness was inversely related to moisture content, indicating that crispness intensity was increasing, while moisture content was decreasing when the samples were held under a heat lamp (Fig. 4.2). 10 9

Sensory crispness

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Holding time (min) (b)

Fig. 4.1 Effect of holding time on the mean sensory crispness intensity ratings of breaded fried chicken nuggets for each cooking method: (a) under a heat lamp and (b) under ambient conditions (Adopted from Antonova et al., 2003).

98 Texture in food 1.3

Normalized sensory scores

1.2

1.1

1

0.9

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50

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Moisture content, % w.b.

Fig. 4.2

Effect of moisture content on sensory crispness (Adopted from Antonova et al., 2003).

4.4.4 Ultrasonic velocity The significant differences in the ultrasonic velocity for the breaded chicken nuggets cooked by three different methods and stored under either a heat lamp or ambient conditions were observed. The samples cooked in a deepfat fryer had higher ultrasonic velocities (431.56–715.38 m/sec) than the samples cooked in an oven (221.41–533.83 m/sec), while the samples cooked in a microwave oven had much lower velocities (90.22–306.92 m/sec). This can be attributed to the changes in crust moisture content among the samples. The samples cooked in a deep-fat fryer had the lowest crust moisture content, while the samples cooked in a microwave oven had the highest. The ultrasonic velocity increased with increasing holding time under a heat lamp as the moisture content decreased. This implied that samples became crispier during holding under a heat lamp. On the other hand, ultrasonic velocity decreased with holding samples under ambient conditions as samples became soggier and softer. The changes in the mean ultrasonic velocities of the breaded fried chicken nuggets are plotted in Fig. 4.3. The relationships between ultrasonic velocity and moisture content are shown in Fig. 4.4. The decrease in the velocity with respect to an increase in moisture content can be attributed to the transmission loss in liquid rather than the transmission loss in a solid. Ultrasound can propagate better in solids than liquids.

Understanding and measuring consumer perceptions of crispness

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Fig. 4.3 Effect of holding time on ultrasonic velocity for each cooking method: (a) under a heat lamp and (b) under ambient conditions (Adopted from Antonova et al., 2003).

4.4.5 Mechanical property measurements Normalization of the peak force and total energy was done by dividing the values by the mass of the sample. The normalized peak force and normalized total energy significantly varied among the samples cooked by different

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Texture in food 800

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Fig. 4.4 Effect of moisture content on ultrasonic velocity (Adopted from Antonova et al., 2003).

methods and stored under different conditions. Samples cooked in a deep-fat fryer had higher values for the peak force and total energy than samples cooked in an oven and microwave oven. However, holding time under either a heat lamp or ambient conditions did not have a significant effect on either peak force or total energy. The effect of holding time under a heat lamp and under ambient conditions for each cooking method on the peak force is shown in Fig. 4.5. The peak force slightly increased with holding time under either a heat lamp or ambient conditions.

4.4.6 Relating instrumental parameters to sensory crispness To investigate the relationship between sensory crispness and objective quality parameters, the linear regression procedure was performed. Except for the relationship between ultrasonic velocity and sensory crispness, none of the objective parameters had an improvement in the correlation coefficient when analyzed separately against each cooking method. The relationship between sensory crispness and peak force is shown in Fig. 4.6 and described by Eq. [4.2]. The positive sign preceding the slope implied that sensory crispness increased when a high peak force was required. Sensory Crispness = 3.35 (Peak Force) – 5.20

(R2 = 0.64) [4.2]

The positive relationship between sensory crispness and ultrasonic velocity is described by Eq. [4.3], showing that sensory crispness and ultrasonic

Understanding and measuring consumer perceptions of crispness 101 50 45

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Holding time (min) (b)

Fig. 4.5 Effect of holding time on peak force for each cooking method: (a) under a heat lamp, and (b) under ambient conditions (Adopted from Antonova et al., 2003).

velocity are closely related. Increasing sensory crispness of the breaded fried chicken nuggets is illustrated by increasing mean ultrasonic velocities, as presented in Fig. 4.7. When analyzed separately for each cooking method the R2 values for the predictive model were 0.87, 0.94 and 0.85 for fryer, oven and microwave oven, respectively. Sensory Crispness = 0.013 (Velocity) – 0.67

(R2 = 0.83)

[4.3]

The models were significant at the 0.05 confidence level. This indicates that

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Texture in food 9 8

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Peak force (N/g)

Fig. 4.6 Correlation between peak force and sensory crispness (Adopted from Antonova et al., 2003). 9 8

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Ultrasonic velocity (m/sec)

Fig. 4.7 Correlation between ultrasonic velocity and sensory crispness (Adopted from Antonova et al., 2003).

sensory crispness could be explained based on the ultrasonic and mechanical parameters. The ultrasonic velocity had high correlation to the sensory crispness (R2 = 0.83). This implies that sensory crispness could be reasonably well predicted by the ultrasonic velocity.

Understanding and measuring consumer perceptions of crispness 103

4.5 Future trends As with any development of methods to characterize a complex textural attribute, like crispness, further research work is needed to validate these methods by collaborative research on various types of instruments and products. Many researchers are working on describing crispness in relation to other measurable attributes like moisture content and mechanical and acoustic properties. Further work is underway in relating various texture-attributes to glass transition temperature. Novel numerical methods, such as fractal analysis and Fourier transformation, have been employed to describe the objective measurements, especially for acoustic properties. Research related to using ultrasonic techniques with air-coupled transducers will lead to a true nondestructive, non-contact rapid crispness evaluation method. However, use of air-coupled transducers has been very much limited to low-power diagnostic measurement methods while crispness measurement requires a high-power diagnostic system and this might result in the use of the through-transmission system described in the case study of this chapter. Future research could also focus on the use of artificial neural network (ANN) techniques to process multiple attributes (such as moisture content, ultrasonic velocity, color, mechanical properties, and acoustic properties) from the sample to arrive at a comprehensive crispness evaluation that is well correlated with the sensory crispness.

4.6 References AMERINE M A, PANGBORN R M

and ROESSLER E B (1965) Principles of Sensory Evaluation. New York, Academic Press. ANON (1998) Frozen in food service (http://www.affi.com/facts/foodserv.htm). Amercican Frozen Food Institute. ANTONOVA I (2001) Determination of crispness in breaded fried chicken nuggets using ultrasonic technique (MS Thesis, Virginia Polytechnic Institute and State University). ANTONOVA I, MALLIKARJUNAN P and DUNCAN S E (2003) Correlating objective measurements of crispness in breaded fried chicken nuggets with sensory crispness, J Food Sci, 68(4), 1308–15. ATTENBURROW G E, DAVIES A P, GOODBAND R M and INGMAN S J (1992) The fracture behaviour of starch and gluten in the glassy state, J Cereal Sci, 16(1), 1–12. BARRETT A H, and PELEG M (1992) Extrudate cell structure-texture relationships, J Food Sci, 57(5), 1253–7. BARRETT A H, CARDELLO A V, LESHER L L, and TAUB I A (1994) Cellularity, mechanical failure, and textural perception of corn meal extrudates, J Texture Stud, 25, 77–95. BOURNE, M C (1975) Texture properties and evaluations of fabricated foods. In Fabricated Foods. Ed. G E Inglett, Westport, C T, AVI Publishing Co. BOURNE M C and COMSTOCK S H (1981) Effects of compression on texture profile parameters, J Texture Stud 12(2), 201–16. BOUVIER J M (1996) Engineering analysis of preconditioning in the extrusion-cooking process, Cereal Foods World, 41(9), 737–40. BOUVIER J M, BONNEVILLE R and GOULLIEUX A (1997) Instrumental methods for the measurement of extrudate crispness, Agro-Food-Industry Hi-Tech, 8(1), 16–19.

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CHAKRA W A, ALLAF K

and JEMAI A B (1996) Characterization of brittle food products: application of the acoustical emission method, J Texture Stud, 27(3), 327–48. CHRISTENSEN C M and VICKERS Z M (1981) Relationships of chewing sounds to judgments of food crispness, J Food Sci, 45, 574–8. COPPOCK J B M and CARNFORD S J (1960) Texture in Foods, SCI Monograph, No. 7, p. 64. DACREMONT C (1995) Spectral composition of eating sounds generated by crispy, crunchy and crackly foods, J Texture Stud, 26(1), 27–43. DONAHOO P (1970) The evolution in ingredients. Canner-Packer, May, 139(5): 20–22. DRAKE B K (1963) Food crushing sounds – an introductory study, J Food Sci, 28, 233–41. DRAKE B K (1965) Food crushing sounds: comparisons of objective and subjective data, J Food Sci, 30, 556–9. EDMISTER J A and VICKERS Z M (1985) Instrumental acoustical measures of crispness in foods, J Texture Stud, 16, 153–67. ELSTON E (1975) Why fish fingers top the market, Fishing News International, 14, 30. GAINES C S, KASSUBA A, FINNEY P L and DONELSON J R (1992) Instrumental measurement of cookie hardness. II. Application to product quality variables, Cereal Chem, 69(2),120– 25. GAO X and TAN J (1996a) Analysis of expanded-food texture by image processing. Part I: geometrical properties, J, Food Process Eng, 19, 425–44. GAO X and TAN J (1996b) Analysis of expanded-food texture by image processing. Part II: mechanical properties, J Food Process Eng, 19, 445–56. HANSON H L and FLETCHER L R (1963) Adhesion of coatings on frozen fried chicken, Food Technol, 17, 793. HUNG Y-C and CHINNAN M S (1989) Mechanical texture measurement of whole and chopped peanuts, Peanut Sci, 16, 32–7. KATZ E E and LABUZA T P (1981) Effect of water activity on the sensory crispness and mechanical deformation of snack food products, J Food Sci, 46, 403–9. KULP K and LOEWE R (1992) Batters and Breadings in Food Processing. St Paul, MN American Association of Cereal Chemists. LABELL F (1994) Coating systems for shrimp, Prepared Foods, 163(2), 79–80. LEE III W E, DEIBEL A E, GLEMBIN C T and MUNDAY E G (1988) Analysis of food crushing sounds during mastication frequency – time studies, J Texture Stud, 19, 27–38. LOEWE R (1993) Role of ingredients in batter systems, Cereal Food World, 38(9), 673–7. MATZ S A (1962) Food Texture. Westport, CT, AVI Publishing Co. Inc. MEILGAARD M, CIVILLE G V and CARR B T (1999) Sensory Evaluation Techniques. Boca Raton, FL, CRC Press LLC. MOHAMED A A A, JOWITT R and BRENNAN J G (1982) Instrumental and sensory evaluations of crispness: 1 – in friable foods, J Food Eng, 1, 55–75. PELEG M (1978) Some mathematical aspects of mastication and its simulation by machines, J Food Sci, 43, 1093–5. PELEG M (1997) Effect of absorbed moisture on the mechanical properties of cereal foods, instant coffee, legumes and nuts. Fifth Confererence of Food Engineering (1997) Annual Meeting of the American Institute of Chemical Engineers (AIChE). Paper no. 68d. POVEY M J W and HARDEN C A (1981) An application of the ultrasonic pulse echo technique to the measurement of crispness of biscuits, J Food Technol, 16, 167–75. SAUVAGEOT F and BLOND G (1991) Effect of water activity on crispness of breakfast cereals, J Texture Stud, 22(4), 423–42. SEYMOUR S K (1985) Studies on the relationships between the mechanical, acoustical and sensory properties in low moisture food products (Ph. D. Thesis, North Carolina State University, Raleigh, NC). SEYMOUR S K and HAMANN D D (1988) Crispness and crunchiness of selected low moisture foods, J Texture Stud, 19, 79–95.

Understanding and measuring consumer perceptions of crispness 105 and TUNG M A (1976) Microstructure of food and its relationship to texture. In Rheology and Texture in Food Quality. Eds J M de Man, P W Voisey, V F Rasper and D W Stanley, Westport, CT, AVI Publishing Co., Inc. SUDERMAN D R (1983) Use of batters and breadings on food products : a review. In Batter and Breading. Eds D R Suderman and F E Cunningham, Westport, CT, AVI Publishing Co. Inc. SZCZESNIAK A S (1963) Classification of textural characteristics. J Food Sci, 38, 385–9. SZCZESNIAK A S (1988) The meaning of textural characteristics – crispness, J Texture Stud, 19, 51–9. SZCZESNIAK A S and KAHN E L (1971) Consumer awareness and attitudes to food texture, J Texture Stud, 1, 280–95. TAHNPOONSUK P (1999) Determination of crispness in breaded shrimp (M. S. Thesis, The University of Georgia, Athens, GA). TESCH R, NORMAND M D and PELEG M (1996) Comparison of the acoustic and mechanical signatures of two cellular crunchy cereal foods at various water activity levels, J Sci Food Agric, 67, 453–9. The American Heritage College Dictionary (Third Edition) (1997) Boston, New York, Houghton Mifflin Co. VICKERS Z M (1979) Crispness and crunchiness of foods. In Food Texture and Rheology. Ed. P Sherman, London, Academic Press. VICKERS Z M (1984) Crispness and crunchiness – a difference in pitch? J Texture Stud, 15, 157–63. VICKERS Z M (1987) Crispness and crunchiness. In Food Texture: Instrumental and Sensory Measurement. Ed. H R Moskowitz, New York, Marcel Dekker, Inc. VICKERS Z M (1988) Instrumental measures of crispness and their correlation with sensory assessment, J Texture Stud, 19, 1–14. VICKERS Z M and BOURNE M C (1976a) A psychoacoustical theory of crispness, J Food Sci, 41, 1158–64. VICKERS Z M and BOURNE M C (1976b) Crispness in foods – a review, J Food Sci, 41, 1153– 7. VOISEY P W (1976) Engineering assessment and critique of instruments used for meat tenderness evaluation, J Texture Stud, 7, 11–48. VOISEY P W and STANLEY D W (1979) Interpretation of instrumental results in measuring bacon crispness and brittleness, J Can Inst Food Sci Technol, 12(1), 7–15. WARD C D W, RESURRECCION A V A and MCWATTERS K H (1998) Comparison of acceptance of snack chips containing cornmeal, wheat flour and cowpea meal by US and West African consumers, Food Qual Prefer, 9(5), 327–32. WELLER P (1997) Cargill foods introduces breaded beef steak (http://www.cargill .com/ today/pressrel/0517c97.htm). ZWIERCAN G A (1974) Case of the weeping pies (and others), Food Eng, 46, 79, 81. STANLEY D W

Part II Instrumental techniques for analysing texture

5 Force/deformation techniques for measuring texture Renfu Lu and Judith A. Abbott1, USDA Agricultural Research Service, USA

5.1 Introduction Force/deformation methods are widely used for objective measurement of the textural properties of solid foods. They directly measure either single or multiple (composite) mechanical properties of food that are important to the sensory perception of texture by humans in the hand or mouth and to the resistance to mechanical damage during handling. Compared to indirect methods such as optical and electrical, force/deformation methods offer the advantages of lower costs in instrumentation, ease of carrying out experiments, and simpler interpretation of measurement results. Since there is a vast range of foods with vastly different textural and mechanical properties, it is not surprising that a large variety of force/deformation methods and techniques are available for different types of foods. These force/deformation methods, based on their measurement principles, may be classified into fundamental, empirical, and/or imitative. Fundamental force/deformation methods are developed based on the engineering theory of materials and measure welldefined mechanical properties of food. On the other hand, empirical methods measure those mechanical properties that are not well defined and/or are poorly understood, but have been found to correlate with the sensory evaluation of the food. There are two approaches to force/deformation measurement of food texture: destructive versus non-destructive. Destructive force/deformation methods 1

Mention of the names of commercial products is solely for providing factual information for the reader and does not imply the endorsement of the United States Department of Agriculture.

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are considered by many to be a preferred means of measuring the texture of food because they are usually better related to the sensory evaluation than are non-destructive methods (Bourne, 2002). Destructive measurements are often used as the standard against which a new non-destructive technique is compared. Destructive techniques are useful for providing information about the average quality for a batch of food items. However, they suffer a major shortcoming in that the food samples are destroyed in the process of measurement. Many foods, particularly fresh, raw or unprocessed food products, are inherently variable in texture among individual items. Measurements of ‘average’ texture are not sufficient to guarantee the quality and consistency of individual items of the food. Non-destructive sensing would provide us with a means to better manage the harvest time for optimal food quality and to monitor, grade, and sort food products to ensure their consistency and superior quality. Consequently, considerable recent research activity has been focused on non-destructive techniques for quality evaluation of fresh, raw food products.

5.2

Mechanical characterization of solid foods

5.2.1 Basic concepts Texture is a quality attribute that is closely related to the structural and mechanical properties of a food. It is, therefore, essential to understand the mechanical properties of foods, in studying their textural properties and measurement techniques. The study of mechanical behavior, i.e. deformation and flow, of foods under applied forces falls within the scope of food rheology, which is a broad, currently active research area covering both solid and liquid foods. A number of textbooks and monographs have been written about the rheology of agricultural and food products and engineering materials with various degrees of mathematical requirements (Ferry, 1980; Mohsenin, 1989; Rao and Steffe, 1992; Sherman, 1970). Those who are interested in the topic are recommended to read one or more of these textbooks to gain a better, more comprehensive understanding of this important subject area. The force/deformation relationship for most food materials is dependent on time or loading rate. Force (F ), deformation (D), and time (t) are three basic variables used in studying the mechanical properties of foods. Force, often expressed in N (newton), is considered an external variable because it is acting and/or measured at the surface (or at the surface point) of an object. (Gravitational and magnetic or electric forces are exceptions, which act on the entire body of the object.) In engineering applications, the force and deformation on a plane in the interior of an object are of considerable interest in quantifying the mechanical response of the object subjected to external loading. Corresponding to the force is stress, expressed in force per unit of area (N/m2 or Pa (pascal)), which has the same unit as pressure. Stress is

Force/deformation techniques for measuring texture

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caused by, and accompanied by, external forces and/or other factors such as temperature (thermal stress) and humidity (hygroscopic stress). Strain is the measure of deformation at a point on a plane in an object; it measures the unit change, due to force, in the size or shape of an object with respect to its original size or shape and is a dimensionless quantity. There are two basic types of stresses; one is the normal stress, designated as σ, that acts in a direction normal (perpendicular) to the plane of an object and the other is the shear stress, τ, tangential to the plane on which the forces act (Fig. 5.1). For example, twisting of a rod by applying torsional force at the two opposite ends induces pure shear stresses on the transverse crosssection of the rod, whereas uniaxial compression or tension causes the normal stress either toward or away from the plane perpendicular to the direction of the applied force. As a sharp knife cuts through a food sample, shear stress is created along the two shearing surfaces of the food sample, whereas forces used to bend a beam create both normal compressive and tensile stresses as well as shear stress on the cross-section of the beam. Most destructive force/ deformation measurements involve a complex form of loads, which often induce both normal and shear stresses in a food sample. Corresponding to the two types of stresses are normal strain (ε) and shear strain (γ). Normal stresses are primarily responsible for the expansion or contraction, i.e. the size change, of an object (Fig. 5.1a) and shear stresses cause distortions or the angle change between two planes in the object (Fig. 5.1b). Foods come from biological origins, and they, whether raw or processed, constantly change with time due to chemical reactions (such as oxidation), microbial actions, and physical interactions with the environment (such as heat and moisture). Consequently, the mechanical properties of foods also change with time and are influenced by external or ambient conditions such

F (or σ) D

A0

L0

L

L0

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A0

θ

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F D (b) τ = A = G γ = G tan θ = G L 0 0

Fig. 5.1 Uniaxial compression of a specimen with an original length L0 and area A0 and the Young’s modulus E (a) and shear stress τ acting on opposite planes causing the distortion of the specimen with shear modulus G and area A0 (b)

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as temperature, humidity, air composition and pressure, and the supply and consumption of energy. Accurate and complete description of the mechanical behavior of agricultural and food materials is an extremely difficult task and fortunately, for many applications, it is not required. The engineering characterization of solid materials is based on some basic assumptions and simplifications – such as linearity in the stress/strain relationship, and homogeneity and/or isotropy in the material properties – which may or may not be valid for biological materials and food products. The theory of rheology describes the basic mechanical behavior of many food products with reasonable accuracy, especially when deformation is small. For many engineering materials, small deformation would mean having the strain level less than 0.2%. For solid foods, deformation as high as 5% may still be considered to be small. During the masticating process (chewing), food items undergo extremely large deformation, well beyond the limit of elasticity or the normal ‘small deformation’ level. No adequate theory is currently available for describing the mechanical response of food under large deformation that takes place during mastication. Despite this, the theory of rheology can help us better understand the underlying principles and processes of measuring the textural properties of food. And it offers a useful guide for designing a better and more efficient texture measurement method or system, which is especially true for non-destructive sensing of food texture. Solid foods, depending on their mechanical responses, may be classified into time-independent elastic materials and time-dependent inelastic materials (Fig. 5.2). For time-independent elastic materials, their mechanical response is independent of time or the rate of loading. Upon removal of applied loads (that did not exceed the elastic limit), the deformed body will recover to its original size and shape. Time-independent elastic materials may be further divided into linear elastic materials whose stress/strain relationship is linear Solid foods

Time-independent elastic foods

σ

ε

σ ε

Linear elastic

Fig. 5.2

Time-dependent inelastic foods

ε Non-linear elastic

ε

t

t

Viscoelastic

Viscoplastic

Classification of solid foods based on their mechanical properties.

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and the non-linear elastic with the non-linear stress/strain relationship. Timedependent materials can be classified into viscoelastic and viscoplastic. Viscoelastic materials exhibit both solid-like (elastic) and liquid-like (viscous) behavior in which the stress/strain relationship is time-dependent. Upon removal of the applied loads, the deformed body of a viscoelastic material will recover partially, whereas a viscoplastic material retains its deformation without recovery. Most biological and food materials behave viscoelastically at small and/or intermediate levels of deformation. The viscoplastic theory may be needed for describing the mechanical behavior of food at large deformation. When the time-dependent response is not critical and can be ignored, the elastic theory offers considerable simplification in analyzing the mechanical responses of food when they are subjected to different forms of loading.

5.2.2 Elastic materials Different foods can exhibit very different mechanical behaviors under uniaxial compressive or tensile loading (Fig. 5.3). The force/deformation response for a cylindrical apple tissue specimen under compressive loading (Fig. 5.3a) may be divided into three phases. During the first phase of deformation, the relationship between force (or stress) and deformation (or strain) is linear and elastic. (Non-linearity at the beginning of the force/deformation curve is mainly caused by the imperfect contact between the loading device and the specimen and, therefore, is ignored in the discussion.) Since most food materials are not truly elastic, they often cannot recover completely to their original size and shape upon removal of the load even under small deformation. Despite this, the theory of linear elasticity applies to this phase of deformation. There are three material constants or parameters for characterizing a linear elastic material: the modulus of elasticity (also called Young’s modulus), designated as E; the shear modulus, G; and the Poisson’s ratio, µ. Since the three parameters are interrelated, once any two of the three parameters are determined, the third one can be calculated using an appropriate equation. The elastic modulus is the ratio of the normal stress to normal strain, which can be determined from the slope of the linear portion of the force/deformation curve in Fig. 5.3a using the following equation:

F / A0 E= σ = ∆ L/ L ε

[5.1]

where E has the unit of N/m2 or Pa; F is the applied force in N; A0 is the original, undeformed cross-sectional area of the specimen in m2; ∆L is the net deformation of the specimen in m; and L is the original length in m. The Poisson’s ratio is the absolute value of the ratio of transverse strain to the corresponding axial strain under uniaxial loading. It ranges from 0.0 for completely compressible materials (i.e. no lateral expansion under uniaxial compression of a constant cross-sectional specimen) to 0.5 for completely

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III

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300

150

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Fig. 5.3 Stress–strain (or F/D) curves of a cylindrical apple tissue specimen under uniaxial compression (a) and a raw beef specimen of rectangular cross-section under uniaxial tensile loading (b) (Lu et al., 1998). The stress–strain curves may be approximately divided into three phases of deformation: elastic I, yielding II, and post-yielding (or stiffening for beef III).

incompressible materials. The Poisson’s ratio for most food materials is between 0.2 and 0.5 (Mohsenin, 1989). For example, apple flesh has a Poisson’s ratio of 0.25–0.35 whereas the Poisson’s ratio of potato tissue is as high as 0.49, close to that for incompressible materials. The Poisson’s ratio can be directly determined by simultaneously measuring both axial and lateral deformations from a constant cross-sectional specimen under uniaxial loading. The measurement error associated with this method can be great, due to the difficulty in accurate measurement of the small lateral deformation during uniaxial compression. A better approach is to prepare two same-size specimens; one is used for uniaxial loading with no lateral constraints and the other

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measured under uniaxial loading with lateral constraints (i.e. no lateral deformation). From the force/deformation responses of the two specimens, the Poisson’s ratio can be determined using the following equation (Gyasi et al., 1981; Hughes and Segerlind, 1972): µ = 1 (R + 4

R 2 – 8R )

[5.2]

in which R=

Eu –1 Ec

[5.3]

where Eu and Ec are the Young’s moduli determined using Eq. [5.1] from the unconstrained and constrained uniaxial loading test, respectively. Beyond the first phase of deformation, the theory of elasticity is no longer valid. During the second phase of deformation, the force/deformation relationship starts to deviate from linearity and becomes increasingly nonlinear as the load increases. When the load is removed during this phase of loading, the specimen will recover only partially. As deformation continues to increase, a noticeable drop or sometimes no increase in the force occurs to many biological and/or food materials. The point at the force/deformation curve where a drop or no increase in force takes place with an increase in deformation is called the bioyield point. The bioyield point indicates the initial cell rupture in the cellular structure of the specimen. For some food materials, the bioyield point does not show clearly on the force/deformation curve. For example, the bioyield point may appear merely as a change in slope. Beyond the bioyield point, the third phase of deformation starts; the force/deformation relationship becomes irregular and jagged, with numerous peaks and valleys until complete breakdown of the specimen. Fresh fruits and vegetables often undergo all three phases of deformation under compressive loading. Other food products such as grains at the normal storage moisture content or even lower often only go through the first and second phase of deformation, followed by a sudden, complete failure of the specimen during the second phase of deformation (Wouters and de Baerdemaeker, 1988). Many muscle foods, when subjected to tensile loads, exhibit a mechanical behavior (Fig. 5.3b) that is significantly different from the one shown in Fig. 5.3a. Muscle foods can withstand large deformation and exhibit a prominent non-linear behavior under uniaxial tensile loading. The force/deformation behavior of raw beef under tensile loads may also be divided into three phases of deformation: linear elastic at small deformation, stress yielding at intermediate deformation, and work hardening (or stiffening) at large deformation (Lu et al., 1998). Depending upon muscle type and post-rigor treatment, raw muscle meats may exhibit all or some of the three phases of deformation.

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5.2.3 Viscoelastic materials Although many foods can be approximated as elastic materials, most are in fact viscoelastic. The mechanical response of viscoelastic materials is timedependent; it depends not only on the current loading level but also on the rate and/or history of loading. Two types of tests are often used to characterize the viscoelastic properties of food materials (Fig. 5.4). One is the creep test, which measures the deformation of a specimen with time when a constant load is instantaneously applied to the specimen (Fig. 5.4a). The second type of test is the stress relaxation, which measures the change in stress over time when the specimen is subjected to a constant deformation (Fig. 5.4b). Mechanical models are often used to help us understand or visualize the mechanical behavior of linear viscoelastic materials. A large number of mechanical models can be constructed to describe the viscoelastic material; a vast majority of them consist of two basic elements: the spring (E) representing the elastic component of the material and the dashpot (η) for the viscous component to account for the time effect in the material. The viscosity η is related to the shear stress τ and shear strain rate γ˙ for the Newtonian liquid by the following equation: η= τ γ˙

[5.4]

E0 η

E1

Stress, σ

Strain, ε

Figure 5.4 shows two simple mechanical models that may be used to describe the creep and stress relaxation behavior of food materials. To better describe a viscoelastic material, a more complex model with additional spring and dashpot elements is often required. As more mechanical elements are added to the model, the mathematical equation becomes increasingly complicated. For more detailed discussion about characterizing the viscoelastic properties of foods and other solid materials, readers are recommended to consult the textbooks of Mohsenin (1989) and Ferry (1980).

E1

E0

η Time (a) Creep under constant load

Time (b) Stress relaxation under constant deformation

Fig. 5.4 Creep (a) and stress relaxation (b) response curves and two simple mechanical models for describing the viscoelastic behavior of solid foods.

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5.2.4 Quasi-static versus dynamic measurement Since viscoelastic material is time or loading-rate dependent, its mechanical behavior will be different for different loading rates. For example, the elastic modulus of apple tissue generally increases with loading rate (Petrell et al., 1980). To completely describe this rate-dependent behavior, the test specimen may have to undergo an extended time period (e.g. from many minutes to days) under the creep and/or stress relaxation tests. This is not only timeconsuming but also impractical, as food specimens can experience a significant change in their mechanical properties over time due to chemical and/or physiological activities, and loss or gain of moisture. In addition, it is often difficult to apply a truly instantaneous load or deformation to the specimen. Dynamic tests allow the elastic modulus (to be exact, the complex modulus consisting of the storage modulus and loss modulus) to be determined for a range of frequencies over a short time period. With the commonly used dynamic test, a food specimen is subjected to a sinusoidal varying stress (or force) given by the following equation: σ = σ0 sin(ω t)

[5.5]

The resulting strain will be a sinusoidal response of the same frequency as the stress, but out of phase by a lag phase ϕ: ε = ε0 sin(ωt + ϕ)

[5.6]

The complex modulus E*(iω), also called dynamic modulus, is determined from the following equation: E *(iω ) =

σ0 (cos ϕ + i sin ϕ ) = E ′( ω ) + i E ′′( ω ) ε0

[5.7]

where E′ is called the storage modulus representing the elastic component of the material, E′′ is the loss or imaginary modulus representing the viscous component or loss of mechanical energy as in a dashpot, and i is the imaginary unit equal to – 1. In dynamic tests, the applied loads or deformations are generally very small and confined to the elastic limit. The loading rate can vary greatly over several scales of magnitude, depending on the vibration amplitude and frequency used. The test often requires a number of specimens to cover a desired range of frequencies, which is prone to experimental errors due to the variability among the test specimens. Lu and Abbott (1995) proposed a new method for fast measurement of the dynamic properties of solid foods. They showed that under certain constraints (i.e. specimen size and frequency range), the dynamic viscoelastic properties of solid foods can be measured over a range of frequencies from one single specimen using an impulse load (or transient load). This transient test makes it faster and easier to obtain the dynamic properties of solid foods.

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In measuring basic mechanical properties of solid foods, several important issues need to be considered. First, many food products exhibit significantly different force/deformation behaviors under compressive and tensile loads. For example, apple flesh exhibits the force/deformation relationship shown in Fig. 5.3a under compressive loading. When subjected to tensile or bending loads, the apple flesh behaves more like a brittle material (Vincent et al., 1991). Secondly, many foods exhibit different mechanical properties when measured in different directions. Abbott and Lu (1996) studied the anisotropic property of apple fruit and found that the mechanical properties of apple tissues, as measured by the elastic modulus and failure strength, are significantly different in three perpendicular directions. Third, the mechanical properties of raw food products such as fresh fruits and vegetables and muscle meats are variable from location to location on the same food sample. Care must be taken in experimental design and reporting results to clearly indicate the location and direction from which specimens are taken.

5.3 Destructive measurements There are a wide variety of methods and/or techniques for destructive measurements of solid foods. Destructive methods can be empirical or fundamental. Empirical destructive methods are often somewhat imitative of methods used in sensory analyses or in the preparation of the food, such as spreading butter on bread, slicing meat, or cutting asparagus. They involve a complex form of loading with the stress and strain levels well beyond the initial failure, and the quantity measured often cannot be adequately interpreted in terms of basic engineering parameters or properties. Fundamental destructive methods, on the other hand, measure basic mechanical properties including the Young’s modulus, Poisson’s ratio, and shear modulus as well as yield strength, failure strength and others; but these often cannot be adequately interpreted in terms of human perception of ‘texture’. When the time- and/ or loading rate-dependent effect is important, the creep test, the relaxation test, and/or the dynamic test is required to measure the viscoelastic properties. Fundamental tests may be performed directly on original food samples that have well-defined dimensions but often require using specimens of specific dimensions excised from food samples. Therefore, the tests are more timeconsuming compared to many empirical tests. In addition it can sometimes be difficult to prepare specimens and mount them onto the testing device in such tests as tension and torsion. Fundamental tests are usually conducted using a universal testing machine. Overall, empirical methods tend to correlate better with sensory textural properties of foods and fundamental methods can help us better understand the mechanical behavior of a food, its structural features, and their changes. Based on the pattern of loading, destructive methods may include puncture, compression, shear, twisting/torsion, tension, bending, and so on.

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5.3.1 Puncture The puncture test measures the force required for a probe to penetrate into a food sample for a pre-specified depth. The test involves both compression and shearing of a food sample; it is an empirical technique that is somewhat imitative of the biting of a food item in the mouth. Puncture measurements depend on a number of factors such as probe size and shape, type of food, speed of loading, and number of probes on the tester. Bourne (1966) proposed the following empirical equation relating the puncture yield force to the area and perimeter of the probe: Fs = KcA + Ks P + C

[5.8]

where Fs is the force in N acting on the probe; A and P are the cross-sectional area (mm2) and the perimeter (mm) of the probe, respectively; Kc is the compression coefficient in N/mm2; Ks is the shear coefficient in N/mm; and C is a constant in N. The first term in Eq. [5.8] represents the contribution of compression to the puncture force and the second term represents the shear contribution. Equation [5.8] suggests that as the ratio of area to perimeter increases, the proportion of compression contributing to the overall puncture force will increase. Conversely, as the ratio of area to perimeter decreases, the relative contribution of shear to the overall puncture force increases. Therefore, by using different probe geometries, such as circular, square, star, and polygonal, we can manipulate the relative contributions of compression and shear to the overall puncture force measurement. However, the edge effect (or stress concentration) with different probe geometries on texture measurements should also be considered. Most commonly used puncture probes are of circular shape, which gives the maximum area to perimeter ratio among all geometries, and thus measures the puncture force with the maximum compression to shear ratio. At the other extreme is the thin blade probe, which gives the minimum ratio of area to perimeter, and the test in fact becomes a shear test. Different foods have different compression and shear properties. The ratio Kc/Ks may be useful for comparing the relative contributions of compression and shear to the puncture force among different foods. For example, the ratio Kc/Ks is approximately five for apples, two for potatoes, and one for bananas (Bourne, 1966). The different ratios of Kc/Ks for different foods may explain why the puncture test works well for some foods but not for others. Equation [5.8] also suggests that, in order to optimize the texture measurement, different probe geometries should be considered for different types of foods. The geometry of the probe tip is also important in the puncture test. Commonly used probe tip geometries include flat end, hemispherical (both full and partial), and conical (Fig. 5.5). In measuring the firmness of many intact fresh fruits and vegetables, a partial hemispherical probe is widely used (Fig. 5.5b), as exemplified by the two standard Magness-Taylor probes. On the other hand, the flat-end probe has been suggested for testing fresh-cut commodities (cut slices or chunks), which would ensure constant contact

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(a) Flat

(b) Partial hemispherical

(c) Full hemispherical

(d) Conical

Fig. 5.5 Different probe tips used in puncture tests. The Magness-Taylor probe, represented by (b) is popular for fruits and vegetables.

area between the probe and flat cut surface of the piece during the test (Wu and Abbott, 2002). Multiple conical probes have been used for measuring the tenderness of meat, such as the Armour Tenderometer that uses ten 3.2mm (1/8-inch) diameter probes with sharp points (conical probes) (Hansen, 1972). The Magness-Taylor (MT) firmness tester, which uses a partial hemispherical tip probe (Fig. 5.5b), is the best-known puncture method for measuring the texture of foods. The MT tester is widely used for estimating the harvest maturity or post-harvest firmness of many fruits and vegetables. There are several variations of the MT tester for measuring different fresh fruits and vegetables. A number of recent reviews have given detailed discussion of various forms of the MT tester (Abbott, 1999; Bourne, 2002). There are basically three types of MT tester available: the handheld MT tester with a mechanical force gauge, the portable MT tester equipped with an electronic force gauge, and the MT tester that uses a universal testing machine with a MT probe attached to it (Fig. 5.6). Two probe diameters, 11.1 and 7.9 mm, are used for different commodities. There are two types of MT testers with mechanical force gauges – the original Magness-Taylor with a helical spring and the Effe-gi with a spiral spring (Effe-gi testers may be marketed under the names McCormick, Wagner, R. Bryce, and others) – but the testers are fundamentally the same. Mechanical MT testers are popularly used for measuring the firmness of fruit in the orchard and at the packinghouse or the shipping point as well as in some laboratories. They are low in cost but prone to operational error because MT measurements are affected by the rate of loading and operator (Harker et al., 1996; Lehman-Salada, 1996). MT testing with a universal testing machine, such as Instron or Texture Analyzer, can accurately control the loading rate and record the force/deformation curve for each food sample. Thus, the measurements are more reliable and reproducible. But these machines are expensive and only suitable for laboratory uses. Many portable MT testers with an electronic force gauge are low in cost and can accurately record the force during the puncture test, and therefore they provide a good alternative to either the mechanical MT tester or the universal testing machine-based MT tester. One problem with these testers is that the loading rate is usually not controllable, although some reject excessively fast or slow measurements.

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Fig. 5.6 Three Magness-Taylor (MT) firmness testers: (a) a handheld mechanical MT tester based on a calibrated spring that may be helical or spiral; (b) a portable MT tester with an electronic gauge; and (c) a MT probe mounted on a standard laboratory testing machine.

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5.3.2 Compression Compression is widely used for measuring the basic mechanical properties of a large variety of solid foods including fruits (Abbott and Lu, 1996; Khan and Vincent, 1993), vegetables (Alvarez and Canet, 2000), grains (Wouters and de Baerdemaeker, 1988), and processed foods (Moiny et al., 2002). These tests are often conducted on cylindrical specimens excised from food samples under uniaxial loading with a universal testing machine. Compression tests may also be used to measure the basic mechanical properties of intact food samples with well-defined geometry. The American Society of Agricultural Engineers (ASAE) has provided a standard for conducting compression tests with intact food samples and the method for calculating the elastic modulus from the compressive force/deformation curve (ASAE, 2000). In texture measurement of intact foods, two types of compression are often used: the uniaxial compression test of food samples between two plates and the confined compression test, such as extrusion (Fig. 5.7). During uniaxial compression, force is applied to the sample in one direction and the sample is allowed to expand freely in the other two directions. The sample is compressed until it breaks or is completely crushed. The initial portion of the force/deformation curve can be used to estimate the modulus of elasticity of the food sample (Mohsenin, 1989). Uniaxial compression is easy to perform and is useful for such foods as grains. The single kernel wheat characterization system developed by the US Department of Agriculture (Martin et al., 1993) measures the hardness of individual wheat kernels by crushing them between two surfaces that move closer together through a rotor and a crescent. The crushing force profile is recorded and analyzed for predicting wheat kernel hardness. Gaines et al. (1996) reported a high correlation between predicted softness equivalent (SE) values from the single kernel wheat characterization system and actual SE milling values. In the simple compression–extrusion test shown in Fig. 5.7, force is applied through a plunger to compress the food sample in the test cell until it is

F

F

Plunger

Extrusion cell

F (a)

(b)

Fig. 5.7 Two types of compression tests: (a) uniaxial compression test between two plates and (b) simple compression–back extrusion test.

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crushed and flows through the gap (or annulus) between the plunger and the test cell. The force required to compress the food sample not only depends on the properties of the food but also the size of the annulus. This type of test is often used for measuring viscous liquids, gels, fats, and some fresh and processed fruits and vegetables. A variation to the simple compression– extrusion tester is to have slits or a grid of holes in the bottom of the extrusion cell so that some of the food will be extruded forward through the slits or holes during compression by the plunger. The Kramer Shear Press (Kramer, 1951) and the Ottawa Texture Measuring System (Voisey, 1971) are two popularly used compression–extrusion testers for measuring the texture of many solid foods (Chen and Marks, 1998; Mafuleka et al., 1991; Rodrigo et al., 1997; Strange and Whiting, 1998). The measurement process involves a complex form of mechanical loading, including shear, compression and extrusion. For this reason, these testers are more often considered to be a shear device rather than a compression device. The Kramer Shear Press is generally more popular for processed products (S. Lakeway, Jr., Pres., Food Technology Corp.) than for fresh fruits and vegetables for several reasons: 1. it is intuitively appealing since each test can measure many pieces of cutup food; 2. the user does not need to be concerned about the choice of free or constrained compression, tension etc.; probe geometry; sample size; and measurement parameters to report; and 3. many practitioners come from a food science rather than horticultural background.

5.3.3 Shear Shear often refers to the action of applying force to cut an object into two separate pieces. This loose definition is different from the strict engineering definition of pure shear, which is difficult to conduct experimentally, except under torsion and some special loading conditions. Shear tests are especially useful for measuring the textural properties of muscle foods. The WarnerBratzler (WB) shear tester is the standard device recommended by the American Meat Science Association for measuring the tenderness of meat. The device (Fig. 5.8) consists of a thin blade with a triangular opening and slotted base (of specified geometry and dimensions) (Bratzler, 1949; Voisey, 1976). Cylindrical specimens of 13 mm diameter are excised from cooked meat. As the blade moves through the slot, the meat specimen is compressed and changes the cross-sectional shape to conform to the restriction imposed by the triangular opening of the blade until it is eventually sheared into two pieces. The maximum force recorded during shearing is considered to be a measure of meat tenderness. The measurement process involves shear, tension and compression. The conventional interpretation, as implied in the name, is that shear forces are primarily responsible for cutting the meat specimen during WB measurement. Other researchers (Voisey, 1976; Zhang and Mittal,

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Blade

Specimen

Slot bars

Fig. 5.8 Schematic of the Warner-Bratzler shear tester for measuring the tenderness of meat.

1993) argue that tensile strength is the primary contributor in the WB shearing process. A number of different shear devices have been developed for measuring meat tenderness; they include the Volodkevich tenderometer (Volodkevich, 1938), the MIRINZ tenderometer (McFarlane and Marer, 1966), and the NIP tenderometer (Smith and Carpenter, 1973). Eckhoff et al. (1988) developed a rapid single-kernel wheat hardness tester. The tester consists of a rotary knife sitting in a groove. As the plate that contains kernels rotates, the knife slices through the kernels. The force required to cut the kernels over time is recorded and analyzed for differentiating hard wheat from soft wheat. Brusewitz et al. (1997) investigated a shearing technique to quantify the texture of several fruits: apples, peaches and bananas. A cylindrical specimen was cut through with a wire probe. A frequency analysis method, i.e. fast Fourier transform or FFT, was used to analyze the force–displacement curves generated as the wire cut through the specimen. They reported that FFT peak energies at frequencies below 4 Hz could be used to detect the change in the texture of fruits.

5.3.4 Torsion/twisting Torsion/twisting is another form of measuring texture that is based on the shear properties of foods. In a torsion test, force is applied to an object to cause the rotation or twisting of one part relative to another part of the object. Torsion tests are not commonly used for solid foods because of special requirements in preparing and mounting specimens and the difficulty of applying torsional force to food samples. Diehl et al. (1980) developed a torsion test to study the structural failure of selected raw fruits and vegetables.

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The test required preparation of specimens of special dimensions. Phillips (1992) developed a rotating pin shear device to measure the tenderness of meat. The device consisted of two sets of concentric pins that can be rotated relative to each other. The force required to rotate the inner set was measured against the angle of rotation and used as a measure of meat tenderness. Studman and Yuwana (1992) developed a twist device for measuring fruit firmness. The device consists of a blade on a spindle, which is pushed into the fruit and rotated about the axis of the spindle. Fruit firmness is estimated by measuring the maximum moment (or rotational angle) required to crush the flesh. The twist test is quite different from a torsion test as the properties tested with the former are likely to be a combination of shear and compression. Hopkirk et al. (1996) suggested that puncture and twist tests may produce quite different firmness judgments. Harker et al. (1996) found the twist test to be more precise than several testers using the MT puncture probe. The twist test has the advantage of being able to measure strength of tissue zones at specific depths from the surface without requiring the excision of tissue samples.

5.3.5 Tension/bending Tensile tests measure the force required to stretch a food specimen or an intact food sample apart. Tensile tests are useful for studying the tensile mechanical properties and, particularly, the structural failure characteristics of foods. Many foods have tensile properties that are quite distinct from those in compression. For example, the tensile properties of muscle foods are very different from those in compression (Lepetit and Culioli, 1994), and tensile tests are considered to be especially valuable for understanding the structural changes of muscle food during such processing treatments as aging and cooking (Dransfield et al., 1986; Lu et al., 1998; Mutungi et al., 1995; Penfield et al., 1976; Purslow, 1991). Researchers also used tensile tests to examine the structural and/or textural changes and the mode of fracture in fruits (Hallett and Harker, 1998; Harker and Hallett, 1992, 1994; Schoorl and Holt, 1983; Stow, 1989). Tensile tests are not widely used for measuring the texture of foods because: 1. the process of mastication primarily involves compression and shearing, not tension, of the food; and 2. tensile tests are more difficult to perform than other techniques discussed above; they present a special challenge in gripping or holding a food specimen without causing tissue damage or slippage during the test. Bending tests provide an alternative for overcoming the specimen-gripping problem to study the tensile properties of foods. There are two types of bending tests available for measuring foods: the cantilever beam bending and the three-point bending. The latter seems to be preferred over the former due to the consideration of specimen mounting. Lu and Siebenmorgen (1995)

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used a three-point bending test to measure the bending strength of rough and milled rice and related bending strength to rice milling quality. Rice kernels were placed on a sample holding device with the distance between the two supporting points at 4.5 mm. Loads were applied to the middle section of the kernel through a loading head mounted on an Instron universal testing machine. Abbott and Buta (2002) used three-point bending to measure the firmness of fresh cut pear slices and found that the bending test was less suitable and convenient compared to the puncture test. Alvarez et al. (2000) determined the fracture toughness and fracture energy of fresh fruits and vegetables from rectangular cross-section beam specimens by using a standard engineering method known as the single-edge notched bend test (a form of three-point bending). Suhendro et al. (1998) developed a bending technique to measure corn tortilla texture. Tortilla strips were bent to a 40° angle and the force required to bend them was used to detect tortilla texture (i.e. rollability and flexibility). In conducting bending tests, the specimen length to thickness (or diameter for a circular beam) ratio is an important consideration. To minimize the shear and compressive stress, the specimen length should be sufficiently large in comparison with its thickness. Standard bending tests for many engineering materials often require the length to thickness ratio to be at least 16 (Van Hecke et al., 1995). For many food products, this requirement can be difficult, if not impossible, to achieve and one has to balance various factors (such as sample geometry, the easiness of preparing test specimens, etc.) in selecting an appropriate length to thickness ratio.

5.3.6 Methods in data analysis In most destructive methods discussed above, the data extracted from the force/deformation curves is maximum force or, sometimes, the slope or the area up to the maximum force. This approach may not be good enough for certain applications since information embedded in the force/deformation curve, especially after initial failure, has not been effectively utilized or has even been totally discarded. A notable deviation from the conventional single parameter approach is the texture profile analysis (TPA) technique developed by Szczesniak and co-workers in the 1960s (Friedman et al., 1963; Szczesniak et al., 1963). The TPA test involves two complete cycles of compression and decompression of a food sample. The degree of compression of a food sample can be as high as 90%. The force/time or force/deformation relationship is recorded during the cycles of compression and decompression. From this force/time curve, a number of texture parameters such as fracturability, hardness, stringiness, and springiness are extracted. Szczesniak et al. (1963) reported that these parameters were closely related to sensory evaluation results. Bourne (1968; 1974) used an Instron universal machine to perform a modified TPA for pears and peaches. TPA tests have been used to quantify the textural properties of many other solid foods, including beef and beef loaves (Brady

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et al., 1985), restructured beef steaks (Chen and Trout, 1991), Cheddar cheese (Bryant et al., 1995; Lakhani et al., 1991), pecans (Ocon et al., 1995), potatoes (Alvarez and Canet, 2000), and cooked rice (Champagne et al., 1999; Lyon et al., 2000). Abbott et al. (1982) examined puncture and compression force/deformation curves of apple tissue and extracted numerous data including forces, areas, slopes and locations of specific events, and estimated jaggedness after fracture. These data were compared to sensory evaluations of oral texture (crispness, hardness, toughness, mealiness, etc.) using stepwise regression (Abbott et al., 1984). Maximum force was never the first-selected variable. Variables often selected were peak force of the first peak (not necessarily bioyield force), mean force near mid-compression, force about the maximum compression (75% strain), and total work in compression. Slopes were seldom selected, indicating less relationship of oral texture to modulus of elasticity than to strength variables in apples. They concluded that inclusion of two or more force/deformation variables in regression equations generally improved prediction of sensory textural attributes over maximum force alone. Recently, several new methods have been reported for analyzing the force/ deformation (or stress/strain) curve to predict the textural properties of foods. These methods include the use of FFT, fractal analysis, and stress/strain spectral analysis. One significant feature of these new data analysis methods is that they utilize the entire force/deformation curve or the portion of the curve after initial failure that shows considerable jaggedness and irregularity. Barrett et al. (1992) used FFT and fractal methods to analyze the jagged stress/strain curves to measure the crispness of food. The FFT method gave qualitative representation of the jaggedness of the force/deformation curve, but it lacked quantitative interpretation of food texture such as crispness. Peleg (1997) recommended that the jaggedness of force/deformation curves be determined by at least two methods simultaneously for mutual verification. Meullenet et al. (1999) treated the force/deformation curves obtained from extrusion tests of cooked rice samples as if they were spectral data. Subsequently, a spectral analysis technique was used to develop a partial least square (PLS) model to predict eight sensory texture characteristics of cooked rice, including adhesion, hardness, cohesiveness, roughness, etc. Their results showed that the method has the potential to predict multiple sensory texture characteristics. These new data analysis methods are useful for improving the prediction of specific textural properties of foods since the entire force/deformation curve is used in analysis. One major drawback with these methods is that they are more involved mathematically and often cannot provide meaningful interpretation on how textural attributes are predicted. However, once the methods and appropriate algorithms are developed, it should not present a great challenge to food technologists who may not have a good mathematical background.

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5.4 Non-destructive measurements Mastication is a process of destroying food, from which humans enjoy and sense the texture and flavor of the food. Hence it seems logical to food technologists that food texture should be measured through destructive means (Bourne, 2002). Any new techniques, especially those which are nondestructive, should be compared against the standard destructive techniques. This is understandable but should be put in perspective. Most destructive methods measure some combination of mechanical properties that are usually poorly understood, and they often measure some specific attributes of interest to the developer of the measurement but which cannot always be clearly articulated or properly considered by users adopting the method. Destructive methods often lack the sensitivity required to detect subtle changes in the texture of food, whereas non-destructive methods tend to better measure the mechanical properties of food, especially in ascertaining their changes at the structural level. For many fresh, unprocessed food products, grading or sorting them into consistent quality or texture categories or groups is even more important than measuring the actual texture. Non-destructive force/deformation sensing requires that food samples not be destroyed or degraded during the process of measurement. As a result of this basic requirement, the levels of force and/or deformation must be such that they will not exceed the limit that would cause permanent damage to the food product. The engineering theory of elasticity and viscoelasticity works well for describing the mechanical response of foods under non-destructive loading conditions. In fact, most non-destructive methods measure specific engineering properties, such as the modulus of elasticity and impact energy, either directly or indirectly. Voluminous literature is available on non-destructive force/deformation sensing techniques for measuring different types of foods, and it would need more space than is permitted here to give a comprehensive review of this important topic. Consequently, our following discussion is primarily focused on non-destructive firmness sensing of fresh fruits and vegetables, which is the primary focus of our research. Firmness of many fruits and vegetables changes during ripening and, hence, it is used for determining the optimal harvest time. Firmness is an important sensory attribute that directly influences eating and cooking quality and consumer acceptance. Firmness is also important in determining the resistance of a food product to mechanical damage in handling and shipping; for example, soft fruit are susceptible to mechanical damage in long-distance shipping and may have a shorter shelf life than firmer ones of the same type. Firmness is closely related to the mechanical properties of the product, and it, like many textural properties, does not have a universally accepted definition. For example, sensory scientists define firmness as the force required to break or bite into the food in the mouth, whereas horticulturists consider the force required to penetrate a mechanical probe – such as the standard MagnessTaylor firmness tester (see Section 5.3.1) – into a fresh product for a prespecified distance as a measure of firmness. To engineers, firmness may

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mean the capacity of a food to resist elastic deformation; the modulus of elasticity is often used as a measure of firmness. In this section, we use the term firmness according to its common usage by engineers. We discuss nondestructive firmness measurement methods and techniques based on the rate of loading, which include quasi-static, dynamic, vibration and impact. Acoustic sensing also falls within the force/deformation category and is related to dynamic, but it is covered in Chapter 6.

5.4.1 Quasi-static loading Quasi-static methods measure the force at a specific level of deformation, or the deformation at a specific level of force, or both. Force measurement methods require controlling the displacement of the probe during testing so that reliable force measurements can be obtained. Verner (1931) developed a firmness tester for stone fruits, which squeezed the fruit between two flat plates for a specified distance. Ross (1949) reported on a fruit firmness tester that used a pneumatic system to press a rounded-end piston (4.0 mm diameter) onto the sample for a depth of 0.79 mm so that no apparent bruises or injuries would occur to the fruit. Schomer and Olsen (1962) developed a mechanical thumb that was attached to the MT tester so that the indentation of the probe would be limited to 1.4 mm. Firmness readings were comparable to those obtained with the MT tester but were less reliable. Fekete (1993) reported on a fruit firmness tester based on measuring applied force for a pre-set penetration depth (0.3 mm). The ratio of stress (force per unit area) to deformation was calculated as a measure of fruit firmness. Mohsenin (1989) reasoned that the bioyield point, which occurs to many fresh fruits and vegetables during compression, could be a better indicator of fruit firmness than MT puncture, because it goes beyond the elastic deformation but has not resulted in a complete rupture of fruit tissue (see discussion in Section 5.2.2). Mohsenin et al. (1965) used a 6.4-mm flat-tip steel probe to measure the bioyield point of apple fruit to monitor the maturity of fruit on the tree. When a rigid probe is compressed against a fruit, the stress distribution is not uniform within the contact area and stress concentration occurs, based on the Hertz and Boussinesq contact theory (Mohsenin, 1989). As a result, the tissue failure will occur gradually within and below the contact surface and go beyond the immediate contact region. Further, the bioyield point often cannot be detected from the force/deformation curve due to the gradual failure of fruit tissue with a rigid probe (Fig. 5.9a). Ababneh (2002) applied the finite element method – a powerful engineering simulation technique – to analyze the stress/strain distribution in the apple fruit when different probe designs (probe size, the elasticity and thickness of the tip material, and tip geometry) were used in the indentation (or penetration) test. His simulations showed that a 3.2-mm thick flat tip with an elastic modulus about one-half that of an apple fruit would lead to a uniform stress distribution within the contact region of intact apple fruit. This uniform stress distribution is desirable

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because the tissue in the contact region tends to fail simultaneously, resulting in a conspicuous drop in force at the bioyield point and less damage to the tissue (Fig. 5.9b). Ababneh (2002) tested six probes with different tip materials with an Instron machine and confirmed that the probe with a diameter of 6.4 mm and a soft tip about 3 mm thick would be optimal for bioyield measurement. Lu and coworkers (unpublished data) further evaluated a new bioyield tester by coupling the 6.4-mm diameter probe with a 3.2-mm thick rubber tip (with an elastic modulus of about 150 kPa, about 40 % or less of that of apples) to a digital force gauge, which was in turn mounted onto a tabletop motor-driven stand. Bioyield force correlated reasonably well with 80

Force (N)

60

40

20

0 0

2

4

6

8

10

Displacement (mm) (a) 40

Force (N)

30

20

10

0 0

2

0

2

0

2

0

2

Displacement (mm) (b)

Fig. 5.9 Force–deformation curves obtained from two apples using an 11-mm diameter Magness-Taylor firmness probe mounted on a laboratory material testing machine (a) and from four locations on the same apple using a bioyield tester with a 6.4-mm diameter and a 3.2-mm thick soft rubber tip probe (b) (Lu unpublished data).

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MT firmness. The bioyield tester was able to monitor the texture change in apples stored in refrigerated storage as well as the MT tester. However, the bioyield tester was found to be less sensitive than the MT tester in monitoring apples stored at room temperature. The force-type firmness devices are mainly used for firm fruits and vegetables such as apples, kiwifruits (at the stage of commercial interest – well before eating ripeness – they are very firm), pears, peaches and cucumbers. Their applications to softer commodities are less successful because the testers are often not sensitive enough to measure the firmness of soft products, and the rate of loading can significantly affect the firmness reading. Devices that measure deformation at a pre-set force are popular for soft fruits. Hamson (1952) developed a device for measuring tomato firmness by applying a constant weight through an 11.1-mm diameter flat-head plunger for five seconds to record the deformation of the tomato fruit. The five-second interval under the constant load was important because tomatoes are viscoelastic and their deformation increases as the time of loading increases. Kattan (1957) designed a tomato firmness tester by encircling the fruit with a chain through which a constant load was applied to the circumference of the fruit for a fixed time. The amount of compression was measured as a measure of tomato firmness. The Asco Firmness Meter, based on the same principle, was introduced commercially in 1959 (Garrett et al., 1960). Others (Diener et al., 1971; Shafshak and Winsor, 1964) also reported on firmness testers that were based on a similar principle. However, these methods have not been widely adopted, largely because of the waiting time required for each measurement. Wu and Abbott (2002) developed a method and procedure for measuring the firmness of fresh-cut tomato slices. Compressive loads were first applied through a cylindrical probe to the tomato slice at a loading rate of 1.0 mm/s. After the pre-specified deformation level was reached, the tomato slice was held for ten seconds to record the stress relaxation (Fig. 5.10). Wu and Abbott reported that a 4-mm cylindrical probe with an indentation of 3.0 mm provided more consistent firmness measurements than a spherical probe or a 1-mm indentation. The maximum force was significantly different for ripeness stages from mature green to red tomatoes. The responses of the relaxation parameters over storage time were dependent on the initial maturity of the tomatoes; values differed significantly between tomatoes stored intact or sliced. Considerable research has been reported on firmness measurement of small fruits such as cherries and berries based on the deformation measurement corresponding to a specific load (Bernstein and Lustig, 1981; Diener et al., 1969; Lusting and Bernstein, 1987; Parker et al., 1966; Rohrbach, 1981; Rohrbach and Mainland, 1993; Timm et al., 1996; Wolfe et al., 1980). An instrument developed by BioWorks, Inc. (Stillwater, Oklahoma, USA) based on the original design of Timm et al. (1996) was recently available for measuring the firmness of small fruits. Fruit are placed on 25 shallow pockets

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Fmax

F0

Force (N)

3

2

Y = 1 – A ln(1 + t ) – B t /(C + t )

1

0 0 1 2 3 Loading distance 0 (mm)

2

4

6

8

10

Relaxation time (s) (a)

Force relative to initial force at 3-mm deformation

1.0 Ripeness stage Green

Tomato Tissue Relaxation under 3-mm deformation

0.9

Breaker

0.8

Turning Pink

0.7

Light-red 0.6

Red

0.5 0.4 0.3 0

2

4

6

8

10

Time (s) (b)

Fig. 5.10 Typical force/deformation curve during the loading and holding (stress relaxation) period (a) and the relaxation curves (b) of tomatoes harvested and measured at various stages of ripeness. Tissue at the junction of the outer pericarp and radial pericarp rays of 7-mm thick slices of tomato loaded with a 4-mm diameter cylindrical punch at 1–3 mm/s; each curve in (b) is a mean of three sites per slice × 10 tomatoes. Force shown is actual force divided by force at completion of loading (Wu and Abbott, 2002).

on an aluminum turntable that automatically rotates and aligns each fruit under the probe. Each fruit is compressed with a sensing probe from the preselected minimum force to the pre-selected maximum force. The force/ deformation curve is recorded and the slope between the minimum and maximum force is calculated as a measure of firmness. Several studies

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(Donahue and Work, 1998; Mitcham et al., 1998; Tetteh et al., 2001) showed that the instrument was able to measure the firmness of small fruits, but measurements were affected by factors such as fruit orientation, size, and pre-set minimum and maximum force (Tetteh et al., 2001). Quasi-static techniques discussed above require physical contact between the loading probe and the food sample, which can be slow and inconvenient. A novel non-contact laser air-puff firmness tester was developed and patented by Prussia et al. (1994) for measuring the firmness of foods and other products. The laser air-puff firmness tester uses a brief puff of compressed air to deform the product surface, which is measured remotely with a laser displacement sensor. The modulus of elasticity is calculated from applied air pressure and the deformation and is used as a measure of firmness. For a constant-pressure air, soft foods would deflect more than firmer foods. Therefore, by adjusting the air pressure, the tester can be suited for different types of food products. The laser displacement sensor is not suitable for highly translucent tissues, such as tomato slices, due to excessive light scatter (S. Prussia, personal communication).

5.4.2 Dynamic loading Most quasi-static methods measure either force or deformation or both from a small local area of food samples. Many foods, especially meats and raw fruits and vegetables, exhibit considerable variability in the mechanical properties within the same food sample. The dynamic force/deformation methods discussed herein are less influenced by the local properties and are more representative of the overall mechanical properties of a food sample. Dynamic force/deformation methods are achieved by applying sinusoidal force to the food over a range of frequencies and recording the corresponding displacement, acceleration or velocity (Fig. 5.11). Firmness is determined by analyzing the frequency spectrum of the ratio of force to deformation (or velocity or acceleration). Rohrbach and Glass (1980) determined the firmness of blueberries by measuring the mechanical impedance using a dynamic device. The mechanical impedance, the ratio of force to velocity in the frequency domain, was measured with a swept sinusoidal vibration over a range of frequencies. An impedance model was developed to fit the experimental data. Rohrbach and Glass found that the spring constant in the model correlated reasonably well with the firmness measurement from quasistatic compression tests. Abbott and Massie (1993) studied a dynamic testing technique for measuring the firmness of apples by applying a swept sinusoidal vibration (40–440 Hz) to the apple fruit through a flat plate (Fig. 5.11). The frequency response, the ratio of output (force) to input (acceleration), was recorded and calculated using a dynamic signal analyzer. Abbott and Massie reported that the dynamic measurement had a higher correlation with the slope of the MT force/ deformation curve (r = 0.61) than with the maximum force (r = 0.41). Later,

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Vibrator

Accelerometer Force transducer

Fruit

Lab jack

Fig. 5.11 Schematic of a dynamic test device for measuring the firmness of food products (adapted from Abbott and Massie, 1993).

Abbott and Massie (1995) used the device to test kiwifruits and reported a better correlation (r = 0.91) between dynamic measurements and MT firmness. Dynamic tests require a vibration device and a signal analyzer or a similar device to record both the input and output signals, which can be costly in instrumentation. Dynamic measurements are susceptible to the influence of fruit mass, size and shape. In addition, dynamic tests are slow in measurements and special care needs to be taken in loading the sample.

5.4.3 Vibration In both quasi-static and dynamic force/deformation tests, the force and/or deformation acting on the food sample is controlled, that is, the food samples have to conform to the constraint imposed upon them. As a result, the tests are not suitable for high-speed sorting or grading applications (with the exception of the laser air-puff tester). Vibration tests, on the other hand, are conducted by applying cyclic motions at certain frequencies to generate free vibrations in individual food items. When a periodic force is applied to a food item, it will vibrate at the same frequency as the applied force. At a particular frequency, the food item will vibrate vigorously and the maximal vibration amplitude is observed; such a condition is called resonance. The vibration behavior of the food is directly related to vibration frequency and the physical and mechanical properties of the food including the modulus of elasticity, mass, size and shape. Hamann and Carroll (1971) developed a sorting device for muscadine grapes using low-frequency vibration energy. The grapes were vibrated in an inclined separation trough at various frequencies between 100 Hz and 200 Hz. Ripe, soft fruit bounced from the trough at a lower frequency while

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firmer fruit bounced out at higher frequencies. The same device was used to sort blueberries based on firmness and shelf life (Hamann et al., 1973). They reported that bruised berries bounced from the vibrating trough at lower frequencies. Bower and Rohrbach (1976) also developed and tested a similar vibration device to sort blueberries. They reported that vibration sorting was effective in separating most good fruit from damaged fruit, but it was not effective in separating blueberries based on firmness and shelf life. Wolfe et al. (1980) reported that vibration sorting of fruit into different quality grades was not accurate enough to meet the marketing requirements. This is not surprising since the vibration behavior is also affected by the mass, size and shape of the fruit. Vibration tests are suitable when products are relatively uniform in size and shape, and when there exists a large variation in the textural attributes. A commercial pilot tomato sorting system was developed, which was capable of sorting 9.0–13.6 tonnes of tomatoes per hour with 90–95% accuracy (Anon., 1976). Vibration tests were found to be effective in separating tomatoes into different firmness groups (Holmes, 1979; McClure et al., 1979).

5.4.4 Impact Impact occurs when two objects collide with each other in a very short time interval, resulting in rapid changes in the momentum and velocity of the impacting objects accompanied by relatively large forces exerted between them. During impact, the object may undergo three phases of deformation: initial elastic deformation, partial plastic or permanent deformation, and the final elastic recovery. The impact response of the object depends on its mechanical properties, mass, size and shape, and thus can be used to measure the texture of food products. Typical impact force responses (IFR) for different firmness levels of tomatoes with approximately the same mass are shown in Fig. 5.12 when they were dropped onto a rigid plate from the same height (therefore the same initial impact acceleration or velocity). The green tomato had the highest impact force and the shortest contact time and the red tomato had the lowest impact force and the longest contact duration. The firm fruit exhibited a relatively symmetric IFR curve, whereas soft fruit had a more skewed IFR curve. The degree of skewness is related to the type of food, firmness and degree of impact (i.e. drop height or initial impact velocity). Numerous studies have been conducted on the impact responses of fruits and vegetables and their relationship with firmness and bruise susceptibility (Bajema and Hyde, 1998; Baritelle and Hyde, 2001; Bartsch and Askariaman, 1982; Delwiche, 1987; Finney and Massie, 1975; Lichtensteiger et al., 1988; Mohsenin et al., 1978; Rohrbach et al., 1982; Zhang and Brusewitz, 1991; Zhang et al., 1994). Impact parameters that can be extracted from the IFR curve include peak force; the ratio of peak force to the time from initial contact to peak force (C1); the ratio of peak force to the square of the time from initial contact to

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Green tomato mass = 103 g

80 70

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60 Pink tomato mass = 103 g

50 40

Red tomato mass = 101 g

30 20 10

0

1

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Time (mSec)

Fig. 5.12 Impact force response curves for three tomatoes of the same mass dropping 10 cm onto a rigid plate (Lichtensteiger et al., 1988).

peak force (C2); the ratio of peak force to the contact time (C3); coefficient of restitution, which is the ratio of the velocities of the product before and after impact and reflects the energy absorbed during impact; contact time; and IFR frequency spectrum. Table 5.1 summarizes various impact parameters used by researchers to measure the firmness of fruits and vegetables. Impact firmness measurement can be implemented with different approaches, which will lead to different instrument designs. There are two basic types of impact test used for measuring the firmness of fruits and vegetables: the drop test and the probe impact test. In the drop impact test, the sample is dropped onto an impact-sensing unit that records the impact force with time. The mass and stiffness of the impact-sensing unit must be significantly greater than those of the impacting fruit so that the influence of the sensing element on the IFR is minimal. This type of impact test is relatively easy to implement, but measurements are affected by the mass and shape (especially the contact geometry) of the sample. The second type of impact test uses an impact probe with a sensing element to impact a sample. This approach reduces the effect of fruit mass and radius of curvature on the IFR curve; however, probe design is critical for measuring the impact response of the fruit reliably and consistently. The drop test has been used to measure the firmness of a number of fruits and vegetables. Rohrbach (1981) developed a computer-controlled impact device that recorded the IFR of blueberries dropping 40 mm onto a rigid plate sensor. Seventy five percent of berries with firmness defects (soft berries) were correctly sorted into appropriate firmness grades by using C2 as a sorting criterion. Delwiche et al. (1987) studied the IFR of peaches

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Table 5.1 Impact parameters used to measure the firmness of fresh fruits and vegetables. (Adopted and expanded from Abbott et al., 1997) Impact parameter

Product

Reference

Peak force

apples, pears, peaches

C1a

peaches, kiwifruits

C2b

apples, blueberries, peaches, pears

Delwiche et al. (1991), Delwiche and Sarig (1991), Sugiyama et al. (1994) Brusewitz et al. (1991), Chen and Tjan (1996), Delwiche (1987), Delwiche et al. (1987), Zhang et al. (1994) Delwiche (1987), Delwiche et al. (1987), Delwiche and Sarig (1991), Delwiche et al. (1989), Rohrbach et al. (1982) Ozer et al. (1998) Brusewitz et al. (1991), McGlone and Schaare (1993), Patel et al. (1993), Zhang et al. (1994) Brusewitz et al. (1991), Zhang et al. (1994) Brusewitz et al. (1991), Zhang et al. (1994) Meredith et al. (1990), Sugiyama et al. (1994)

c

C3 Contact time Absorbed energy d IFR skewness Coefficient of restitutione IFR frequency spectrum Stiffness parameter Initial acceleration a b c d e

cantaloupe apricots, blueberries, kiwifruits, peaches, raspberries peaches peaches apples, peaches apples, peaches, pears tomatoes cherries

De Baerdemaeker et al. (1982), Delwiche (1987), Delwiche et al. (1987) Nahir et al. (1986) Younce and Davis (1995)

C1 is the ratio of the peak force to the time to the peak force C2 is the ratio of the peak force to the square of the time to the peak force. C3 is the ratio of the peak force to the contact time. IFR = impact force response. Coefficient of restitution is the ratio of the velocities of the specimen just before and after impact and reflects the energy absorbed in the product during impact.

impacting on a rigid surface. After comparing the results using different impact parameters, they concluded that C2 and force at 295 Hz were most highly correlated with elastic modulus and MT firmness. Based on this study, Delwiche et al. (1989) developed a prototype sorting line that could sort peaches and pears at a rate of five fruit/s. Approximately 74% of the peaches were sorted into the correct firmness grades. Meredith et al. (1990) used the coefficient of restitution determined from two consecutive bounces of peaches to determine fruit firmness, and they reported that the parameter correlated well with MT firmness. Brusewitz et al. (1991) investigated the relationship between peach ripeness and various impact parameters obtained from the drop test. They reported that peach ripeness correlated with C1, percent energy absorbed, and skewness of the IFR curve. McGlone and colleagues (McGlone and Schaare, 1993; Patel et al., 1993) developed three impact firmness devices for various applications to different fruits: ‘SoftSense’ for research uses and off-line quality control in industry; ‘BerryBounce’ designed for measuring the average firmness of batches of small-sized fruit; and ‘SoftSort’ for grading medium-size fruit such as kiwifruit. Impact firmness measurements for the three devices are based on the dwell time or contact time, defined as the peak width at half height of the first IFR curve. Ozer et

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al. (1998) reported that the drop impact test based on C3, the ratio of peak force to the contact time from three consecutive impacts, had a good correlation with flesh firmness and elasticity of cantaloupe fruit. The impact probe method has been studied by a number of researchers for various fruits and vegetables. Delwiche and Sarig (1991) developed a probe impact sensor, which was controlled by an air cylinder. Good correlation was obtained between peak acceleration (or force) and penetrometer firmness for pears and peaches, but not for apples. Chen and Tjan (1996) and Jaren et al. (1992) proposed an improved approach to impact firmness measurement. The fruit was impacted with a small spherical impactor of known mass and radius of curvature, and the acceleration of the impactor was measured. Chen and Tjan (1996) reported that the IFR was independent of the fruit mass and was less sensitive to the variation of the radius of curvature of the fruit. A prototype sorter was developed, which can sort fruit at a rate of five fruit/s, and good firmness predictions were reported for peaches and kiwifruit (Chen and Tjan, 1996). Ortiz-Cañavate et al. (2001) tested this impact sorter in an experimental packing line. A commercial firmness sorting system was recently developed by Sinclare International Ltd, Norfolk, UK (Briggs, 1998). The sorting device uses expandable resilient bellows, which are expanded to bring the sensor in contact with the food sample to detect its firmness. In most impact devices, using either the drop test or impact probe test mode, the fruit is impacting against a rigid sensing device, which could cause bruising to the fruit. Thai (1994) tested a soft foam sensor, whose electrical resistance properties change when being deformed, for selected fruits and vegetables. Younce and Davis (1995) developed a firmness sensor for cherries, which measured the voltage signal generated from dropping a cherry fruit onto a modified intercom speaker. Compared to other force/deformation methods, the impact method is fast and generates no or minimal damage to the fruit if the system is properly designed. The impact technique has great potential for sorting fruits and vegetables for firmness. One inherent problem with the impact method is that it measures the firmness of fruit at a particular location, which may not be representative of the overall firmness of the fruit. Multiple measurements from the same fruit are needed to overcome the property variability problem.

5.5 Conclusions The ability to measure texture accurately and quickly enables the food industry to set standards for quality and to monitor deterioration that occurs during storage and distribution. The study of chemical and physiological changes that determine texture has been underpinned by the development of methods for quantifying texture. Since texture can be defined as the

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human perception of the mechanical properties of the food, most commercial and research methods to measure texture have focused on the mechanical properties of the foodstuff. The diversity of foods, the variety of attributes required to fully describe textural properties, and the changes in these attributes as the product senesces (raw fruits and vegetables) or ages (processed foods) or undergoes microbial breakdown contribute to the complexity of texture measurement. The complexity of texture can still only be fully detected and described by sensory evaluation, which involves using a panel of people who have been trained to quantify defined attributes. However, instrumental measurements are preferred over sensory evaluations for both commercial and research applications because instruments are more convenient to use, widely available, tend to provide consistent values when used by different (often untrained) people, and are less expensive than sensory panels. We reviewed the principles of instrumental texture measurements and provided some practical applications that have been reported to give guidance in selecting among the many methods that have been developed. These instrumental measurements are widely understood and can provide a common language among researchers, producers and customers (retailers or consumers). There are numerous empirical and fundamental measurements that relate to textural attributes. Mechanical methods measure functions of force, deformation and time. Destructive mechanical methods generally relate more closely to sensory evaluations than do non-destructive measurements; but, by their destructive nature, they cannot be used for sorting products. Therefore, the commodity, the purpose of the measurement, sometimes tradition, and sometimes regulations guide the choice of the textural measurement method.

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and JACKSON P J (1996) Penetrometer measurement of apple and kiwifruit firmness: operator and instrument differences, J Am Soc Hort Sci, 121(5), 927–36. HOLMES R G (1979) Vibratory sorting of process tomatoes, Am Soc Agri Eng Paper 796543, St Joseph, Michigan, ASAE. HOPKIRK G, MAINDONALD J H and WHITE A (1996) Comparison of four new devices for measuring kiwifruit firmness, N Z J Crop Hort Sci, 24(3), 273–86. HUGHES H and SEGERLIND L J (1972) A rapid mechanical method for determining Poisson’s ratio in biological materials, Am Soc Agri Eng Paper 72-310, St Joseph, Michigan, ASAE. JAREN C, RUIT-ALTISENT M and PEREZ DE RUEDA R (1992) Sensing physical stage of fruits by their response to nondestructive impacts, AgEng 92 Paper 9211-113, Uppsala, Sweden, June 1–40. KATTAN A A (1957) Changes in color and firmness during ripening of detached tomatoes, and the use of a new instrument for measuring firmness, Proc Am Soc Hort Sci, 70, 379–83. KHAN A A and VINCENT J F V (1993) Compressive stiffness and fracture properties of apple and potato paranchyma, J Texture Stud, 24(4), 423–35. KRAMER A (1951) Objective testing of vegetable quality, Food Technol, 5(7), 265–9. LAKHANI S, GULLETT E A, FERRIER L K and HILL A R (1991) Texture analysis of Cheddar cheese made from ultrafiltered milk, J Food Qual, 14(3), 257–71. LEHMAN-SALADA L (1996) Instrument and operator effects on apple firmness readings, HortScience, 31(6), 994–7. LEPETIT J and CULIOLI J (1994) Mechanical properties of meat, Meat Science, 36(1/2), 203– 37. LICHTENSTEIGER M J, HOLMES R G, HAMDY M Y and BLAISDELL J L (1988) Impact parameters of spherical viscoelastic objects and tomatoes, Trans Am Soc Agri Eng, 31(2), 595–602. LU R and ABBOTT J A (1995) A transient method for determining dynamic viscoelastic properties of solid foods, Trans Am Soc Agri Eng, 39(4), 1461–7. LU R and SIEBENMORGEN T J (1995) Correlation of head rice yield to selected physical and mechanical properties of rice kernels, Trans Am Soc Agri Eng, 38(3), 889–94. LU R, CHEN Y R, SOLOMON M B and BERRY B W (1998) Tensile properties and Warner-Bratzler tenderness measurement of raw and cooked beef, Trans Am Soc Agri Eng, 41(5), 1431–9. LUSTIG I and BERNSTEIN Z (1987) An improved firmness tester for juicy fruit, HortScience, 22(4), 653–5. LYON B G, CHAMPAGNE E T, VINYARD B T and WINDHAM W R (2000) Sensory and instrumental relationships of texture of cooked rice from selected cultivars and postharvest handling practices, Cereal Chem, 77(1), 64–9. MAFULEKA M M, OTT D B, HOSFIELD G L and UEBERSAX M A (1991) Dry bean (Phaseolus vulgaris) hardening and the consequences of pectin methylensterase activity in storage, J Food Proc Preservation, 15(1), 1–18. MARTIN C R, ROUSSER R and BRABEC D L (1993) Development of a single-kernel wheat characterization system, Trans Am Soc Agri Eng, 36(5), 1399–404. MCGLONE V A and SCHAARE P N (1993) The application of impact response analysis in the New Zealand fruit industry, Am Soc Agri Eng Paper 936537, St Joseph, Michigan, ASAE. MCCLURE J R, HOLMES R G and HAMDY M Y (1979) Inclined vibrating plate tomato sorter, Am Soc Agri Eng Paper 796008, St Joseph, Michigan, ASAE. MCFARLANE P G and MARER J M (1966) An apparatus for determining the tenderness of meat, Food Technol, 20(6), 838–9. MEREDITH F I, LEFFLER R G and LYON C E (1990) Detection of firmness in peaches by impact force response, Trans Am Soc Agri Eng, 33(1), 186–8. MEULLENET J C, SITAKALIN C and MARKS B P (1999) Prediction of rice texture by spectral

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stress strain analysis: a novel technique for treating instrumental extrusion data used for predicting sensory texture profiles, J Texture Stud, 30(4), 435–50. MITCHAM E J , CLAYTON M and BIASI W V (1998) Comparison of devices for measuring cherry fruit firmness, HortScience, 33(4), 723–7. MOHSENIN N N (1989) Physical Properties of Plant and Animal Materials, (Second Edition). New York, Gordon and Breach Science Publisher. MOHSENIN N N, COOPER H E, HAMMERLE J R, FLETCHER S W and TUKEY L D (1965) “Readiness for harvest” of apples as affected by physical and mechanical properties of the fruit, Penn State Univ Agri Exp Sta Bul, 721. MOHSENIN N N, JINDAL V K and MANOR A N (1978) Mechanics of impact of a falling fruit on a cushioned surface, Trans Am Soc Agri Eng, 21(3), 594–600. MOINY V, MEULLENET J F and XIONG R (2002) Uniaxial compression of Cheddar cheese at various loading rates and its correlation to sensory texture profiles, J Texture Stud, 33(3), 237–54. MUTUNGI G, PURSLOW P and WARKUP C (1995) Structural and mechanical changes in raw and cooked single porcine muscle fibers extended to fracture, Meat Science, 40(2), 217– 34. NAHIR D, SCHMILOVITCH Z and RONEN B (1986) Tomato grading by impact force response, Am Soc Agri Eng Paper 863028, St Joseph, Michigan, ASAE. OCON A, ANZALDUA-MORALES A, QUINTERO A and GASTELUM G (1995) Texture of pecans measured by sensory and instrumental means, J Food Sci, 60(6),1333–6. ORTIZ-CAÑAVATE J, HOMER I, GARCIA-RAMOS F J and RUIZ-ALTISENT M (2001) Determination of firmness in a fruit packing line using a non-destructive impact sensor. In Proc 6th International Symposium on Fruit, Nut, and Vegetable Production Engineering, Potsdam, Germany, September 11–14. OZER N, ENGEL B A and SIMON J E (1998) A multiple impact approach for non-destructive measurement of fruit firmness and maturity, Trans Am Soc Agri Eng, 41(3), 871–6. PARKER R E, LEVIN J H and WHITTENBERGER R T (1966) An instrument for measuring firmness of red tart cherries, Mich Agri Exp Sta Quart Bul, 48(3), 471–82. PATEL N, MCGLONE V A, SCHAARE P N and HALL H (1993) “BerryBounce”: a technique for the rapid and non-destructive measurement of firmness in small fruit, Int Soc Hort Sci, 352, 189–98. PELEG M (1997) Measures of line jaggedness and their use in foods textural evaluation, Crit Rev Food Sci Nutr, 37(6), 491–518. PENFIELD M P, BARKER C L and MEYER B H (1976) Tensile properties of beef semitendinosus muscle as affected by heating rate and end point temperature, J Texture Stud, 7(1), 77– 85. PETRELL R J, MOHSENIN N N and WALLNER S (1980) Dynamic mechanical properties of the apple cortex in relation to sample location and ripening, J Texture Stud, 10(3), 217–29. PHILLIPS D M (1992) A new technique for measuring meat texture and tenderness. In Proceedings of the 38th ICoMST, Clermont-Ferrand, France, 959–62. PRUSSIA S E, ASTLEFORD J J, HEWLETT B and HUNG Y C (1994) Non-destructive firmness measuring device, US Patent No 5,372,030. PURSLOW P P (1991) Measuring meat texture and understanding its structural basis. In Feeding and the Texture of Food, Eds J F V Vincent and P J Lillford Cambridge, Cambridge Unveristy Press, 35–56. RAO M A and STEFFE J F (1992) Viscoelastic Properties of Foods. New York, Elsevier Applied Science. RODRIGO C, RODRIGO M, FISZMAN S and SANCHEZ T (1997) Thermal degradation of green asparagus texture, J Food Protection, 60(3), 315–20. ROHRBACH R P (1981) Sorting blueberries to improve fresh market shelf life, Am Soc Agri Eng Paper 811501, St Joseph, Michigan, ASAE. ROHRBACH R P and GLASS III S W (1980) Driving point mechanical impedance of blueberries, Trans Am Soc Agri Eng, 23(2), 298–302.

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6 Sound input techniques for measuring texture L. M. Duizer, Massey University, New Zealand

6.1

Introduction

Acoustic emissions are an important aspect of food texture perception. Quality and acceptability of food products are often assessed based on the sounds produced during crushing or biting of the food. For crisp food products in particular, when the sound is not appropriate, the food may be considered unacceptable and of poor quality (Szczesniak, 1990). Two acoustic emission techniques exist in the literature for measuring the sounds produced by manipulation of a food product. The first is destructive testing, which, as the name implies, refers to the permanent deformation of the food such that it can no longer be used for further texture research. The second, in which sound is transmitted through a food product to obtain the natural resonant frequencies of the product, is predominantly used for the assessment of fruit ripeness and quality. Consumers subjectively evaluate fruit ripeness and quality by tapping products and listening to the sounds they make in order to assess their texture. This approach has led to the development of objective instrumental techniques for collecting the same information. This chapter will focus on the destructive and non-destructive acoustic measures of food products. The equipment required for each test will be discussed and important factors to consider will be addressed. Various applications of these tests will be presented. The chapter will conclude with a brief discussion on the types of equipment most often used as well as books referenced to aid in the understanding of the contribution of sound to textural properties of foods. Key to understanding acoustic measurements is the physics of sound and the sensation of hearing. These topics will be discussed prior to explanation of the acoustic techniques.

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Sound and its detection: what is sound?

Sound originates due to the vibration of a sound source moving through its surrounding medium. During eating or compressing food, the sound source is the product being crushed. During non-destructive testing, the sound source is a signal generator attached to the food being tested. The medium through which the sound wave travels is typically air; however, in the case of nondestructive testing, the sound medium is the flesh and skin of the fruit which the sound is being transmitted through. The vibrations from the sound source cause a disturbance in the surrounding medium, setting particles in motion, and thereby transporting energy through the medium. This motion is produced due to the vibration around the equilibrium of one molecule displacing others around it. This then causes a pressure wave to be produced. The pressure wave is composed of periods of rarefaction (regions of low air pressure due to decreased particle density) and compression (regions of high air pressure due to increased particle density) (Speaks, 1999). It is this pressure wave that is detected by the ear for the perception of sound and is also detected by microphones during recording of bite sounds. All elastic objects vibrate best at natural frequencies. A natural frequency is defined as the characteristic frequency at which an elastic structure is free to vibrate (Speaks, 1999). An object can have more than one natural frequency, and this natural frequency has significant implications during both destructive and non-destructive testing. Resonance occurs when a forced frequency, such as that occurring during non-destructive testing, coincides with a natural frequency, resulting in amplification of sounds at that frequency. The resonant properties of an object are dependent on the density of the object, as well as its shape and size (Abbott et al., 1997). 6.2.1 The detection of sound Within humans, sound detection occurs via two mechanisms; bone-conduction or air-conduction. Air-conducted sound is detected via the movement of sound waves throughout the auditory system. There are three sections to the auditory system; the outer ear, the middle ear and the inner ear. The auditory canal in the outer ear functions as a funnel for incoming sound waves (Stevens and Davis, 1938). At the end of the outer ear is the tympanic membrane, or eardrum. Internal to the tympanic membrane is the middle ear. This portion of the auditory system contains the ossicles which are three small bones called the malleus, incus and stapes (commonly referred to as the hammer, the anvil and the stirrups) (Kiang and Peake, 1988). The major function of the middle ear is to ensure the efficient transfer of sound from the air to the fluid in the cochlea within the inner ear (Moore, 1982). The last section of the auditory system, the inner ear, lies within the temporal bone of the skull. Located within the inner ear is the cochlea, which contains the oval and round windows, the helicotrema, the basilar membrane and the organ of corti. The cochlea is the most important part of the auditory system. It is here

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that the sound pressure variations are transformed into neural impulses (Rossing, 1990). During air-conduction, sound waves enter the auditory canal and cause the tympanic membrane (eardrum) to vibrate. The ossicles transmit the vibrations through the middle ear. The movement of the foot-plate of the stapes vibrates the membranous covering of the oval window, causing pressure changes in the cochlear fluids. The inward movement of the oval window causes a flow of fluid around the helicotrema and an outward movement of the round window. This flow of fluid causes the basilar membrane to move in waves from the base of the cochlea toward the apex or end of the cochlea. The waves build slowly, increasing in amplitude as they move down the cochlea. Upon reaching maximum amplitude, the magnitude of the waves decreases abruptly. Sounds of different frequencies produce maximum amplitude at different places along the basilar membrane. High frequency sounds produce maximum intensity displacement near the oval window with little activity further along the membrane. Low frequency sounds produce vibrations along the entire membrane, with maximum amplitude near the apex of the cochlea (Moore, 1982). Resting on the basilar membrane is the organ of corti. The organ of corti is responsible for converting mechanical activity into neural activity. Hair cells within the organ of corti translate the wave motions of the basilar membrane into nerve impulses. Sound waves are also transmitted to the inner ear via bone conduction. For bone-conduction, the vibrations of the sound source do not enter the auditory system via the auditory canal. Instead they are picked up by the bones of the skull around the middle ear. These vibrations trigger the movement of the endolymphatic fluid and the basilar membrane within the inner ear similar to the movement triggered by air-conducted sounds (Stevens and Davis, 1938)

6.3

Destructive testing

Sound is produced due to the application of a force on the food product. The cell walls of the product snap and energy is released. It is this energy moving through the air (or other sound medium) which is detected and recorded. Destructive testing has been conducted in two ways; via recording the sounds produced by the application of a force on the product either by the teeth (during biting and chewing) or by instrumental shear or compression probes (the instrumental element). To record the sound emission produced via destructive means an experimental set-up such as that shown in Figs 6.1 and 6.2 is required. Requirements for this set-up include; a means of crushing the food product (either by eating as shown in Fig. 6.1 or by crushing as shown in Fig. 6.2), a microphone interfaced with an amplifier and sometimes a filter. Attached

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Amplifier

Filter Data acquisition unit

Fig. 6.1

Example of acoustic apparatus used for destructive testing when biting with teeth.

Instron testing machine

Data acquisition unit Compression probe Amplifier

Filter Sound-proof chamber

Fig. 6.2

Example of acoustic apparatus used for destructive testing by instrumental compression.

to the amplifier and filter is a computer or other data acquisition system (such as a strip chart recorder). Data analysis software or equipment is then required to analyse the collected data. Each of these pieces of equipment will be discussed in turn. The microphone selected for use should be able to detect frequencies within the audible frequency range (20–20 000 Hz). The microphone acts to convert the acoustic energy of the sound wave produced during crushing of the food into electrical voltage. The voltage signal is then amplified and filtered. Amplification increases the signal in order to process the information while filtering reduces noise and gives a smoother signal for processing. In some systems, preamplifiers and amplifiers are interfaced with the microphone for this purpose and the data is processed prior to storage. However, it is also possible to use a data acquisition unit where the amplification and filtering may be done automatically. One such system that can be used is the Powerlab system developed by ADInstruments Pty Ltd (Castle Hill, Australia). Others have developed their own proprietary data acquisition systems (Seymour and Hamann, 1984). Once the signal has been amplified (and possibly filtered) the data is stored on either audio tape (Drake, 1963; Drake, 1965; Kapur, 1971; Vickers, 1987; Dacremont et al., 1991; Dacremont, 1995) or as a data file on a computer (Lee et al., 1990; Duizer et al., 1998). If saved onto tape, Fourier transformation of the data takes place using fast Fourier transformation (FFT)

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signal analysers (Lee et al., 1988; Dacremont et al., 1991; Dacremont, 1995, or audio spectrometers (Drake, 1963; Kapur, 1971). FFT determines the important frequencies (in hertz (Hz)) of the sound wave. If stored on a computer, the saved data can be analysed using computer programs for determining the FFT of the sound wave (Seymour and Hamann, 1984; Bouvier et al., 1997; Liu and Tan, 1999; De Belie et al., 2000). This experimental setup can be used for recording data during testing with individuals biting and chewing samples as well as during instrumental compression and puncture. However, there are factors specific for each type of testing situation which must be considered.

6.3.1 Recording of sounds produced during physical biting of samples In order to fully record all vibrations produced during biting of foods, both air-conducted and bone-conducted sounds must be recorded. The location of the microphone for the recording of air-conducted sounds has included placement directly in front of the mouth (Drake, 1963; Lee et al., 1988; Lee et al., 1990) or beside the ear canal (Dacremont et al., 1991; Dacremont, 1995; De Belie et al., 2000). The majority of research into bone-conducted sounds has used a contact microphone as the sound detector. However, Kapur (1971) first devised a system for measuring bone-conducted vibrations where a very fine stainless steel needle was inserted on the bone under the skin in three areas of the face; the forehead, the mastoid process and the lower border of the mandible (Kapur, 1971). This needle was attached to a phonograph cartridge for picking up the signals from the bony surfaces. Although this is an invasive approach to collecting the information, it was felt that a needle placed on the bone was the only means of measuring bone-conducted vibrations. The vibrations were recorded onto magnetic tape and the amplitude of the vibrations plotted against frequency. Now, bone-conducted sounds are recorded via a contact microphone attached to the skin above the facial bones of subjects. This is a much less invasive technique than the needle used by Kapur (1971). A contact microphone detects vibrations at the location to which it is attached and no airborne sounds are detected. The frequency range of a contact microphone for recording bone-conducted sounds does not have to be as broad as that for air-conducted sounds, as bone-conducted sounds are lower in frequency than air-conducted sounds (Dacremont, 1995). Sounds have been recorded from contact microphones placed on the cheek of the subject near the maxillar angle of the jaw directly in front of the ear (Dacremont et al., 1991) or attached to the mastoid bone directly behind the ear of the subject (Dacremont, 1995). The eating technique employed during testing has an effect on recorded acoustic signals. High-frequency air conducted sounds are most prominent during biting of samples with the front teeth. The physiological tissue of the

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cheeks will dampen air-conducted sounds produced during chewing food with the back molars (Dacremont et al., 1991). Also, open mouth chewing results in louder sound recordings than close mouthed chewing during recording of both air-conducted sounds and bone-conducted sounds as vibration transmission through the bone increases when biting with an open jaw (Lee et al., 1990; Hashimoto and Clark, 2001). When recording bone-conducted sounds, care must be taken to ensure that only the bite sounds are being recorded. When the teeth come into contact with each other during biting, an increase in the recorded sound amplitude has been noted (Hashimoto and Clark, 2001). It is important when evaluating physiological acoustic responses that the placement of the microphone and the eating technique (bite vs chew, open mouth vs closed mouth) be carefully considered for correlating acoustic results with sensory measures. When analysing bone-conducted sounds, it is important to note that the acoustic properties of this sound may be altered due to the resonant frequency of the mandible. Kapur (1971) showed that the jaw resonates at 160 Hz meaning that recordings at this frequency may be louder than what is actually heard during chewing. This has been further supported by Dacremont et al. (1991) who noted that when bone- and air-conducted sound recordings are combined to match what is heard by an individual during chewing, the boneconducted sounds are attenuated at 160 Hz.

6.3.2 Recording of acoustics produced during mechanical testing One of the stated advantages of recording an acoustic signature by mechanical testing is the reproducibility and consistency of mechanical tests (Mohamed et al., 1982; van Hecke et al., 1998). However, it is important that the instrumental test selected simulates the conditions to which a food is subjected during chewing (Mohamed et al., 1982). Factors such as the type of probe used and the speed of the crosshead during testing must be carefully considered to ensure that the instrument adequately mimics the sensations occurring in the mouth during chewing. Various instruments and probes have been used for the production of acoustic signatures via mechanical crushing. These have included the Instron Universal Testing Machine with a compression probe (Roudaut et al., 1998) or a Kramer shear cell (Seymour and Hamann, 1988), the Steven’s testing machine with a compression probe (Tesch et al., 1995) as well as other proprietary instruments (Mohamed et al., 1982; Al Chakra et al., 1996; Liu and Tan, 1999). Researchers have argued that mechanical tests such as puncture and compression are similar to tooth impact and molar action during mastication (Van Hecke et al., 1998; Liu and Tan, 1999). However, more recent debates point out that there is a difference between strain that occurs during chewing compared to that possible in instrumental tests (van Vliet, 2002). In order to remove extraneous noise from the collected signature during testing, the samples and the probe should be placed in an insulated chamber,

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as shown in Fig. 6.2 (Mohamed et al., 1982; Seymour and Hamann, 1988; Tesch et al., 1995). This prevents the noise of the machine from interfering with the acoustic signal of the sample. This noise appears to originate from the electronic drive control as well as the toothed belts and drive mechanism required to move the probe into the sample. Additionally, background noise present in the room can be eliminated by using the insulated chamber. In order to ensure that instrumental acoustic recordings are similar to the acoustics produced during biting, a similar rate of fracture should occur. Sharp cracking sounds occur due to the high energy sound waves produced when a material fractures rapidly and is broken (Bruns and Bourne, 1975). On average, a human compression rate is 20 mm.s–1 (Bourne, 1982) whereas, instrumental compression varies between 0.3 mm.s–1 (Roudaut et al., 1998) and 3.33 mm.s–1 (Seymour and Hamann, 1988) depending on the instrument used. When allowing the sample to break at its own rate, Mohamed et al. (1982) observed that a Malteser fractured at a rate of 83.4 mm.s–1. The fracture rates may have an impact on the resulting acoustic properties of the sample, and care should be taken to ensure that consistency in the rate of compression or shear occurs.

6.3.3 Analysis of sound waves produced during destructive testing The sound wave recorded during destructive testing is presented as a plot of amplitude vs time. An example of an amplitude–time plot of sounds produced during biting of an extruded snack product stored at water activities of 0.11 and 0.44 is shown in Fig. 6.3. The sound wave from the 0.11 water activity sample represents the sounds made when biting a crisp product and the sound wave from the 0.44 water activity sample represents the sound made when biting a less crisp product. There are visual differences apparent between the two curves. The crisp product in Fig. 6.3(a) has more peaks as well as having peaks of a higher amplitude than the less crisp product in Fig. 6.3(b). Many different properties of sound waves have been measured and correlated with sensory properties, and a list of these acoustic parameters published in food literature is shown in Table 6.1. The amplitude of the sound wave refers to the amount of energy the sound source produces. A high amount of energy is reflected in a high-amplitude wave and a low amount of energy is reflected in a low-amplitude wave. The amount of energy transported across a given area per unit of time is known as the sound intensity (measured in decibels or dB) (Speaks, 1999). This measure, and its related measure, sound pressure level (also measured in dB), are related to the perceived loudness of the sound, a more subjective measure than the measured sound intensity and sound pressure (Rossing, 1990). There is a great deal of variability between individuals in terms of how loud a sound is perceived to be. In general, for one individual, the more intense the sound, the louder it is perceived as being. This will vary between individuals due to differences in ability to detect sounds because of age as well as other physiological variables inherent

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15

10

Amplitude (mvolts)

5

0

–5

–10

–15 0

1000

2000 3000 Time (µs) (a)

4000

5000

15

Amplitude (mvolts)

10

5

0

–5

–10

–15 0

1000

2000

3000 4000 Time (µs)

5000

6000

7000

(b)

Fig. 6.3

Sound waves produced while biting extruded corn-based samples stored at water activities of (a) 0.11 and (b) 0.44.

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Table 6.1

Acoustic parameters used to define crisp products

Author

Parameter

Drake (1965)

Mean frequency Bite time Sound level at 1.2 kHz Equivalent continuous sound level (Leq) Mean height of the peaks (mhp) Duration of sound Number of peaks (NP) Mean sound pressure (N/m2) Mean sound pressure level (dB) Acoustic intensity (watts/m2) Sound level (dB) for each chew Total sound level (dB) Frequencies Fractal analysis

Mohamed et al. (1982) Edmister and Vickers (1985); Vickers (1987) Seymour and Hamann (1988)

Lee et al. (1998) Lee et al. (1990) Dacremont (1995) Duizer et al. (1998)

Averaged over frequency ranges of: 0.5–3.3 kHz Averaged over frequency ranges of: 0.5–3.3 kHz Sum over frequency ranges of: 0.5–3.3 kHz

to a person. Additionally, different frequencies of sounds are perceived to be louder or softer than other frequencies (Rossing, 1990). The amplitude–time plot of sounds collected during destructive testing has been described as a jagged curve (Vickers and Bourne, 1976). And in fact, some of the measures of the amplitude–time curve can be used to characterise the jaggedness of the curve (such as number of peaks and mean height of the peaks). A technique which can characterise the jaggedness of peaks, and has recently been applied to food research, is fractal analysis (Barrett et al., 1992; Barrett et al., 1994; Duizer et al., 1998). Fractal analysis has been defined as a quantitative technique for assessing the overall ruggedness or jaggedness of irregular objects (Barrett et al., 1992). Briefly, fractal analysis involves the use of mathematical algorithms to determine the degree of jaggedness of a line in order to give a fractal dimension of the line. An area around the line is measured and, through various iterations of the algorithm, the space occupied by the line within that area is determined. This space is plotted against the measured area and the slope of the linear relationship is determined. The resulting number is the fractal dimension. The fractal dimension of a jagged line can range from one, which is a smooth line, to two, which occurs when the line is so jagged that it occupies the entire area being measured by the algorithm. Although there are many different algorithms which can be used for fractal analysis, the algorithm most often used for acoustic research is the Kolmogorov algorithm. This algorithm is a box counting technique that involves dividing the signature into a grid and counting the number of squares which contain

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a part of the object. The size of the boxes of the grid is halved and again the occupied squares are counted. This process continues for numerous iterations. The size of the boxes cannot be smaller than the resolution of the object (Russ, 1994). The slope of a log:log plot of the number of filled boxes vs the size of the box gives the Kolmogorov fractal dimension. For an object to be truly fractal, it must be self-similar. This means that any part of the object cannot be distinguished from the whole object or another part of the object (i.e. it has the same scaling factor in all directions). In the case of an acoustic signature, it is not possible to achieve self-similarity due to restrictions in sampling rate. Despite this, Barrett et al. (1992) and Tesch et al. (1995) concluded that it was possible to use fractal analysis as an objective measure of the degree of jaggedness of acoustic signatures. In this instance, the fractal dimension must be termed ‘apparent’ rather than ‘true’. Frequency is an integral part of an acoustic signature. The frequency of the vibratory motion of the sound wave is the rate at which the particles in the medium vibrate as the wave moves through the surrounding medium. It is measured in hertz (Hz) where one hertz equals one vibration per second (Speaks, 1999). The particles within the sound medium vibrate at the same frequency at which the sound source vibrates. The objective frequency measurement of a sound corresponds with the subjective measure of pitch (Rossing, 1990). A high-frequency sound source is perceived to have higher pitch than a low-frequency sound source. The frequency of the sound is determined using FFT. During Fourier analysis, the original data points are transformed into underlying frequencies. These frequencies are then plotted as amplitude vs frequency. From the plot, it is possible to identify special or important frequencies as these are characterised by higher amplitude on the plot. FFT plots of the sound waves shown in Fig. 6.3 are shown in Fig. 6.4. The sample stored at a water activity of 0.11 which is a more crisp sample shows more high frequency peaks (Fig. 6.4(a)) than the sample stored at a water activity of 0.44, a less crisp sample (Fig. 6.4(b)). The frequencies of sound have been used to compare various textural properties in order to obtain a better understanding of how the characteristics are perceived (Mohamed et al., 1982; Dacremont, 1995).

6.4

Non-destructive testing

Non-destructive sonic transmission tests measure the dynamic resonant behaviour in the audio-frequency range of the products. This is based on the principle of resonance, where the object vibrates vigorously at a particular frequency. For each resonant frequency there is one (and sometimes more than one) vibration mode. This mode and the frequency can provide information regarding the mechanical properties of the food.

156

Texture in food 0.000 20

Amplitude (volts)

0.000 15

0.000 10

0.000 05

0.000 00 0

5000

10 000

15 000

20 000

25 000

Frequency (Hz) (a) 0.000 20

Amplitude (volts)

0.000 15

0.000 10

0.000 05

0.000 00 0

5000

10 000

15 000

20 000

25 000

Frequency (Hz) (b)

Fig. 6.4 Power spectrum analysis of sound waves produced while biting extruded corn-based samples stored at water activities of (a) 0.11 and (b) 0.44.

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In the sonic transmission test developed by Abbott et al. (1968) the signal generator produces a pulse signal of uniform amplitude over a variety of consecutive frequency ranges. This is interfaced with an acoustic driver which imparts vibrational energy through the fruit, supported on a pedestal. An accelerometer attached on the opposite side of the fruit detects the vibrations which are converted to a signal and analysed via FFT. Due to the timeconsuming nature of the test, the acoustic impulse technique has been developed (Yamamoto et al., 1980). In the experimental set-up for the acoustic impulse technique, a microphone replaces the accelerometer. The microphone does not require attachment to the fruit, thereby eliminating the effect of detector placement on resonant frequencies. Additionally, in place of a signal generator, a ball pendulum is swung into the fruit at an equatorial location, thereby producing the sounds which are picked up by the microphone and analysed for resonant frequencies. A number of commercial bench top machines for non-destructive testing of fruit (such as the Acoustic Firmness Sensor™ by Aweta BV, The Netherlands) have been produced and high-speed machines for grading of fruit on packing lines are imminent. The test location around the fruit and the holding position of the fruit does not have an effect on the resonance frequencies but does affect their amplitude (Chen et al., 1992). Additionally, the hitting strength and location of the microphone do not have an effect on the peak frequency recorded during testing (Yamamoto et al., 1980). Fruit shape will have an effect on resonance and it has been recommended that three measurements be recorded and averaged in order to reduce the error due to variance in fruit shape (Chen and De Baerdemaeker, 1993).

6.4.1 Analysis of data collected during non-destructive testing The resonant frequencies resulting from the sound input signal have been used to calculate a stiffness factor of the fruit (Eq. 6.1). Abbott et al. (1968) found the second resonant frequency to be related to changes in the apple texture. This frequency was found to be the most reproducible, being less affected by the position of the driver and pickup than the other natural frequencies. The inclusion of mass in this stiffness coefficient equation compensates for the effect of the size of the apple on the resonant frequencies recorded. S = f 2m 2

[6.1]

where f is the second frequency squared and m is the mass of the sample in grams. Others (Cooke and Rand, 1973) have modified this equation to that shown in Eq. 6.2. However, Abbott et al. (1997) indicate that this stiffness coefficient equation is designed for fruit with a large variation in sizes and may not provide any more useful results for fruit with small variations in size than the original equation.

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S = f 2m2/3

6.5

[6.2]

Application of sound measurement techniques

Both destructive and non-destructive acoustic results have been applied to various food products in order to gain an understanding of the texture and quality of these products. In particular, destructive testing has provided key information regarding the perception of various textures of food while nondestructive testing has allowed for the determination of stiffness, leading to the evaluation of fruit quality. 6.5.1 The use of acoustics for understanding crispness The fact that crushing sounds might have an effect on the perception of texture was first suggested by Drake (1963). He proposed that when correlated to sensory data, vibrations produced during compression of foods could be helpful in the area of texture research. In 1976, Vickers and Bourne first published a psychoacoustical theory of crispness that led to numerous research publications exploring the use of acoustics and their relationship to the perception of crispness, crunchiness and crackliness of food products. All of the tests conducted have resulted in non-recoverable damage to the tested food samples. Structurally, crisp foods have a tendency to be cellular (Brennan et al., 1974). When a force is applied to such a cellular product, each cell ruptures, creating a sound and the overall rupture pattern produces an irregular frequency and amplitude signature (Vickers and Bourne, 1976; Vickers and Christensen, 1980; Vickers, 1981, 1987). Crisp food products can be classified into two general categories based on their structure; wet crisp (those which have water in their cells such as carrots, apples, water chestnuts and cabbage) and dry crisp (those which contain air within their cells such as toast, biscuits, extruded products and potato chips). The sound pattern generated by foods in these product categories is similar, and can be best characterised by the loudness of the sound and the number of sound occurrences. This is shown by the significant correlations between crispness and the log (number of peaks × mean height of the peaks) for wet and dry crisp products (r = 0.89 for wet and dry together, r = 0.95 for wet alone and r = 0.88 for dry alone (Edmister and Vickers, 1985)). Acoustics have been applied much more for understanding the textural properties of dry crisp products than wet crisp products, and the majority of published literature has been in the area of crispness perception of dry snack foods such as potato chips or extrusions (Mohamed et al., 1982; Vickers, 1987; Seymour and Hamann, 1988; Duizer et al., 1998). One reason for this is that dry crisp products have a texture which is easy to manipulate, usually through modifying the water activity, while keeping the structural properties of the samples constant.

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Mohamed et al. (1982) suggested that crispness of potato chips could be understood through combining acoustical results with force–deformation results. Various instrumental parameters have been applied to regression equations in order to test this theory. These instrumental parameters include the ratio of work done during fracture to total work (Mohamed et al., 1982), the total work to fracture (Seymour and Hamann, 1988) and peak force (Vickers, 1987). The regression equations developed by these authors and the products studied are shown in Table 6.2. Each of the three regression equations shown in Table 6.2 has strong predictive ability, with R2 values greater that 0.85, indicating that a measure of force as well as the acoustic element is important for prediction of crispness. The negative relationship between crispness and peak force indicates that the hardness or toughness of the potato chip samples studied must counteract the auditory sensation of crispness, leading to the belief that crispness must have a vibratory component and a non-vibratory component and the non-vibratory component is counteracting crispness (Vickers, 1987).

6.5.2 The use of acoustics to differentiate between crispness, crunchiness and crackliness In addition to developing an understanding of the sensation of crispness, acoustic techniques have been used to differentiate between the sensation of crispness and those of crunchiness and crackliness. Crunchiness has been shown to be strongly related to crispness (Vickers and Wasserman, 1979; Vickers, 1981), and various definitions indicate their close association. Recently, Fillion and Kilcast (2002) proposed that the crispness of fruit and vegetables can be defined as ‘a light and thin texture producing a sharp clean break with Table 6.2

Regression equations used for predicting crispness of dry crisp products

Author

Foods tested

Regression equation

R2 value

Mohamed et al. (1982)

Sponge fingers, wafer biscuits, crisbakes, ice cream wafers, Maltesers varying in water activity

Log crispness = 0.59 + 0.49 log (Leq) + 0.50 (WF/WT)

R2 = 0.85

Vickers (1987)

Potato chips

Oral crispness = –15.6 + 5.35 (np) + 133 (mhp) – 6.21 (peak force)

R2 = 0.98

Seymour and Hamann (1988)

Pringles potato chips

Crispness = 9.904 – 0.134 (work) + 0.025 (mean sound pressure 2.6–3.3 kHz)

R2 = 0.95

Crunch twists

Crispness = 16.47 – 0.064 (force) – 0.110 (sound pressure level 0.5–1.2 kHz)

R2 = 0.95

Note: Leq is continuous sound level; WF /WT is the ratio of work done during fracture to total work; np is number of peaks and mhp is mean height of peaks

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Texture in food

a high-pitch sound when a force is applied’. Crunchiness was associated with products with hard, dense textures producing a repeated loud, lowpitched sound (Fillion and Kilcast, 2002). During sensory evaluation of apples, crispness has been defined as ‘the amount and pitch of sound generated when the sample is first bitten with the front teeth’ and crunchiness as ‘the amount of sound generated when chewing with the back teeth’ (Harker et al., 1997). Vincent et al. (2002) differentiated between crispness and crunchiness based on the amount of force applied to the samples, with crisp products fracturing after the application of a lower force than that required for crunchy products. Certainly, there appears to be an association between the two textures. To differentiate between crispness and crunchiness, Vickers (1984a) asked judges to bite various crisp and crunchy foods and to indicate whether they were crisp or crunchy and then, in a separate experiment, to indicate which sample in a pair of crisp-crunchy samples was higher in pitch during biting. Foods which were classified as more crisp than crunchy nearly always produced a higher pitched sound than those which were classified as more crunchy than crisp. This was later confirmed by Dacremont (1995) who, through FFT of acoustic data produced during biting of food products, determined the actual frequencies of the sounds produced. Amongst the food products tested were extruded flat breads (crisp), carrot (crunchy) and dry biscuits (crackly). Crisp foods generated high-pitched frequencies (from 5–12.8 kHz) and these sounds were predominantly air-conducted. In contrast, crunchy foods produce low-frequency sounds when bitten, with frequencies within the frequency range of 1.25–2 kHz (Dacremont, 1995). Crackliness is not a texture term found frequently in sensory evaluation literature. Vickers (1984b) stated that a crackly product could be characterised by the number of sharp repeated noises produced when a food is bitten and chewed. This could be judged using either oral tactile or auditory cues. Dacremont (1995) showed that crackly food products produce a great deal of low-frequency sounds, similar to crunchiness, and can be differentiated from crunchiness because of the high level of bone-conducted sounds generated during biting.

6.5.3 The use of acoustics for the determination of fruit quality Both destructive testing and non-destructive acoustic testing have been used to determine the textural properties of fruit, predominantly firmness and crispness. Both of these properties have been related to the quality and ripeness of the fruit. For apples, texture is an important characteristic that directly influences consumer preferences for crisp, juicy fruit (Liu and King, 1978). However, for many other fruits changes in hardness are used to follow the ripening process. As the fruit ripens, it becomes softer, starch converts to sugars, acidity is lost and flavour develops. In these cases, consumers tend to have distinct preferences for softer and riper fruit (Jaeger et al., 2003). The different consumer needs from apples (crisp and hard texture) and other fruit

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(ripe and flavourful) explain the different research approaches. In apples, both destructive and non-destructive approaches have been used to investigate the properties of fruit. However, for most other fruit, the focus has been on the use of non-destructive measurements to monitor the softening/ripening process. There has been a concentration of this type of research in fruit but not in manufactured foods, due to the fact that fruits are subject to high levels of biological variability (Dever et al., 1995). Therefore, there is commercial interest in methods that can sort for products that are more homogenous in quality. Manufactured foods do not face this high level of product variability. Using destructive acoustic techniques, a method for classifying apples based on crispness has been suggested (De Belie et al., 2000). Cox’s Orange Pippin apples were stored under various conditions to modify the texture. The apples were grouped into mealy and crisp categories and the sounds produced during biting were recorded. Crisp apples produced sounds which were higher in frequency than mealy apples. Through the use of principle components analysis of the analysed frequencies, the two groups of fruit could be separated based on frequencies, indicating that acoustics can be useful for the determination of fruit texture and therefore quality. The authors stated that with more extensive experiments, including sensory panels, the acoustic technique could be used to develop a method for objective crispness evaluation of apples, removing the necessity for trained sensory panels. Others, however, have concluded that chewing sounds were no better than other instrumental tests (in particular, puncture testing) for measuring apple texture (Harker et al., 2002). The stiffness coefficient calculated from results of the non-destructive acoustic tests has been related to firmness and ripeness measurements of fruit. The relationship between the stiffness coefficient and sensory firmness/ ripeness has been determined for Starking Delicious apples (r = 0.66) (Yamamoto et al., 1980), Golden Delicious apples (r = 0.6) (Abbott et al., 1992), Red Delicious apples immediately after picking (r = 0.84) (Finney, 1971a). Although different sensory scales have been used for the sensory portion of the research, each of these tests showed a strong relationship between the stiffness coefficient and sensory scores. Acceptability has also been related to the stiffness coefficient. Van Woensel et al. (1987) showed that the acceptability of Golden Delicious apples could be related to the stiffness coefficient, stating that it was important to find a relationship between the stiffness coefficient and the acceptability of the fruit for the consumer. While apples are a relevant subject to review in terms of methodologies for measuring crispness, other fruit are perhaps not so relevant. Research on measuring firmness as a method to follow ripening has involved kiwifruit (McGlone and Shaane, 1993), avocado (Peleg et al., 1990) and stonefruit (Finney, 1971b; Landahl et al., 2000). Furthermore, a number of studies have compared the ability of non-destructive acoustic testing to grade for quality with that of trained inspectors used to assess fruit against official

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standards for tomatoes (Schotte et al., 1999) and apples (Abbott et al., 1992). Schotte et al. (1999) found that graders from different fruit auction houses showed a large amount of variability in the use of the scale. All results had to be rescaled prior to comparison with the stiffness data. The stiffness score was related to tomato firmness (R2 = 0.79), and the authors felt that it was a more objective technique than grading and could be used without requiring a rescaling of data.

6.6

Future trends

The area of acoustic research shows a promising future. Crispness perception has been studied from a mechanical and acoustical perspective and the contribution of the acoustic properties to the perceived crispness has largely been addressed. The focus can now shift to the relationship between these crispness components and structure. Acoustics will play a large role in furthering the understanding of crispness and how, by manipulating various structural properties of the food product, its acoustic properties and therefore its perceived crispness can be altered. Additionally, the use of acoustics during processing is an emerging trend. One area where this is receiving attention is in the acoustic emissions produced during extrusion of food products (Francis et al., 1998). The motor output of an extruder produces certain sounds when extruding samples. The output changes as the texture of the product changes. This is due to the amount of work that the extruder must do in order to produce the samples. An experienced extruder operator can tell by listening to the machine if it is making the correct noises during production and then modify the settings on the extruder (screw speed, feed rate) in order to ensure that the machine is making the correct noise. Extruder sounds have been recorded and related to the physical properties of the final extruded product (Francis et al., 1998). Quantifying these noises and analysing their frequencies can be a useful tool for predicting the quality attributes of the processed food product.

6.7

Sources of further information and advice

The following is a list of equipment commonly used for recording chewing sounds. It is compiled from published research papers and is not exhaustive. Inclusion in this list is by no means indicative of the quality of the equipment. It is merely an indication that it has been used for acoustic recordings. 6.7.1 Microphones Condenser microphones used to record air-conducted sounds include the AKG C414EB or Bruel and Kjaer 4133. These microphones have a suitable

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frequency range for recording air-conducted sounds (within the audible frequency range). Contact microphones for recording bone-conducted sounds include the AKGC401B and the Shadow 4001. These microphones do not have as broad a frequency range as the condenser microphones; however, such a broad range is not required for bone-conducted sounds 6.7.2 Data acquisition equipment for recording and analysis of sound waves Various amplifiers have been interfaced with the microphones. Most often this amplifier is a Bruel and Kjaer. These amplifiers must be interfaced either with signal conditioners or to a computer with appropriate software. For recording and analysis of acoustic sound waves, one software package which has been mentioned is Soundedit (Macromedia, San Francisco, California). This software can be used to set up the testing situation as well as for analysis of the data by FFT. Another means for recording the data is to use a Powerlab (ADInstruments, Castle Hill, Australia) with associated software (Chart4) for analysis of data. This data acquisition unit has built in amplifiers and filters therefore the microphone can merely be plugged into the system for data collection. 6.7.3 Data analysis FFT can be analysed using any number of data programs. SigmaPlot (SPSS Inc, Chicago, Illinois) and MATLAB (The MathWorks Inc, Natick, Massachusetts) can be used to convert incoming signals into frequencies. 6.7.4 Useful references For further understanding of fractal dimensions, the book Fractal Surfaces by Russ (1994) provides an overview of fractal analysis. This book also contains a computer program which can be used for calculating fractal dimensions. Additionally, the fractal dimension of a sound wave can be calculated by using MATLAB (The MathWorks Inc, Natick, Massachusetts). There are also many books which can be of use for gaining an understanding of hearing, and the physics of sound. Two books which this author has found quite useful are: The Science of Sound by Rossing (1990) and An Introduction to Sound by Speaks (1999).

6.8

References

ABBOTT J A, BACHMAN G S, CHILDERS R F, FITZGERALD J V

and MATUSIK F J (1968) Sonic techniques for measuring texture of fruits and vegetables, Food Technol, 22(5), 635– 45.

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ABBOTT J A, AFFELDT H A and LILJEDAHL L A (1992) Firmness measurement of stored ‘Delicious’

apples by sensory methods, Magness-Taylor, and sonic transmission, J Amer Soc Hort Sci, 117(4), 590–595. ABBOTT J A, LU R, UPCHURCH B L and STROSHINE R L (1997) Technologies for non-destructive quality evaluation of fruits and vegetables, Hort Reviews, 20, 1–120. AL CHAKRA W, ALLAF K and JEMAI A B (1996) Characterization of brittle food products: Application of the acoustical emission method, J Text Studies, 27(3), 327–48. BARRETT A H, CARDELLO A V, LESHER L L and TAUB I A (1994) Cellularity, mechanical failure, and textural perception of corn meal extrudates, J Text Studies, 25(1), 77–95. BARRETT A M, NORMAND M D, PELEG M and ROSS E (1992) Characterization of the jagged stress-strain relationships of puffed extrudates using the fast Fourier transform and fractal analysis, J Food Sci, 57(1), 231–2, 235. BOUVIER J M, BONNEVILLE R and GOULLIEUX A (1997) Instrumental methods for the measurement of extrudate crispness Agro Food Industry Hi Tech, Jan/Feb, 16–19. BOURNE M C (1982) Food Texture and Viscosity. New York, Academic Press. BRENNAN J G, JOWITT R and WILLIAMS A (1974) Sensory and instrumental measurement of brittleness and crispness, Proceedings of the IV International Congress of Food Science and Technology, 2, 130–43. BRUNS A J and BOURNE M C (1975) Effects of sample dimensions on the snapping force of crisp foods, J Text Studies, 6(4), 445–58. CHEN H and DE BAERDEMAEKER J (1993) Effect of apple shape on acoustic measurements of firmness, J Agric Eng Res, 56(3), 253–66. CHEN P, SUN Z and HUARNG L (1992) Factors affecting acoustic responses of apples, Trans of the ASAE, 35(6), 1915–20. COOKE J R and RAND R H (1973) A mathematical study of resonance in intact fruits and vegetables using a 3-media elastic sphere model, J Agric Eng Res, 18(2), 141–57. DACREMONT C (1995) Spectral composition of eating sounds generated by crispy, crunchy and crackly foods, J Text Studies, 26(1), 27–43. DACREMONT C, COLAS B and SAUVAGEOT F (1991) Contribution of air- and bone-conduction to the creation of sounds perceived during sensory evaluation of foods, J Text Studies, 22(4), 443–56. DE BELIE N, DE SMEDT V and DE BAERDEMAEKER J (2000) Principal component analysis of chewing sounds to detect differences in apple crispness, Postharvest Biology and Technology, 18(2), 109–19. DEVER M C, CLIFF M A and HALL J W (1995) Analysis of variation and multivariate relationships among analytical and sensory characteristics in whole apple evaluation, J Sci Food and Agric, 69(3), 329–38. DRAKE B K (1963) Food crushing sounds. An introductory study, J Food Sci, 28, 233–41. DRAKE B K (1965) Food crushing sounds: comparisons of objective and subjective data, J Food Sci, 30(3), 556–9. DUIZER L M, CAMPANELLA O and BARNES G R G (1998) Sensory, textural and acoustic characteristics of extruded snack food products, J Text Studies, 29(4), 397–411. EDMISTER J A and VICKERS Z M (1985) Instrumental acoustical measures of crispness in foods, J Text Studies, 16(2), 153–67. FILLION L and KILCAST D (2002) Consumer perception of crispness and crunchiness in fruits and vegetables, Food Qual Pref, 13(1), 23–9. FINNNEY E E (1971a) Dynamic elastic properties and sensory quality of apple fruit, J Text Studies, 2(1), 62–74. FINNEY E E (1971b) Random vibration techniques for non-destructive evaluation of peach firmness, J Agric Eng Res, 16(1), 81–7. FRANCIS G, SELLAHEWA J and CHESSARI C (1998) Use of acoustic emissions as a novel on-line sensor to evaluate the quality of extruded products, Presented at the 3rd Annual Smart Extrusion Seminar, Sydney, Australia. HARKER F R, REDGEWELL R J, HALLETT I C, MURRAY S H and CARTER G (1997) Texture of fresh fruit, Hort Reviews, 20, 121–224.

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and WALKER S B (2002) Sensory interpretation of instrumental measurements 1.Texture of apple fruit, Postharvest Biology and Technology, 24(3), 225–39. HASHIMOTO K and CLARK G T (2001) The effect of altering jaw position on the transmission of vibration between the skull and teeth in humans, Arch Oral Biol, 46(11), 1031–8. JAEGER S R, ROSSITER K L, WISMER W V and HARKER F R (2003) Consumer-driven product development in the kiwifruit industry, Food Qual Pref, 14(3), 187–98. KAPUR K K (1971) Frequency spectrographic analysis of bone conducted chewing sounds in persons with natural and artificial dentitions, J Text Studies, 2(1), 50–61. KIANG N Y S and PEAKE W T (1988) Physics and physiology of hearing. In Stevens’ Handbook of Experimental Psychology Second edition. Eds R C Atkinson, R J Herrnstein, G Lindzey and R D Luce, New York, John Wiley and Sons, 297–302. LANDAHL S, DE BELIE N, DE BAERDEMAEKER J, PEIRS A and NICOLAÏ B M (2000) Non-destructive and destructive firmness measurements on apples and peaches, Agricontrol 2000: IFAC International Conference on Modelling and Control in Agriculture, Horticulture and Post-Harvest Processing, Wageningen, The Netherlands, 297–302. LEE W E, DEIBEL A E, GLEMBIN, C T and MUNDAY E G (1988) Analysis of food crushing sounds during mastication: frequency-time studies, J Text Studies, 19(1), 27–38. LEE W E, SCHWEITZER M A, MORGAN G M and SHEPHERD D C (1990) Analysis of food crushing sounds during mastication: total sound level studies, J Text Studies, 21(2), 165–78. LIU F W and KING M M (1978) Consumer evaluations of McIntosh apple firmness, Hortsci, 13(2), 162–3. LIU X and TAN J (1999) Acoustic wave analysis for food crispness evaluation, J Text Studies, 30(4), 397–408. MCGLONE V A and SHAANE P N (1993) The application of impact response analysis in the NZ fruit industry, Am Soc Agr Eng, Paper#93-6537. MOHAMED A A A, JOWITT R and BRENNAN J G (1982) Instrumental and sensory evaluation of crispness: I – In friable foods, J Food Eng, 1(1), 55–75. MOORE B J (1982) An Introduction to the Psychology of Hearing, (Second Edition). London, Academic Press. PELEG K, BEN-HANAN U and HINGA S (1990) Classification of avocado by firmness and maturity, J Text Studies, 21(2), 123–39. ROSSING T D (1990) The Science of Sound (Second Edition). Boston, M A Addison-Wesley Publishing Company. ROUDAUT G, DACREMONT C and LE MESTE M (1998) Influence of water on the crispness of cereal-based foods: acoustic, mechanical, and sensory studies, J Text Studies, 29(2), 199–213. RUSS J C (1994) Fractal Surfaces, New York, Plenum Press. SCHOTTE S, DE BELIE N and DE BAERDEMAEKER J (1999) Acoustic impulse-response technique for evaluation of modelling of firmness of tomato fruit, Postharvest Biol and Tech, 17(2), 105–15. SEYMOUR S K and HAMANN D D (1984) Design of a microcomputer based instrument for crispness evaluation of food products, Trans of the ASAE, 27(4), 1245–50. SEYMOUR S K and HAMANN D D (1988) Crispness and crunchiness of selected low moisture foods, J Text Studies, 19(1), 79–95. SPEAKS C E (1999) Introduction to Sound: Acoustics for the Hearing and Speech Sciences (Third Edition). San Diego, Singular Publishing Group. STEVENS S S and DAVIS H (1938) Hearing: Its Psychology and Physiology, New York, John Wiley and Sons. SZCZESNIAK A S (1990) Texture: is it still an overlooked food attribute? Food Technol, 44(9), 86–95. TESCH R, NORMAND M D and PELEG M (1995) On the apparent fractal dimension of sound bursts in acoustic signatures of two crunchy foods, J Text Studies, 26(6), 685–94. VAN HECKE E, ALLAF K and BOUVIER J M (1998) Texture and structure of crispy-puffed food products: Part II Mechanical properties in puncture, J Text Studies, 29(6), 617–32.

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parameters for liquids and time dependent solids, Food Qual Pref, 13(4), 227–36. and DE BAERDEMAEKER J (1987) Relation between mechanical properties of apple fruit and sensory quality, J Food Process Eng, 9(3), 173–89. VICKERS Z M (1981) Relationships of chewing sounds to judgements of crispness, crunchiness and hardness, J Food Sci, 47(1), 121–4. VICKERS Z M (1984a) Crispness and crunchiness – a difference in pitch?, J Text Studies, 15(2), 157–63. VICKERS Z M (1984b) Crackliness: relationships of auditory judgments to tactile judgments and instrumental acoustical measurements, J Text Studies, 15(1), 49–58. VICKERS Z M (1987) Sensory, acoustical, and force-deformation measurements of potato chip crispness, J Food Sci, 52(1), 138–40. VICKERS Z M and BOURNE M C (1976) A psychoacoustical theory of crispness, J Food Sci, 41(5), 1158–64. VICKERS Z M and CHRISTENSEN C M (1980) Relationships between sensory crispness and other sensory and instrumental parameters, J Text Studies, 11(3), 291–307. VICKERS Z M and WASSERMAN S S (1979) Sensory qualities of food sounds based on individual perceptions, J Text Studies, 10(4), 319–32. VINCENT J F V, SAUNDERS D E J and BEYTS P (2002) The use of critical stress intensity factor to quantify ‘hardness’ and ‘crunchiness’ objectively, J Text Studies, 33(2), 149–59. YAMAMOTO H, IWAMOTO M and HAGINUMA S (1980) Acoustic impulse response method for measuring natural frequency of intact fruits and preliminary applications to internal quality evaluation of apples and watermelons, J Text Studies, 11(2), 117–36. VAN WOENSEL G, WOUTERS A

7 Near infrared (NIR) diffuse reflectance in texture measurement S. Millar, Campden and Chorleywood Food Research Association, UK

7.1

Introduction

Along with appearance, the texture of foodstuffs is a key defining quality parameter. It is important not only in terms of consumer acceptance (whether an apple has a crisp and refreshing or dry and woolly mouthfeel, for example) but also for quality assurance during processing. Where internal damage to fruit and vegetables may be determined during processing, for example, appropriate decisions on the best use of the material may be taken. Although the rapid quantification of food texture attributes would be desirable, therefore, the technological means of effecting such determinations are not universally available. One of the suite of spectroscopy techniques which has become widely used over the last 30 or so years is near infrared spectroscopy (NIR). This technique continues to have many advantages as a practical tool under commercial situations. The reasons for this are rapid assessment times, the ability to obtain a number of separate pieces of information from one analysis (protein content, moisture content and texture of wheat for example), the relatively low cost of the analysers and the generally rugged nature of the equipment when compared with many of the high end, laboratory-based spectroscopic systems. This chapter reviews the use of NIR and related spectroscopic techniques for the assessment of food texture. A general introduction detailing the underlying principles of spectroscopy leads into a section describing the use of the near infrared region. There then follows specific reviews of the technique’s application in three main areas: cereals, fruits and vegetables, and muscle foods. Finally the current state of knowledge is summarised and some likely future trends highlighted.

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7.1.1 Background to spectroscopy The term spectroscopy encompasses a wide range of techniques which have as their common denominator the function of determining the interaction of electromagnetic radiation with matter. Two main aspects of this interaction are recognised: • the absorption of energy by particular atoms, molecules or bonds; • the reflection and refraction of the incident energy as a result of its interaction with the physical structure of the material to be assessed. Although the importance of both aspects is generally recognised by those using one of the spectroscopic techniques, it is usually the former which achieves greater prominence. Readers will undoubtedly be familiar with examples such as ultraviolet (UV) and fluorescence spectroscopy [an example being the detectors commonly used in high performance liquid chromatography (HPLC) systems for the quantification of proteins], visible spectroscopy, where electronic transitions which result in colour as perceived by human beings may be quantified, and infrared spectroscopy, where energy is absorbed in accordance with the modes of vibration of particular bonds. When used as a means of determining texture, however, it is generally the second of these aspects which is the more important, i.e. the interaction of the incident energy with the physical structure of the material to be assessed. As the way in which this interaction takes place is dependent on the wavelength of the energy source as well as the physical structure of the material, different spectroscopic techniques have differing levels of sensitivity to the physical aspects of the material. The subject of this chapter is the use of NIR spectroscopy for the assessment of food texture. This technique is interesting in this context as it provides a good balance of physical and chemical interactions with the incident energy. In fact, in many cases it is the strength of the interaction with the physical aspects of the samples assessed which becomes problematic in the use of the technique. An example of this is in the assessment of properties of materials derived from cereal sources such as wheat flour. Here, the accuracy of the determination of protein content is dependent on the effectiveness of the techniques used to reduce the sizeable effects of light scattering on the spectrum. Conversely, however, this feature makes the technique a strong one for application to the problem of assessing food texture.

7.1.2 Near infrared spectroscopy The region of the electromagnetic spectrum associated with NIR is broadly that covering 800–2500 nm (Fig. 7.1). The region from 800–1100 nm is often referred to as the near visible and represents energy having wavelengths just slightly longer than those generally seen as red by the human eye. The use of this region is primarily in those instruments used to determine the proportion of light transmitted through food materials. There is some debate

Near infrared (NIR) diffuse reflectance in texture measurement 169 0.6 0.5

Log 1/R

0.4 0.3 0.2 0.1 0 800

970

1140

1310

1480

1650

1820

1990

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2330 2500

Wavelength (nm)

Fig. 7.1 Example NIR spectrum of ground wheat.

as to how much of the passage of energy is due to true transmission as opposed to a combination of transmission and reflectance. Nevertheless, the penetrating power of the energy in this region is greater than that at longer wavelengths (Osborne et al., 1993). The region beyond 1100 nm is recognised as the true near infrared and is primarily used for sample assessment by diffuse reflectance. NIR instruments contain a source of radiation in the NIR region of the spectrum, a means of splitting this energy into wavelengths or wavelength regions and a system of detection to allow the energy which has been reflected from or has passed through the sample to be quantified. More expensive instruments generally have systems that allow continuous spectra to be recorded which have regular spacing between wavelengths. Such instruments tend to be more commonly used in research laboratories or technical centres where their flexibility justifies their higher cost. For more general usage, however, simpler instruments tend to be used, particularly in situations where a high degree of robustness for everyday use is required. Although there are a number of operating principles which may be used, these instruments tend to be based around the use of interference filters which capture discontinuous ‘slices’ of information from the spectrum. The wavelengths represented by the filters are generally pre-selected during manufacture in accordance with known regions of absorbance for the most important constituents such as protein, moisture and lipid. These and other absorbers may be quantified as a function of the modes of vibration generated in particular bonds as a result of the absorption of radiation at specific wavelengths. The most important absorbers are N–H (protein), O–H (moisture) and C–H (lipids). The NIR region does not contain any of the fundamental modes of vibration for such molecules, this information being found in the mid infrared region [covering the region 4000–400 cm–1

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or 2500–25 000 nm (by convention the NIR region is referred to in terms of wavelength while the mid infrared is measured as a function of frequency)]. However, second and third overtones as well as combination bands due to interference between different modes of vibration may be found, and it is these that form the basis of the technique. Generally speaking, the physical characteristics of a given sample tend to cause variation in the overall spectral response such that during calibration for physical characteristics wavelengths are chosen which contain little chemical information but which will still vary systematically with the structural components of a food system. Irrespective of whether the information of interest is related to the chemical or physical characteristics of the sample, however, a means of relating it to meaningful units is required. NIR information is by nature empirical and generally needs to be calibrated against an accepted reference method to allow it to be used in a meaningful way. As a result, this process becomes defining in the subsequent performance. The precision of the reference method chosen (as well as the precision of the NIR technique), the meaningfulness (or otherwise) of the method for description of the parameter of interest, as well as the underlying relationship between the spectral data and the parameter of interest all impact on the accuracy and precision of the final calibration developed. Consequently, great care must be taken at this stage and, even where this is the case, an inappropriate choice of reference method may result in disappointing results which, although apparently due to the NIR method, actually have as root cause the inadequacy of the method to which the spectral output has been calibrated.

7.2

Application of NIR to cereals and their products

In many cases, cereals undergo a milling or comminution step to reduce their particle size for further processing. The way in which cereals are transformed during this stage is a function of a wide range of physical characteristics including size and shape. However, for a number of cereal species, the texture of the grain determines how the grain will function at subsequent stages of the production chain (Evers and Millar, 2003). Depending on the manifestation of the textural attributes in question, a number of different descriptors have been used. Examples include horny/flinty as opposed to mealy for maize and vitreous versus mealy for barley. Both these ‘scales’ have a component related to grain appearance, with vitreous grains tending to appear so due to the relative lack of ‘void’ regions in the endosperm, the central plant storage reserves, resulting in greater density. In contrast, mealy grains are those where air spaces in the endosperm cause light scattering which gives rise to a more diffuse appearance. Perhaps the most interesting cereal in this context, however, is wheat which not only exhibits variation in endosperm density as described above (Fig. 7.2) but also a separate phenomenon commonly termed as ‘hardness’.

Near infrared (NIR) diffuse reflectance in texture measurement 171

(a)

(b)

Fig. 7.2 Examples of (a) vitreous and (b) mealy wheat grains.

The existence of these two descriptor scales for wheat texture and the assertion that they did not result from the same underlying cause (Simmonds, 1974) has been recognised for some time. However, the functional importance of these characters has arguably been recognised for as long as wheat has been milled. Hard milling grains require longer conditioning times (the process by which a controlled amount of water is allowed to penetrate the grain to ease the subsequent milling process) prior to milling and ultimately results in flour and intermediate products having larger, more angular particles which flow more easily than those from soft milling wheats. When examined microscopically, flour particles milled from hard wheats exhibit particles where the endosperm cells may be fractured randomly across the plane of the cell. This contrasts with those from soft wheats where a greater proportion of free, undamaged starch granules result (Greer and Hinton, 1950). Within the endosperm cells, starch granules are contained within a protein matrix which is more or less continuous depending on how vitreous the grains are. The way in which forces are transmitted to starch granules during milling is clearly a function of grain hardness as hard milling wheats result in more damaged starch granules. This term relates to apparently irreversible changes in starch granule physical structure which allow the granules to absorb more water and to be more susceptible to enzymatic attack. Up to a point this is desirable in wheats for breadmaking as they allow more water to be included (increasing yield) and result in sugars being available for yeast to feed on. Harder wheats are thus favoured for breadmaking. It could be argued on this evidence that the strength of the protein matrix or starch granules must vary between hard and soft milling wheats. In fact, Simmonds (1974) demonstrated that this was not the case, the relative strengths being similar. It was some time later that workers at the former Flour Milling and Baking Research Association (FMBRA), now part of Campden and Chorleywood Food Research Association (CCFRA), demonstrated that a suite of proteins having molecular weights of approximately 15 kDa could be extracted from water washed wheat starch (Greenwell and Schofield, 1986,

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1989). When separated using electrophoresis, the band associated with these proteins was more intense for starches from soft milling wheats than those classified as hard milling. When durum wheats were assessed (the very hard wheats used for pasta production), the band was completely absent. Initially the ‘protein’ was termed friabilin due to the apparent relationship with grain texture, but further work has classified these proteins as part of the puroindoline family (Rahman et al., 1994). It was postulated that these proteins constituted a ‘non-stick’ coating which aided the release of starch from protein in the grain during milling. Subsequent work has demonstrated the genetic basis for this phenomenon (Giroux and Morris, 1997), and it remains an active area of research for cereal science. The use of NIR as a means of assessing the texture of cereals was recognised at an early stage of the adoption of the technique. Although the earliest work applying NIR to food and agriculture-based applications used instruments which collected spectra for whole cereal grains, it was not until diffuse reflectance instruments became available that the technique gained mass appeal (Osborne et al., 1993). This change not only provided the technique for use in applications such as the determination of the composition of the ground material, it also opened the way for rapid assessment of the particle size of the meal and thus the physical characteristics of the whole grains with respect to grinding. It is the light scattering properties of particulate material which result in the spectral variation which may be related to particle size. As the particle size of a given material decreases, the effective pathlength of the incident radiation through particles decreases causing a decrease in absorption by the chemical components within each particle. As light scatter is inversely proportional to mean pathlength, smaller particles lead to more scattered radiation. The scatter results from the many reflections and refractions at all the optical interfaces within the sample (Birth and Hecht, 1987). The net effect of this is that more, smaller particles show greater reflectance (less absorbance and more scatter) than few, large particles. As NIR data are commonly expressed as the log reciprocal of reflectance (to give an approximation of absorbance), this means that few, large particles give higher log 1/R values than many, small particles. This is consistent across all wavelengths in terms of order even if the absolute magnitude of the differences may vary, and so in theory any wavelength may be used to effect a measurement of particle size. In practice, however, those wavelengths which contain limited information about chemical absorbers give the best results as the information about physical characteristics and chemical composition is not combined. Early work showed that NIR could be used to discriminate between samples of hard and soft milling wheat (Hart, 1976; Williams, 1979). Later workers developed calibrations for both wheat and wheat flour using a number of reference measurements including sieving values (Osborne et al., 1981, 1982), pearling resistance (Svensson, 1981) and grinding resistance (Starr et al., 1981). Given the compositional information that may be collected at some wavelengths, the use of the difference in response between composition-

Near infrared (NIR) diffuse reflectance in texture measurement 173

dependent and composition-independent wavelengths may be used to develop calibrations (Wetzel, 1984). In 1986, a method based on a new approach for the determination of wheat hardness was developed and approved by the American Association of Cereal Chemists (AACC). This approach was novel in that the NIR data were not related to another definable reference method (such as the proportion of material passing through a sieve of defined aperture). To ensure agreement between instruments, a set of five hard and five soft milling wheat standards were developed to allow ‘calibration’ by users (AACC, 1994). Subsequently workers (Windham et al., 1993; Le Bars et al., 1994) developed moisture correction factors to take into account the effect of differing moisture contents on the way in which grain is reduced in size during grinding. Although this part of the chapter has been concentrated on the assessment of wheat endosperm texture due to its widespread importance, the technique has been applied to the assessment of texture of other cereals, such as maize (Eckhoff and Paulsen, 1996). However, the widespread reporting of these applications has been lesser, even though the principles of the use of the technique are essentially the same.

7.3

Application of NIR to fruit and vegetables

While the texture of cereals tends to be an indicator of processing requirements, the texture of fruit and vegetables may often be used as an indicator of ripeness or the presence of damage. In some cases, it also may be used for the prediction of end-use properties such as when differentiating between waxy and floury potatoes. Fundamental to producer and processor, however, is an understanding of the integrity of the material produced or delivered. This has historically been a difficult area for the entire supply chain, particularly as in some cases bruising or damage may go undetected until the consumer purchases the item in question resulting in, at the very least, disappointment in the product. Interestingly one of the very first applications for NIR was to the assessment of egg contents and quality (Osborne et al., 1993). While this is clearly not in the fruit or vegetable area, it serves as an illustration of the way in which the technique may be applied in such situations. Although the penetrating power of the true NIR region is limited, that of the near visible is better. This has recently been used by a team which has developed a new method to allow the interior of fruit and vegetables to be assessed more accurately. The method is called the V-method and uses a system of transmitted and reflected light to ‘virtually peel’ fruit or vegetables where skin or peel serves to render the contents ‘invisible’ (Krivoshiev et al., 2000)). The method may be understood with reference to Fig. 7.3. Here the composite nature of the transmitted light passing through a schematic diagram of a fruit or vegetable may be seen. The incident energy interacts with the peel or skin on both sides of the object as well as the fleshy interior. By using

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T ≈ TA TO TB IA

TA 45°

RA

TO

TB 45°

IB

RB

Fig. 7.3 Diagrammatic representation of the V-method. Where: IA and IB represent the incident radiation from either side of the tuber; RA and RB represent the reflected radiation from either side of the tuber; TA, TO and TB represent the transmittance of light through peel from either side of the tuber (TA and TB) and the flesh (TO). The contribution of the peel (RA and RB) is removed from the optical density of the entire tuber (OD) to allow an estimation of the optical density of the flesh (ODO) using an equation of the general form: ODO ≈ a0 + a1 OD + a2 ln(1/RA) + a3 ln(1/RB)

reflected energy directed at both sides of the object, however, a description of the spectral properties of the external surface may be derived and thus removed from the transmitted energy. Thus the spectra of the flesh may be separated from that of the peel or skin. To date, this method has been applied to both potatoes and apples for the assessment of internal bruising of the materials. For potatoes, a set of 194 samples has been assessed within which the tubers were classified into one of three groups. The first of these (C1) comprises tubers which have low levels of internal damage and which may be used for structured products such as chips or potato crisps where appearance is of paramount importance. The second class (C2) contains those tubers which do show some evidence of internal damage but which may be used for comminuted or homogenised food products such as pureed potatoes. Finally, those tubers which were so severely damaged as to make them unsuitable for food use were classified in a third group (C3). Spectra covering the range 600–1100 nm were collected from the tubers both before and after peeling. The tubers were then dissected and classified according to the system described. Discriminant analysis was then used to classify the tubers on the basis of their spectral properties. The overall classification accuracy of these models was 83% which indicated the strong potential of the V-method for the routine assessment of potato quality (Krivoshiev et al., 2002). This work is still developing but the potential for instrumentation to rapidly grade fruit or vegetables with peel or skin is clear. Additionally, the technique will undoubtedly be extended to the assessment of other compositional characteristics, such as moisture or sugar contents, because the tissue or tissues of most direct interest, the flesh, may be isolated spectroscopically.

Near infrared (NIR) diffuse reflectance in texture measurement 175

Another group of workers has reported the development of NIR calibrations for a range of properties of potatoes as assessed sensorily (Van de Laer et al., 2000). In addition to flouriness, the disintegration of the cooked flesh and its granulation could be modelled by NIR. As for many of the other applications reported, the accuracy of this calibration was limited in comparison with compositional analysis of lower moisture foods. Nevertheless, the level of performance was consistent with the repeatability errors of the sensory analysis and so would be unlikely to be improved upon unless significant improvements in the precision of reference determinations was demonstrated. More traditional approaches have previously been applied to the measurement of a number of parameters for fruit and vegetables. In many cases, the calibrations developed have been targeted at the measurement of overall acceptability whether using instrumental or sensory means of quality assessment. An example of how a range of attributes may be used for the assessment of global parameters is the use of NIR for the assessment of the maturity of vining peas (Scotter, 1996). The traditional method for assessing pea maturity is the Martin Tenderometer, an instrument which is essentially a shear force gauge. This instrument has been widely-used in Europe since the 1950s for assessment of the readiness for harvest of pea crops (Arthey and Dennis, 1991). A programme of work was undertaken at CCFRA under which calibrations were developed for the UK pea producers for pea firmness (calibrated against Tenderometer data), alcohol insoluble solids (AIS), which was closely correlated with Tenderometer data, and a number of sensory parameters. The work on peas was subsequently extended to include sweetcorn. Spectra were collected from both intact as well as macerated samples. Calibrations developed using this latter material were shown to be slightly better (Scotter, 1998), although the performance of calibrations representing both approaches was sufficient to allow them to be used commercially. In addition, calibrations were developed for a number of sensory attributes of peas including skin and flesh firmness and mealiness (increasing levels of starch result in more mealy textures). The textural characteristics for a number of other types of produce have also been studied by workers at CCFRA. For avocados, penetrometer values were supplied by commercial graders and processors along with samples for NIR scanning. Calibrations were developed which had acceptable levels of performance. However, it was found that the performance of these calibrations was highly dependent on information collected from the visible region of the spectrum (Scotter, 2000). Studies assessing the penetration depth of NIR radiation on intact avocados demonstrated that the skin contained inorganic material which largely blocked that from the true NIR region. It would seem, therefore, that the performance of these calibrations was not dependent on a straightforward relationship between NIR data and, for example, the texture of the flesh. Rather there appears to be a secondary correlation between colour of the skin or flesh and the texture of the flesh. Calibrations for the measurement of the texture of apples have also been

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reported, again using penetrometer readings as the reference method (Cho et al., 1996). In this work, calibrations were developed using both full range scanning instrumentation as well as a less expensive filter instrument. Scanning instrumentation has also been used to determine nectarine and strawberry texture (Scotter, 2000). This latter work also included an interesting study on the use of NIR as a technique for grading potatoes on the basis of whether their eating quality was described as waxy or floury. A number of other groups have published on the use of NIR for the rapid assessment of apple texture, including one where the emphasis was on the use of the technique for rapid grading (Moons and Dardenne, 2000). One of the most interesting studies has used the technique for the assessment of changes in texture over time rather than simply at a fixed point (Sohn and Cho, 2000). The texture of both intact (including peel) and peeled fruits was determined using a texture analyser as well as a fruit hardness tester. The measurement of firmness of nectarine flesh by NIR has also been reported (Costa et al., 2000). Although the reference method was not described in detail and so the units of measurement and their errors may not be readily understood, an R2 of 0.75 was reported which indicates some potential for rapid grading or screening applications. Nevertheless, such squared correlation coefficients should be recognised as being at the lower end of acceptability for NIR calibration performance. It was interesting to note, however, that the performance for measurement of flesh firmness was superior to that for Brix (the % sucrose by weight of a solution at a given temperature) which, being dependent on the composition of the fruits, may have been expected to exhibit better performance than measures of texture. Increased reliance on spectroscopic techniques for grading of produce is likely to be a trend for the future. A survey presented at the 9th International NIR Conference in Verona, indicated that the grading of high-moisture plant products such as intact fruit and vegetables had the potential to be a multimillion $US industry (Kays et al., 2000). Although the instruments commercialised to date represent only a fraction of this potential, as technological challenges with instrumentation design and manufacture are addressed, it is clear that this will be a very significant development over the next years and decades. In 1999, the estimated grading cost was < $0.02 per product unit and it may be envisaged that this cost can only fall as the takeup of the technology increases.

7.4

Application of NIR to meat

The texture of meat is important ultimately as an indicator of acceptability to the consumer when eaten (Morgan et al., 1991). However, the appearance and texture of meat post mortem is influenced by the slaughter process as well as by the inherent characteristics of the meat. This becomes a stage,

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therefore, at which measurement and ultimately control are desirable. Two conditions may arise as a result of increased pre-slaughter stress endured by the animal. Low pH caused by a rapid development of stress in pigs prior to slaughter results in PSE (pale, soft, exudative) meat which has an open texture. Where the stress is of a longer duration, DFD (dark, firm, dry) meat results in both pork and beef. In this case the meat has a higher than average pH and the muscle fibres are more closely packed. The texture of meat following slaughter has been investigated using spectroscopic means, and a number of instruments have been commercialised on this basis. Generally, such systems rely on changes in texture causing differences in the light scattering nature of the muscle fibres. Typically this has been achieved for the interior of joints using visible spectroscopy with wavelengths in the range 600–690 nm (Fortin, 1989) and at 700 nm (Murray et al., 1989) having been suggested. In addition, devices based on colorimetric assessment rather than information about a specific wavelength have also been developed (Irie and Swatland, 1992). More recently, however, NIR has also been applied to the assessment of meat texture. Given the published use of visible spectroscopy, it is not surprising that a number of studies have also been directed at the development of NIR calibrations for the assessment of meat tenderness (Lanza, 1983; Hildrum et al., 1994; Byrne et al., 1998). For the most part, these studies have resulted in calibrations relating meat texture to NIR properties for samples assessed at the same point in time. The last of these, however (Byrne et al., 1998), investigated the use of NIR spectra collected at earlier times post mortem to predict the tenderness of beef aged for 14 days. This approach was extended by Rødbotten et al. (2000) who developed NIR calibrations based on an assessment of meat tenderness using a Warner Bratzler shear-press device for beef M. longissimus dorsi stored for two and seven days. In this case, however, NIR spectra were collected both pre and post mortem. Although further work was felt to be required, the results indicated that the method may have potential. In addition to the use of instrumented methods of assessing tenderness, Hildrum and Nilsen (2000), developed calibrations against sensory measures. Both reference method approaches gave results having squared correlation coefficients in the range ~0.5–0.7. While again this would be seen as being at the lower limit of acceptability for use of such calibrations, the potential of the technique for such assessment was further demonstrated. In addition to work using the higher wavelengths, the near visible region (750–1100 nm) has also been applied to the analysis of meat tenderness (Venel et al., 2001). When a range of M. longissimus dorsi samples was assessed, calibrations for tenderness (Warner-Bratzler shear force) gave the best performance, although this indicated limited potential for a global calibration solution (R = 0.51). However, when specific calibrations were developed for segregated sample sets (e.g. separated by animal grade or pHu), slightly better performance was observed.

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The storage history of meat is also an important consideration for processors in terms of both microbiological safety and the texture of the meat. The use of ‘fresh’ or ‘frozen’ labels has, historically, been challenging due to difficulty in defining general temperature limits on which to base a definition (Windham et al., 1996). Nevertheless, these workers investigated NIR as a means of assessing the storage conditions under which chicken breast meat had been kept. The meat was classified as falling into one of five classes representing samples stored at 4, 0, –3, –12 and –18 °C. Discriminant analysis based on principal components analysis (PCA) functions was shown to allow correct classification of 85 and 75 % of the unfrozen and frozen chicken breasts respectively.

7.5

Application of NIR to other foods

7.5.1 Pulses In addition to cereals, the textural attributes of pulses have also been characterised. An example of this is the prediction of the cooked texture of chickpeas (Flinn et al., 1998). Cooked samples were compressed in bulk using an Instron instrument and the results expressed as newtons per seed. NIR calibrations were developed for both whole and ground, uncooked material. Interestingly, the spectra collected from whole chickpeas gave slightly better performance than those for ground material. 7.5.2 Milk-based products Although compositional analysis of milk by NIR is commonly practised, there have been few reports on the use of spectroscopic methods for the assessment of the texture of milk based products such as yoghurts or cheese. However, there is evidence that such applications may be of interest as both NIR and UV (ultraviolet)/VIS (visible) spectroscopy have been applied to the analysis of the onset of clotting in milk (Giangiacomo et al., 1998). It was felt that, although the texture of the material changed as coagulation occurred, the ability of NIR to follow such variation was dependent on the changes in the amount of free water generated due to the differences in structure. 7.5.3 Chocolate Fourier transform NIR spectroscopy (FT-NIR) has also been applied to the assessment of a range of chocolate quality parameters including viscosity ∨ ∨ ( Tarkosová and Copíková, 2000). The calibration performance was encouraging although the cross-validation results were poorer than expected. This may have been due to the relatively low (n = 84) number of samples used for calibration development. Nevertheless as a rapid indication of the

Near infrared (NIR) diffuse reflectance in texture measurement 179

flow properties of chocolate during processing, this may have significant value.

7.6

Conclusions and future trends

The use of spectroscopic approaches for texture measurement may at first seem counter-intuitive to many readers as many of these techniques have traditionally been seen as tools to probe the chemistry of materials rather than their physical structures. Nevertheless, certain regions of the electromagnetic spectrum allow the interaction of light with the physical structure of the material to be quantified relatively easily. Both the visible and near infrared regions contain relevant information for this purpose as has been demonstrated by many of the papers cited in this chapter. The application of NIR to the assessment of cereal grain texture is not that surprising given the fact that the technique was developed extensively as a result of its adoption as the method of choice for rapid analysis of grain protein and moisture contents. Applying the technique to particle size and ultimately textural attributes was a natural consequence of this. There are two key areas of interest in this scientific field for the future. The first is the extension of the technique when applied to powders to include assessment of particle size distributions as has been previously demonstrated by Hareland (1994). The second is the estimation of grain texture from the whole grain without recourse to grinding. This is already a commercial reality and, although detailed results have yet to be published, it appears that the accuracy is less than that achieved using ground material. Nevertheless, improvements in these calibrations are likely to be the next step. The assessment of fresh produce quality using spectroscopic approaches is well-established scientifically and appears to be gaining momentum commercially. There have been considerable gains in this area, most notably in the Far East where in-store grading of produce has been reported. One of the areas in which future development may be envisaged is through the use of techniques such as virtual peeling to obtain a more precise estimate of the spectral contribution of the flesh alone rather than the combined peel and flesh. Although less widely used, the application of spectroscopic techniques to meat quality assessment has been reported. The use of visible spectroscopy or colorimetry has been acknowledged while NIR approaches have also been reported. The advantage of the NIR technique in having the ability to estimate meat composition may actually have resulted in the technique having a lower level of application to the assessment of textural properties. It would seem logical to suggest, therefore, that the combination of these approaches will continue to grow in significance. Finally, it is surprising to see few reported attempts to apply spectroscopic approaches to the assessment of dairy product texture. It should be recognised,

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however, that its use for the compositional analysis of products such as cheese is more common. It may only be a matter of time, therefore, before its application to texture analysis becomes more widespread.

7.7

Sources of further information

7.7.1 Other books There have been many books covering the application of NIR to analysis of foods with those cited below being good general introductions to the area. Practical NIR Spectroscopy with Applications in Food and Beverage Analysis. Longman Scientific & Technical, Longman House, Harlow, Essex, England. Near Infrared Technology in the Agricultural and Food Industries. American Association of Cereal Chemists, Inc., 3340 Pilot Knob Road, St Paul, MN, USA.

7.7.2 Journals Journal of Near Infrared Spectroscopy. NIR Publications, 6 Charlton Mill, Charlton, Chichester, West Sussex, England. Applied Spectroscopy. Society for Applied Spectroscopy, 201B Broadway Street, Frederick, MD 21701-6501, USA.

7.8

References

American Association of Cereal Chemists (AACC) (1994) Wheat Hardness as Determined by Near-Infrared Reflectance. AACC Method 39-70A. ARTHEY D and DENNIS C (1991) Vegetables Processing. Glasgow and London, Blackie; 18– 20. BIRTH G S and HECHT H G (1987) The physics of near infrared reflectance. In Near Infrared Technology in the Agricultural and Food Industries. Eds P Williams and K Norris, St Paul, MN, American Association of Cereal Chemists, Inc., 1–15. BYRNE C E, DOWNEY G, TROY D J and BUCKLEY D (1998) Non-destructive prediction of selected quality parameters of beef by near-infrared reflectance spectroscopy between 750 and 1098 nm, Meat Science, 49, 399–409. CHO R K, KWON Y K, LEE K H and IWAMOTO M (1996) Application of near infrared spectroscopy for quality evaluation of an intact apple. In AMC Davis and P Williams Eds Near Infrared Spectroscopy: The Future Waves, Proceedings of the 7th International Conference on Near Infrared Spectroscopy, Montréal, Canada, 6–11 August 1995 Chichester, NIR Publications, 629–31. COSTA G, NOFERINI M, ANDREOTTI C and MAZZOTTI F (2000) Non-destructive determination of soluble solids and flesh firmness in nectarines by near infrared spectroscopy. In AMC Davis and R Giangiacomo Eds, Near Infrared Spectroscopy: The Future Waves, Proceedings of the 9th International Conference on Near Infrared Spectroscopy, Verona, Italy, 13–18 June, 1999, Chichester, NIR Publications, 863–866. ECKHOFF S R and PAULSEN M R (1996) Maize. In Cereal Grain Quality. Eds R J Henry and P S Kettlewell, London, Chapman & Hall, 77–112.

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(2003) Cereal grain structure and development: some implications for quality, Journal of Cereal Science, 36, 261–84. FLINN P C, BLACK R G, IYER L, BROUWER J B and MEARES C (1998) Estimating the food processing characteristics of pulses by near infrared spectroscopy, using ground or whole samples, Journal of Near Infrared Spectroscopy, 6, 213–20. FORTIN A (1989) Detection of PSE pork under field conditions using the Colormet registered meat probe, Proceedings of the International Congress of Meat Science and Technology, 35, 195–200. GIANGIACOMO R, LIZZANO R, BARZAGHI S, CATTANEO T M P and BARROS A S (1998) NIR and other luminometric methods to monitor the primary clotting phase of milk, Journal of Near Infrared Spectroscopy, 6, 205–12. GIROUX M J and MORRIS C F (1997) A glycine to serine change in puroindoline b is associated with wheat grain hardness and low levels of starch-surface friabilin, Theoretical and Applied Genetics, 95, 857–64. GREENWELL P and SCHOFIELD J D (1986) A starch-granule protein associated with endosperm softness in wheat, Cereal Chemistry, 63, 379–80. GREENWELL P and SCHOFIELD J D (1989) The chemical basis of grain hardness and softness. In H Salovaara Ed., Wheat End-use Properties, Proceedings of the 1989 International Association of Cereal Science Technology Symposium, Helsinki, Helsinki Press, 59– 72. GREER E N and HINTON J J C (1950) The two types of wheat endosperm, Nature, 165, 746– 48. HARELAND G A (1994) Evaluation of flour particle size distribution by laser diffraction, sieve analysis and near-infrared reflectance spectroscopy, Journal of Cereal Science, 21, 183–90. HART H V (1976) Protein determination in (home-grown) wheat by the Infra Alyzer, FMBRA Bulletin, 6, 192–204. HILDRUM K I and NILSEN B N (2000) Near infrared reflectance spectroscopy for the assessment of meat tenderness. In AMC Davis and R Giangiacomo Eds, Near Infrared Spectroscopy: The Future Waves, Proceedings of the 9th International Conference on Near Infrared Spectroscopy, Verona, Italy, 13–18 June, 1999, Chichester, NIR Publications, 855–7. HILDRUM K I, NILSEN B N, MIELNIK M and NAES T (1994) Prediction of sensory characteristics of beef by near-infrared spectroscopy, Meat Science, 38, 67–80. IRIE M and SWATLAND H J (1992) Relationships between Japanese pork colour standards and optical properties of pork before and after frozen storage, Food Research International, 25, 21–30. KAYS S J, DULL G G and LEFFLER R G (2000) Challenges and opportunities in the use of near infrared for the analysis of intact, high moisture products. In AMC Davis and R Giangiacomo Eds, Near Infrared Spectroscopy: The Future Waves, Proceedings of the 9th International Conference on Near Infrared Spectroscopy, Verona, Italy, 13–18 June, 1999, Chichester, NIR Publications, 841–47. KRIVOSHIEV G P, CHALUCOVA R P and MUKAREV M IV (2000) Seeing through layers, NIR News, 11, 7–11. KRIVOSHIEV G, CHALUCOVA R, DAHM D, MILLAR S, EVANS D, GEGOV Y, BOJILOV P and LUNGOV A (2002) Application of the method “seeing through layers” to improve the accuracy of potato sorting, The 11th International Diffuse Reflectance Conference, August 10–16, Chambersburg, PA. LANZA E (1983) Determination of moisture, protein, fat, and calories in raw pork and beef by near infrared spectroscopy, Journal of Food Science, 48, 471–4. LE BARS C B, BROWN G and CURTIS P (1994) The influence of moisture content on wheat hardness testing, Chorleywood Digest, 141, 98–101. MOONS E and DARDENNE P (2000) Determination of internal apple quality by non-destructive visible and near infrared spectroscopy. In AMC Davis and R Giangiacomo Eds, Near Infrared Spectroscopy: The Future Waves, Proceedings of the 9th International

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Conference, on Near Infrared Spectroscopy, Verona, Italy, 13–18 June, 1999. Chichester, NIR Publications, 785–9. MORGAN J B, SAVELL J W, HALE D S, MILLER R K, GRIFFIN D B, CROSS H R and SHACKLEFORD S D (1991) National beef tenderness survey, Journal of Animal Science, 69, 3274–83. MURRAY A C, JONES S D M and TONG A K W (1989) Evaluation of the Colormet reflectance meter for the measurement of pork muscle quality. Proceedings of the International Congress on Meat Science and Technology, 35, 188–94. OSBORNE B G, DOUGLAS S and FEARN T (1981) Assessment of wheat grain texture by near infrared reflectance measurements on Bühler-milled flour, Journal of the Science of Food and Agriculture, 32, 200–202. OSBORNE B G, DOUGLAS S and FEARN T (1982) The application of near infrared reflectance analysis to rapid flour testing, Journal of Food Technology, 17, 355–63. OSBORNE B G, FEARN T and HINDLE P H (1993) Near Infrared Spectroscopy in Food Analysis. Harlow, Essex, Longman Scientific & Technical. RAHMAN S, JOLLY C J, SKERRITT J H and WALLOSHECK A (1994) Cloning of a wheat 15-kDa grain softness protein (GSP). GSP is a mixture of puroindoline-like polypeptides, European Journal of Biochemistry, 223, 917–25. RøDBOTTEN R, NILSEN B N and HILDRUM K I (2000) Prediction of beef quality attributes from early post mortem near infrared reflectance spectra. In AMC Davis and R Giangiacomo Eds., Near Infrared Spectroscopy: The Future Waves, Proceedings of the 9th International Conference, on Near Infrared Spectroscopy, Verona, Italy, 13–18 June, 1999, Chichester, NIR Publications, 809–12. SCOTTER C N G (1996) Approaches to the measurement of fruit and vegetable quality parameters using near infrared spectroscopy. In AMC Davis and P Williams Eds, Near Infrared Spectroscopy: The Future Waves, Proceedings of the 7th International Conference on Near Infrared Spectroscopy, Montréal, Canada, 6–11 August 1995, NIR Publications, Chichester, 625–8. SCOTTER C N G (1998) Fruit and vegetable quality measurement, New Food, 1(1), 57–60. SCOTTER C N G (2000) Rapid non-destructive assessment of fruit and vegetable quality by near infrared (NIR) spectroscopy, CCFRA R&D Report,104. SIMMONDS D H (1974) Chemical basis of hardness and vitreosity in the wheat kernel, Bakers Digest, 48, 16–18, 20, 22, 26–29, 63. SOHN M R and CHO R K (2000) Development of calibration equation for firmness and determination of cell wall components in apple fruits using near infrared spectroscopy. In AMC Davis and R Giangiacomo Eds, Near Infrared Spectroscopy: The Future Waves, Proceedings of the 9th International Conference on Near Infrared Spectroscopy, Verona, Italy, 13–18 June, 1999, Chichester, NIR Publications, 791–5. STARR C, MORGAN A G and SMITH D B (1981) An evaluation of near infrared reflectance analysis in some plant breeding programmes, Journal of Agricultural Science (Cambridge), 97, 107–18. SVENSSON G (1981) Varietal and environmental effects on wheat milling quality, Agri hortique genetica, 39, 1–103. ∨ ∨ TARKOSOVÁ and COPÍKOVÁ J (2000) Fourier transform near infrared spectroscopy applied to analysis of chocolate, Journal of Near Infrared Spectroscopy, 8, 251–7. VAN DE LAER G, DARDENNE P, AGNEESSENS R and ROLOT J -L (2000) Prediction of potato sensory properties by near infrared spectroscopy. In AMC Davis and R Giangiacomo Eds, Near Infrared Spectroscopy: The Future Waves, Proceedings of the 9th International Conference on Near Infrared Spectroscopy, Verona, Italy, 13–18 June, 1999, Chichester, NIR Publications, 879–82. VENEL C, MULLEN A M, DOWNEY G and TROY D J (2001) Prediction of tenderness and other quality attributes of beef by near infrared reflectance spectroscopy between 750 and 1100 nm; further studies, Journal of Near Infrared Spectroscopy, 9, 185–98. WETZEL D L (1984) Physical sample characterization by granulation sorting from diffuse reflectance measurements in the near-infrared. In Proceedings of Third Annual Users Conference for NIR Researchers, February, Pacific Scientific Inc., Silver Spring, MD.

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8 Nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) in texture measurement A. K. Thybo, A. H. Karlsson, H. C. Bertram and H. J. Andersen, Danish Institute of Agricultural Sciences, P. M. Szczypinski, Technical University of Lodz, Poland and S Donstrup, Aarhus University Hospital, Denmark

8.1

Introduction

8.1.1 Texture of foods and demands for non-destructive measurements Texture is a very important factor in food quality and indeed is perceived as the ultimate quality attribute in several foods. Moreover it is considered to be one of the most essential technological quality parameters of semi-processed/ composite foods during processing. The development of rapid and robust online and at-line methods for grading both raw material and semi-processed/ composite foods with differences in their technological and sensory texture qualities is ‘therefore’ a challenge to the industry, because such grading will allow optimal use. Over the years an extensive number of at-/on-line methods have been developed and successfully implemented in the food industry. However, only a few are able to predict and/or characterize quality attributes which are significant for the technological and sensory specifications of foods. Water characteristics, e.g. water distribution and mobility, and the anatomical structures of raw materials in processed foods have been identified as the physical/chemical properties of most importance for the technological and sensory properties of the final foods (Levine and Slade, 1991). Nuclear magnetic resonance (NMR) is one of the few instrumental techniques to have proven high potential in the characterisation of both water distribution and the anatomical interior of foods, both known to be critical for texture (Chen et al., 1989; Berendsen, 1992; Cornillon, 1998; Hills, 1998; Ruan and

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Chen, 1998). Consequently, this unique technique has attracted increasing attention within food research over recent years, and interest in it is further reinforced by its non-destructive sampling nature. The NMR technique includes NMR spectroscopy, MR-imaging and NMR relaxometry. Low-field NMR relaxation and MR-imaging are especially relevant in the context of food science due to the relatively low cost of the instruments used. The present paper gives a state of the art overview of the potential of these techniques in texture analysis of foods. 8.1.2 Principles in 1H low-field NMR relaxation In 1H low-field nuclear magnetic resonance relaxation (proton NMR) the specimen is investigated in a low magnetic field (Ruan and Chen, 1998). In a magnetic field, the magnetic moments of the nuclei will align with the magnetic field (like small compass needles). Following the application of a radio frequency, the protons (hydrogen molecules) in the material are excitated and consequently perturbed from their equilibrium state, to which they will subsequently return by a process described as relaxation. Two types of processes are involved in the return to equilibrium state, longitudinal relaxation (T1) and transverse relaxation (T2). These can consequently be used to characterize the material. In common low-field NMR studies, only the contribution from protons of mobile compounds, i.e. water and fat (non-crystallized) is ‘visualized’. Chemically, water is simple with its two protons but, more interestingly, water behaves differently in different environments. The individual water protons are differently associated to macromolecules providing distinct ‘availabilities’ and thus distinct mobilities for water, and this may be responsible for the various physical properties and functionalities of different food items. As an example, the water protons are less mobile in a solution with e.g. sugar compared with pure water, as the protons will form interactions with the sugar molecules. Thus, the water protons in the sugar solution will be restricted in motion, and an NMR relaxation measurement will display a faster relaxation of the water protons compared with pure water. Water is the most abundant compound in food materials (Isengard, 2001), and therefore water protons are found in high amounts in foods. During recent years, NMR relaxation has been used as an experimental technique for the study of the nature of water in food as well as non-food products (Fullerton and Cameron, 1988; Berendsen, 1992; Rutledge, 1992; McCarthy, 1994; Clark et al., 1997; Cornillon, 1998, Vackier et al., 1999; Ibanez and Cifuentes, 2001; Bertram et al., 2001a). Different water populations/compartments can be extracted from the NMR relaxation curves by e.g. exponential fitting of the relaxation decay. In the case of only one water compartment, a mono-exponential fitting is performed, which can be described as: S = Ae – t /T n

[8.1]

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where S is the total relaxation signal, A is the amplitude representing the proportional water amount (equals protons in the water) and Tn is the relaxation time of the water protons. If several types of protons decaying at different rates exist in a material, each decay (or each type of proton) will have its own Tn describing the intrinsic relaxation rate. If two water compartments (two types of protons) are found in a system, e.g. bulk water and water in association with macromolecules (restricted water), a bi-exponential fitting may be performed, expressed as: [8.2]

SΤ = A1 e – t /T n 1 + A2 e – t /T n 2

where ST is the total relaxation signal, A1 and A2 are the amplitudes representing the proportional amounts of bulk and restricted water, and Tn1 and Tn2 are the relaxation times of bulk and restricted water compartments. As an alternative to the discrete exponential fitting described above, a ontinuously distributed exponential fitting has sometimes been applied (Kroeker and Henkelman, 1986). With this method, the number of exponential components is not pre-defined, and the result is expressed as a continuous distribution of relaxation times. Figure 8.1 shows an example of a continuous spectrum with three water components with transverse relaxation times T21, 6 1 % drip 5 % drip

5

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10 % drip 4

3

2

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0 1

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Fig. 8.1 Illustration of a continuous spectrum from low-field 1H NMR relaxation of three water compartments in pork with relaxation times T21, T22 and T23 (x-axis) and the relative amount of the water compartments presented by the amplitudes (A21, A22 and A23) on the y-axis. The three relaxation time constants reflect three types of water “associations” in pork, which are represented in different amounts (A21–23) and influenced by the water-holding capacity (Source: Bertram et al., 2002a with permission from Elsevier).

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T22 and T23 (x-axis) and amplitudes (A21, A22 and A23) on the y-axis for three types of meat with different water-holding capacities (1, 5, 10 % drip losses) (Bertram et al., 2002a). A minor water component was detected with relaxation time between 1 and 10 ms (T21), a major water component between 40 and 60 ms (T22), and finally a water component with relaxation time between 150 and 400 ms (T23). 8.1.3 Principles in MRI An MR scanner used for magnetic resonance imaging (MR-imaging, NMRI or MRI) is in principle an NMR spectrometer equipped with gradient coils, which allows shaping of the magnetic field in a controlled manner. In this way it is possible to obtain images of the specimen placed in the scanner, where the intensity of the image reflects the value of various MR parameters for the corresponding position in the sample. Just as the intensity of the MR signal can be made dependent on a series of parameters, so can the intensity of the image. The most common of these parameters are proton-density, relaxation times and diffusion properties; these then make it possible to construct images showing the spatial distribution of molecules within a specimen. Consequently, all three contribute to the intensity of the image to a varying, but selectable, degree in a normal image. However, it is possible to combine a series of MR images and to obtain images that reflect separate physical properties. The proton-density reflects the concentration of water and fat in the specimen being imaged. These two contributions can be separated and the protons from non-mobile compounds, e.g. proteins and starch, will not contribute to the MR image. The relaxation times, T1 and T2, primarily reflect how water interacts with macromolecules, and a diffusion-weighted image can show to which extent barriers against free diffusion exist.

8.2

Methods and analysis

8.2.1 Low-field NMR methods Low-field NMR systems are typically benchtop systems, equipped with a permanent magnet, with field strength in the range 50–500 mT. The systems require sample sizes in the order of mL, and the domain for these instruments is relaxation and diffusion measurements. Due to the low magnetic field strength, these systems are in general not suited to carrying out chemical analysis, i.e. exploiting spectral differences between chemical groups. This is the domain of high-resolution–high-field NMR spectroscopy, a subject not covered in this chapter. However, low-field NMR is able to accomplish spectral differentiation between two of the most abundant compounds in foods, water and fat.

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8.2.2 MRI methods MRI instruments can be roughly grouped by two factors; sample size and magnetic field strength. Small-bore MR scanners able to image samples in the mL range are available as special probes to low-field NMR spectrometers. Such systems should have a spatial resolution in the order of tenths of µL. Low-cost, low-field imagers are now available on the market and are increasingly being used for more applied purposes in the food industry. Alternatively, small-bore MR scanners with a field strength above 15 T can obtain a maximum resolution around 30 × 30 × 30 µm. These are of vast interest in basic research, but are much too expensive to be used in food industry applications. Wide-bore MR scanners are systems normally used for medical imaging, with a maximum sample diameter around 0.5 m. These systems are available with a magnetic field strength in the range from 0.2 to 3 T. In general, the high cost of wide-bore imagers is a hindrance but, realizing that it is not always necessary to use state-of-the-art wide-bore imagers in food science, an ever-accelerating pace of research activity in the exploitation of MRI in that industry has taken place during the 1990s.

8.2.3 Data analysis Most NMR relaxation data and MR-images are characterized by a complex multivariate structure with both compositional and spatial information. Optimal extraction of information from such data structures requires the use of advanced multivariate statistical data analysis techniques. Novel chemometric techniques extract the relevant information in the NMR data and develop a predictive model between the NMR data and the quality attributes (Bro et al., 2002). As an alternative to the previously mentioned exponential fitting and discrete exponential fitting of low-field NMR relaxation curves those with thousands of data points can also be correlated to e.g. sensory or other quality attributes using multivariate data analysis as Partial Least Squares Regression, and subsequently a predictive model can be built (Martens and Martens, 1986; Bechmann et al., 1998). In some cases all the information in the full relaxation curves has been used to predict quality related parameters, and in other cases the extracted water compartments (T2) and the relative amount of these (A2) have been useful for predictive purposes (Jepsen et al., 1999; Thybo, et al., 2000; Engelsen et al., 2001, Thygesen et al., 2001). In other experiments, more advanced data analysis, such as the so-called slicing-technique (a threeway modelling, Winding and Antalek, 1997; Pedersen et al., 2002), has been used to explore the information in NMR curves (Andersen and Rinnan, 2002; Jensen et al., 2002; Povlsen et al., 2003). Qualitative visual investigation of the MR-images is the most common way of interpreting them (Ishida et al., 1989; Clark et al., 1997; Bonny et al. 2000). Very few quantitative descriptions of these images have been provided (Barreiro et al., 2000; Thybo et al., 2003, 2004). A quantification of the

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information in an MR-image can be determined by computer-assisted image texture (structure) analysis; afterwards multivariate statistical methods can be used to find correlations with food quality attributes. Many sophisticated image analysis features have been developed, but application is mainly in the medical field with determination of anatomical structures and boundaries and size of tissues. The most common method for computing image characteristics is the analysis of image histogram (image brightness distribution). This kind of analysis usually reveals statistics on image brightness, contrast and the ratio of light to dark areas within an image. Unfortunately the histogram itself does not carry any information on spatial relations between image elements (pixels). To overcome the problem of lack of spatial information in an image histogram, two-dimensional histogram (co-occurrence matrix) analysis may be applied. Co-occurrence matrix estimates the joint probability of two pixels having particular intensities and being related to each other with specified distance and direction. Therefore, co-occurrence-matrix-derived features provide quantitative information on the structure of an image texture pattern, including texture directivity. Another method used for texture evaluation is based on the run-length matrix. The matrix is defined as the number of times that a chain consisting of a specified number of pixels (in a given direction) has a certain grey level. Run-length-matrix-derived statistics provide some knowledge about texture directivity and homogeneity. Textural features of an image, such as histogram, gradient, co-occurrence matrix, run-length matrix, autoregressive and wavelet-based features, can be determined by e.g. MaZda software (ver. 3.20 by Politechnika Lodzka, Lodz, Poland). The program was created within the EU COST B11 project “Quantitative Analysis of Magnetic Resonance Image Texture” devised for the years 1998–2002. It is under continuous development, and new texture computation techniques are being implemented in this software. Further, development of appropriate mathematical tools for quantitative data analysis is a future challenge, if MRI is to become a successful non-invasive rapid online/at-line method for food quality attribute measurements.

8.3 Application of NMR: texture determination of solid foods 8.3.1 Determination of texture by low-field NMR relaxation As mentioned above, NMR relaxation can be used to display different water compartmentalizations in food materials by measuring the relaxation rates of the water protons. Several researchers have stated that NMR relaxation is a rapid, non-invasive and non-destructive method of determining the distribution of water pools with different relaxation rates in foods (Cornillon, 1998; Ruan and Chen, 1998). Water distribution in foods is an important characteristic in relation to food texture quality. The number of relaxation studies focusing

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on water compartments has increased enormously during recent years. However, only in a minority of these studies have the results been correlated to actual texture attributes; most are focussed on the potential of determining water distributions within a food product, its water-holding characteristics and the effects of processing conditions, e.g. heating, cooking and freezing, on water exudation and migration. Nevertheless, it has been shown that low-field NMR relaxation measurements demonstrate a high correlation with texture properties in rice, bread, meat, fish and potatoes.

8.3.2 Fish Several researchers have applied NMR relaxation to fish in order to study water contents (Steen and Lambelet, 1997; Bechmann et al., 1998; Jepsen et al., 1999; Andersen and Rinnan, 2002; Jensen et al., 2002). However, only Steen and Lambelet (1997) correlated the transverse NMR relaxation data obtained with the sensory texture attributes of toughness, fibrousness and dryness. In cod mince exposed to different freezing conditions, the correlations using either sensory texture attributes or texture determined by compression test were r = 0.76–0.95 and r = 0.93–0.96, respectively. The conclusion was that transverse NMR relaxation is a relevant method both in determination of texture changes in cod mince upon freezing and in prediction of the final texture quality of cod patties.

8.3.3 Meat Fjelkner-Modig and Tornberg (1986) were the first to address the potential of transverse NMR relaxation in the context of raw meat to predict the sensory texture properties in cooked meat. With respect to the water distribution in pork, they revealed three compartments of water, which they designated as free, extra-cellular and intra-cellular water. In raw meat from the Hampshire breed, the sensory toughness was related to the relaxation time of intracellular water, while in raw meat from the Yorkshire breed, toughness was correlated with the amount of intra-cellular and extra-cellular water. Moreover, juiciness in meat from Yorkshire pigs tended to be related to the relaxation time of the intra-cellular water. Accordingly, the study demonstrated how NMR relaxation could be used as a tool to unravel the basis for differences in sensory textural properties among different meat qualities. Recently, it has also been demonstrated that NMR relaxation measurements reflect the changes in water characteristics in meat during cooking (Micklander et al., 2002; Bertram et al., 2004a) which are known to occur as a result of textural changes due to myosin denaturation and shrinkage of connective tissue. Finally, textural alterations of meat as a result of freezing/thawing have also been shown to be detectable by NMR relaxation (Yano et al., 2002).

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8.3.4 Bread and starch-based products Hardness and water mobility in cooked rice were investigated by transverse NMR relaxation and correlated to texture measurement determined by TPA (Ruan et al., 1997). Furthermore, the change in firmness of the rice samples upon storage, which was due to retrogradation, was described by transverse NMR relaxation data. Seow and Teo (1996) showed that pulsed NMR could be used to monitor staling in different starch-based products e.g. cornstarch gel, bread and rice cup cake. With significant correlations (r = 0.96–0.98) between NMR measurements and firmness determined by a compression test, it was concluded that NMR (solid phase signal) can be used to predict staling in starch gels and starch-based products. Moreover, staling and changes in firmness of sweet rolls were correlated with water characteristics determined by transverse NMR relaxation (r = 0.87–0.98) (Ruan et al., 1996). The authors found that the change in the mobility of the water in the different water compartments was the basic reason for the staling process. Recent papers have confirmed the high correlation between transverse NMR relaxation measurements and changes in elasticity and firmness in bread during eight days of storage (Chen et al., 1997; Engelsen et al., 2001) (Fig. 8.2). Moreover, the study by Engelsen et al. (2001) showed that NMR relaxation data could be used to predict the texture in the bread at day 8 from changes in relaxation data between days 0 and 3, which is highly interesting due to the potential for the 2000 1800

r r2 RMSECV BIAS

= 0.95 = 0.90 = 147 = –9.6

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Fig. 8.2 Prediction of firmness of bread samples by Partial Least Squares Regression on low-field 1H NMR relaxation curves. Reference values versus predicted values. The middle diagonal line is the target line and the two other lines indicate the (+/–) RMSECV errors (Source: Engelsen et al., 2001 with permission from Elsevier).

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prediction of shelf life. Finally, Engelsen et al. (2001) carried out on-line NMR relaxation measurement on dough during the full baking process and showed that changes in water characteristics could be used to follow the onset and off-set of the gelatinisation process and to understand the water associations with the proteins, starch and pentosans which reflect textural changes and which may be relevant to understanding the quality of the final baked product.

8.3.5 Potatoes Several recent studies have shown that NMR relaxation data obtained from raw potatoes can predict sensory-determined texture attributes in cooked potato samples. Transverse NMR relaxation measurements on raw potatoes have shown very high correlations with the sensory attributes of firmness, springiness, adhesiveness, mealiness (see Fig. 8.3), graininess and moistness in cooked potatoes (Bro et al., 2002; Thybo et al., 2000; Povlsen et al., 2003). Previous studies showed that NMR relaxation measurements correlated to some of the texture attributes, as the NMR relaxation data gave information about water content in the raw potatoes, which is inversely correlated with starch and dry matter content which are known to determine the specific texture attributes in the cooked product (Thygesen et al., 2001; Thybo et al., 2003, 2004). However, adhesiveness and springiness correlated better with NMR data than with the content of the chemical constituents. This indicated that NMR apparently gives information about certain mouthfeel variables which are difficult to measure by any other instrumental technique.

8.3.6 Cheese NMR relaxation has also been shown to reflect rheological properties of cheese analogs (Budiman et al., 2000). Transverse NMR relaxation measurements showed good correlation (R2 = 0.81–0.84 corresponding to r = 0.90–0.92) with texture determined as initial stress in a compression test of cheese analogous samples containing either milk fat or vegetable oil (Fig. 8.4). Moreover, the elastic constant g1 of the cheese analogs was also highly correlated to NMR measurements (r = 0.91–0.96). These results clearly indicate that transverse NMR relaxation might also have a high potential in determining cheese texture, but this needs further investigation.

8.3.7 Determination of water and water-holding capacity in solid food by low-field NMR relaxation As pointed out previously, many of the investigations using NMR to predict the contents of water and fat have been carried out without any attempt to predict texture. However, these studies are also relevant in the present context, as water and fat are of importance for texture, and they should be able to give

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5000

Signal amplitude

4000

3000 2000 1000 0 0

9

1000

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3000

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8 7

Predicted

6 5 4 3 2 1 0 0

1

2

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4 5 Measured

6

7

8

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Fig. 8.3 Low-field 1H NMR relaxation curves of four different potato varieties (up); prediction of mealiness and moistness in cooked potatoes from NMR relaxation curves determined on raw potatoes – reference values versus predicted values (down). (Source: Bro et al., 2002 with permission from Elsevier).

appropriate information for future applications. Prediction of fat and water content (Bechmann et al., 1998; Jepsen et al., 1999; Andersen and Rinnan, 2002) in raw fish using NMR relaxation in combination with multivariate data analysis has shown intriguing results with an r-value up to 0.998 despite large sample variation with respect to muscle structure (Andersen and Rinnan, 2002). Other studies have shown that transverse NMR relaxation can be used to determine changes in water compartmentalization in fish during freezer storage (Lambelet et al., 1995; Jensen et al., 2002). NMR longitudinal relaxation data have been shown to reflect the water content of marinated chicken breast, and a good correlation between expressible moisture (water-holding capacity) and NMR transverse relaxation data has

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Texture in food 45 Milkfat Vegetable oil

40 35

R 2 = 0.848 R 2 = 0.816

σ0 (kPa)

30 25 20 15 10 5 0 14

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Fig. 8.4 Texture of cheese analog samples containing either milk fat or vegetable oil determined as stress in uniaxial compression versus relaxation time T21 from low-field 1H NMR (Source: Budiman et al., 2000 with permission from Food and Nutrition Press).

been established (Rongrong et al., 2000). As in fish (Jepsen et al., 1999), several studies have displayed correlations (r = 0.60–0.85) between NMR transverse relaxation data and the water-holding capacity of pork (Renou et al., 1985; Tornberg et al., 1993; Brøndum et al., 2000; Brown et al., 2000; Bertram et al., 2001b). In some of these studies, correlations were established using multivariate statistical regression analysis on the relaxation decay (Brøndum et al., 2000; Brown et al., 2000), and accordingly no information was obtained about the specific transverse relaxation parameters to which the correlation could be ascribed. However, when discrete bi-exponential fitting was carried out on the relaxation decay, the highest correlation was obtained on the transverse relaxation parameter representing the most loosely ‘entrapped’ water (Tornberg et al., 1993; Bertram et al., 2001b), or the relative fraction of this component (A2 in Eq. 8.2) (Renou et al., 1985), which is in agreement with the hypothesis that it is the least restricted water which is related to potential drip loss (Bertram et al., 2002a). Besides waterholding capacity, high correlations between NMR relaxation data and protein content in fresh meat (Tipping, 1982; Bertram et al., 2002b), fat content in minced meat and meat products (Renou et al., 1985; Pedersen et al., 2001), and moisture content in sausages (Gerbanowski et al., 1997) have been reported. Finally, an indirect relationship between transverse NMR relaxation data and meat texture has been displayed in a recent study, where a strong relationship between meat structure, a decisive factor for meat texture, and NMR transverse relaxation was established (Bertram et al., 2002a; Fig. 8.5). Similar studies solely focussed on the changes in the distribution of water as a reflection of structural/anatomical changes will be very important tools for further

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Sarcomere length (µm)

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1 40

42

44

46

48

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T21 (ms)

Fig. 8.5 Relationship between the relaxation time T21 from low-field 1H NMR relaxation and meat texture displaying a strong relationship between T21 and meat structure (sacromere length), a decisive factor for meat texture (Source: Bertram et al., 2002c with permission from American Chemical Society).

understanding of the texture changes during processing in the future application of NMR in food science.

8.4 Application of MRI: texture determination of solid foods 8.4.1 Determination of texture by low-field MR-imaging The combination of non-invasiveness and visualization of water and lipid distribution makes MRI unique as a diagnostic tool. In the past, MR-imaging has mostly been applied successfully within the medical field, and it has achieved general acceptance as a powerful tool in the diagnosis and assessment of tumours in the human brain and body by visual interpretation of images (Lerski et al., 1999). As the technology has matured, new applications have been developed directed at non-medical areas, such as plant physiology and anatomy (MacFall and Johnson, 1994; MacFall and Van As, 1996). More recently, the potential of MR-imaging in studying anatomical details and changes in water and transport of solutes in foods during processing has begun to emerge.

8.4.2 Qualitative evaluation of structural/textural changes in food The first attempts to reveal the potential of MRI in food science were observations of the static food structures in a way which was analogous to

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imaging of human anatomy. The observations were made in relation to the quality of fresh fruits and vegetables in terms of internal breakdown, bruises, voids and post-harvest studies (Chen et al., 1989; McCarthy, 1994; McCarthy et al., 1995; Clark et al., 1997). For apples this non-destructive method was used to investigate internal changes in water cores and the development of browning during storage (Clark and Burmeister, 1999; Clark and Richardson, 1999). The maturity of tomatoes has been followed by MR-imaging (Ishida et al., 1989; Saltveit, 1991), and MR-imaging has been used as a method of observing changes in internal structure during compression of tomatoes (Gonzalez et al., 1998). In the latter study, the macro-structural changes were quantified and this revealed registered changes in MRI signal intensity from specific tomato tissues. Changes in water mobility during storage in starch-based systems in the form of sweet rolls have been followed by MR-imaging. This showed that water migrated from the crumb to the crust, which was associated with the firming of the crumb (Ruan et al., 1996). Moreover, a spatial redistribution of water mobility within the sweet rolls during storage was also observed. A recent study revealed how MR-imaging can visualize and so be a valuable tool in the study of the adiposity distribution in fish (Collewet et al., 2001). The effects of high-pressurization, known to severely alter the texture of meat (Ratcliff et al., 1977), have also been studied and compared with nonpressured meat using MR-imaging (Bertram et al., 2004b). Finally, in a study of changes in the texture and structure of lasagne after cooking, as investigated by an instrumental compression devise, MR-imaging clearly demonstrated the differences in internal structure of the pasta in relation to cooking time and thereby facilitated the interpretation of the texture measurement (Gonzales et al., 2000). The above listed subjects show that MR-imaging is a useful technique to interpret defects and structural changes during maturity, processing and staling, all of which are very important factors for texture. However, further use of MRI in food science requires full exploitation of the quantitative aspects of MRI, as qualitative observations of changes are not always sufficient in foods, where transport of mass, heat and momentum and their effect on functionality are also of major importance. The use of MR-imaging in the quantitative determination of food quality is a new area with huge potential and one which deserves attention. The very few papers published within this area are presented below.

8.4.3 Quantitative evaluation of structural/textural changes in food Quantitative interpretation of MR-images is necessary in order to obtain correlations between MR-images and texture. When correlations are established, predictive models for texture using MR-imaging can be developed and subsequent MR-imaging can be used for on-line or at-line quality monitoring. In the following the few papers currently published which deal with a direct

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correlation between texture of food and MR-images through quantitative interpretation of the MR-images are described. Mealiness in apples and peaches has been related to MR-imaging data from the fruits and to subsequent mechanical texture analysis of the fruit (Barreiro et al., 2000) (Fig. 8.6). Histogram features from MR-images of the mealy apples were more skewed than the histogram of fresh and crispy apples, and subsequent multivariate discriminant analysis of the MR-image T2 histograms

T2 images

d13 (mealy + internal breakdown)

3b 17 (mealy)

3b 12 (intermediate)

d16 (fresh & crispy)

Fig. 8.6 Examples of T2 MR-images and T2 histograms from two mealy apples (first, second), an intermediate (third) and a fresh (fourth) apple showing large visual differences in appearance of the images and more skewed histograms of the mealy apples compared with the fresh and crispy apples. (Source: Barreiro et al., 2000 with permission from Elsevier).

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features clearly discriminated between the mealy and the fresh apples determined by instrumental compression test. In the previously mentioned MRI study on fish by Collewet et al. (2001), they also managed to quantify the adiposity distribution, which is highly interesting as the relative deposition of fat in the different edible flesh parts greatly influences the smoothness of the texture and the flavour of fish muscle. Another recent study describes the use of MRI coupled with image analysis in the prediction of soft cheese texture characteristics obtained by a sensory panel (Mariette and Collewet, 2001). This study revealed that several sensory attributes were correlated (r = 0.74–0.95) with variables from the MR images. It clearly demonstrated that the MRI technique associated with image analysis methods is a powerful tool to establish relationships with sensory texture. The prediction of cooked potato texture and dry matter content from MRimages of raw potatoes was also recently revealed (Thybo et al., 2003, 2004). The large visual variation in a T1-weighted MR-image of raw potatoes obtained in one of these studies is given in Fig. 8.7. Low signal intensity (dark areas) indicates relatively free water, while high signal intensity (bright areas) reflects a tighter ‘association’ between water and macromolecules. This large variation in MRI signal intensity within a potato has previously been reported where the

Folva

Sava

Primula

Ukama

Fig. 8.7 MR-images (T1-Weighted) of two mature raw potato varieties (Folva and Sava) and two immature raw potato varieties (Primula and Ukama). Low signal intensity (dark areas) indicates relatively free water, while high signal intensity (bright areas) reflects tighter “association” between water and macromolecules (Source: Thybo et al., 2003 with permission from Elsevier).

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bright areas were stated to be the vascular tissue (MacFall and Van As, 1996). In the study no correlation was established between dry matter content and simple image analysis features (grey scale intensities) in the MR-images (Thybo et al., 2003, 2004). However, when approximately 260 image analysis features (Materka et al., 1999a, b, 2000) were subjected to multivariate data analysis a prediction of cooked potato texture attributes was possible, which resulted in a correlation coefficient of r = 0.70–0.85 for hardness, adhesiveness and moistness (Thybo et al., 2004). In contrast, the geometrical attributes, i.e. mealiness and graininess, were not well predicted from the MR-images. Present studies deal with a quantitative description of MR-images, showing that structural information of relevance for the sensory texture attributes can be extracted from MR-images of raw potatoes. Despite their preliminary nature, these studies display the potential of MR-imaging as a future method for quantitative quality evaluation. Finally, MRI has also been used to follow dynamic changes in the structure of foods during processing, e.g. cooking, drying, rehydration, freezing and freeze-thawing. MR-images have been shown to provide relevant information on structural changes (Hall et al., 1998; Nott et al., 1999a,b; Evans et al., 2002).

8.5

Future trends

The 1990s has seen NMR move into the area of modern food science due to its huge potential to reveal structural and dynamic changes of importance to many quality attributes. There is a growing awareness that low-field NMR relaxation and imaging of water provide useful insight into many structural aspects of raw and processed foods. Most of the results presented above are based on the fact that low-field NMR parameters, especially water, in foods are dependent on the cellular architecture of the food product and hence indirectly measure textural properties. The food industry demands rapid on-line methods to predict the sensory or technological quality of the raw material in order to obtain a final food product with the most optimal and uniform quality. In a recent paper the applicability of NMR relaxation, NIR and spectrofluorometry and multivariate handling of large data sets was presented in relation to characterization and prediction of quality parameters (Bro et al., 2002). The present review has addressed examples of applications of low-field NMR relaxation and MRI to the determination of texture and structure in food. However, can NMR techniques be developed which can offer at-line and on-line sensors of relevance for the industry? Until recently, the high cost of most NMR equipment has turned out to be the major hindrance, but the supply in recent years of lowcost, benchtop NMR models suitable for both relaxation measurements and imaging has indeed made NMR relevant to the food industry. At present NMR relaxometry has already shown potential as a natural at-line method in

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fat determination of meat (Pedersen et al., 2001), and several other applications are apparent on the basis of our present knowledge. In particular, the preliminary results from the use of an NMR mobile surface device (the so-called NMRmouse) in food analysis indicate that, with further development of the hardware part, the introduction of on-line NMR relaxometry may be a reality in the near future (Martin et al., 2002). Moreover, the possibilities for exploiting MRI as an on-line sensor for process control and quality assurance throughout the food manufacturing chain are intriguing, and the next few years will probably see rapid development in this area. As mentioned previously, quantitative MRI has great potential in the food industry and use of the technique is set to increase. It is a very demanding technique and development of appropriate mathematical models for quantitative data analysis is going to be a crucial element within this area. Furthermore, the development within rheo-NMR (Hills, 1998; Callaghan and Gil, 2001) and solid imaging (Hills, 1998), even though both techniques are in a rudimentary stage, will undoubtedly contribute to a much better insight regarding the role of the intermolecular interactions known to be of great significance for the texture of foods and to be factors of importance in low-water-content foods, e.g. cookies, crackers, pasties, etc. In conclusion, NMR technology has proven to be dynamic and flexible. Its applications have been proven useful in the area of food science and texture analysis. Future development in hardware, exploitation in image analysis methods and development in multivariate data analysis tools will certainly make NMR the tool of the future within food science and food analyses.

8.6

References

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and SCHAD L (1999b) Toward texture feature selection of Magnetic Resonance Phantom Images. In Proceedings Computers in Medicine, Lodz, Poland, 23–25 Sept., 23–5. MATERKA A, STRZELECKI M, LERSKI R and SCHAD L (2000) Toward automatic feature selection of texture test objects for Magnetic Resonance Imaging. In Proceeding 11th Portuguese Conference on Pattern Recognition, Porto, Portugal, 11–12 May, 11–16. MCCARTHY M J (1994) Magnetic Resonance Imaging in Foods, New York, Chapman and Hall. MCCARTHY M J, ZION B, CHEN P, ABLETT S, DARKE A H and LILLFORD P J (1995) Diamagnetic susceptibility changes in apple tissue after bruising. J Sci Food Agric, 67, 13–20. MICKLANDER E, PESHLOV B PURSLOW P P and ENGELSEN S B (2002) NMR-cooking monitoring the changes in meat during cooking by low-field 1H-NMR, Trends Food Sci Technol, 13, 341–6. NOTT K P, EVANS S D and HALL L D (1999a) The effect of freeze-thawing on the magnetic resonance imaging parameters of cod and mackerel, Lebensm -Wiss u –Technol, 32, 261–8. NOTT K P, EVANS S D and HALL L D (1999b) Quantitative magnetic resonance imaging of fresh and frozen-thawed trout, Magn Reson Imag, 17, 445–55. PEDERSEN H T, BERG H, LUNDBY F and ENGELSEN S B (2001) The multivariate advantage in fat determination in meat by bench-top NMR, Inn Food Sci Emerging Technol, 2, 87–94. PEDERSEN H T, BRO R and ENGELSEN S B (2002) Towards rapid and unique curve resolution of low-field NMR relaxation data: trilinear SLICING versus two-dimensional curve fitting, J Magn Reson, 157(1), 141–55. POVLSEN V T, RINNAN Å, VAN DEN BERG F, ANDERSEN H J and THYBO A K (2003) Direct decomposition of NMR relaxation profiles and prediction of sensory attributes of potato samples, Lebensm -Wiss u –Technol, 36, 423–32. RATCLIFF D, BOUTON P E, FORD A L, HARRIS P V, MACFARLANE J J and O’SHEA J M (1977) Pressureheat treatment of post-rigor muscle: objective-subjective measurements, J Food Sci, 42, 857–9. RENOU J P, MONIN G and SELLIER P (1985) Nuclear magnetic resonance measurements on pork of various qualities, Meat Sci, 15, 225–33. RONGRONG L, KERR W L, TOLEDO R T and CARPENTER J A (2000) 1H NMR studies of water in chicken breast marinated with different phosphates, J Food Sci, 65, 575–80. RUAN R R and CHEN P L (1998) Water in Foods and Biological Materials, Loncaster, PA, Technomic Publishing Company Inc. RUAN R, ALMAER S, HUANG V T, PERKINS P, CHEN P and FULCHER R G (1996) Relationship between firming and water mobility in starch-based food systems during storage, Cereal Chem, 73, 328–32. RUAN R R, ZOU C, WADHAWAN C, MARTINEZ B, CHEN P L and ADDIS P (1997) Studies of hardness and water mobility of cooked wild rice using nuclear magnetic resonance, J Food Process Preserv, 21, 91–104. RUTLEDGE D N (1992) Low resolution pulse nuclear magnetic resonance (LRP-NMR), Analusis Magazine, 20, 58–62. SALTVEIT M E JR (1991) Determining tomato fruit maturity with nondestructive in vivo nuclear magnetic resonance imaging, Postharvest Biol Technol, 1, 153–9. SEOW C C and TEO C H (1996) Staling of starch-based products: A comparative study by firmness and pulsed NMR measurements, Starch/Stärke, 48, 90–93. STEEN C and LAMBELET P (1997) Texture changes in frozen cod mince measured by lowfield nuclear magnetic resonance spectroscopy, J Sci Food Agric, 75, 268–72. THYBO A K, BECHMANN I E, MARTENS M and ENGELSEN S B (2000) Prediction of sensory texture of cooked potatoes using uniaxial compression, near infrared spectroscopy and low field 1H NMR spectroscopy, Lebensm-Wiss u –Technol, 33, 103–111. THYBO A K, ANDERSEN H J, KARLSSON A H, DØNSTRUP S and STØDKILDE-JØRGENSEN H (2003) Low-field NMR relaxation and NMR-imaging as tools in differentiation between

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potato sample and determination of dry matter content in potatoes, Lebensm-Wiss u – Technol, 36, 315–22. THYBO A K, SZCZYPINSKI P M, KARLSSON A H, DØNSTRUP S, STØDKILDE-JØRGENSEN H S and ANDERSEN H J (2004) Prediction of sensory texture quality attributes of cooked potatoes by NMR-imaging (MRI) of raw potatoes in combination with different image analysis methods, J Food Eng, 61, 91–100. THYGESEN L G, THYBO A K and ENGELSEN S B (2001) Prediction of sensory texture quality of boiled potatoes from low-field 1H NMR of raw potatoes. The role of chemical constituents, Lebensm-Wiss u –Technol, 34, 469–77. TIPPING L R H (1982) The analysis of protein in fresh meats using pulsed NMR, Meat Sci, 7, 279–83. TORNBERG E, ANDERSSON A, GÖRANSSON Å and VON SETH G (1993) Water and Fat Distribution in Pork in Relation to Sensory Properties. In Pork Quality: Genetic and Metabolic Factors. Eds E Puolanne and D I Demeyer with M Ruusunen and S Ellis, Oxon, CAB International, 239–58. VACKIER M-C, HILLS B P and RUTLEDGE D N (1999) An NMR relaxation study of the state of water in gelatin gels, J Magn Reson, 138, 36–42. WINDING W and ANTALEK B (1997) Direct exponential curve resolution algorithm (DECRA): A novel application of the generalized rank annihilation method for a single spectral mixture data set with exponentially decaying contribution profiles, Chem Intell Lab Syst, 37, 241–54. YANO S, TANAKA M, SUZUKI N and KANZAKI Y (2002) Texture changes of beef and salmon meats caused by refrigeration and use of pulse NMR as an index of taste, Food Sci Technol Res, 8, 137–43.

9 Modelling food texture L. M. M. Tijskens and H. Luyten, Wageningen University and Research Centre, The Netherlands

9.1

Introduction

9.1.1 What is texture Texture is one of the major characteristics in determining and defining the quality of horticultural crops and is also a major attribute for man-made products (Hutchings and Lillford, 1988). Researchers in the area of mechanical properties like to see texture as a property of food products that can be measured adequately by simple and objective techniques. The relations, however, between objective measures of mechanical properties, including all effects of physical conditions, and their sensorial counterparts are notoriously difficult to establish. For researchers in the area of sensory sciences, texture is just one of the many attributes that have to be estimated by panels tasting and chewing the product in more or less controlled conditions. Textural issues are sensory categories, defined as reactions to stress (force) or tactile feeling (Meilgaard et al., 1991). In other words, the notion of texture, in all its aspects, is as ill-defined and elusive as that of quality itself. Different people often use different definitions. Still, one has to work with texture in research and in practice. The textural properties of our food need to be communicated in such a way that scientists and growers and manufacturers, and all the actors in the food supply chain can at least understand one another. Since the analogy with quality in general seems to be so apt an approach similar to that used by Sloof et al. (1996), Tijskens et al. (1994) and Wilkinson and Tijskens (2001) can be followed. This approach is based on a decomposition of the problem of quality into product properties and associated quality attributes. Unlike other, better-defined attributes of food products, e.g. colour, infections and defects, the relationship between product properties and textural attributes

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Texture in food

is rather ill-defined. Product properties can affect more than one attribute, while at the same time attributes can rely on more than one property. A rather incomplete picture for semi-solid foods is shown in Fig. 9.1 (Luyten, 2003 based on data from de Wijk et al., 2003). In addition to this labyrinth of relations among purely textural issues, texture of food products affects other senses as well. Texture can modulate flavour perception, e.g. sweetness as a function of sugar content and juiciness (Brückner and Auerswald, 2000; Brückner, 2003). Texture of food determines how fast or slow we chew and how much force we apply. And that in turn determines how the product reacts and how we perceive the texture. So, research on texture should be connected to knowledge of oral physiology (Hutchings and Lillford, 1988; Lucas et al., 2002). But Szczesniak (2002) provided the most important aspect of sensory perception of texture in terms of quality and acceptance, stating that an acceptable texture is hardly noticed, but negative texture experiences are strongly linked with a negative hedonic loading. In practice, this means that when the perceived texture is considered sufficient, it hardly adds anything to the preference and acceptance of consumers. However, when texture is lacking consumers will reject the product regardless of the intensity of its other quality attributes. It is a so-called critical attribute (Molnár, 1995 cited in Wilkinson and Tijskens, 2001). So, in dealing with texture, a systematic distinction has to be made between texturerelated chemical and physical properties which are intrinsic to the food, the perception of trained sensory panels, who judge attribute intensities regardless of their own likes and dislikes, and acceptance by consumers, who cannot detach their own preferences from their perception, and who always respond in complex combinations. 9.1.2 Assigned Quality versus Acceptance, Properties versus Attributes In an attempt to develop a framework for modelling quality in general, a philosophical view on quality was developed (Tijskens et al., 1994; Sloof et Property

Attribute

Viscosity

Thickness

Density

Melting

Particle size

Compact

Flavour concentration

Creamy

Adhesion

Sticky

Fig. 9.1 An illustration of the complexity in the attribute–property relation for mayonnaise and custards. Based on the results of de Wijk et al. (2003). (Picture is not complete.)

Modelling food texture 207

al., 1996; Wilkinson and Tijskens, 2001) that proved to be highly efficient, profitable and far more generally applicable than anticipated. The innate notion of product quality has three separate aspects. Product, user and market all influence the ‘product quality’. In this philosophical view a product does not have quality of its own; quality is assigned to a product by the user. The assignment of quality to a product can be decomposed into separate processes of perception, evaluation and appreciation (see Fig. 9.2). During these processes, the intrinsic properties of a product are converted into quality attributes that are evaluated and appreciated in the context of socio-psychological factors and the market situation. After a quality is assigned to the product the user decides whether or not to accept it. So, the complete quality scheme (see Fig. 9.3) consists of intrinsic product properties, quality attributes, quality assignment and product acceptance. Each of these steps is influenced to a greater or lesser degree by external factors. With this decomposition of the concepts of quality and of quality assignment a generic architecture for modelling quality change of agricultural products can be defined. From this view on quality it becomes evident that the buyer/consumer plays the active role in the process of quality evaluation. The consumer assigns quality to a product. So, quality does not exist other than in the mind of the observer (buyer/consumer) as a virtual property of the product. In other words, quality is the result of the act of assigning a uni-dimensional quality measure to a product by a given person. The assignment, however, is, Product Product properties

Attributes

Perception

Perception

Evaluation

Evaluation

Appreciation

Appreciation

Properties

Assigned quality

Human Quality assignment process

Fig. 9.2

Steps in assigning quality to a product by a human (from Wilkinson et al. 2001).

208

Texture in food Physical Gas conditions Relative humidity Temperature

Cultivar Intrinsic product properties

Quality

Cost

Growth conditions

Assignment

Product acceptance

Status symbol Fashion

on

Customs

om

Prior experience

Preference

ic

User

ps S yc oc ho io lo gi ca l

Ec

Availability

Climate

Fig. 9.3 Quality assignment of natural food products as affected by product properties, economic and socio-psychological circumstances (from Wilkinson et al., 2001).

as far as the product is concerned, completely based on intrinsic product properties. In other words, the assigned quality reflects (almost) solely the interaction between the product on the one side and the perceiving buyer/ user on the other. Based on the quality assigned to the product, the customer will decide whether or not to accept it. This decision is influenced by external factors, which are largely determined by the market situation (economic factors) and the psychological and social circumstances of the consumer. So, all these economic and socio-psychological effects are coming into play when the consumer has to decide whether or not to buy a particular product, in a particular market situation, in the particular circumstances that he/she happens to be in at a particular moment. Among these factors are the relative abundance of available produce and, equally important, the presence of a possibility of choice: if there are only bad-quality peaches (too hard, no taste) from southern Europe available in central Europe they will be sold due to lack of alternatives (Bohling and Adam, 1994). On the other hand, as long as poor-quality products can be sold, transport and sale of poor-quality produce will continue. A number of major issues that affect the modelling of quality (and texture) can be taken from this viewpoint on the issue. • Assigned quality is entirely based on intrinsic properties. Conversion of properties to attributes is done by the consumer/user/buyer/observer. • Conversion of intrinsic properties to attributes is complex and up to now unclear (see Fig. 9.1). • Quality (not acceptability) can and may be modelled based on the behaviour of relevant intrinsic properties only.

Modelling food texture 209

• What is normally called product quality is, in the majority of cases, the acceptability, where all the effects of economic and psychological influences combine with the assigned quality.

9.1.3 Role of modelling in texture analysis The ultimate goal of modelling is to provide reliable predictions of occurrences that have not yet taken place, for any product, from any source and in any situation. This goal, although agreed upon by every modeller and every user of models, is, however, currently a long way off and is far from being fulfilled by the present technology in modelling. Modellers have achieved major progress in mathematics, modelling techniques and modelling tricks during the past few decades, from the early 1970s (Thornley, 1976; Edelstein-Keshet 1988; Thornley and Johnson, 1990; Keen and Spain, 1992; Brown and Rothery, 1993) up to the 21st century. What technology can deliver at the moment is a fairly accurate mathematical description of the behaviour of a product or commodity in conditions not too far away from those in which the tests have been conducted and in which the results have been gathered, based upon which the model is so-called validated or calibrated. For modelling the quality (and texture) behaviour of our food from the growing site through the different handling sequences, the distribution chain, storage, and processing, up to consumption and the final judgement of consumers, the situation is no different. A vast number of models, submodels and applications have been built, and many of them have been reported in detail. Most of these models are very valuable for exactly this but only this application: predicting the future behaviour of a product in circumstances similar to the test circumstances. These are the so-called dedicated models. Once the application is to be extended to fairly new circumstances, the sometimes huge task of developing, creating and calibrating new models has to be repeated. In terms of efficiency of resource utilisation, this approach seems efficient in the short term; in the long run, however, it is neither very efficient nor very satisfying. Moreover, in terms of comprehension of the problem at hand and generation of knowledge, this approach leads only to indirect and incomplete understanding. To achieve the ultimate goal of modelling quality – predicting future behaviour of any product and its quality from any source in any circumstance while generating more knowledge about the process under study – we must include all the available knowledge, fundamental as well as practical, that is at our disposal. The results of this kind of approach are the so-called fundamental (or rather fundamental-oriented) models. These do indeed generate more knowledge and understanding, and can direct scientific research to those areas where information and understanding seem to be lacking. The benefit to be gained from the practical application of modelling lies exactly in the potential it offers for optimising processes, procedures and supply chains. The main advantage of modelling product behaviour is, in our

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Texture in food

opinion, to be found in the steering and directing of research towards those areas where essential knowledge is lacking and where possible bottlenecks can be resolved in a more efficient and economic way (Tijskens et al., 1998a). Also process-oriented modelling greatly increases the efficacy of data analysis and interpretation.

9.1.4 Fundamental models and disciplines Fundamental-oriented models can be built in many ways and within many scientific disciplines. Disciplines, however, are a result of the deep-rooted urge of man and scientists to bring some order to our view and perception of the world. Nature, on the other hand, does not have or need disciplines at all. Processes occurring in nature “naturally” comply with the laws of nature. The ultimate drive of science is to detect and understand these laws, and to convert that understanding into some practical application in order to increase the quality of our lives. So, within the realm of each discipline, the same laws of nature will be present and known in one form or another. The direct consequence is that, in modelling and understanding processes of nature, it does not really matter which particular discipline is used as a framework, as long as its rules are used consistently and thoroughly. For chemists, the choice of kinetic modelling is self-evident. The general rules of kinetic modelling are summarised and described in van Boekel and Tijskens (2001). The rules of the discipline and the laws of nature in the area of chemistry were all discovered and formulated in the early days of chemical discoveries, and theories were formulated. They are now a standard part of textbooks on chemistry, physical chemistry, kinetics and enzyme kinetics (Chang, 1981; Fersht, 1984; Whittaker, 1994; etc.) Although this knowledge has been available for so many years, when building empirical models old-fashioned style, it is the first information that consistently is not used at all. Every time new data need to be analysed, mathematical and statistical relations are invented and tested all over again, and the model with the statistical best fit to the data at hand is chosen to represent the behaviour of that system, without recourse to any expert knowledge whatsoever. Modern science is based on the most important rule of all: the repeatability of a process. That is, in the same conditions the same process will occur at the same rate. In making models, we should use this, the very basis of science, and search for process rate constants that are true constants for any condition encountered. In other words, rate constants of processes will be the same for the same processes, no matter what the conditions are. Some general rules can be drawn up when modelling the complexity of interacting processes that occur in nature. When trying to model interactive processes, it is of the utmost importance to apply the old Roman rule “divide et impera” (divide and rule). In modern terminology, that is problem decomposition. Having used this technique almost unconsciously for several years in developing process-oriented models, a young information technologist

Modelling food texture 211

devoted a major part of his PhD thesis to developing a more consistent, understandable and applicable system in that respect (Sloof, 1999; Sloof, 2001). Still, the process of detecting possible and plausible mechanisms in rather unstructured data is apparently quite a difficult task for most people. In our opinion, that, rather than the necessary requirements of mathematical skills or product expertise, is what makes a good modeller. Another general rule for developing process-oriented models is the consistent application at all costs of the laws of a particular discipline. Adapting mathematical equations, whether at the level of differential equations or at the level of functions or analytical solutions of these differential equations, invariably changes the fundamental nature of a model into an empirical one. Changes are only permitted at the level of developing appropriate mechanisms. Changes at a mathematical level inevitably prevent any further development along fundamental lines. Process-oriented modelling does offer not only the advantage of an increased understanding of processes and interactions, it also ensures a reusability of models and, above all, the transferability of parameter values to all kinds of different situations and conditions in which the product is used. And that is the ultimate goal of modelling: providing a prediction of future behaviour of products from any provenance, from any growing conditions and in any circumstances during the food supply chain. Based on these ideas, using the vast knowledge product experts already have on the processes which take place and the applicability of the laws of the various disciplines, modelling product behaviour and food quality is not at all difficult – it merely takes a lot of time and effort. The reason why this technique has not been used consistently in every case is the idea that our food is too complex to be described by these simple principles.

9.2

Factors affecting texture

9.2.1 Texture of fresh produce As already pointed out in the introduction, texture can originate from different sources, physical and chemical as well as structural. Van Dijk and Tijskens (2000) provided an overview of the possible sources of texture for cellular food materials, that is food products from living tissues, at three levels of abstraction: molecular, cellular and organ. Processes that cause changes in texture occur mainly at the molecular level. Available information, however, is predominantly gathered at organ level and at best at cellular level. Due to this discrepancy it remains very difficult to understand what is going on with the texture in our food. It makes it almost impossible to interpret the information that is contained in phenomenological observations. Unravelling the sources of texture into processes at different levels may be helpful. Some examples are given below.

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Texture in food

The possible sources of texture of cellular plant materials (fruits, vegetables), as measured at an organ level, have their roots in the cellular and molecular level (Vincent, 1991; Tijskens et al., 1999c; Van Dijk and Tijskens, 2000). This is shown in Table 9.1. At the lowest level the molecular composition of the cell wall determines the properties of the material. The composition and behaviour of polysaccharides like pectin are especially important at this level. Food properties can be understood from cell wall behaviour as well as tissue tension (or turgor pressure), the overall structure and shape of the cell and the presence of special compounds, like starch. The way cells are organised and arranged in tissues and organs (the histology and morphology) ultimately affects the textural properties of organs. The texture of cellular plant materials like fruits and vegetables is thus built upon molecular behaviour. But the actual stiffness and the strength of organ tissue, as perceived by humans as well as measuring devices, is largely determined by the physical structure of the tissue (Vincent, 1991). Inhomogeneities in the structure, like air channels in apples and vascular bundles in asparagus, play a particularly important role in the ultimate texture (Vincent et al., 1991). In a very similar way the texture of meat can be understood from the chemical composition and physical structure (Purslow, 1991). Here proteins are the most important structural molecules. In living material, processes that change all these items and properties occur simultaneously. Depending on the type of product some items are more important for the texture than others. In some products items may not be present at all; sometimes the processes are not active. Depending on the Table 9.1

The source of texture of cellular plant materials on different structural levels

Level Organ

Different tissues Adhesion Morphology

Tissue

Different cells Air spaces Adhesion and cohesion between cells Orientation/morphology

Cells

Cell wall Size and shape Turgor Special compounds (i.e. starch)

Cell wall

Constituents Thickness Orientation

Molecules

Polysaccharide types Modification (including enzyme action) Molecular size and conformation

Modelling food texture 213

relative occurrence and importance of one of the mentioned items, a very diverse range of textural behaviour can be depicted, e.g.: • When tissue tension (or cell turgor) is of major importance, cells are ruptured easily during the eating process. Juice will be expelled (which is an important (secondary) item in the texture of the products). An example is fresh strawberries. The cell walls in such products are soft and damage due to the fracturing process occurs in a large volume of the material. Often these fruits bruise easily. • When, in addition to tissue tension, the stiffness and brittleness of cell walls are of major importance (often due to the presence of pectin in the cell walls), the product is essentially crispy and juicy. Rupture occurs through the cell walls and the cell contents are expelled. Damage is often limited to a small volume around the bite. An example is fresh apples or celery. • When cell walls are relatively strong and the adhesion between cells is weak, the fracture path goes around the cells and not all the cell walls break (fracture always occurs at the weakest points). No juice (or only a small amount) is expelled and products are essentially mealy and dry as is sometimes the case in overripe apples. • When the morphological or histological structures are relatively important, the sensory texture, perceived during eating, may be affected by the orientation of product tissue. When biting parallel to the radial air spaces, apples were found to fracture at smaller deformations and were more brittle than when the bite was perpendicular to these air spaces (Vincent et al., 2002). This clearly shows the importance for the fruit texture of structural features at a higher level of aggregation. The presence of air channels was also used to explain differences in texture between different types of apples. • Dolores Alvarez and co-workers (2000) studied the differences in texture between different types of fruits and vegetables like carrot, apple, celery and cucumber. They concluded that both cell size and inter-cellular spaces affect texture. The effects of the position of the test-piece in the fruit or vegetable and of orientation could also be explained based on the same type of information. 9.2.2 Effects of processing Many fruits and vegetables are stored or processed before eating, these processes have major effects on the final texture of the food material, and they are studied and modelled by many scientists and technologists. The most important reason is the urge to optimise the texture, and with it the quality, of the food. It was shown above that the origin of texture is very complex. To understand it, both chemical and physical properties on different structural levels, as well as the structure itself, have to be taken into account. This implies that it will be very difficult or even impossible to model textural changes due to

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Texture in food

storage or processing. Fortunately most changes due to processing are limited to a specific entity or compound, which facilitates our understanding and offers the potential to adjust texture. • The heating process applied to vegetables like carrots and beans changes particularly the molecular properties of the pectin in the cell walls. Enzyme activity plays an essential part in this process. Some examples are worked out in this chapter. • Leafy vegetables wither during storage. The main reason is the change in tissue tension upon loss of water and turgor pressure. • The effect of processing on the texture of products like tomato ketchup and apple sauce can be understood better when the structural changes are taken into account. Depending on the applied heating, the concentration, the homogenisation and the applied shear processes there is either a suspension of cells or a dispersion of cell contents and cell wall fragments (den Ouden, 1995; Schijvens et al., 1998), both with their own rheological properties. • The eating process is very difficult to describe. Often the breakdown occurs on different successive levels of aggregation. Textural attributes concerning the first bite are often governed by structures at a high level of aggregation and the existence of inhomogeneities in these structures. The fracture as a result of the deformation during the first bite is very important in perceiving texture. Large flaws and holes, where stresses are concentrated and fracture consequently starts (van Vliet and Luyten, 1995), govern the process of fracturing. A clear example is the difference in texture between apples from different cultivars and the effect of orientation (see above). • For nutritional reasons, the breakdown of cells and the accompanied release of the cell content (juice) is a very important aspect of the eating process. The point at which this happens, whether in the first bite, during further mastication or later during digestion, affects the perceived texture. The attributes as juiciness and mealiness are especially prone to this phenomenon.

9.2.3 Man-made products Although products with a natural origin, like meat, fruits and vegetables, are eaten fresh, the actual structure of many food products is obtained by processing. Examples of such man-made products are bread, cheese, mayonnaise, etc. The texture of this type of product depends on both composition and structure. Shear, pressure and temperature are the processing parameters that have most effect on the structure. Spontaneous compartmentalisation has to be taken into account (for instance phase separation). Models developed to understand and regulate the mechanical properties of non-food materials can often be used successfully for man-made food products. Some examples are given below.

Modelling food texture 215

Different food materials are so-called cellular solids1 (Jeronimidis, 1991). In this type of food commodity, cellular refers to the pocket foam-like structure. The structure of these products consists of a solid phase surrounding air pockets. Examples are bread, dairy dessert foams and different types of snacks. The air pockets can be closed or interconnecting. The presence or absence of interconnections between the air pockets and the density of the material are the most important parameters affecting the mechanical properties of such products (Gibson and Ashby, 1988). The theory developed for cellular solids was successfully applied to unravel the effects of ingredients and processing conditions on the texture of bread crumb (Scanlon and Zghal, 2001). Understanding was gained by separating the effects on the air cells (size, shape, porosity) and the physical properties of the solid phase (stiffness, brittleness, starch retrogradation, gluten behaviour). The textural quality of bread depends on the texture of both the crumb and the crust. Attributes like crispiness and crunchiness are clearly related to crust properties; the softness of bread is more affected by crumb properties. An overview of the different structural levels affecting bread texture is shown in Table 9.2 (Luyten et al., 2003). Changes in crispiness accompanying the ageing of bread are related to water transport from the crumb towards the crust. The transport may be affected by structure on an aggregated structural level, but the ultimate change due to the presence of water is on a molecular scale, increasing the mobility of the bread macro-molecules. Many solid or solid-like food materials can be considered as composite materials, that is, as a continuous phase interspersed with filler particles (Luyten and Van Vliet, 1990). Roughly the properties of such a material can be understood by the behaviour of the matrix material, the amount and properties of the filler particles and the interaction between filler and matrix. In this way it is possible to model the changes in cheese texture with temperature Table 9.2 The source of texture of cellular man-made food products on different structural levels. An example for bread Level Food product Macro level Meso-level Molecules

1

Visible structure (crumb/crust, other ingredients) Type of porous structure Size and shape of air cells/pores Cell wall shape and thickness Composition Localisation of ingredients Small air cells Ingredients, including water Functionality of ingredients, including mobility

Cellular in this case has to be understood differently from the meaning of cellular in fresh and natural materials.

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(melting of the fat-filler), fat content (= filler content) and compositional factors like the water content, pH or amount of salt present (behaviour of the protein matrix) (Luyten, 1988; Luyten et al., 1991). This modelling allows us to separate effects on a molecular level (for instance the effect of pH on protein functionality) and on food structure (for instance amount and size of fat particles). Models were found to be applicable to a large range of food products. Ponne et al. (1996) provided a nice example on the effect of fibre enrichment on the texture of different gel-like foods. In that paper, the effects of type of fibre (via the swelling behaviour and the volume of filler), rigidity of the fibre, rheological behaviour of the gel matrix and molecular interactions between the matrix material and the fibre particles could very neatly be separated. The latter effect is shown in Fig. 9.4. Depending on the existence of interactions with the matrix material, pea fibres could either increase or decrease the modulus of the food model.

Slope of compression curve (N/mm)

9.2.4 Biopolymers The majority of firmness in living material is built up on biopolymers. The degree of polymerisation (DP), i.e. the number of units building up the polymer backbone, is generally considered to be the actual source of firmness and strength. In fruits and vegetables the most frequently occurring biopolymers are cellulose, pectins and starch. In meat this is mainly collagen. Firmness decay is then the results of a decrease in the degree of polymerisation by mechanical, chemical or enzymatic action. Every time the length of the polymers is diminished by ‘cutting’, the strength of that chain is diminished. However, a major difference exists between linear two-dimensional and three-dimensional polymers. For predominantly linear polymers, each cut in the backbone will roughly halve the remaining chain length. This behaviour is pictured in Fig. 9.5. However, when the same chain is cross-linked with covalent or ionic bonds, depending 0.3

Pectin Carrageenan

0.2

Gelatin Marmalade Gelatin-2

0.1

0.0 0

Fig. 9.4

1

2 Fibre (w/w %)

3

The effect of added pea fibre on the slope of the compression curve of different food gels (from Ponne et al., 1996).

Modelling food texture 217 0 cut 100 % 1 cut 50% 2 cuts 33% 3 cuts 25%

Fig. 9.5

Effect of increasing number of cuts in a linear polymer.

on where the cuts happen to be made, it takes some cuts before the chain length (or degree of polymerisation) starts to decrease. This is visualised in Fig. 9.6. The consequence for the changes in associated strength is shown in Fig. 9.7. Based on the theory of Markov processes, the cutting of a linear polymer was modelled by Saedt et al. (1991). The model was validated on data obtained from pectin degradation by a bacterial endo-transeliminase. Not only could an equation for the average DP be developed (see Eq. 9.1), but also the distributions of the various fractions of remaining chain lengths could be visualised.

DP =

1   1 –  1 – 1  ⋅ e – α⋅t  DP0 

[9.1]

Although Eq. 9.1 mathematically equals exactly the well-known logistic function, often used to describe sigmoidal behaviour, its behaviour in time

0 cut

100 %

3 cuts 100 %

2 cuts 50 %

Fig. 9.6

Retardation in decreasing chain length for cross-linked polymer chains.

218

Texture in food 100

Strength (%)

75

50

25

0 0

Fig. 9.7

10 Number of cuts

20

Decrease in chain length and strength per cut for linear and cross-linked polymer chains.

(t) does reflect the more normally encountered exponential decay. The reason is that the initial condition (DP0) is much larger than the asymptotic value obtained at infinite time. Of course when cutting down polymer chains, the final length will inevitably be unity. For a better understanding of firmness originating from biopolymers, it would certainly be worthwhile taking the distribution of chain length of the cut remains into account.

9.3

Effects of enzymes on texture

Enzymes play a prominent role in our food. They are essential in the development and decay of fresh fruits and vegetables and hence crucial in all quality attributes, including texture. However, in man-made products too the action of enzymes, or its prevention, is of prime importance. Conservation of fresh food to prolong product lifespan, by sterilisation, pasteurisation and freezing, relies to a large extent on the complete inactivation of those enzymes which enhance quality deterioration (polygalacturonases (PG), lipases, lipoxygenases, polyphenoloxidases, etc.). The most often used indicator for enzyme inactivation is peroxidase, since it is one of the most heat-resistant enzymes. At the same time, efforts are made to stimulate the action of quality-improving enzymes such as e.g. pectin methyl esterase. In the storage of fresh produce at low temperatures and low respiration conditions e.g. in controlled atmosphere and modified atmosphere (CA, MA) enzyme-catalysed quality deterioration is the main source of quality decay, apart from that induced by microorganisms. To model the very complex pathways involved in texture changes during growth, ripening, storage and processing, an understanding of the actions and peculiarities of the enzymes involved in these conversions is an absolute necessity. As far as the denaturation of enzymes during heat treatments like

Modelling food texture 219

blanching, cooking, pasteurisation and sterilisation is concerned, the simplest mechanism which is still plausible is: ks S + E→ P+E kd E → E na

[9.2]

The first reaction represents the conversion of a substrate (S) into a product (P) by the action of some enzyme (E). The second reaction represents the heat-induced inactivation of the active enzyme (E) into an inactive form. Based on the laws of chemical kinetics, this set of reactions can be converted into a set of differential equations:   dt   dS ( t ) = – ks ⋅ S ( t ) ⋅ E ( t )  dt   dP ( t ) = ks ⋅ S ( t ) ⋅ E ( t )   dt

dE ( t )

= – kd ⋅ E ( t )

[9.3]

Upon integration at constant external conditions (mainly temperature), the solution for substrate and enzyme is: S( t ) = S 0 ⋅ e

  e – k d ⋅t – 1    ks ⋅ E 0 ⋅     kd    

E( t ) = E 0 ⋅ e – k d ⋅t

[9.4]

As in all chemical and biochemical reactions, the rate constants (kd and ks) depend on temperature according to Arrhenius: k i = k i, ref ⋅ e

E ⋅ 1 – 1    R  T ref T 

[9.5]

This principle has been applied frequently in modelling enzyme behaviour during all kinds of technological treatments (Tijskens and Rodis, 1997; Tijskens et al., 1997a,b, 1998a,b, 1999a,b). Based on this mechanism, the activity of enzymes, as determined in practice, is represented by ks · E(t). In Fig. 9.8 a three-dimensional example is shown for the activity of an enzyme during inactivation at various different but constant temperatures. At relatively low temperatures the activity increases with temperature as expected. At a certain temperature the enzyme starts to denature, thereby effectively decreasing the molar amount of active enzyme present (E(t)). Even at these denaturing temperatures, the reaction rate constant of the remaining active enzyme (ks) continues to increase with temperature. It can be clearly seen that the temperature at which the maximum activity is exerted by the enzyme depends on the time of denaturation. This type of behaviour of enzymes acting during

220

Texture in food

Activity

0.04

0.02

0

20 40 Time

60 80 0 10 40

Fig. 9.8

50

60

70 Temperature

80

90

Example of enzyme activity during inactivation at different but constant temperatures. Based on Eq. 9.4.

a denaturing process is quite generic in nature. It has been observed, and the model has been applied in data analysis with remarkable success for a large range of food-related enzymes and fruit and vegetable types. It is expected to work equally well for enzymes in other types of foods like meat and manmade products. Polakovic∨ and Bryjak (2002) describe in detail a more elaborate model for enzyme denaturation (the Lumry–Eyring model), comprising an inactive but reversible intermediate. With this approach, the sometimesobserved regeneration of enzymes could be explained by the reversible conversion from intermediate inactive enzyme (Einter) back into the active enzyme (E). k

kd E← f→ E inter → E na

[9.6]

kb

The simple model on enzyme denaturation (Eq. 9.2) can be extended with a concurrent formation of enzyme. This is frequently observed as the so-called enzyme turnover (Eq. 9.7). Depending on the initial amount of enzyme present, the pattern of firmness decrease can now turn from a simple exponential type of decay (as for the simple model) to a sigmoidal type of decay (Tijskens, 2003)(see Figs 9.9 and 9.10). k

p S + E→ P+E kf →E kd E→ decay

[9.7]

Modelling food texture 221 10

8

Increasing rate of enzyme formation

S

6

4

2

0 20

Fig. 9.9

40

60 Time

80

100

Behaviour of substrate under the action of an enzyme exhibiting ‘simple’ turnover. Initial amount of enzyme = 0.

10

8

S

6 Increasing rate of enzyme formation

4

2

0 20

Fig. 9.10

40

60 Time

80

100

Same simulation as Figure 9.9, but now with a moderate initial level of enzyme.

The analytical solution at constant external conditions (mainly temperature) of the system of concurrent formation and decay of an active enzyme is:  (e – k d ⋅t – 1) ⋅ E 0 – kf ⋅ t k ⋅ (e – k d ⋅ t – 1)  S = S 0 ⋅ exp  – f  ⋅kp kd kd2  

[9.8]

222

Texture in food

9.4

Applying models to predict texture

Based on the premises depicted in previous sections on biopolymers and enzymatic effects, a couple of models and applications were built. Two of these models will be highlighted in the next sections. The first one is on firmness of apples during all kinds of storage conditions (CA, MA) and the effect of enzymes accumulating during that storage (Tijskens et al., 1999c). The second one is the result of a EU project (AIR1-CT92-0278) on the effects that different combinations of time and temperature of blanching have on the firmness of fruits and vegetables after cooking (Van Dijk and Tijskens, 2000).

9.4.1 QUAST: Quality of apples during storage and transport During the storage and transport of apples, all kinds of processes remain active in the fruit tissue to a varying degree of intensity. The different treatments and conditions apples are given to prolong their natural life (maturity at harvest, low temperature, CA conditions, climacteric stage, ethylene scrubbing) do slow down the majority of these processes, but not all to the same degree. Processes occurring in the fruit, which are important in quality and firmness decay, comprise: • • • •

pectin degradation with effect of oxygen pectin degradation without effect of oxygen water loss cellulose degradation

Since, with modern technology, water loss is not the major source of firmness decay, and can be kept to a very low level, it will not be considered in this example. Cellulose decay can be completely neglected due to its very slow rate. So, in this example the main emphasis is on firmness loss caused by the decay of pectin. The conditions in which the apples are stored and those at harvest may affect the quality decay and firmness deterioration. Lowering the temperature slows down ripening and softening. Controlled atmosphere storage in low oxygen and elevated carbon dioxide conditions will also slow down these processes. One of the consequences of CA will, however, (probably) be an accumulation of firmness decaying enzymes (e.g. PG). Ethylene (during storage) and climacteric stage (at harvest) will enhance these processes. So in short the model can be represented (not necessarily the absolute truth) by the following basic processes: k

p Pect → decay

k

pO  Pect + O 2 → decay

[9.9]

Modelling food texture 223

The first one always occurs, regardless of the gas conditions applied. The second process needs oxygen to proceed, and will consequently be slowed down by CA conditions. The effect of oxygen and carbon dioxide on respiration and decay in CA conditions can be described by the respiration models of Peppelenbos (Peppelenbos et al., 1993, 1996) and Hertog (Hertog et al., 1997). Based on these models a relative respiration was proposed (Tijskens, 1995) that is virtually independent of temperature. The relative respiration is the ratio between the respiration at actual temperatures, and the respiration at the same temperature in normal gas conditions:

RelResp =

1 + K mo ⋅ 21 O2 ⋅ 21 1 + K mo ⋅ O 2 + K moc ⋅ O 2 ⋅ CO 2

[9.10]

As for all chemical and biochemical reactions, all reaction rates (kp and kpO) depend on temperature according to Arrhenius’ law (see Eq. 9.5). The equilibrium constants (Kmo and Kmoc) in Eq. 9.10 were found to be virtually independent of temperature. In Fig. 9.11 an example is shown for the relative respiration of Elstar apples as a function of oxygen and carbon dioxide levels in the atmosphere. The model on relative respiration (Eq. 9.10) is used to modulate all rate constants of oxygen-consuming reactions. Although not very well documented in the literature, experts in CA storage know that apples which have been stored in CA conditions generally soften

1

Relative respiration

0.8 0.6 0.4 0.2 0 0

20

1

15

2 CO2(%)

10 O2(%)

3

5

4 5

0

Fig. 9.11 Relative respiration in Elstar apples as simulated by the model (Eq. 9.10).

224

Texture in food

faster during subsequent shelf life. The longer the apples have been stored, and the more respiration-suppressing the conditions are, the faster the poststorage softening. This is modelled as the accumulation of an enzyme (maybe PG, more probably related to ACC production and accumulation) that enhances pectin decay in the ex-store life.

∂ PG = kPG ⋅ PG ∂t The oxygen-induced decay of apple pectin then becomes: ∂Pect pO ∂t

[9.11]

= – k pO ⋅ Pect pO ⋅ PG ⋅ RelResp

+ k pO ⋅ Pect pO ⋅ (1 – RelResp)

[9.12]

This formulation, however, is too empirical and is probably an incorrect representation of the processes involved. Studies to correct this situation are being conducted. It is more probable that the amount of active PG (or any other pectin-decaying enzyme system) is the result of an interactive production and degradation system where the degradation is affected by oxygen, while the production is not or much less (see Eq. 9.13). k

f → PG k dO  O2 PG + O 2 →

[9.13]

kd

PG → decay Against a constant production (reaction 1 in Eq. 9.13) a decay increasing with increasing oxygen levels (reaction 2) effectively lowers the amount of active enzyme in high oxygen systems, while increasing its activity at low oxygen levels. The effect of stage of maturity is included in the model by means of a logistic approximation of ethylene production.

∂ Eth = kEth ⋅ Eth ⋅ ∂t

 Eth  1 –   Eth max 

[9.14]

In this over-simplified equation for ethylene production, the time has to be seen as relative to a fixed state of the product at harvest, let’s say the climacteric stage (inflection point of the logistic function). The exerted effect of ethylene is expressed by the integral of this equation over the variable temperature, CA and ethylene conditions. The climacteric stage of the product, both pre- and post-harvest, depends on the history of ethylene production and action. In its turn, the climacteric stage, expressed as a fraction of the maximal climacteric stage, affects a number of processes like pectin decay.

Modelling food texture 225

Again assuming that the firmness of apples can be approximated by the decay of pectins under variable storage conditions, the firmness of Elstar apples can now be simulated. The model describes the behaviour of firmness based on the processes possibly occurring in any part of the lifespan of an apple, from growing through harvest to storage and transport. The model can now be applied to predict the firmness in any known or unknown scenario of temperature and applied gas conditions, from any starting point with regard to maturity at harvest or growing condition. Example 1: variable temperatures In Figs 9.12 to 9.15 a scenario (shown in Fig. 9.15) was applied to apples with a harvest maturity of 18 days before climacterium, with in total 1.5 days at 20 °C in air to simulate harvest, transport and auction before commercial 8

Firmness (kg)

7.5 Temp (°C) –1

7 6.5

–2 –3

6

–4 –5

5.5

–6 5

0

50

100

150

Time (days)

Fig. 9.12

Firmness during storage and ex-store life at different storage temperatures. 2.8

Firmness (kg)

2.6 Temp (°C) –1 2.4

–2 –3 –4

2.2

–5 –6 2 0

50

100

150

Time (days)

Fig. 9.13

Firmness generated by oxygen-sensitive pectin decay.

226

Texture in food 3

Firmness (kg)

2.5 2

Temp (°C) –1

1.5

–2 –3

1 –4 –5

0.5

–6 0 0

50

Fig. 9.14

100 Time (days)

150

Firmness as generated by oxygen-insensitive pectin decay.

Temperature (°C)

20

15

10

5

0 0

50

100

150

Time (days)

Fig. 9.15 Temperature scenario during storage and ex-store life.

storage, followed by commercial storage at 1–6 °C in an atmosphere of 1% O2 and 3% CO2 at a relative humidity of 90%. The effect on firmness decay of different storage conditions can clearly be seen (see Figs 9.12 and 9.14). The figures also show that firmness decreases only by the action of oxygen insensitive pectin decay. Moreover the (small) difference in the rate of firmness decrease during the ex-store period of 15 days at 20 °C (which is a simulation of the shelf-life at the consumers) should also be noted. Example 2: Variable harvest maturity In the same chain set-up as in Example 1, apples with a harvest maturity from 25 to 0 days before reaching the climacteric stages were stored at 1 °C in 1% O2 and 3% CO2. The simulations are illustrated in Figs 9.16 and 9.17.

Modelling food texture 227 8

Firmness (kg)

7

Harvest maturity (days) – 25

6

– 20 – 15

5

– 10 4

–5 –0

3

0

50

Fig. 9.16

100 Time (days)

150

Firmness as affected by maturity at harvest.

3

Firmness (kg)

2.5 Harvest maturity (days) – 25

2 1.5

– 20 – 15

1

– 10

0.5 0

–5 –0 0

Fig. 9.17

50

100 Time (days)

150

Firmness generated by oxygen-sensitive pectin decay.

The effects of harvest maturity are dramatically visible in Fig. 9.18 showing the development of the climacteric stage at the scenario shown in Fig. 9.19. The riper the apples are at harvest, the sooner climacterium is reached, even in CA conditions. The effects on firmness during storage are not that dramatic, but the riper the apples were harvested the faster the decrease in firmness during the shelf life of the product. 9.4.2 BRAM: Blanching Response Amplification Model The complexity of firmness changes as a consequence of technological actions is clear from the increase in firmness that is often encountered when cooking or sterilising fruits and vegetables that are rich in pectins (Stolle-Smits, 1996). This increase is observed both in objective measurements (StolleSmits, 1996 p. 96) and in sensorial evaluation (Stolle-Smits, 1996 p. 113). Whether or not the increase in firmness is an agreeable one remains to be seen. The phenomenon cannot only be observed in firmness, but is also reflected in the amount of pectin building blocks (galacturonic acid) that are

228

Texture in food 1

Harvest maturity (days)

Climacteric stage (–)

– 25 0.8

– 20 – 15

0.6

– 10 0.4

–5 –0

0.2 0

0

50

100

150

Time (days)

Fig. 9.18

Development of climacteric stage for different stages of maturity at harvest.

Temperature (°C)

20

15

10

5

0 0

Fig. 9.19

50

100 Time (days)

150

Temperature scenario for apples with different stages of maturity at harvest.

found in the treatment water (Stolle-Smits, 1996 p. 99). This already provides the modeller with a clue to what could be happening. The whole process is clearly related to the degradation of pectin chains, most probably under the influence of an enzyme. The effect of the cooking or sterilisation process, however, has to be a chemical non-enzymatic reaction, since at these high temperatures enzyme activity very rapidly ceases to be important. The most prominent of the enzymes that change the structure of the pectin biopolymer without actually decreasing its DP is pectin methyl esterase (PE). PE decreases the degree of methylation of the C6 carboxyl group of the pectin backbone. As a consequence of this action, Ca++ bridges could be formed between adjacent chains (the so-called egg-box model) effectively resulting in a higher degree of polymerisation. At the same time the enzyme PE will denature during prolonged exposure at high temperatures (see previous section). During the cooking process, β-degradation takes place, preferably

Modelling food texture 229

at places containing adjacent methylated carboxylic groups. So, in short, the action of PE enhanced during blanching effectively prevents depolymerisation at cooking time. This whole complex (but still massively simplified) process can schematically be represented as: ks DE + PE → PE kd PE → PE na

[9.15]

kβ

DP + DE → WSP + DE The first reaction in Eq. 9.15 represent the demethylation of pectins, the second the denaturation of the enzyme PE, and the third the β-degradation of the pectin chain. This last reaction contains the degree of esterification as a catalyst for the β-degradation, thereby effectively blocking the process when all ester groups have been removed. Based on this assumed simplified mechanism, and using the fundamental rules of chemical kinetics, the set of differential equations can be deduced: ∂ PE = – kd ⋅ PE ∂t p ∂ DE = – ks ⋅ DE ⋅ PE ∂t p ∂ DP = – k β ⋅ DP ⋅ DE ∂ tc

[9.16]

At constant conditions of treatment time and temperature, the set of differential equations can be solved analytically. The resulting equation, however, is very cumbersome to use in normal statistical analysis. It contains several occurrences of the mathematical exponential integral that can be handled by mathematical packages like MapleV (www.maplesoft.com) and Mathematica (www. wolfram.com), but not by standard statistical packages. It was therefore assumed that at blanching conditions the β-degradation can still be neglected, and that at cooking conditions the enzyme activity has been stopped by complete denaturation. This assumption has the additional benefit that we can split up the processes into separate activities, each with its own time and temperature. The input situation for the cooking/sterilisation process then becomes the output of the blanching treatment. This assumption results at constant external conditions in the following analytical solution for the degree of esterification:  k ⋅ PE 0 ⋅ (e – k d ⋅t b – 1)  DE ( t b ) = DE 0 ⋅ exp  s  [9.17] kd   The solution for the remaining enzyme activity is shown in Eq. 9.4. Of

230

Texture in food

course all reaction rate constants depend on blanching temperature according to Arrhenius’ law (Eq. 9.5). The third differential equation in Eq. 9.16 can also be solved analytically for constant external conditions, now during the cooking process: DP ( t c ) = DP0 ⋅ exp [– k β ⋅ DE ( t b )]

[9.18]

During this cooking/sterilisation process, the initial situation with respect to DE (DE0) is provided by the previous equation (Eq. 9.17). Combining Eq. 9.17 and Eq. 9.18 integrates the effect of both the pre-heating and cooking time and results in:   k ⋅ PE 0 ⋅ (e – k d ⋅ t b – 1)   DP ( t c ) = DP0 ⋅ exp  – k β ⋅ t c ⋅ DE 0 ⋅ exp  s     kd  [9.19]

Assuming that the decrease in DP only affects the variable part of firmness (in this case firmness originating from pectins), and that the remaining firmness is unchanged by the process under study, DP0 is converted into Firmvar, and a term Firmfix has to be added (see Eq. 9.20) for the analysis of experimental results. Firm (tc)

  k ⋅ PE 0 ⋅ (e – k d ⋅ t b – 1)   = Firm fix + Firm var ⋅ exp  – k β ⋅ t c ⋅ DE 0 ⋅ exp  s  kd     [9.20] In Fig. 9.20 the three-dimensional behaviour of firmness is shown. In Table 9.3 the results from the statistical analysis of experimental data on carrots during different blanching time–temperature combinations, followed by a 30 min cooking at 100 °C using multiple non-linear regression analysis based on Eq. 9.20 (for time dependence) and Eq. 9.5 (for temperature dependence) is shown. Not all the parameters could be estimated on the experimental data. Therefore, a number of (less important) parameters were fixed at some plausible value, some obtained from visual inspection of the data (Firmfix), some from literature (kβ), and some from similar products 2 ) is (PE0, DE0). The exceptionally high explained part of the model ( Radj both amazing and encouraging. Taking into account the high measuring error when measuring objective firmness, a percentage variance accounted for of well over 95% is indeed high. All standard errors (s.e.) are relatively very low (well below 6%). All this statistical information leads to the conclusion that the model and the assumptions upon which it is based are valid. Since the development of the model is entirely based on processes occurring in the product, this model could well be extended to other (similar) products, provided reliable values for the parameters are available. Meanwhile the model has been applied to products like green beans and potatoes. Although the number

Modelling food texture 231

6

Firmness (kg)

5

4

3

2 120 30

40

50 Tempera 60 ture

60 70 (°C)

40 80

90 0

20

80

100

Time (min)

Fig. 9.20 Three-dimensional representation of the measure (symbols) and simulated (solid lines) firmness of carrots as function of pre-heating time and temperature followed by a cooking process.

of data points available and the number of blanching time–temperature combinations was considerably smaller, similar results were obtained. Again this indicates the generic applicability of the model and of the line of reasoning in developing it. More research in this respect is needed. 9.4.3 Optimisation and reversed engineering Modern society requires a different approach to consumers’ wishes. Nowadays, it is no longer technology push, i.e. putting new products on the market based entirely upon technological possibilities, that determines what consumers buy and eat; the technology has to adapt far more to the demands of the market in order to produce new and acceptable products (market pull). So, for food engineers, this changes the entire framework of their actions. The question is no longer: What can we produce? It is turned around as in: How can we produce a product with certain properties? On a fundamental level, the question is no longer: What happens when process circumstances are changed? It is now: How do we have to change the circumstances to obtain a certain property? And that question relies much more on fundamental knowledge of all the processes involved than was the case before. This process of reversion is called reversed engineering. Based on the model BRAM, it was shown that reversed engineering is relatively easy to achieve, if (and only if) we can rely on basic and fundamentally sound models.

232

Texture in food

Table 9.3 Results of the statistical analysis firmness modulation during pre-heating and cooking (BRAM model) Parameter

kd,ref Ead ks Eas Firmvar (≡ DP0)

Estimated Estimate 0.0487 162.12 0.242 113.90 4.73

s.e. 0.00268 3.86 0.00571 1.81 0.159

kβ PEsol DE0 Firmfix

Fixed 0.023 1 60 1.5

n.a. n.a. n.a. n.a

R 2adj Tref tc Nobs

Administrative information 95.4 60 30 294

n.a. = not applicable s.e. = Standard error of estimate

By combining the obtained firmness (left-hand side of Eq. 9.20) with the desired firmness values (Firmlim), and solving for the blanching time, all the solutions in the form of processing conditions that can deliver the desired product firmness are obtained (Eq. 9.21)

tb = –

  k PE + ln s 0 ln    

  Firm lim – Firm fix  ln  Firm var –  k β DE 0 t c  ks PE 0 kd

    kd       

[9.21]

In Fig. 9.21 the results are shown for the processing circumstances required to achieve two levels of targeted firmness after a blanching/cooking sequence, based on the parameter values provided in Table 9.3. It can be clearly seen that achieving a higher targeted firmness requires longer blanching at lower temperatures. The higher temperatures rapidly become unable to provide the desired firmness. So, fundamental modelling not only provides easy and clear descriptions of intricate problems. It also provides the means to accommodate reversed engineering, in developing products with the properties asked for by the consumer.

Modelling food texture 233

Target 4.5 kg 140

Blanching time (min)

120 100 Target 4 kg 80 60 40 20 20

0

50

Fig. 9.21

9.5

55 60 65 Blanching temperature (°C)

70

0

10 Cooking time (min)

Behaviour of the blanching time (tb) to achieve a desired firmness at two different levels.

Future trends

9.5.1 Modelling as a directive of research As already stated in the introduction, the ultimate goal of modelling is to provide reliable predictions of occurrences that have not yet taken place, for any product, from any source and in any situation. If anything is made clear in the previous sections, it is that modelling texture and texture behaviour is not yet capable of fulfilling this target, especially not the last part. Progress has been made, but it is far from being sufficient to predict reliably future behaviour. When only one issue predominantly determines texture, some success has been achieved, as can be seen from the examples provided. When more then one interacting issue determines texture and texture behaviour, that behaviour becomes quite rapidly incomprehensible, and beyond the reach of understanding and modelling. However, based on current viewpoints and ideas, it is possible to model parts of that behaviour, in such a way that modelling can provide information on what could possibly be going on, and where the lack in knowledge actually is. The examples on enzyme activity, important in all changes in our food, whether fresh or processed, make this point evident. Far too little reliable information and knowledge is available on the behaviour of texture-affecting enzymes. But the examples also indicate strongly that not only the type of action that enzymes exert on the texture-generating biopolymers, but also

234

Texture in food

their own rate and mechanism of formation and decay is of major importance. Strong indications are emerging that this is at least one of the reasons for the difference in behaviour of similar food products which have come from completely different growing and/or production sites and systems. The intricate problem of texture origins and textural changes has to be decomposed into generic processes, that can be studied in their own right, and modelled in a fundamentally correct fashion in order to guarantee transferability of model parameter values from one situation and study to another one (Tijskens et al., 1998a). In our opinion that is the only way in which texture in all its forms and shapes can be understood and modelled consistently.

9.5.2 Consistent theory Consistency is the crucial issue in developing theoretical views and fundamental models, especially when interactions play their confusing role as they do in texture behaviour. The more interactions there are, and the more compounds or processes which could be the major source of change, the more a consistent theory is needed to understand all these interactions. But at the same time, the more difficult it is to develop a consistent viewpoint. But that is exactly what is needed to bring the understanding, the modelling and the prediction of texture behaviour for all kinds of food products from all kinds of growing and processing conditions to an acceptable and generic level. Advances in isolated targets have been achieved, and quite some progress has been made over the last decades, but still a consistent theory on texture, its sources, its conversion from chemical compounds to physical measurements, the interactions within the different sources of texture, the role and effect of enzymes in changes in texture is not available. And there lies in our opinion the major challenge for texture research for the coming decades.

9.6

Notation

Variable DE decay DP E Ea Eth k K O2 P PG

degree of esterification any decay products degree of polymerisation molar concentration enzyme activation energy ethylene rate constant (pseudo) equilibrium constant oxygen concentration product molar concentration polygalacturonase (Cont’d.)

Modelling food texture 235 R RelResp S t T WSP α

universal gas constant (8.314 J/mol/K) relative respiration substrate time temperature water soluble pectin degradation products rate constant of depolymerisation

Subscript 0 b c d dO Eth f Firm fix i inter max mo moc na p PG pO ref s var β

initial backward/of blanching of cooking denaturation oxygen induced denaturation of ethylene forward/formation firmness invariable part any intermediary maximal obtainable Michaelis Menten for oxygen inhibition by carbon dioxide not active product or pectin (eg 9) of polygalacturonase of pectin, consuming oxygen at reference temperature substrate variable part For β-elimination

9.7

References

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and

ASHBY M F

HERTOG M L A T M, PEPPELENBOS H W, TIJSKENS L M M

Modelling food texture 237 (2001) Problem decomposition. In Food Process Modelling. Eds L M M Tijskens, M L A T M Hertog and B M Nicolaï, Cambridge, Woodhead Publishing, 19–34. SLOOF M, TIJSKENS L M M and WILKINSON E C (1996) Concepts for modelling quality of perishable products, Trends in Food Science & Technology, 7, 165–71. STOLLE-SMITS T (1996) Effects of thermal processing on cell walls of green beans: a chemical and structural study (PhD thesis, Katholieke Universiteit Nijmegen, NL). SZCZESNIAK A S (2002) Texture is a sensory property, Food Quality and Preference 13, 215– 25. THORNLEY J H M (1976) Mathematical Models in Plant Physiology: a Quantitative Approach to Problems in Plant and Crop Physiology, London, Academic Press. THORNLEY J H M and JOHNSON I R (1990) Plant and Crop Modelling: a Mathematical Approach to Plant and Crop Physiology, Oxford, Clarendon. TIJSKENS L M M (1995) A model on the respiration of vegetable produce during postharvest treatments, Proceedings International Conference on AGRI-FOOD Quality, June 25– 28, Norwich, 322–7. rd TIJSKENS L M M (2003) Modelling quality, Proceedings 3 International Conference an Integrated View on Fruit and Vegetable Quality: Quality in Chains, 6–9 July 2003, Wageningen, NL, Acta Horticulturae, 604, 123–36. TIJSKENS L M M and RODIS P S (1997) Kinetics of enzyme activity in peaches during storage and processing, Food Technol & Biotechnol, 35, 45–50. TIJSKENS L M M, SLOOF M and WILKINSON E C (1994) Quality of perishable produce. A philosophical approach, Proceedings of the Sixth International Symposium of the European Concerted Action Program COST94 The Post-Harvest Treatment of Fruit and Vegetables – Current Status and Future Prospects, 19–22 October, Oosterbeek, NL, 493–502. TIJSKENS L M M, RODIS P S, HERTOG M L A T M, WALDRON K W, INGHAM L, PROXENIA N and VAN DIJK C (1997a) Activity of peroxidase during blanching of peaches, carrots and potatoes, J Food Eng, 34, 355–70. TIJSKENS L M M, WALDRON K W, NG A, INGHAM L and VAN DIJK C (1997b) The kinetics of pectin methyl esterase in potatoes and carrots during blanching, J Food Eng, 34, 371–85. TIJSKENS L M M, HERTOG M L A T M, DIJK C VAN (1998a) Generic modelling and practical applications. In EUR 18183 – COST Action 915, Consumer Oriented Quality Improvement of Fruit and Vegetable Products, Food Quality Modelling. Eds B M Nicolai and D de Baerdemaeker, Luxembourg, Office for Official Publications of the European Communities, 145–51. TIJSKENS L M M, RODIS P S, HERTOG M L A T M, KALANTZI U and VAN DIJK C (1998b) Kinetics of polygalacturonase activity and firmness of peaches during storage, J Food Eng, 35, 111–26. TIJSKENS L M M, HERTOG M L A T M and VAN DIJK C (1999a) GESSI: a Generic Enzyme System for Stimulation and Inactivation during storage and processing. Hägg M, Ahvenainen R, Evers A M and Tiilikkata K (Eds), Proceedings Second International Conference AGRI-FOOD QUALITY II, 22–25 April 1998, Turku, Finland, 81–86. TIJSKENS L M M, RODIS P S, HERTOG M L A T M, PROXENIA N and VAN DIJK C (1999b) Activity of pectin methyl esterase during blanching of peaches, J Food Eng, 39, 167–77. TIJSKENS L M M, HERTOG M L A T M, VAN SCHAIK A C R and DE JAGER A (1999c) Modelling the firmness of Elstar apples during storage and transport, International Symposium on Effect of Preharvest and Postharvest factors on Storage of Fruits and Vegetables, August 1997, Warsaw, Poland, Acta Horticulturae, 485, 363–72. VAN BOEKEL M A J S and TIJSKENS L M M (2001) Kinetic modelling. In Food Process Modelling. Eds L M M Tijskens, M L A T M Hertog and B M Nicolaï, Cambridge, Woodhead Publishing, 35–59. VAN DIJK C and TIJSKENS L M M (2000) Mathematical modeling of enzymatic reactions as related to the texture of fruits and vegetables after storage and mild preheat treatments. In Design of Minimal Processing Technologies for Fruit and Vegetables. Eds S M SLOOF M

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Alzamora, S M Tapia and A López-Malo, Gaithersburg, M D, Aspen Publishers Inc., 127–52. VAN VLIET T, LUYTEN H (1995) Fracture mechanics of solid foods. In New Physico-chemical Techniques for the Characterization of Complex Food Systems. Ed. E Dickinson, Cambridge, Chapman & Hall, 157–76. VINCENT J F V (1991) Texture of plants and fruits. In: JFV Vincent & PJ Lillford (Eds.): Feeding and the texture of food, Soc Exp Biol, 44, 19–33. VINCENT J F V, JERONIMIDIS G, KHAN A A and LUYTEN H (1991) The wedge fracture test a new method for measuring food texture, J Text Stud, 22, 45–57. VINCENT J F V, SAUNDERS D E J and BEYTS P (2002) The use of critical stress intensity factor to quantify “hardness” and “crunchiness” objectively, J Text Stud, 33, 149–59. WHITTAKER J R (1994) Principles of Enzymology for Food Sciences (Second Edition), New York, M. Dekker. WIJK R DE, RASING F and WILKINSON C (2003) Texture of semi-solids 2: Sensory flavortexture interactions for custard desserts, J Text Stud (in press). WILKINSON E C and TIJSKENS L M M (2001) Modelling food quality. In Food Process Modelling. Eds L M M Tijskens, M L A T M Hertog and B M Nicolaï, Cambridge, Woodhead Publishing, 367–82.

Part III Understanding and improving the texture of particular foods

10 Plant structure and fruit and vegetable texture K. W. Waldron, Institute of Food Research, UK

10.1

Introduction

Texture is probably one of the most important quality characteristics of edible fruits and vegetables. It is known that plant structure plays a key role in determining texture, and numerous researchers have sought to elucidate the relationship(s) between these aspects in the hope of identifying means of control. However, such relationships have been difficult to expose. As a result, methods and processes to control texture have often been developed empirically. This chapter starts by considering the meaning of texture, with special reference to sensory texture and analytical texture. This is followed by a brief description of plant structure and how it contributes to texture in relation to different length scales. In particular, the importance of cell adhesion is emphasised in relation to crispness and juiciness. The influence of postharvest treatments and processing are considered together with how our increased understanding of cell-wall microstructure and polymer chemistry is enabling us to identify new potential routes to control texture. Finally, the chapter ends by assessing where there may be key deficiencies in our understanding of texture and plant structure, and offers suggested areas of research. 10.1.1 Texture For decades, researchers have sought to define food texture, usually in relation to the food properties and the sensations created during eating. Texture is an important quality attribute of fruits and vegetables and is based on sensory perceptions. The term was originally used to describe the visual and tactile

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characteristics of fabrics and applied later to other materials such as foods (Guinard and Mazzucchelli, 1996). Texture perception includes the appraisal of food in the mouth and involves the skin, muscles and connective tissues in and around the face. Bourne (1982) defined the textural properties of a food as the group of physical characteristics that: • arise from the structural elements of the food; • are sensed by the feeling of touch; • are related to the deformation, disintegration and flow of food under force; • are measured objectively by functions of mass, time and distance. More recently, Szczesniak (1990) has defined food texture as “the sensory manifestation of the structure of food and the manner in which this structure reacts to the applied forces, the specific senses involved being vision, kinaesthesia and hearing” (kinaesthesia comprises the sensation of presence, movement and position as resulting from nerve ending stimulation). The importance of the sensory basis of texture has stimulated research into the consumer perception of texture in foods. Indeed, Guinard and Mazzucchelli (1996) state that “analytical sensory evaluation and consumer testing provided the most meaningful and reliable information about the textural qualities and acceptability of a food or beverage”. Interestingly, visual and tactile information from handling and cutting are also important, enabling the appraisal of food texture to begin prior to ingestion (Bech et al., 2001).

10.2

Measurement of texture

10.2.1 Measurement of sensory texture Sensory assessment involves the codification of sensory experiences, and is commonly carried out by trained panels. These can provide an objective assessment of sensory attributes or characteristics (Civille and Szczesniak, 1973). Szczesniak (1963) developed a well-organised approach for the classification of textural characteristics according to three groups: 1. mechanical (hardness, cohesiveness, viscosity, elasticity and adhesiveness; brittleness, chewiness and gumminess); 2. geometrical (those related to size and shape, and those related to shape and orientation); 3. compositional characteristics (moisture and fat contents). For vegetables, Szczesniak also emphasised that the mechanical characteristics within the different components of a multi-phase food should be considered.

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10.2.2 Measurement of mechanical texture The development of instrumental methods has arisen mainly from the desire to monitor and evaluate the texture of foods during food production and processing. It has also been driven by the need to relate texture to underlying food composition. Thus, a considerable amount of research has sought to elucidate the mechanical properties of plant-based foods in relation to composition and structure. The Scott-Blair approach identifies three types of texture measurement: empirical, imitative and fundamental (Szczesniak, 1963; Brennan and Jowitt, 1977). Empirical tests have been developed from practical experience and are often considered to be arbitrary and poorly defined (Bourne, 1994). Imitative tests have been considered to be a sub-set of empirical tests that seek to mimic the consumer (Peleg, 1983). In contrast, fundamental tests are usually defined in engineering units and include measurement of precise mechanical properties with universal testing apparatus (Smith et al., 2002).

10.2.3 Acoustic emission and crispness Crispness is a particularly important quality characteristic in many fruits and vegetables. There is a considerable body of research on crispness of foods, but most of it involves dry materials such as breakfast cereals and snack foods (Fillion and Kilcast, 2002) where it is significantly affected by the degree of hydration. This research suggests that crispness is poorly defined (Roudaut et al., 2002). The role of auditory sensations in crispness has been reviewed by Vickers (1985, 1988). Vickers and Bourne (1976) suggested that the number of emitted sounds per unit biting distance and the loudness of the sounds changed with perceived crispness. Crisp products are characterised by sudden, clean and total fractures; the rapid fracture propagation of dry crisp foods underlying the generation of sound is commensurate with Vincent’s (1998) definition of crispness as a sudden drop in load experienced in muscles and teeth sensors when a crisp item breaks in the mouth. As a result, one means of assessing sensory attributes such as crispness is to measure the sounds produced during compression of foods. Regarding fruits and vegetables, Fillion and Kilcast (2002) have studied the consumer perception of crispness and crunchiness, and have confirmed the complexity of these concepts and the important contributions made from a wide range of peceptions including fracture characteristics, sound, density and geometry.

10.2.4 Comparing instrumental and sensory measurement of texture Instrumental methods for measuring texture are valid only if they can predict sensory textural attributes. This aspect has been studied in depth (e.g. Szczesniak, 1987; Peleg, 1983; Brennan and Jowitt, 1977). However, a number of recent studies have indicated that in fruits and vegetables sensory measurement cannot yet be replaced by instrumental approaches (e.g. Harker et al. (2002). Nevertheless, recent advances in data handling and interpretation may provide

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further opportunities to explore the relationships and associations between physical measurements and sensory perceptions (e.g. DeBelie et al., 2002). Hence, texture is a very difficult concept to describe or measure. This presents significant difficulties when attempting to develop food-chain strategies to modify the texture of fruits and vegetables. However, it is generally accepted that sensory texture of edible plant organs is dependent on the structure of those organs and, by understanding plant structure, it may be possible to develop methods to improve sensory texture.

10.3 Plant structure The fracture mechanics, and therefore textural characteristics, of edible plant organs relate to the way they deform and disrupt during mastication (e.g. Jackman and Stanley, 1995). Edible plant organs are highly complex. They comprise a range of contributory structures at different length scales: organs are made up of tissues, which exist in a range of shapes and sizes. These, in turn, consist of plant cells and intercellular air-spaces that also differ in shape, size and packing. Each cell is surrounded by its own cell wall, the dimensions of which, and the mode and extent of adhesion to one another, can also vary. Within the cell wall, there is a further level of complexity comprising substructures such as plasmodesmata which are involved in intercellular communication, in addition to the layered character of the middle lamella (between cells), primary and secondary walls (Fig. 10.1 a&b). Finally, the (bio)chemical composition of cell walls differs, not only from cell to cell, but between the substructures within any one wall. This hierarchy of structures is summarised in Fig. 10.2. This heterogeneity reflects the varying functionality of the tissues, cells and cell walls in the plant (Gibeaut and Carpita, 1993; Brett and Waldron, 1996). Edible fruits and vegetables are generally rich in weak, non-structural parenchyma cells. The strength and texture of such tissues is determined by the mechanical properties of the cell walls in conjunction with the internal turgor pressure of the cells and intercellular adhesion (Van Buren, 1979; Brett and Waldron, 1996). The functions of some tissues can change during the life of a cell or organ, and textural characteristics will result from the natural structural characteristics of the plant organ as modified by growth, development and post-harvest treatments.

10.4 Cellular basis of crispness, juiciness and mealiness in fruit tissue 10.4.1 Crispness and turgor pressure Whilst crispness is not fully understood as a perception, and cannot yet be measured directly by instrumental means, there is little doubt that the crispness

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Fig. 10.1 (a) Electron micrograph of a cell wall of Chinese waterchestnut. Abbreviations: m: middle lamella; p: primary wall. The cell wall is approximately 0.5 µm across.

Fig. 10.1 (b) Electron micrograph of a cell wall of asparagus collenchyma cell. Abbreviations: m: middle lamella; p: primary wall; s: secondary wall. The cell wall is approximately 2 µm across.

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Texture in food Root, Stem, Fruit, Leaf

Organ

Texture

Parenchyma, Epidermis, Vascular Tissue Adhesion, Shape, Transport Cell Strength, Elasticity, Porosity

Cell wall

Wall domains

Polymers

Adhesion, Communication Chemistry, Rheology

Physiological / processing events

Fig. 10.2 The hierarchy of structures within plants.

in fruits and vegetables relies on the sudden fracturing of the intact plant structure during masticatory activities. This is consistent with Szczesniak’s (1988) description of fresh celery which asserts that celery is crisp because it snaps cleanly, and it is crunchy because its cellular structure facilitates a series of successive fractures when eaten. The fracture characteristics of crisp plant tissues therefore depend on the crack propagation properties of the tissues. In the case of edible plant tissues, this will depend on the characteristics of the cellular structure in conjunction with turgor pressure. Turgor pressure results from the osmotically-driven influx of water into a cell as a result of the higher concentration of solutes within the protoplast which is bounded by a semi-permeable (plasma) membrane. In the native plant tissue, turgor pressure is the key driver of cell expansion, which then occurs as a result of controlled slippage between cell-wall polymers. For a cell to exhibit turgor, the cell wall must exhibit elasticity over a range of tensions which counters the osmotic influx of water. Without elasticity, there can be no turgidity, only a rigid state or a flaccid state (Brett and Waldron, 1996). The internal pressure of cells affects the mechanical properties of tissues, and Lin and Pitt (1986) argued that turgid cells cause the cell wall to be stressed. Crack propagation in crisp tissues involves cell wall breakage. Pressure on the tissue from the teeth increases the hydrostatic pressure in the nearest cells, the walls of which are stretched to the extremes of their elasticity and then fracture catastrophically. Because numerous cells around the point of force from the tooth are pressurised in this way, a crack will propagate

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through them, rupturing the cell walls as it travels and releasing juice. If the tissue is being crushed between molars, then the pressure will be increased in many cells between the teeth, providing a potential path of fully stretched cells along which a crack can travel. If a tissue has lost moisture through evaporation (e.g. wilted celery), or if the extent of elastic extension of the cell walls has increased for whatever reason (e.g. asparagus stored under anaerobic conditions as described by Waldron and Selvendran, 1990), then turgidity will be low. In such a situation, a much greater degree of tissue deformation will have to be induced before the walls can be stretched to breaking point. Such tissues will have a rubbery texture. Hence, tissues containing turgid cells are crisper and are characterised by greater stiffness and lower toughness or work of fracture than flaccid tissues containing low turgor pressure cells (Hiller and Jeronimidis, 1996).

10.4.2 Juiciness and mealiness Juiciness and mealiness are terms commonly used to describe edible fruit and vegetable texture. Juiciness is used mainly in describing fruits. It results from the release of juice from the fruit tissues during mastication and is generally considered to be a highly positive attribute. The release of juice and associated flavour compounds will generally depend on cell rupture, although wall porosity will obviously play a role. If forces adhering cells to one another are stronger than the cell walls, then failure will occur in the walls. Alternatively, if the cell walls are stronger than the forces holding cells together, cell separation, tissue softening and mealiness will result (Fig. 10.3). Mealiness, thus involves encapsulation of juice, flavours and other cell contents within the cells and is usually considered to be a negative quality attribute, associated with fluffy appearance and a floury and granular texture (Jaeger et al., 1998; Barreiro et al., 1998). Like many sensory characteristics, there are other closely related expressions. For example the term ‘succulent’ is often used in reference to young vegetables (Szczesniak and Ilker, 1988). Mealiness is a term also applied to some vegetables after cooking (see below). Juiciness and mealiness are considered to be inversely related to each other (e.g. Kuhn and Thybo, 2001). The more mealy a ripe fruit tissue is, the less juicy it is often perceived to be. However, ripeninginduced softness is not always associated with mealiness as in the case of Spanish pears (Martin-Cabrejas et al., 1994). This has been studied sensorialy by Barreiro et al. (1998). In studies on apples, the sensory procedure profiled mealiness as a loss of crispness and juiciness, and an increase in the floury sensation in the mouth.

10.4.3 Ripening-related changes in cell adhesion Ripening-related softening of fruits is associated with a high degree of wall disruption (e.g. Ben-Arie et al., 1979; Brett and Waldron, 1996; Harker

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Fig. 10.3 Phase contrast light micrograph of separated cells from mealy apple. The cells are approximately 100 µm across.

et al., 1997; Tucker, 2003). This involves loss of electron-density of the middle lamella and the solubilisation of pectic polysaccharides. It is usually accompanied by an overall decrease in non-cellulosic neutral sugars, for example galactose and arabinose (Redgwell et al., 1997). A resultant decrease in the strength of cell adhesion results in an increase in cell separation and tissue softening. Extreme cell separation results in a mealy character (see above) as in the case of certain apple varieties. Here, the juice remains inside the cells. There has been a considerable amount of research to try to correlate such changes in cell-wall chemistry with changes in texture in order to elucidate the biochemical mechanism and identify possible points of control. So far, evidence suggests that ripening-related softening involves the actions of a number of cell wall-degrading enzymes. These include endopolygalacturonase (endo-PG), pectin methyl-esterase (PME), and β-galactosidase in the main, but others, including cellulase, xyloglucan endotransglycosylase (XET) and expansins have also been identified. Brummel and Harpster (2001) have succinctly summarised a number of conclusions that can be drawn from molecular-genetic studies on such enzymes, mainly in tomato. In particular, PG, in conjunction with PME, is the main enzyme involved in the depolymerisation and associated solubilisation of pectic polysaccharides. Interestingly, its suppression appears to reduce fruit softening only slightly, but has a considerable impact on shelf life. In addition, early suppression of β-galactosidase activity reduces softening, presumably as a result of steric hindrance of the side chains in relation to endo-PG activity. Also, ripening-

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related expansin levels correlate with softening, and may help to stimulate pectin depolymerisation. However, changes in the integrity of cell membranes and resultant leakage of water and electrolytes may also be important.

10.5 Cellular stability during processing High temperature heating of fruit and vegetable tissues causes an initial loss of instrumental firmness due to the disruption of the plasmalemma and an associated loss of turgor (Greve et al., 1994). This may result in the development of a rubbery character. However, the most notable change involves tissue softening which results from an increase in the ease of cell separation in many non-lignified tissues (Van Buren, 1979). Thermally induced cell separation is believed to be due to the β-eliminative degradation of pectic polysaccharides involved in cell adhesion (BeMiller and Kumar, 1972; Keijbets and Pilnik, 1974). Such depolymerisation is strongly influenced by pH, and is enhanced considerably under increasingly alkaline conditions (Neukom and Deuel, 1958). It is also strongly influenced by other ions and is enhanced by e.g. Ca, Mg, K ions, citrate, malate and phytate organic acids (Keijbets and Pilnik, 1974). The softening of acidic fruit tissues during heating has been attributed to acid hydrolysis of cell-wall polysaccharides, although this has been refuted by Krall and McFeeters (1998). Thermal treatment of plant tissues is often accompanied by wall swelling (Warren and Woodman, 1974; LeCain et al., 1997). This may be due to the thermal degradation of polymers that laterally stabilise the wall, facilitating disruption, and may also be affected by changes in the ionic composition of the cell wall. The ability of calcium to cross-link pectic polysaccharides and thereby reduce their solubilisation provides a dual role for this ion as a promoter of pectin degradation through eliminative degradation, whilst enhancing texture through cross-linking. 10.5.1 Pre-cooking If some edible plant tissues are thermally treated at 50–60 °C for approximately 30 minutes or more, they fail to soften to the same degree as non pre-treated tissues during subsequent high-temperature processing. This is thought to be due to the thermal-stimulation of wall-bound PME which de-esterifies pectic polysaccharides involved in cell adhesion (Van Buren, 1979). The result is a greater stability against β-eliminative degradation at high temperatures, which is augmented by a greater ability of the pectic polymers to be ionically crosslinked by divalent cations. Similar effects can be induced by chemical deesterication in cold, dilute alkali (Van Buren and Pitifer, 1992). The precooking effect can be sizeable in vegetables such as carrots (Ng and Waldron, 1997a). In some others, e.g. potatoes, it is often less and it is often variety dependent. However, the changes in cell-wall chemistry are similar (Ng and Waldron, 1997b). It is likely that the pre-cooking effect on thermal stability

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of cell adhesion relies not only on the PME-induced de-esterification of pectic polysaccharides, but also on the availability of divalent cations to produce extra ionic cross-links (Ng and Waldron, 1997b), and the impact of their chelation by intracellular organic acids. This is in keeping with the observation that the pre-cooking effect in carrots can be reversed by soaking in CDTA (chelator of calcium) (Ng and Waldron, 1997b). There have been relatively few studies on the molecular mechanism of pre-cooking, although Sajjaanantakul et al. (1989) investigated the effect on solubilised polymers. More recently, Tijskens et al. (1997 a, b) studied the kinetics of PME activity to the firming process. Nevertheless, industrial exploitation of pre-cooking has been approached in an empirical manner, and is often carried out in conjunction with the addition of calcium salts to control texture. There is little understanding of the precise (bio)chemical basis for pre-cooking, and this area lends itself to further investigation in the light of the increase in demand for pre-prepared vegetables.

10.5.2 Resistance to thermal softening Some edible, non-lignified parenchyma tissues fail to soften during thermal processing. Chinese water chestnut (CWC), Chufa and mature sugarbeet and beetroot contain examples of these tissues (Parker and Waldron, 1995; Parr et al., 1997; Waldron et al., 1997a; Ng et al., 1998; Parker et al., 2000). In CWC, the edible tissues consist of thin-walled, starch-rich storage parenchyma with a few thin vascular bundles. After thermal treatments such as cooking or canning, CWC retains its firm and crisp texture in terms of both sensory perception and mechanically-measured tensile strength and toughness (Waldron et al., 1997a). This is because its cells fail to separate and tissue fracture occurs only by cell-wall fracture. A number of studies have been carried out to investigate this characteristic and to elucidate the nature of the polymers involved. It appears that in CWC, arabinoxylans, which are cross-linked by ferulic acid (FA) (Parr et al., 1997), are responsible for thermal stability of texture. Chufa, which is closely related to CWC (Parker et al., 2000), demonstrates similar properties. Sugarbeet and beetroot, both dicotyledonous plants, can exhibit similar characteristics as a result of the ferulate-crosslinking of pectic polysaccharides (Waldron et al., 1997a, b). The role of this cross-linking has been highlighted by the observation that increasing the cross-linking by peroxidation also increases the thermal stability of texture (cooking time) (Ng et al., 1998). In all these examples, the ferulic acid interpolymeric cross-links include a family of six dehydrodimers which make up to 4% of the FA. Interestingly, the level of FA in the cell walls is relatively low (about 2% dwt) as compared with the polysaccharide components. Perhaps this low level explains why it was overlooked in earlier studies (e.g. Loh and Breene, 1981). At the time of writing there are studies underway to investigate the molecular-genetic control of such cross-linking.

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Tissues in immature edible organs such as those in immature stems and leaves often undergo post-harvest toughening (Burton, 1981). In many cases, this has usually been attributed to continued maturation. For example, the continued development of vascular and support tissues in edible stems such as asparagus and cauliflower involves secondary wall development with associated lignification. This may be augmented by the permeation of lignin precursor phenolics into the middle lamella between walls of adjacent nonlignified (e.g. parenchyma) cells. The resulting intercellular cross-links are resistant to thermal treatments, and reduce the extent of tissue softening during thermal processing (Femenia et al., 1999a, b). In addition, cinnamic acids may play a role in post-harvest toughening. In asparagus, this characteristic is accompanied by, and generally considered to be due to, an increase in lignification and the formation of interpolymeric cross-linking (Waldron and Selvendran, 1992). However, recent research has demonstrated that there is an additional increase in feruloylated polysaccharides which become crosslinked with diferulic acid (Rodriguez-Arcos et al., 2002). This is likely to be a wound response, stimulated by the harvesting process; the levels of FA are much lower in all parts of the stem during normal growth. This suggests that wound responses may also contribute to post-harvest toughening of vegetable tissues and could provide additional metabolic routes for control. An additional example of resistance to thermal softening concerns the development of the Hard-To-Cook (HTC) defect in grain legumes (Mattson et al., 1950). Storage of legume seeds under conditions of high humidity and high temperature as found in tropical countries results in an increase in cooking time of the seeds. This has a consequential impact on water and fuel use. Whilst much research has been carried out on this problem (Aguilera and Stanley, 1985; Reyes-Moreno and Liu, 1995), the mechanism is still poorly understood. Nevertheless, a possible multi-mechanism hypothesis has been suggested by Liu (1995). The way in which plant organs fracture is complicated further by the heterogeneity of component cell types. These can have very different mechanical and fracture properties which reflect their physiological function. In an onion leaf base, for example, the tissues are relatively homogeneous, comprising storage parenchyma cells. However, the tissues inside an asparagus spear reflect the immature stage of a rapidly-growing stem with vascular and strengthening cells. The walls of such cells are stronger and tougher than the thin, non-lignified walls of parenchyma cells and will strongly influence the mechanical and fracture properties of the stem.

10.6 Improving cell adhesion The above sections have described some of the many factors that contribute towards the textural characteristics of fruits and vegetables. The texture of a fruit or vegetable will depend predominantly on tissue fracture characteristics.

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These will be influenced by a number of criteria including cell type, cell and tissue heterogeneity, cell adhesion, cell contents and turgor, and all as modified by food-chain and post-harvest events. Furthermore, the sensory texture is itself poorly defined. However, it is clear that a key determinant of texture is the degree to which cells separate or rupture during mastication. Hence, it is generally accepted that controlling cell adhesion is an important route to controlling texture. Many attempts to control cell adhesion in ripening fruits and processed vegetables have involved empirical approaches, for example modifying agronomic and processing events, or breeding, or genetic modification, followed by assessment of the end product quality. It is often assumed that by gathering large bodies of information, and applying statistical analysis, the mechanisms of cell separation during ripening or processing might be further understood, and control routes revealed. Unfortunately, this approach has highlighted the complexity of the softening process, and presented more questions than answers. We are still a long way from being able to precisely control texture in a predictive manner. One of the key reasons for this is that the architecture of cell adhesion at the molecular level is still poorly understood. For decades, cell adhesion has been considered to be the domain of the entire middle lamella generally. Like cement between bricks in a wall, the middle lamella has been thought of as the glue joining the faces of adjacent cells (e.g. Van Buren, 1979; Harker et al., 1997). This has stimulated much research into the physicochemical properties of pectic polysaccharides, particularly in relation to molecular weight, degree of methyl esterification, and propensity for βeliminative degradation. There have been numerous attempts to correlate changes in such properties with changes in tissue texture (e.g. Ng et al., 1997 a, b). However, there is an increasing body of evidence which indicates that cell adhesion is controlled by precise aspects of wall architecture. In the remaining sections, some of the findings supporting this are presented, and considered in relation to the possible future approaches to controlling texture.

10.6.1 The relationship between wall architecture and cell adhesion Several lines of research suggest that cell adhesion is not dependent on the entire middle lamella layer, but on components that are located predominantly at the edges of cell faces, and which therefore involve only a small fraction of middle lamella polymers. Kolloffel and Linssen (1984) investigated cell wall formation by TEM (transmission electron microscopy) during cell division. Cell adhesion between daughter cells begins at the later stages of cell division. A specific region of the parent cell wall is degraded, thereby enabling connection of the newlyformed cell plate (which separates the daughter cells), to the middle lamella surrounding the parent cell (Kolloffel and Linssen, 1984; Knox, 1992). The cell plate is fated to become the new middle lamella between the daughter cells. Afterwards, an inter-cellular space develops in the tricellular junction

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region between the two daughter cells and the adjacent cell. The new intercellular space then spreads along the middle lamella. The extent of the space is regulated by electron-dense intra-wall structures at which space formation terminates (Kolloffel and Linssen, 1984). These regions are thus located at the corners of pre-determined intracellular spaces. If separation failed to cease at these points, it would continue until the cells separated completely. These regions represent sections of continuous arcs along the edges of intercellular spaces. However, the observations did not preclude the rest of the middle lamella from being involved in cell adhesion, and did not provide any information on the chemical or biochemical composition or nature of the arcs. There is now significant evidence to support the leading role of these arcs in cell adhesion. The importance of calcium in the cross-linking of low-ester pectic polysaccharides involved in cell adhesion is well established, and chelation of calcium from tissues such as potato parenchyma results in cell separation (Waldron et al., 1997a). Investigations into the localisation of calcium-binding, low-ester pectic polysaccharides were pioneered by the development of monoclonal antibody (Mab) technology. Cell walls of carrot tissues (Knox et al., 1990), suspension-cultured carrot cells (Liners and Van-Cutsem, 1992) and ripe cherry tomato (Roy et al., 1992) were immunolabelled with the anti-pectin Mabs JIM5 and JIM7. JIM5 Mabs bound preferentially to the electron-dense regions of the cell wall were highlighted by Kolloffel and Linssen (1984) indicating that they contained low-esterified homogalacturonan sequences. In contrast, the highly-esterified pectins to which JIM7 bound did not exhibit such localisation. This characteristic has been explored on the surface of separated cells by SEM (scanning electron microscopy) (Parker et al., 2001) and by using fluorescein isothiocyanate (FITC)-tagged JIM5 antibodies (Waldron et al., unpublished). The JIM5 Mabs bind preferentially to the edges of the cell faces. A number of more highly-characterised Mabs have also been raised to calcium-binding homogalacturonan. Of particular note is LM7 which is specific to a non-blockwise pattern of methyl esterification. This Mab binds specifically at the corners of all parenchyma inter-cellular spaces examined to date (Willats et al., 2001). In keeping with the distribution of unesterified pectic polysaccharides which can be crosslinked by divalent cations, calcium has been found to be located in the middle lamella, particularly at the corners of inter-cellular spaces (Roy et al., 1992). Additional evidence for the role of the edge of the cell face has come from studies on phenolic esters. If CWC tissues are extracted with hot, dilute alkali, cell separation can be induced. Under these conditions, only a proportion of the phenolics are released from the cell wall. Analysis of the separated cells by fluorescence microscopy has revealed that the remaining phenolic moieties are concentrated at the edges of the cell faces (Waldron et al., 1997a; Fig. 10.4). This strongly supports the hypothesis that the edge of the cell face is particularly important in cell adhesion. Interestingly, analysis of

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Fig. 10.4 Fluorescence micrograph of a single cell of Chinese waterchestnut. The cell was released from parenchyma tissue after extraction in mild alkali. Abbreviations: f: cell faces; e: edges of cell faces. The cell is approximately 100 µm across.

the phenolic moieties remaining in the cell wall indicated that they were enriched in 8,8’-DiFA in the aryltetralyn form (Waldron et al., 1997a) suggesting that there may be some specificity in the role of this dehydrodimer as later highlighted by Parker et al. (2003). The role of the bulk of the middle lamella in cell adhesion has also been studied. Parker et al. (2000) have investigated the structures remaining on the surface of separated potato cells and have observed that the middle lamella is a separate entity which is attached to the cell walls only at the edges of the cell faces. This result strongly supports the dominant role of the edge of the cell face in adhesion, and indicates that the bulk of the middle lamella is probably not involved in adhesion. The importance of the edges of the face is also supported by mathematical considerations by Jarvis (1998).

10.7 Future trends Texture is a sensorial issue and relates to the interaction of food in the mouth during ingestion. As yet, it is not possible to use instrumental methods to replace a trained sensory panel. Furthermore, the relationships between the

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different levels of plant structure and texture are a long way from being understood. However, the increase in understanding of wall architecture in relation to cell adhesion provides a useful focus for research since this area is likely to provide some control over texture. There is a paucity of information concerning the chemistry of cell-wall components at the edges of the cell faces. Apart from indirect evidence from epitope localisation studies and fluorescence microscopy, the composition of edge-of-face polymers is largely unknown. Moreover, the biochemical activities in this domain remain to be elucidated. This has significance not only from the textural perspective, but also as the basis of multi-cellularity in higher plants. The edge of the cell face results from metabolic events that occur during cell division. It provides the means by which the two daughter cells can adhere to each other and it is consequently fundamental to nearly every aspect of multi-cellular plant growth and development. Therefore this concept lends itself to basic research activities which would not only provide a greater understanding of plant morphology, but would also provide a potential leverage on the textural quality of plant-based foods.

10.8 Acknowledgements The author wishes to thank Dr M L Parker for the electron, light and fluorescent micrographs. This writing of this article was supported by the UK Biotechnology and Biological Science Research Council.

10.9 References and STANLEY D W (1985) A review of textural defects on cooking reconstituted legumes – the influence of storage and processing, J Food Proc Preserv, 47, 144–69. BARREIRO P, ORTIZ C, RUIZ-ALTISENT M, DE SMEDT V, SCHOTTE S, ANDANI Z, WAKELING I and BEYTS P K (1998) Comparison between sensory and instrumental measurements for mealiness assessment in apples. A collaborative test, J Text Stud, 29, 509–25. BECH A C, GRUNERT K G, BREDAHL L, JUHL H J and POULSEN C S (2001) Consumers’ quality perception. In Food, People and Society – A European Perspective of Consumer’s Food Choices. Eds L J Frewer, E Risvik and H Schifferstein Berlin, Heidelberg, Springer, 97–113. BEMILLER J N and KUMAR G V (1972) β-elimination of uronic acids: evidence for an ElcB mechanisms, Carb Res, 25, 419–28. BEN-ARIE R, KISLEV N and FRENKEL C (1979) Ultrastructural changes in the cell wall of ripening apple and pear fruit, Plant Physiol, 64, 197–202. BOURNE M C (1982) Food Texture and Viscosity: Concept and Measurement. London, Academic Press. BOURNE M C (1994) Converting from empirical to rheological tests on foods: it’s a matter of time, Cereal Foods World, 39, 37–9. BRENNAN J G and JOWITT R (1977) Some factors affecting the objective study of food texture. In Sensory Properties of Foods. Eds G G Birch , J G Brennan and K J Parker, London, Applied Science, 277–45. AGUILERA J M

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and WALDRON K W (1996) Physiology and Biochemistry of Plant Cell Walls. Topics in Plant Functional Biology: 1. Series Eds M Black and B Charlwood, London, Chapman and Hall. BRUMMEL D A and HARPSTER M H (2001) Cell wall metabolism in fruit softening and quality and its manipulation in transgenic plants, Plant Mol Biol, 47, 311–40. BURTON, W G (1981) Post-harvest Physiology of Food Crops. London, Longman. CIVILLE G V and SZCZESNIAK A S (1973) Guidelines to training a texture profile panel, J Text Stud, 4, 204–23. DE BELIE N, LAUSTEN A M, MARTENS M, BRO R and DE BAERDEMAEKER J (2002) Use of physicochemical methods for assessment of sensory changes in carrot texture and sweetness during cooking, J Text Stud, 33, 367–88. FEMENIA A, WALDRON K W, ROBERTSON J A and SELVENDRAN R R (1999a) Compositional and structural modification of the cell wall of cauliflower (Brassica oleracea L. var botrytis) during tissue development and plant maturation, Carb Polym, 39, 101–8. FEMENIA A, RIGBY N M, SELVENDRAN R R and WALDRON K W (1999b) Investigation of the occurrence of pectic-xylan-xyloglucan complexes in the cell walls of cauliflower stem tissues, Carb Polym, 39, 151–64. FILLION L and KILCAST D (2002) Consumer perception of crispness and crunchiness in fruits and vegetables, Food Qual Pref, 13, 23–9. GIBEAUT D M and CARPITA N C (1993) Structural models of primary cell walls: consistency of molecular structure with the physical properties of the walls during growth, Plant J, 3, 1–30. GREVE L C, SHACKEL K A, AHMADI H, MCARDLE R N, GOHLKE J R and LABAVITCH J M (1994) Impact of heating on carrot firmness: contribution of cellular turgor, J Agric Food Chem, 42, 2896–9. GUINARD J X and MAZZUCCHELLI R (1996) The sensory perception of texture and mouthfeel, Trends, Food Sci Technol, 7, 213–19. HARKER F R, REDGWELL R J, HALLETT I C and MURRAY S H (1997) Texture of fresh fruit, Horticultural Reviews, 20, 121–224. HARKER F R, MACDONALD J, MURRAY S H, GUNSON F A, HALLETT I C and WALKER S B (2002) Sensory interpretation of instrumental measurements 1: texture of apple fruit, Postharv Biol Technol, 24, 225–39. HILLER S and JERONIMIDIS G (1996) Fracture in potato tuber parenchyma, J Mater Sci, 31, 2779–96. JACKMAN R L and STANLEY D (1995) Perspectives in the textural evaluation of plant foods, Trends Food Sci Technol, 6,187–94. JAEGER S R, ANDANI Z, WAKELING I N and MACFIE H J H (1998) Consumer preferences for fresh and aged apples: a cross-cultural comparison, Food Qual Pref, 9, 355–66. JARVIS M C (1998) Intercellular separation forces generated by intracellular pressure, Pl Cell Env, 21, 1307–10. KEIJBETS M J H and PILNIK W (1974) β-elimination of pectin in the presence of anions and cations, Carb Res, 33, 359–62. KNOX J P (1992) Cell adhesion, cell separation and plant morphogenesis, Plant J, 2, 137– 41. KNOX J P, LINSTEAD P J, KING J, COOPER C and ROBERTS K (1990) Pectin esterification is spatially regulated both within cell-walls and between developing tissues of root apices, Planta, 181, 512–21. KOLLOFFEL C and LINSSEN P W T (1984) The formation of intercellular spaces in the cotyledons of developing and germinating pea-seeds, Protoplasma, 120, 12–19. KRALL S M and MCFEETERS R F (1998) Pectin hydrolysis: Effect of temperature, degree of methylation, pH, and calcium on hydrolysis rates, J Ag Food Chem, 46, 1311–15. KUHN B F and THYBO A K (2001) The influence of sensory and physiochemical quality on Danish children’s preference for apples, Food Qual Pref, 12, 543–50. BRETT C T

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11 Plant compounds and fruit texture: the case of pear T. Kojima, S. Fujita and M. Tanaka, Saga University, Japan and P. Sirisomboon,* King Mongkut’s Institute of Technology Ladkrabang, Thailand

11.1 Introduction: variations in pear texture Pears have particularly interesting texture properties which are intermediate between those of apples and potatoes (Baritelle et al., 2000). Pear varieties which are well known for their different texture are European pears and Asian pears (sometimes known as apple pears, Japanese pears, Chinese pears, or Oriental pears). European pears such as Bartlett and Comice become soft when ripe while Asian pears such as Chojuro (Choujuurou), Twentieth Century (Nijisseiki), Ya Li, and Tsu Li are firm, sweet, and juicy, and more easily bruised than European pears (Chen et al., 1987). European pears mostly have a bell shape. They improve in both texture and flavor after being picked. The fruit of the Japanese pear has a sandy texture and a round shape. The Chinese pears Ya Li and Tsu Li have an obovate shape. Japanese pears can be eaten just after harvest because they ripen on the tree and not during postharvest storage (Yamaki et al., 1979a cited in Tanaka and Kojima, 1996). 11.1.1 European pear texture Ben-Arie et al. (1979a) reported the change in firmness in pear (Pyrus Communis L. cv. Spadona) during ripening on and off the tree. The softening of the harvested fruit was slightly delayed compared to that of fruit which was allowed to ripen on the tree. In both cases, however, the change in firmness was roughly steady state. Fruit kept for about a week at 20 °C showed a marked acceleration in softening. The firmness of Spadona pears declined gradually in cold storage at a fairly constant rate throughout a 25 week period. The subsequent decline in fruit firmness during one week ripening at 20 °C following cold storage was very rapid compared to that of

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fruit ripened at 20 °C directly after harvest, even though little change in firmness had occurred during cold storage. Ma and Chen (2003) found that, for long-term storage, pear Pyrus communis L. (Doyenne du Comice) stored in air developed both flesh breakdown and senescent scald disorders after four months storage, and those disorders were increased after five and six months storage. Controlled atmosphere (CA) stored fruit were free from scald regardless of CA regime and storage time but developed internal browning after four months of storage which increased after five and six months of CA storage. Fruit stored in different CA conditions showed different disorders. Change in ethylene production and flesh firmness during post-storage ripening at 20 °C was found to be the best objective measurement of storage life of Doyenne du Comice fruit. Fruit stored in refrigerated air for 1–3 months exhibited typical climacteric-like patterns of ethylene production during 15 days at 20 °C and softened to proper ripeness with desirable eating quality on day 5 at 20 °C. When the fruit was initially transferred to a temperature of 20 °C, the magnitude of ethylene production increased with storage time. Fruit stored in air for four months or longer did not exhibit climacteric-like patterns of ethylene production during ripening and nor did it soften improperly with a coarse and dry texture. Fruit stored in short-term CA storage followed by holding in air at –1 °C for one, two, and three months were capable of ripening normally with desirable dessert quality without developing any physical disorders. The differences in mechanical property measurements between quasistatic (slow) and dynamic (fast) loading rates in pear tissue were studied by Fridley and Adrian (1966) (Baritelle et al., 2000). They stated that pears could withstand more compressive force before tissue damage occurs than other commodities such as apples, peaches or apricots. Baritelle and Hyde (2000) investigated the effect of strain rate and size on mechanical tissue failure properties of D’ Anjou and Bosc pear under dynamic axial compression testing. Their results indicated that the more rapidly the Bosc (not D’ Anjou) tissue sample was deformed (higher strain rate) the tougher the tissue. This tends to agree with Dal Fabbro et al. (1980). The shock wave propagation speed was not affected by fruit size and did not vary with strain rate, as long as strain rate was sufficient to cause impulse loading. This finding is important in developing non-destructive testing procedures. The larger size fruit had higher failure stress and higher failure strain resulting in a greater tissue toughness and secant elastic modulus. Shock wave speed was determined from differences in the force profiles at each end of the sample during compression. The secant elastic modulus is failure stress divided by failure strain. Tissue toughness is the area under the stress–strain profile. The important finding of this experiment concerns the locus of failure points as strain rate increases. Generally as the strain rate increases the failure stress also increases while the failure strain remains nearly the same. Thus the toughness and stiffness (secant elastic modulus) both increase with increasing strain rate. At increasing strain rates, the failure stress increases

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and there is a small decrease in the failure strain. These in turn influence the calculated parameters of stiffness and tissue toughness. Baritelle et al. (2000) investigated the tissue failure properties of Bartlett and D’Anjou pears. The D’Anjou cultivar had higher failure stresses and failure strains and thus greater tissue toughness, while the Bartlett cultivar consistently had higher secant moduli. To counter the effect of hydration and ripeness when testing the dynamic (impact) mechanical properties of climacteric fruit, Baritelle et al. (2001) reported the result of using 1-MCP (1-methylcyclopropene) to inhibit the ripening process and reduce the influence of ripening in such testing. In the experiment the samples used were apples and Bartlett and D’Anjou pears. The results showed that treating climateric fruits with 1-MCP slowed the maturation process but did not completely inhibit changes in tissue failure properties. The effects of dehydration could have compounded this. The importance of accounting for ripening can easily be seen in the pears compared to apples from the large and significant differences found in all failure parameters. From Oron et al. (2002), Spadona pear firmness seemed not to change due to water salinity and irrigation technology (irrigation with tap water, irrigation with saline water with surface drip irrigation and subsurface drip irrigation). 11.1.2 Japanese pear texture The effect of harvesting dates, storage time and handling on susceptibility to mechanical injury or pear texture has been studied on several cultivars of Japanese pears. Tsukamoto (1981) reported that the Kikusui was more sensitive to bruising than the Choujuurou and that the flesh tissues under the skin of the Japanese pear were very sensitive to bruising by both impact and compression. Chen et al. (1987) studied impact and compression damage to Asian pears. The flesh firmness, measured with a U.C. fruit firmness tester, of Choujuurou was the highest, followed by those of Ya Li, Tsu Li, and Twentieth Century. Results of impact tests using the impact tester developed by Chen et al. (1985) and compression tests using an Instron universal testing machine also indicated that Choujuurou pears are the firmest and most resistant to mechanical damage. Twentieth Century pears are most sensitive to impact and compression bruising. At the time of harvest, Tsu Li and Ya Li pears could resist mechanical damage nearly as well as Choujuurou pears, but they become more susceptible to bruising in cold storage. Increased time in the ripening room (20 °C) produced more softening and increased bruise resistance in Choujuurou and Twentieth Century pears than in Tsu Li and Ya Li. The firmness of those fruits tended to decrease as the time in cold storage increased. In Choujuurou, Tsu Li and Ya Li firmness tended to decrease at about the same rate, whereas that of Tsu Li decreased faster. Kawano et al. (1984) kept single Nijisseiki pears in polyethylene bags (3µ-thickness), packed in polystyrene pack (PSP), and then in a three-layer corrugated cardboard box. The box was delivered by mail truck from an orchard in Tottori prefecture to the research station and kept in a refrigerator

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at 0 °C. Within 10 days after harvest, the pears were subject to experiment as non-stored fruits. Another set of fruit was kept until 3–4 months before experiment as stored fruits. The conclusions were that the delayed harvest pears were softer and more prone to permanent deformation than the early harvest ones. This was confirmed by the G factor obtained from the impact compression test. The G factor is the limit or critical acceleration at which fruit drop does not result in bruising. When the G factor was small the fruit was easily damaged. The delayed harvest pears, both stored and non-stored, had a smaller G factor than the early harvest ones. The bioyield point of the delayed harvest pears when 112–152 N was applied was less than that of the early harvest. However, Kawano et al. (1984) pointed out that the results could not be used to reach any firm conclusions for stored fruit. The Takayuki Kojima research group at Saga University, Japan, carried out intensive analysis of the texture of Japanese pear (Sirisomboon et al., 2000a). The conclusions from their study (Pyrus serotina Rehder var culta Housui) were as follows. Using the plate compression test the only interpretable indices for the Japanese pear were the deformation ratio, modulus of elasticity and relaxation. However, using the puncture test the interpretable textural indices were the rupture force, deformation at the rupture point, toughness, average firmness, initial firmness, apparent modulus of elasticity, bioyield force, penetrating force and penetrating energy into the flesh. They indicated that the skin of the Japanese pear contributed 70–80% of the firmness of the fruit and, as the fruits enlarged, the strain decreased and the elasticity increased, the fruit skin was more prone to mechanical damage, the fruit flesh became softer, and the firmness and rigidity of the whole fruit was reduced. The effect of vibration and storage temperatures on quality, including hardness via puncture testing of Japanese pears, was also studied (Liu et al., 1998). The vibration test in the laboratory was carried out under conditions identical to those in highway trucks equipped with refrigeration units which commute between Saga and Osaka, and the fruits storage test was at 0 °C, 10 °C and 90% relative humidity. The hardness along with acid and Brix decreased with increased storage temperature (0, 5, 10 and 15 °C) and storage period (one day and three days). Under this condition weight loss was found to increase and peel color became darker. All of these properties were changed both during storage and by vibration. Near infrared spectroscopy provides a non-destructive method of distinguishing difference in storage period for Japanese pears and whether the fruits were subjected to vibration.

11.2 Measuring and modelling fruit firmness 11.2.1 The development of firmness testers for pear The technique of using a small spherical impactor for testing the response of fruits to impact was described for the first time by Chen et al. 1985 (cited in Chen et al., 1996). Chen et al. (1996) determined the effect of impacting

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mass on firmness sensing of fruits both theoretically and experimentally. They pointed out that the desirable features associated with low impacting mass were: increases strength of the measured acceleration signal, thereby facilitating easier detection and maximizing signal-to-noise ratio; increases both the magnitude of the calculated firmness index, A/t, and the rate of change of A/t with respect to the fruit firmness, E; minimizes the error due to movement of the fruit during the impact; and minimizes fruit damage caused by the impact (A is the peak acceleration of the impactor, t is the time required to reach peak force, E is firmness). The development of firmness testers for pears was continued at the University of California at Davis supported by the California Pear Advisory Board and California Canners during 1989–1992 by Delwiche and Sarig in 1989 and by Delwiche et al. in 1990–1992 and during 1998–2000 by Chen et al. The Delwiche model is a probe-type sensor developed to measure the impact response of fruit conveyed on a packing line and to predict firmness. The prototype was tested on Bartlett pears (Delwiche et al., 1996). The Chen models (Chen and Thompson, 1999 and 2000) were the handheld tester (Fig. 11.1a) and the same tester installed as a real-time on-line sorter (Fig 11.1b). The hand-held tester could be used for both on-tree fruit and picked fruit. It was influenced by turgidity of the fruit which is in turn affected by tree moisture status. High turgor results in a high modulus of elasticity of the fruit (the fruit tissue deforms less to a given pressure), and the hand-held unit is very sensitive to change of elastic modulus. The handheld firmness reading should be done during consistent field conditions, i.e. under fairly consistent tree water status conditions for example, in the morning at about the same temperature from day-to-day and readings should not be taken during irrigation periods or when the trees are under moisture stress. The on-line sorter was effective in segregating pears into three distinct firmness categories. The unit operated at speeds of six fruits per second and was nondestructive. The prototype impact system for firmness sorting was modified and installed in an experimental fruit packing line in Spain (García-Ramos et al., 2003). The online sensor is being incorporated into a commercial sorting line in Japan. Based on the same acoustic impulse response method the De Baerdemaeker research group at Katholieke Universiteit te Leuven, Belgium evaluated the method in order to measure the firmness changes of Pyrus communis var. Conference and Doyenné du Comice pears on-tree during two successive years 1995–1996 (De Belie et al., 2000). They indicated that the method appeared to be a valuable means to monitor the firmness evolution of pears on-tree in a non-destructive manner and to determine the optimum harvest date. In both years, the firmness showed a sudden reduction between three weeks and one week before harvest to about 23 kHz2 g2/3 for Conference and on average 31.5 kHz 2 g 2/3 for Doyenné. The optimum harvest date, corresponding to firmness of about 20–21 kHz2 g2/3 for Conference and 26–27 kHz2 g2/3 for Doyenné, occurred within about two weeks from the

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(a)

(b) Fig. 11.1 (a) Experimental hand-held pear firmness tester (from Chen and Thompson 2000); (b) on-line sorting system with dual impact sensors (from Chen and Thompson 1999).

Plant compounds and fruit texture: the case of pear 265

measured sudden drop in firmness which indicated that the fruit was softening ready to eat. Continuation of the firmness measurements after this point and the construction of the first-order degradation model, S = S 0 e – α t s , allowed the optimum harvest date to be more accurately predicted. S is the stiffness factor at time ts in kHz2 g2/3, S0 is the stiffness factor at the initial time 0 in kHz2 g2/3, α is the temperature-dependent deterioration constant in day–1 and ts is the storage time in days. The correlation between the destructive firmness (maximum force at compression to 8 mm deformation at 50 mm/min of 8mm diameter metal cylinder) and the non-destructive firmness (stiffness factor) was high for Doyenné (correlation coefficient = 0.82) and fair for Conference (correlation coeff icient = 0.59). None of the measured meteorological parameters (temperature and rainfall) consistently provided a significant improvement in the predictive firmness model. Based on the impact technique used for the development of a portable instrument to monitor the firmness of melons, the pear firmness tester (Fig. 11.2) was modified by Sugiyama (2001). The experiment was done on La France and Le Lectier pears. Using the firmness tester the ripeness of the pears was evaluated using transmission velocity rather than resonance frequency, in order to compensate for variations in the size of samples. The maximum peak in the impact waveform required to calculate the transmission velocity was also easily detected. The ability to measure firmness was implied by the correlation between the transmission velocity and the apparent elasticity measured by the compression test using the universal testing machine. The firmness tester showed its ability to measure the La France pear firmness better than the Le Lectier pear. Sugiyama (2001) indicated that the result might be due to unequal distribution firmness of Le Lectier; several of the panelists also said that the firmness of Le Lectier was different even within one sample and further investigation was necessary. Sugiyama (2001) concluded, based on the daily change of transmission velocity at different pear storage temperatures and the different score in sensory evaluation, that the firmness tester was able to monitor physiological changes in ripening pears.

Fig. 11.2 Portable firmness tester for pears.

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Bertelsen (2001) reported the development of an acoustic firmness sensor by the grading company Aweta. The firmness value is weight-specific and the fruit is weighed simultaneously with the sound recording. The University of Ghent has developed the mathematics behind the sound-to-firmness conversion. The Research Center Aarslev in Denmark have tested the sensor for a number of apple varieties and for Clar Frijs pear. They found no correlation between the sensor firmness value and fruit firmness measured on the peeled fruit using a penetrometer. Despite the lack of correlation, the newest version of the sensor program included a conversion feature between the sensor and the penetrometer values. Fruit size appeared to affect the firmness value with small fruits exhibiting lower values than the large fruits.

11.2.2 The sensitivity of texture measurement methods and instruments Garcia et al. (1995) reported that Blanquilla pears with higher turgidity had low values of deformation at skin puncture (DSP) compared with those of lower turgidity. The more turgid fruits showed high values of bruise volume because the stresses in the tissue might be higher. Turgid fruits exhibited different impact response compared to less turgid fruits when measured by the impact tester developed by Garcia et al., 1988 (cited from Garcia et al., 1995). For a given impact energy, impact forces were higher and deformations lower in turgid fruits. Stresses in tissue would therefore be higher, while Magness-Taylor firmness, related to tissue strength, did not change greatly between the two groups of fruit. This indicated that the Magness-Taylor sensitivity is not enough for measuring the effect due to the turgidity. Chen and Thompson (2000) reported that penetrometer readings did not differ significantly with the time of day of measuring (morning and afternoon). Time of the day influenced the turgor of fruit. They found that the penetrometer was sensitive to the ultimate strength of pear tissue but that the results were not much influenced by its elastic modulus. High turgor results in a high modulus of elasticity of the fruit and the hand-held tester they developed is very sensitive to change in elastic modulus. However, there appears to be a correlation between elastic modulus and ultimate strength of the pear tissue. Figure 11.3 shows the result of sorting by an on-line sorting system, compared with fruit firmness measured by hand held-firmness tester and by penetrometer. It is interesting to note that there are similarities between Magness-Taylor firmness and failure stress measurements using dynamic axial compression (between pear cultivars D’Anjou and Bosc), with the higher firmness values having the higher failure stress (Baritelle and Hyde, 2000). Dynamic axial compression testing showed highly significant differences between varieties for failure stress, failure strain, secant elastic modulus, and tissue toughness. Pears with higher Magness-Taylor firmness generally had higher failure stress. The Takayuki Kojima research group at Saga University, Japan reported that the puncture test was superior to the plate compression test in analyzing

Plant compounds and fruit texture: the case of pear 267 On-line sorting, 40 fruits 170

Hand-held impact reading

160 150 140 130 120 110 100 90 0

1

2 Sorted group

3

(a) On-line sorting, 40 fruits

20

Ave. penetrometer reading, Ibs

18 16 14 12 10 8 6 4 2 0 0

1

2 Sorted group

3

(b)

Fig. 11.3 The result of sorting made with on-line sorting system, compared with fruit firmness measured (a) by handheld firmness tester and (b) by penetrometer (from Chen and Thompson, 2000).

the textural properties of the Japanese pear fruits with regard to changes in the pectin constituents. Their study (Sirisomboon et al., 2000b) showed that most of the correlation coefficient values between the textural properties from the puncture test and pectin constituents, alcohol insoluble solids and soluble solids were significant and were higher values than those obtained from the plate compression test. Hence, the puncture test would provide a better relationship between texture and pectin constituents of the Japanese pear fruit than the plate compression test.

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11.2.3 The texture model of pear Kawano et al. (1984) proposed using Hertz’s theory of elasticity to analyze impact acceleration using the dynamic modulus of elasticity obtained from impact compression testing more accurately than it is possible to obtain the static modulus of elasticity from quasi-static compression testing. The linear regression model of bruise volume (BV, mm3) with the same impact energy related to Magness-Taylor firmness (F, N) and turgidity in terms of deformation at skin puncture (DSP, mm) for Blanquilla pears (García et al., 1995) was BV = 107.6–0.51 F-32.6 DSP

r2 = 0.39 (n = 720)

To develop the above model, BV was calculated from depth (h) and width of bruise (D) using the equation of Chen and Sun (García et al., 1995 cited from Chen and Sun, 1981) BV =

π hD 2 6

These two models have been analyzed and show that the parameter DSP explains 31% of the total variation in Blanquilla pears. Adding the MagnessTaylor firmness, the models can explain 39 % of the total variation. The results showed that Magness-Taylor firmness was related to bruise susceptibility, although not to the same extent as turgidity. When testing fruit with similar turgidity (for instance, at harvest), firmer fruit were shown to be less susceptible to bruising. The relationship between firmness and bruise susceptibility was closer in pears than in apples, since the ripening rate was faster in pears and the ranges of firmness values wider. The parameter firmness and turgidity exhibited no relationship between each other and influenced bruising independently. According to the models, bruise susceptibility was affected by fruit turgidity and firmness changes during ripening: bruise damage would increase with decreasing firmness, depending on which is the main factor in the ripening process. At Katholieke Universiteit te Lueven, Belgium the Josse De Baerdemaeker research group have carried out intensive work on numerical simulations to facilitate accurate firmness measurement of fruit and vegetables by the nondestructive acoustic impulse response method. They stated that the finite element method coupled with computer vision is well suited for analysis to gain insight into the mechanical vibration of fruit of different shapes (Jancsók et al., 2001). Dewulf et al. (1998) mentioned that the technique could be used to monitor the change of Conference pear fruit quality with time and to evaluate different storage methods. The dynamic characteristics of a Conference pear due to changes in material characteristics was investigated by Dewulf et al. (1999) using finite element simulations to determine the firmness of Conference pears by using the mechanical response technique. Using an image processing technique they obtained the geometrical model of a Conference pear. The dynamic behavior

Plant compounds and fruit texture: the case of pear 269

of the pear and the correlation of its behavior with material characteristics were investigated by using a numerical modal analysis. They found that the oblate-prolate mode is the most sensitive with respect to the material characteristics of the calyx end of the pear and also that the influence of the material characteristics of the stem end of the pear on the eigen-frequency of this mode is small. The first two bending modes are the most sensitive eigenmodes with respect to the material characteristics of the stem end of the pear. Therefore they concluded that the texture quality of the calyx end and the stem end of a Conference pear could be determined separately. The Young’s modulus of the spherical calyx end of the pear could be determined from the oblate-prolate modes and that of the stem end from the first bending modes. A finite element analysis using a conical model for the stem end of the pear indicated that a similar formula for the firmness of the stem end of pear can be derived. The influence of the shape of Conference pears on acoustic response was investigated using finite element modeling by Jancsók et al. (2001). The effect of the easy to measure but imprecise length/diameter (L/D) ratio on the resonant frequencies was related linearly with the normalized frequencies in the first bending, compression and first torsion modes but not in the oblate-prolate mode. This was due to the oblate-prolate mode shape where the bottom part is deformed and nearly spherical and does not vary to a large extent. The first six more accurate Fourier descriptors, where the different pear shapes have their effect, were calculated and correlated with the raw frequencies. It is obvious that the third normalized descriptor correlates well with the modes with lower frequencies (bending, torsion, compression) but that the correlation is low for high-frequency modes (oblate-prolate). To estimate the Young’s modulus, the more accurate Fourier descriptors resulted in a more accurate estimation. When the stiffness factor ( f 2m 2/3 ) for oblateprolate mode is used to estimate the material properties then the error is small. They concluded that the simulated measurement carried out indicated that the Young’s modulus values or the stiffness factor could possibly be estimated based on the resonant frequency measurement, but that great care must be taken to identify the mode shape associated with the resonance frequency. The anisotropic relaxation properties of Dangshan pears were determined based on the relaxation model by Wang (2003). The three-element relaxation model was expressed as σ(t) = ε0E0 + ε0E1e–t/T where σ is the stress of the pear specimen under load in MPa; t is time of conducted experiment under load in s; ε0 is the constant strain, dimensionless; E0 is the equilibrium modulus in MPa; E1 is the decay modulus in MPa; T is the time of relaxation and T = η/E1 in s; η is the specific viscosity in MPa s. The three elements included E0, E1, and η. The results indicated that the decay modulus, the equilibrium modulus, time of relaxation, and specific

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viscosity were significantly influenced by specimen orientation, latitude, and depth. The longitude of the specimen and initial deformation were not significant.

11.3 Chemical compounds affecting firmness: the example of Japanese pear The edible tissue of fleshy fruit is usually composed of parenchymatous cells which contain water and accumulate organic substances such as sugar and organic acids, have large air spaces between them, and are frequently of very large size (Rhodes, 1980). Fruits contain a very high percentage of their fresh weight as water (Tucker, 1993). In addition to the large parenchymatous cells, the flesh of many fruits contains stone cells, which may play a role in the texture of the ripe fruit and which contribute to the flesh of some fruits. This is because they frequently contain tannin-like compounds, leucoanthocyanins, which are responsible for the astringency of these fruits. Japanese pears have a water content of around 90%. The fruit of Japanese pear contains four constituent sugars; namely sucrose, glucose, fructose, and sorbitol (Tanaka and Kojima, 1996 cited from Yamaki et al., 1979a). Yamaki et al. (1979a) stated that, as the fruit ripens, the total sugar concentration increases gradually, but changes in the concentration of each sugar do not occur at the same time. In the Japanese pear, photosynthesis products translocate to fruits as sorbitol, and young fruits store sorbitol during the cellular division stage. At this stage sorbitol concentration was the highest and the sucrose concentration was the lowest. After a cell thickening stage, sorbitol is metabolized vigorously to fructose and glucose, which are stored as starch. In the ripening stage, a climacteric rise occurs, starch decreases, and sucrose increases. Fructose attained the highest concentration, followed by sorbitol, sucrose and glucose. Thus the changes in concentration of each sugar for the Japanese pear during growing season can indicate the growth phase and the degree of ripeness (Tanaka and Kojima, 1996). Generally, Japanese pear inner quality is determined by sweetness (not organic acid), and the quality of the texture. Destructively, sugar content of fruit can be measured directly via HPLC (high performance liquid chromatography) analysis for sucrose, fructose and glucose (Bartolomé et al., 1996). However, since sugars are usually the major component of soluble solids, it is much easier to simply measure soluble solids in extracted juice with a refractometer (Peiris et al., 1998 cited from Wills et al., 1989). Near infrared spectroscopy can be used effectively to measure soluble solids for fruit and vegetables (Kawano et al., 1992, 1993; Kojima et al., 1994; Slaughter et al., 1996; Peiris et al., 1998; Schmilovitch et al., 1999; Slaughter et al., 1999; Lu et al., 2000; Lu, 2001; Sirinnapa et al., 2001; Park et al., 2002). Kojima et al. (1994) reported that the Brix values of the Japanese pear Kosui and Housui could be measured at six wavelengths of 1508, 1668, 1932, 2132, 2212 and 2400 nm. The log

Plant compounds and fruit texture: the case of pear 271

(1/R∞) values or the absorption coefficients at wavelengths of 620 nm may be used to predict the sugar content of the skin-flesh specimens of Japanese pear Nijisseiki (Budiastra et al., 1998).

11.3.1 Cell wall At cell wall, carbohydrate polymers of fruit make up 90–95% of the structural components of the wall, the remaining 5–10% being glycoprotein (Tucker and Grierson, 1987). The carbohydrate polymers or polysaccharides can be grouped together as cellulose, hemicelluloses and pectins. Fruit cell walls contain relatively more pectin material and less hemicellulose than other plant cell walls glycoprotein (Tucker and Grierson, 1987). Pectic substances are the major component of the middle lamella and of the primary cell walls of fruit tissues (Knee and Bartley, 1981; Batisse et al., 1994 cited from Voragen et al., 1983). Pectin is a linear polymer with many α-1,4-linked Dgalacturonic acid moieties, but it also contains neutral sugars such as Larabinose, D-galactose and L-rhamnose (Klavons et al., 1994). The carboxyl groups of the galacturonic acid are partly esterified with methanol and free groups are more or less neutralized. The monomer is thought to have C1 conformation. Yamaki et al. (1979b) investigated changes in polysaccharides and monosaccharide components in cell wall during cell division, cell enlargement and softening in Japanese pear (Pyrus serotina Rehder var. culta Rehder cv. Hosui) fruit. They reported as follows. The polysaccharides of Japanese pear were composed of glucose, uronic acid, xylose, arabinose, galactose, rhamnose, mannose and fucose. The total polysaccharide content of cell wall per cell (DNA content basis) remained constant during cell division but, during the pre-enlargement period, it began to increase rapidly in spite of the slightness of cell enlargement. Thereafter, during the enlargement period, the polysaccharides remained almost constant although the fruits enlarged dramatically, and the polysaccharides increased somewhat with ripening. The quality of the polysaccharides seemed to change actively at each stage, indicating that the extensive fruit enlargement did not require an increase in polysaccharide content, and was rather accompanied by the partial breakdown or partial interconversion of polysaccharide components already present. The loss of arabinose and galactose in acid-soluble hemicellulose was prominent in fruit softening occurring in the ripening stage. The cellulose component decreased with over-ripening. On the other hand, xylose and non-cellulosic glucose residues did not alter with ripening or over-ripening. Non-cellulosic glucose continued to accumulate during cell enlargement.

11.3.2 Pectins The chemical composition of pectins (i.e. degree of esterification, distribution of rhamnopyranoses and amount of specific neutral sugar) and the interrelation

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with other cell wall polysaccharides, determine the firmness of the fresh and processed plant tissue (Boeriu et al., 1998). Change of pectin in degradation of cell wall was characterized by a decrease in the level of insoluble pectin with a concomitant increase in soluble pectin (Ben-Arie et al., 1979a). The degradation of pectin is catalyzed by two groups of enzymes, polygalacturonase (PG) and pectin methyl esterase (PE). The increase in PG activity was accompany by an increase in water-soluble pectin and fruit softening (Eskin, 1990 cited from Pressey et al.,1971). Two types of PG have been identified, endo and exo. The former randomly hydrolyzes the glycosidic bonds in the pectin molecule while the latter acts from the terminal end of the pectin molecule. In the presence of endo-PG the pectin molecules are rapidly degraded into smaller units accompanied by a marked decrease in viscosity. PE is involved in de-esterification of the cell wall galacturonans followed by PG action. The result of these enzyme activities is the release of soluble polyuronide with a corresponding decrease in the molecular weight of the polyuronide polymer (Gross and Wallner, 1979; Huber, 1983; Eskin, 1990 cited from Seymour et al., 1987).

11.3.3 Changes in cell wall structure Rhodes (1980), cited in much research, explained the cell wall structure change during fruit ripening as follows. Changes in cell wall structure in many fruits during ripening have been observed under electron microscope, including avocado (Pesis et al., 1978), pear (Ben-Arie et al., 1979b) and tomato (Crookes and Grierson, 1983). These changes usually consist of an apparent dissolution of the pectin-rich middle lamella region of the cell wall. At the biochemical level, major changes can be observed in the pectin polymers of the wall. There is a loss of neutral sugars during ripening; in most fruit, this is predominantly galactose, but some loss of arabinose also occurs (Tucker and Grierson, 1987). These two sugars are the major components of the wall’s neutral pectin. There are also major changes observed in the acidic pectin or rhamnogalacturonan fraction of the wall. During ripening, there is an increase in the solubility of these polyuronides and, in several cases, they have been shown to become progressively depolymerized. The degree of esterification of the polyuronide fraction can also change during ripening. It was indicated that an increased solubility of the cell wall polyuronides is the characteristic cell wall change occurring during softening in the whole range of ripening fruits (Shewfelt, 1965; Dolendo et al., 1966). It appears that levels of sugars commonly associated with either hemicellulose or cellulosic fractions remain constant throughout ripening. Fruit chemical compositions in cell wall structure change during ripening are reviewed in Table 11.1. Removal of calcium from junction zones between pectin molecules in the middle lamella is a possible mechanism of cell separation and fruit softening, but there is no direct evidence that it occurs (Knee, 1993).

Plant compounds and fruit texture: the case of pear 273 Table 11.1 Change of fruit chemical composition in cell wall structure during ripening (Modified from Rhodes, 1980) Compositions Neutral pectin Acidic pectin (Rhamnogalacturonan)

Change Galactose Arabinose Polyuronides

Predominantly loss Some loss Increase in solubility, progressively depolymerized, change in esterification

Changes in the activities of some cell wall-degrading enzymes during development and ripening of Japanese pear fruit (Pyrus serotina Rehder var. culta Rehder cv. Hosui and 93-3) have been studied intensively by the Yamaki group (Yamaki and Matsuda, 1977; Yamaki et al., 1977; Yamaki and Kakiuchi, 1979). The following is the changing pattern of cell wall-degrading enzyme activities and pectin compound of Japanese pear during fruit development and ripening concluded from the studies of the Yamaki group. The activities per fresh weight of cell wall-degrading enzymes, i.e. endocellulase, PG, PE, and β-galactosidase during the cell division and preenlargement stages, were fairly high, decreased in the enlargement stage, and increased remarkably with ripening or over-ripening, except for PE. The endocellulase was present as an acid type and a neutral type. The former was more active than the latter in the cell division and pre-enlargement stages, but with ripening the reverse was found. On the other hand, the activities, on a DNA content basis (content in cell wall per cell), in all enzymes were roughly constant, not decreasing during the cell division, pre-enlargement and enlargement stages, but increasing extensively with ripening. That is, the lowering of these enzyme activities per gram fresh weight in the enlargement stage seemed not to be due to inactivation or stimulation of enzyme degeneration. The extensive enhancements of cell wall-degrading enzyme activities with ripening or over-ripening seem to be closely related to the softening or pithiness of the fruit. The Yamaki group concluded that in Japanese pear, PG and cellulase activities were mainly related to softening and βgalactosidase, and partial degrading of the hemicellulose component was related to flesh breakdown at over-ripening. In greater depth, Tateishi et al. (1996) investigated the cell wall-bound glycosidase (α-L-Arafase, α and β-galactosidase, β-glucosidase, α- and βmannosidase and α- and β-xylosidase) activities in pre-ripe and ripe fruit of Japanese pears (Pyrus serotina Rehder–var. culta. cv. Hosui). They indicated that α-L-Arafase played a role in fruit softening due to α-L-Arabinofuranosidase (EC.3.2.1.55) activity which increased dramatically (15-fold) with fruit ripening after the fruit enlargement stage, an effect which might be related to the loss of arabinose from cell wall fraction. In addition, they indicated that because the CDTA (tran-1, 2-cyclohexanediamine-N,N,N′,N′-tetra acetic acid) was the most effective in solubilization of α-L-Arafase, the enzyme might be solubilized in association with the pectic polysaccharides released by the

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chelating reagent. In contrast, although galactose was also released from the cell wall, only a little change in the activity of β-galactosidase was observed between the pre-ripe and ripe stages. Kitagawa et al. (1995) (from Tateishi et al., 1996) reported that one of the isoforms of β-galactosidase played a role in the release of galactose from pectic polymers of Japanese pears. Total pectin (TP) content comprised 0.4–0.5% of the fresh weight (FW). Total pectin in FW did not change significantly during the cell division and pre-enlargement stages but decreased rapidly in the enlargement stage and then remained constant in the ripening stage. When expressed on DNA content basis, its value clearly increased in the pre-enlargement stage and ripening stage, with little change in the cell division stage and enlargement stage. Water-soluble pectin (WSP) increased parallel to the increase in total pectin with ripening. Hence the ratio of WSP to TP content was constant, which contrasted with the increased ratio in European pear (Barlette pear) with ripening (Yamaki and Matsuda, 1977 cited from Miura et al., 1963).

11.3.4 Alcohol insoluble solids and solubilization of pectin Pectin constituents in fruits and vegetables are extracted from alcohol insoluble solids (AIS). The AIS form the principal constituents of cell walls and may be partially associated with each other and with some phenolic compounds. They are composites of salts, proteins, starch and different non-starch polysaccharides such as pectin, hemicelluloses and cellulose (Reinders and Their, 1998). The changes in AIS and pectin constituents of the mature Japanese pear (Hosui) at different picking dates were intensively investigated by the Kojima research group at Saga University, Japan (Sirisomboon et al., 2000b). While fruit weight and average diameter increased over the harvest period, the alcohol insoluble solids on fresh weight basis (AIS in FW) decreased. Change in AIS in fruit appears to be related to the change in the rate of decline on a FW basis and the increasing weight of the fruit. The soluble solids (SS) in juice increased consistently during the fruit enlargement. While AIS in FW significantly decreased during fruit enlargement, WSP, OSP (oxalate soluble pectin), NSP (non-soluble pectin), and TP in AIS increased. The increasing rate of WSP was relatively high. There was no significant change in WSP in FW with picking dates, but the OSP, NSP, and TP in FW significantly decreased. There was an increase in the soluble pectin ratios (OSP/TP and WSP/TP) and a decrease in the non-soluble pectin ratio (NSP/ TP). These show the occurrence of the solubilization of pectin in the fruit. Ben-Arie et al (1979a) reported that fruit softening was accompanied by the solubilization of the insoluble pectic substances to a soluble pectic fraction. Though the solubilization occurred, the NSP in AIS gradually increased because of the increase of TP in AIS. Similar patterns of soluble pectin ratios (WSP/TP and OSP/TP) and non-soluble pectin ratio (NSP/TP) changes have been reported by Yamaki and Matsuda (1977). The correlations between AIS in FW and TP in FW were very high. Therefore, the decline in TP in FW

Plant compounds and fruit texture: the case of pear 275

corresponded to the decline in AIS in FW. The decrease in AIS reflected in part the loss in the pectic substance (Ben-Arie et al., 1979a). Furthermore, the correlation between AIS in FW with WSP/TP was very high compared to that with OSP/TP, which indicated that the loss in pectic substances was predominantly due to the solubilization of NSP to WSP and partially due to that of NSP to OSP. This indicated that the binding force of OSP with fruit tissue may be stronger than WSP.

11.3.5 Oxalate soluble pectin In addition, the Takayuki Kojima research group also proposed a simplified method for determining the total OSP content of Japanese pear (Sirisomboon et al., 2001). The OSP is related to fruit firmness with respect to the chelation of calcium ions with carboxyl groups of adjacent polyuronide chains. However, an accurate total OSP content is not easy to attain. They proposed a simple method to define extraction time of total OSP in fruit without repetition based on half-extraction time which is the time when 50% of OSP was extracted. There were two stages of extraction – primary and secondary – and half-extraction was in the former. In the primary stage, different picking dates affected neither the extraction dates nor the ratio of unaccomplished extracted OSP content to total OSP content. However, in the secondary stage, different picking dates affected both (P < 0.01). The extraction time of total OSP could also be estimated. The method recommended that the extraction should be done until half-extraction time was reached and then the total OSP could be estimated by multiplying the pectin content at that time by two. Half-extraction time of Japanese pear was 1.5 hr. The Kojima research group also postulated that the method might prove useful for other fruit and vegetables as well.

11.4 The effect of constituents on fruit texture The development of fruit can be divided into cell division, pre-enlargement, enlargement and ripening stages. Yamaki et al. (1979b) described the importance of cell wall constituents during fruit growth as follows. Cell division in fruit generally continues only for a short period after fruit setting, and the fruit enlarges dramatically by expansion and elongation of cells until ripening. Ripening is the series of changes occurring during the early stages of the senescence of fruits in which the structure and composition of the unripe fruit is altered so that it becomes acceptable to eat (Rhodes, 1980). Ripening involves complex changes in the metabolism of the fruit in which both anabolic and catabolic processes take place. Some of the degradative processes involved in changes in adhesion between the cells lead to tissue softening. Tucker (1993) found that fruit softening arises from three mechanisms: loss of turgor, degradation of starch, or breakdown of the fruit cell walls.

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Loss of turgor is largely a non-physiological process associated with the post-harvest dehydration of the fruit. Loss of water has only a minor effect on the fruit’s biochemistry, equivalent to about 5–10% of the fruit’s fresh weight. Degradation of starch probably results in a pronounced textural change, especially in fruit like banana, where starch accounts for a high percentage of fresh weight. In general, however, texture change during the ripening of most fruit is thought to be largely the result of cell wall degradation. The degradation of cell walls is exceedingly complex and is connected with some cell wall-degrading enzymes and, among these enzymes, pectin was one of the most strongly implicated in fruit softening (Yamaki and Kakiuchi, 1979). Pectin constituents, a complex of non-starch polysaccharides, in fruit and vegetables play an important role in harvest maturity and ripening and storage of fruit and vegetables which is related to the texture change in them (Shewfelt et al., 1971; Batisse et al., 1996; Taylor et al., 1995; Yu et al., 1996; Ketsa and Daengkanit, 1999).

11.4.1 Pear turgidity García et al. (1995) investigated the susceptibility to bruising of Blanquilla and Conference pears. They concluded that fruits at harvest were more susceptible to bruising than fruits after storage, due to a decrease in fruit turgidity; fruits picked early were less susceptible to bruising than those picked later, due to a decrease in fruit firmness; and fruit turgidity and firmness influenced bruise susceptibility independently although their effects combine during fruit ripening (see the texture model of pear in Section 11.2.3). The turgidity was explained in terms of the deformation at skin puncture (DSP). It was the parameter most related to weight loss, mainly due to water, and turgidity decrease was associated with water loss (García et al., 1995 citing from Strasburger et al., 1985). DSP appeared to be related to fruit turgidity. Turgid fruits exhibited different impact response compared to less turgid ones. Blanquilla pears with low values of DSP (turgid fruits) showed high values of bruise volume because the stresses in the tissue might be higher. Baritelle et al. (2000) reported that temperature and turgor effects were highly significant for failure stress, failure strain, secant elastic modulus, tissue toughness and shock wave speed; however, the cultivars responded in different and opposite ways. For Barlette pears, as temperature and turgor increased, the failure stress and strain decreased, while the opposite was true for the D’Anjou cultivar. The important finding of this work was that the small strain property, shock wave speed, decreased significantly with decreasing turgor.

11.4.2 Pectin constituents and related compounds The relationship between texture and pectin constituents and some related compounds of the Japanese pear (Pyrus serotina Rehder var. culta Housui)

Plant compounds and fruit texture: the case of pear 277

during fruit enlargement and ripening was intensively analyzed by the Kojima research group at Saga university, Japan (Sirisomboon et al., 2000b). Among the studied constituents, the alcohol insoluble solids in fresh weight (AIS in FW) appeared to have a significant effect on the textural properties of the fruit. The textural properties, which correlated differently with the pectin content in AIS and with that in FW, indicated the different effects of the fruit cell wall and fruit flesh (including skin) on the texture of the fruit during fruit enlargement. This study showed for the first time evidence for the close relationship of the pectin constituents in determining the fruit texture of the Japanese pear. Three textural properties from the plate compression test – namely, the deformation ratio, modulus of elasticity, and relaxation – and nine from the puncture test – rupture force, deformation at the rupture point, toughness, average firmness, initial firmness, apparent modulus of elasticity, bioyield force, penetrating force in the flesh, and penetrating energy in the flesh of the Japanese pear – changed simultaneously with the average diameter and weight of the fruit and were affected by the pectin constituents, AIS and SS. Hardness and deformation of the fruit obtained from the plate compression test seemed to depend on the combination of the AIS in FW and the weight of the fruit. They found that the oxalate soluble pectin (OSP) seemed to play a more significant role in fruit texture than the water soluble pectin (WSP). The OSP both in AIS and in FW affected the fruit texture more than the WSP. Yu et al. (1996) reported that OSP affected strawberry firmness more than WSP, which might be because OSP exists as pectic acid which could bind calcium and form a cross-linked structure. Neal (1965) indicated that the chelation of calcium ions with carboxyl groups of adjacent polyuronide chains is a key factor for fruit firmness and Knee (1993) described how a major site of calcium deposition is in the junction zones in the middle lamella. However, the solubilization of the WSP appeared to influence the textural properties more than that of the OSP. To verify the effects of the pectin solubilization, the pectin ratio was determined. Due to the highest correlation coefficient (r) value among the pectin ratios, the WSP/TP ratio correlated best with deformation ratio, modulus of elasticity, relaxation value, and energy absorption and all the textural properties obtained from the puncture test (Sirisomboon et al., 2000b). These results confirmed the effect of the solubilization of NSP to WSP during cell wall degradation (Ben-Arie et al., 1979a) on the texture of fruit, whereas the solubilization of NSP to OSP had less effect on fruit texture than that of WSP. In the case of European pears, the relationship between flesh firmness and cell wall polysacharides of Marguerite Marillat and La France pears (Pyrus communis L.) after different storage periods was investigated by Murayama et al. (2002). In both cultivars fruit softened and reached a buttery and juicy texture after short-term storage (one month at 1 °C), while fruit softened but never reached that texture after long-term storage (4–5 months at 1°C). There was a cultivar difference in the relationship between flesh firmness and water-soluble polyuronide content. For La France, the lower the flesh

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firmness, the higher the water-soluble content after short-term storage, while lower flesh firmness was correlated with lower water-soluble polyuronide content in fruit after long-term storage. The amount of water-soluble polyuronides in Marguerite Marillat varied conversely with decreasing flesh firmness for both types of storage, although water-soluble polyuronide levels of fruit after long-term storage tended to be lower than those of fruits after short-term storage when compared to fruit of similar flesh firmness. Murayama et al. (2002) indicated that the lower amount of water-soluble polyuronides seems to be related to the inferior texture of pear fruit after prolonged storage. The chelator-soluble polyuronide content was lower than water-soluble polyuronide and alkali-soluble polyuronide contents, independent of cultivar and storage period, and the amount was similar for both storage periods for both cultivars. Murayama et al. (2002) indicated that the chelatorsoluble polyuronide content seems not to be crucial in inducing dry texture. They found that the alkali-soluble polyuronide contents showed the highest correlation to flesh in both cultivars among cell wall polysaccharides. The lower the flesh firmness, the lower the alkali-soluble polyuronide contents independent of cultivar and storage period. With this result and the report of Murayama et al., 1998 (from Murayama et al., 2002) that the alkali-soluble polyuronide content decreased during softening of pears. Murayama et al., 2002 indicated that the amount of alkali-soluble polyuronides seems to be associated with the softening of pears. Fruit after short-term storage contained more alkali-soluble polyuronides than fruit after long-term storage in both cultivars, when compared with fruit having similar flesh firmness. Murayama et al. (2002) indicated that the extensive decomposition of alkali-soluble polyuronide occurred during storage at 1 °C in both cultivars and suggested that the alkali-soluble polyuronides were thought to metabolize and become water-soluble polyuronides during normal ripening of pears. However, water-soluble polyuronide levels after long-term storage also tend to be lower than those of fruit after short-term storage when compared with fruit having similar flesh firmness as mentioned above. Murayama et al. (2002) described that water-soluble polyuronides were further metabolized so that they were alcohol-soluble and lost from the alcohol-insoluble residue. Again the lower content of pectic polysaccharides after prolonged storage seems to be related to the inferior texture of fruit. In addition, Murayama et al. (2002) indicated that the differences in the amount of hemicellulosic polysaccharides between hard and soft fruit were slight and there was no negative correlation between flesh firmness and cellulose content, independent of cultivar and storage period. This leads to their conclusion that cellulose is not degraded during ripening of pears and that the amounts of hemicellulosic polysaccharides and cellulose seem not to be crucial in inducing inferior texture in pears.

Plant compounds and fruit texture: the case of pear 279

11.5 Use of near infrared spectroscopy (NIR) to evaluate textural properties Near infrared (NIR) spectroscopy which can avoid the complexity of sensor contact and associated handling problems (Delwiche et al., 1996) has recently been studied as a means of evaluating the texture of intact apples by Cho et al., 1996, Onda et al., 1996, Sohn and Cho, 2000, Lu et al., 2000 and Park et al., 2002; plums by Onda et al., 1996; pear by Sirisomboon, 2001; sweet cherries by Lu, 2001. As described by Robert and Cadet (1998), the spectral information obtained in the NIR spectral region corresponds to the harmonics and combinations of the fundamental vibrations observed in the mid-infrared. These absorptions are of lower intensity and are less well-resolved, and they contain only part of the information content available in the mid-infrared region. The NIR spectra of carbohydrates can be split into two distinct and characteristic parts: the first part corresponds to the 1100–1800 nm wavelength region, and this is characteristic of the first and second harmonics of the OH and C-H stretching frequencies. The secondary part corresponds to the 1800–2500 nm wavelength regions and is characteristic of the combination bands of the O-H and C-H stretch. The possibility of determining the degree of esterification of pectic polymers of fresh, pre-heated and sterilized green beans using NIR spectroscopy was reported by Boeriu et al., 1998. They reported that the absorption of the methoxyl group at 2248 nm was clearly seen in the spectra of pectins. This result corresponds with the study of Sohn and Cho (2000), which indicated that the characteristic bands of the methoxyl group were at around 2250 nm. Sohn and Cho (2000) also reported that low accuracy was obtained for the estimation of the water-soluble pectin content of apple compared with that measured by a reference method. In addition, they found that the ability of NIR to predict methoxyl content in AIS was better than in apple fruit. The Takayuki Kojima research group at Saga University, Japan conducted an intensive analysis of texture and pectin constituents of Japanese pears (Pyrus serotina Rehder var. culta Housui) during fruit enlargement and ripening to clarify the relationship between the textural properties and pectin constituents and to develop the non-destructive method of evaluating pectin constituents and textural properties of the fruit using NIR spectroscopy (Sirisomboon, 2001). The NIR spectroscopy (1100–2500 nm at 2 nm intervals at 25 °C room temperature) was applied to estimate pectin constituents and textural properties for both intact Japanese pears and their juice. The NIR reflectance spectra of intact Japanese pears were measured by fiber optics in interactance mode. Before being measured, fruits were placed in a water bath to keep fruit temperature at 28 °C. Fruit was placed in a black box and the equatorial area was scanned. The NIR spectra of the juice were measured by diffuse transflectance. The juice sample was dropped on an aluminum cell for liquid sample (British cup) and pressed with a glass plate. The liquid layer for this

280

Texture in food

cell for data acquisition had a 30 mm diameter and 0.1 mm thickness. The NIR spectra of AIS were also measured using diffuse transflectance. The AIS was put and leveled with an angle spatula on a British cup and pressed with a glass plate as was done for the juice. The 210 spectra of intact Japanese pear and juice (two spectra per fruit) and 105 spectra of AIS (one spectra per fruit) were analyzed. Scanning was performed once per spectrum. Pure pectin from citrus fruits which contained galacturonic acid 76% and methoxy content 8.6% without sucrose or other sugars (Sigma Chemical Co., USA) and D-α-galacturonic acid (Katayama Chemical Co., Ltd, Japan) was dissolved in distilled water at concentrations of 100 µg/ml for the triplicate samples and for 60 µg/ml for the duplicate samples. The NIR spectra of those solution samples and the triplicate samples of pure pectin powder were measured following the methods used for juice and AIS, respectively. The results were as follows. The characteristic pectin absorption bands of the NIR spectra of intact fruit, juice and AIS of Japanese pear were proposed. The absorption at 2110 nm in juice spectra was partly due to the absorption of pectin. However, the 2242 nm might be more suitable for determining the pectin content in juice. For intact fruit spectra, the pectin absorption bands were observed at 1590, 1730 and 2362 nm. The 1590 nm was proposed for determining pectin content in AIS and the 1730 and 2362 nm for pectin content in fresh weight. For AIS spectra, the 1726 nm could be the suitable wavelength for determining the pectin content in AIS. The 1670–1674 nm were proposed for measuring the textural properties by intact fruit spectra. Intact spectra provided fairly accurate predictions for AIS in FW and OSP in AIS. In addition to those constituents, juice spectra could also provide predictions for WSP and TP in AIS. The textural properties capable of prediction included deformation at rupture point, rupture force, toughness, average firmness, penetrating force in the flesh and modulus of elasticity of both intact fruit and juice of Japanese pear and penetration energy in the flesh of juice-only spectra. It seemed that the intact spectra provided an accurate prediction of those textural properties compatible with the juice spectra.

11.5.1 Correlation spectrum Simple correlation coefficients between constituents (textural properties and pectin constituents) and absorbance at each measured wavelength for original intact spectra were improved by second derivative transformation. However, the correlation spectrum of juice spectra did not show any improvement in correlation values. The common three broad bands of the high correlation coefficients between both textural properties and pectin constituents with absorbance at each measured wavelength were observed in juice spectra including 1100–1300 nm, 1550–1850 nm, and 2100–2300 nm (Figs 11.4, and 11.5). Therefore, the pectin constituents in juice and textural properties

–0.60

–0.70

–0.70

–0.80 –0.90

–0.80 2166–2278 1100–1306 1600–1716

–0.90

Rupture force

–1.00

–1.00

0.00 –0.10 –0.20 –0.30 –0.40 –0.50 –0.60 –0.70 –0.80 –0.90 –1.00

–0.40

Toughness

–0.60 –0.70 2188–2274 1686–1752 1100–1300

Average firmness

–0.80 –0.90

–0.40 –0.50

1100–1300

1816 2084 1796

–0.70

1600– 1800

2150– 2250

2228 2252

–0.60

Penetrating energy into the flesh –1.00

1100 1300 1500 1700 1900 2100 2300 2500

Wavelength (nm)

1100–1316 1550–1800

2162–2272

Penetrating force in the flesh

–1.00

–0.50

–0.90

2162–2278 1100–1304 1610–1730

–0.50

–0.40

–0.80

1868

–0.50

–0.60

1776

–0.40

–0.50

1644

–0.30

–0.40

1132 1228 1236 1360 1364

–0.30

1364

Correlation coefficient

Correlation coefficient

Correlation coefficient

Plant compounds and fruit texture: the case of pear 281

–0.60 –0.70 –0.80 –0.90

1100–1362

1596–1822

2162–2272

Modulus of elasticity

–1.00 1100 1300 1500 1700 1900 2100 2300 2500

Wavelength (nm)

Fig. 11.4 Correlation spectrum between textural properties with absorbance at each wavelength measured on juice (original spectra). Indicated wavelengths typed vertically were independent variables in each calibration equation and those typed horizontally were the highest correlation wavelengths.

related to the same vibration bonds. Intact original spectra also showed the same results with narrower broad bands at around 1404–1474 nm and 1900– 1966 nm. This indicated that the textural properties were correlated well with the pectin constituents.

11.5.2 Calibration equations for prediction Using multiple linear regression (MLR) calibration equations developed from intact fruit spectra and juice spectra, AIS in FW and OSP content in AIS

0.70

–0.50

0.60

0.40 0.30

0.80 2210– 2282

0.70 1100– 1300

0.60

0.60

0.50

0.50

1650– 1758

1860

1622– 1828

2332 2348

0.90

1852 2080

0.90

0.70

OSP in AIS

AIS in FW

0.20

1334

2144– 2276

0.50

–0.90

0.80 1100–

1632– 1810

2220

2166– 2280

2388

1632– 1810

1884

–0.70 1100– –0.80 1300

1388

–0.60

1100– 1328

2268 2316

–0.40

1852

0.80

1344

0.90

–0.30

2416

–0.20

1196

Texture in food

1544

Correlation coefficient

Correlation coefficient

282

2156– 2282

0.40

0.40 TP in AIS

WSP in AIS

0.30

0.30 1100 1300 1500 1700 1900 2100 2300 2500

Wavelength (nm)

1100 1300 1500 1700 1900 2100 2300 2500

Wavelength (nm)

Fig. 11.5 Correlation spectrum between pectin constituents and alcohol insoluble solids with absorbance at each wavelength measured on juice (original spectra). Indicated wavelengths typed vertically were independent variables in each calibration equation and those typed horizontally were the highest correlation coefficient wavelengths.

could be fairly accurately predicted. In addition, the equations from juice spectra, the WSP, OSP and TP in AIS were fairly accurately predictable. The textural properties, including rupture force, toughness, average firmness, penetration force in the flesh and modulus of elasticity of both intact and juice spectra, and penetration energy in the flesh of juice spectra, could be sufficiently accurately predicted. The best equation for each constituent was selected based on the lowest standard error of prediction (SEP); multiple correlation coefficient (R), which was equal to or higher than 0.90; the ratio of the standard deviation of reference data for prediction set to SEP (SD/ SEP), which was equal to or higher than 3; and the ratio of the range in reference data for prediction set to the SEP (RER), which was more than 10. However, when SD/SEP was less than 3 and RER was less than 10, the best equation based on an RER of more than 10 for at least one tenth of the range was selected (Williams and Sobering, 1996). The calibration equations obtained could predict only for the year, but could not be used to predict the data in another year. Data collection over many years is highly recommended in order to improve the calibration equations. This study (Sirisomboon, 2001) showed for the first time the

Plant compounds and fruit texture: the case of pear 283

potential of NIR spectroscopy to measure the textural properties and pectin constituents of Japanese pear. The MLR equations (Table 11.2) developed from intact fruit spectra could predict mostly the same constituents of pectin and textural properties as those from juice spectra. The NIR absorbance of the spectra for intact fruit seemed to correlate better with the constituents than that of the spectra for juice. This leads to the effective non-destructive measurement of the texture and pectin constituents of fruit. NIR spectroscopy allows the evaluation of those inner properties of intact fruit. It also enables assessment of the freshness of fruit to be used for the commercial production of juices and beverages.

11.6 Future trends 11.6.1 Role of functional protein on texture of fruit The role of structural protein cross-links is evaluated within the context of cell-wall-mediated changes in texture during fruit ripening (Brownleader et al., 1999). Only a little of the investigation has been done and the conclusions show that intensive research is needed. The investigations of El-Buluk et al. (1995) showed that protein content measured by the AOAC (Association of Analytical Communities) method decreased markedly with fruit growth and development and that the texture measured by plunger-type pressure tester declined gradually during fruit development for all guava cultivars studied. That the softness of the fruit was associated with lower protein might be due to the fruit utilizing protein for growth and development which correspond with a rapid increase in the ratio between protein catabolism and anabolism which the study also found. Bartolome et al. (1995) measured the texture and soluble protein in Red Spanish and Smooth Cayenne pineapple fruit. They found that the values for texture, measured using a Kramer shear test, were lower for Red Spanish than for Smooth Cayenne (although the difference was not statitistically significant) and that the soluble protein, measured using a Bio-red kit, was higher for Red Spanish. It was thought that the soluble protein might be related to the lower total dietary fiber. Cano et al. (1997) measured along with several biochemical and physical constituents the soluble protein (also by Bio-red kit) and texture (by Kramer shear test) of bananas. The texture differences among cultivars did not correspond with their soluble protein. The use of suitable measuring methods and measured constituents may lead to greater understanding of the role of protein in fruit texture. 11.6.2 Application of a simplified method for the determination of total oxalate soluble pectin content in fruit and vegetables The chelation of calcium ions with carboxyl groups of adjacent polyuronide chains is a key factor in fruit firmness (Neal, 1965), with calcium deposition occurring in the junction zones in the middle lamella (Knee, 1993). Any

λ3

1886

1752

1388 1860 2268 1776

Step Best Best Best 1884* 2220 2316* 1368

2228

2258 1360 1806 1658

2416 1544* 1344 2064

2388 2080 1196 1712

2266* 2418

2378

Best pair 2nd derivative 2194

2252* 1816

2084

2214* 1868* 2210 1758*

2362 1776 1886 2086

1658 1132 2266* 1318

λ5

2094 2332 1852 1852 1200* 1452

2306

1796

2274

2210 1364

1322 1228

1238

1604

1736

λ6

1814 1376

2006

1308* 1636 1726 1100 1588 1680* 1804

JUICE SPECTRA

1630* 1542 1622* 1318

1672* 1280

1116 2126 2408 1104

Step up 2nd derivative Step up original spectra Best pair 2nd derivative Step up/Best pair 2nd derivative Best pair original spectra

up original pair original spectra pair original spectra pair original spectra

λ4

INTACT SPECTRA 1128 1666* 1664* 1640

λ2 λ9 R SEC SEP Bias

SEP

SD

0.93 0.95

2348

1878 2042

0.93 0.91 0.91 0.94

0.95

0.91

1554 1574 0.96 1236 1364 1644 0.96 0.91 2038 1614 0.92

2042 1562

0.92

0.62 –0.12 8.48 –0.47

2.74

0.20

2.2**

2.6** 2.9** 2.2** 2.5**

2.1** 1.7**

0.53 0.63 0.13 1.51 1.41 0.16 7.34 7.93 –0.95 9.53 11.52 2.57

2.3** 2.1** 1.9** 2.0**

0.025 0.029 0.005 2.8**

2.91

0.89 1.09 0.11 9.83 10.61 –0.53 0.69 0.64 0.16 0.56 0.50 –0.06

0.57 5.65

0.003 0.003 0.001 2.3**

0.96 0.90 1.28 0.17 2.5** 0.92 13.38 12.96 –0.51 2.3** 0.91 0.69 0.73 –0.008 1.9** 1584 1784 1112 0.96 0.39 0.52 –0.07 2.4**

λ8

2460 1724

λ7

ηWavelengths in bold letters indicate the proposed wavelength ranges of pectin (1522–1638 nm, 1694–1762 nm, 2074–2202 nm and 2334–2410 nm) or methoxyl group (2202–2282 nm). *Most significant wavelength in each calibration equation determined by t-test. **The data predicted at RER > 10 with an accuracy of at least one tenth of the range. AIS: alcohol insoluble solids. OSP: oxalate soluble pectin. WSP: water soluble pectin. TP: total pectin. FW: fresh weight.

Puncture test Rupture force Toughness Average firmness Penetrating force in the flesh Penetrating energy in the flesh Flat plate compression test Modulus of elasticity Pectin constituents AIS in FW WSP in AIS OSP in AIS TP in AIS

1350 2302

Best pair 2nd derivative Step up/Best pair 2nd derivative

2280 1310 1636 1456 1636

up original spectra pair 2nd derivative up original spectra up original spectra

λ1

Wavelength selected

Best pair original spectra

Step Best Step Step

MLR method

MLR of reflectance measurement for textural properties and pectin constituents of intact Japanese pear and juiceη

Puncture test Rupture force Toughness Average firmness Penetrating force in the flesh Flat plate compression test Modulus of elasticity Pectin constituents AIS in FW OSP in AIS

Constituents

Table 11.2

284 Texture in food

Plant compounds and fruit texture: the case of pear 285

increase in free pectin carboxyl groups might be expected to increase the importance of calcium as a firmness increasing agent (Van Buren, 1979). Calcium binding agents, such as solutions of oxalate, ethylenediaminetetraacetic acid (EDTA) or polyphosphate, have been used to extract pectic acid from fruits (Pilnik and Voragen, 1971). For example, solutions of oxalic acid and ammonium oxalate are used to extract the pectic acid in the form of OSP. Oxalate soluble pectin exists as pectic acid which could bind calcium and form a cross-link structure (Yu et al., 1996). Researchers using various methods to extract OSP have reported differing quantites of OSP which might not represent the total. This gave rise to differences in or misinterpretations of the data, such as different correlations between pectin content and fruit firmness, contrasting comparisons of the solubilization of different kinds of pectin and so on. Moreover the time required to completely extract the pectin was possibly too long to be practicable. The method proposed by Sirisomboon et al. (2001) and described in Section 11.3.5 offers the potential of reducing the extraction time. The method recommended that the extraction should be done until half extraction time was reached and the total oxalate soluble pectin could be estimated by multiplying the pectin content at half extraction time by two. The method may be applicable to the extraction of other kinds of pectin or constituents contained in fruit. Therefore, this simplified method for the determination of total OSP content in fruit and vegetables could benefit researchers in different sectors, especially agricultural science, medical science, food science, pharmaceuticals and cosmetics, by enabling accurate and easy extraction of total pectin constituents.

11.6.3 Methodology for measuring actual and estimating bruise volume and bruise threshold The results of Kawano et al. (1984) for Japanese pear Nijisseiki indicated that the quasi-static compression test showed a difference in the absorbed energy per unit bruised volume for different harvesting dates whereas the impact test showed no difference. This might be due to the difference in the shape of the bruise. Although both impact and compression cause bruising to spread inwards from immediately under the skin, the impact bruise spreads radially and the compression bruise conically (Tsukamoto, 1981), a fact which might make the bruise volume estimation incorrect. The compression bruise pattern is similar to that found in European pears (Chen et al., 1987). The difference in patterns of impact and compression bruises indicates that the loading rate greatly affects the pattern of failure in Asian pears, and the irregular pattern of the impact bruise made it difficult to quantify the degree of bruising due to that cause (Chen et al., 1987). Bruise volume was generally described as a parabolic shape which could be used to estimate bruise area or volume. However, it is not capable of estimating the area of the irregularlyshaped impact bruise (Chen et al., 1987). Bollen et al. (1999) compared several methods for estimating the size of apple bruises caused by impact

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test against the actual volume measured using a sectioning and image analysis technique. They concluded that all the volume estimation methods induced errors in prediction and there was not one single method which could be used for the estimation of bruise volume over the whole range of commercially significant impacts. The bruise threshold, which is the drop height at which bruising begins to occur for a given specimen mass, curvature, and impact surface (Varith et al., 2001 citing from Bajema and Hyde (1998), of apples was studied by Varith et al. (2001) to gain information to develop management tools to reduce bruising for fruits and vegetables. They used the dynamic axial compression (DAC) prediction equation for bruise threshold which Baritelle and Hyde (2001) developed based on Horsfield et al. (1972) in which the Hertz contact theory was applied. The model equates tissue failure stress to impact-induced stress at bruise threshold, and it was compared with the paired increasing-height multiple-impacting bruise threshold (PIHMI). The PIHMI detects bruise threshold in individual apples by comparing force profile pairs from successively higher drops of the specimen at the same location onto a rigid surface (Bajema and Hyde, 1998 citing from Varith et al., 2001). Varith et al. (2001) showed that when the drop height reaches the bruise threshold, the pair of profiles for the same drop height will differ because one will have more tissue failure than the other. They indicated that the method works for commodities only such as apples whose tissues fail in compression near the surface but it fails with potato and pear tissues which have higher Poisson’s ratios and fail in shear rather than compression. The results reconfirmed PIHMI as a promising technique for determining bruise threshold in apples, and showed that DAC could also predict bruise threshold. DAC showed the best results when grouped by turgor, reconfirming the strong dependency of bruise threshold on apple turgor. Practically speaking PIHMI bruise threshold is an easier process to use than DAC, although DAC is more sensitive to the post-harvest behavior of fruits and vegetables, e.g. turgor effect. There is a need for more research to find an accurate, sensitive and easy method to detect bruise threshold for pears and other fruit and vegetables. The machine vision procedure using visible and NIR ranges to detect bruising in sweet cherries was studied by the Daniel Guyer research group at Michigan State University, USA (Guyer et al., 1996) and in strawberries by the Masateru Nagata research group at Miyazaki University, Japan (Shrestha, 2002). This image processing technique can be used to detect, measure actual and estimate bruise volume and bruise threshold. For cherries, the bandpass filters that enhance the intensity contrast between bruised and unbruised fruits were determined. An optimum combination of two wavelengths is identified at 750 nm (NIR range) and 500 nm (green range). An optimum single wavelength is identified at 750 nm. Bruise detection via infrared edge detection had the least error potential which suggested the idea of implementing edge detection to detect the transitive area between good and bruised tissues.

Plant compounds and fruit texture: the case of pear 287

For strawberries it was concluded that the a* level of the L*a*b* model was especially able to represent the bruises on the strawberry surfaces and the discoloration on bruises caused by a comprehensive force of less than 2 N was not detected. The images of non-bruised and bruised strawberry were taken at 860 nm and 960 nm filters, respectively. The former image was subtracted from the latter one. The method of image subtraction using the same strawberry has given 100% accuracy. However, for on-line sorting, the subtraction of the standard image from the on-line image resulted in an error of 5–10%. The study has confirmed the possibility of using the standard strawberry image to sort and judge bruises on strawberry.

11.6.4 Cell wall change and sensory evaluation The textural properties measured by mechanical methods may or may not yield a good correlation to human response (Jeon et al., 1973; Diehl and Hamann, 1979; Robertston et al., 1984; Bartolomé et al., 1996). Delwiche et al. (1996) suggested that future research should address the question of which physical properties are most closely related to human perceptions of fruit texture. Any properties or combinations of properties which are related to cell wall change or failure during mastication might be indicators of sensory perception. Cell wall and the middle lamella, their quantity and mechanical properties, are very important in determining how the plant food stuff will behave under the disintegrating action of the forces applied to it during mastication (Ilker and Szczesniak, 1990). Ilker and Szczesniak described as follows. If the cell wall is stronger than the middle lamella, the tissue will yield between the cell and the cell contents will not be released during mastication. The sensory perception will be that of a dry, chalky granular texture, e.g. raw potatoes. If the cell wall is weaker than the middle lamella, the yielding will occur through the cells and, as a result, the liquid contents will be released. The sensory perception will be that of a juicy product, e.g. good quality pears. The cell wall physical change may give a better correlation with the sensory evaluation than the texture properties obtained from whole fruit measurement, and the puncture test may provide better correlation than the plate compression test. Further investigation of how cell wall physical change is correlated with sensory evaluation may help to answer the question of why the textural properties measured by mechanical methods may or may not yield a good correlation to human response. Multivariate calibrations could help with mathematical manipulation of texture property variables to find combinations which offer a positive sensory experience to human consumers. 11.6.5 Development of texture model for non-destructive determination Destructive methods of measuring fruit texture cannot be used for on-line control of fruit quality (Dewulf et al., 1999). At the time of writing the well-

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known, non-destructive methods of detecting fruit texture which have been investigated are the acoustic resonant measurement technique and the NIR spectroscopy technology. With regard to the former De Belie et al. (2000) expressed the following view: the acoustic impulse response technique is a fast, non-destructive firmness measurement normally applied to spherical fruit such as apples, peaches and tomatoes. Even the stiffness factor, which is calculated from the first resonance frequency and the mass of the fruit, is found to be significantly correlated with fruit f irmness and sensory measurement. However, it is not immediately clear whether the acoustic impulse response technique, which was initially developed for spherical fruit, is equally applicable to non-spherical fruits such as pears. This opinion showed that research is needed to clarify whether the technique is applicable to non-spherical fruits as well. The latter, NIR spectroscopy technology, as described in Section 11.5, is effectively able to evaluate the soluble solids of fruit non-destructively via the multivariate model. The soluble solids content of pears was reasonably well (negatively) correlated with the texture measures (De Belie et al., 2000; Sirisomboon et al., 2000b). Progress towards applying NIR spectroscopy technology to estimate textural properties and the flavor of fruit and vegetables in situ will be interesting to watch.

11.7 Sources of further information and advice • Journal of Texture Studies Food and Nutrition Press, 6527 Main Street, PO Box 374, Trumbull, Connecticut, 06611, USA. Publishes original research, reviews, and discussion papers on rheology, psychorheology, physical testing and sensory testing of foods. • Journal of Food Composition and Analysis (http://www.sciencedirect.com/science/journal/08891575) By Academic Press: an international journal, it is the official publication of INFOODS, and is co-sponsored by The United Nations University and the Food and Agriculture Organization of the United Nations. This journal is devoted to all scientific aspects of the chemical composition of human foods, and emphasizes new methods of analysis; data on composition of foods; studies on the manipulation, storage, distribution, and use of food composition data; and studies on the statistics and distribution of such data and data systems. The journal is looking to build on its strong base in nutrient composition and to place increasing emphasis on other food components such as anti-carcinogens, natural toxicants, flavors, colors, functional additives, pesticides, agricultural chemicals, heavy metals, general environmental contaminants, and chemical and biochemical toxicants of microbiological origin. • Postharvest Biology and Technology (http://www.elsevier.nl/locate/inca/503313) It is an international scholarly

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journal covering biological and technological research in the area of post-harvest systems for agronomic and horticultural crops. It is published in three volumes of three issues per year by Elsevier Science. • Journal of Biosystems Engineering (http://www.sciencedirect.com/science/journal/15375110) By Academic Press. • Academic Press The remit of Biosystems Engineering is research in the physical sciences and engineering to understand, model, process or enhance biological systems for sustainable developments in agriculture, food, land use and the environment. Topics are broadly classified under nine interest fields and each paper is allocated to the most appropriate category: Automation and Emerging Technologies (AE), Information Technology and the Human Interface (IT), Precision Agriculture (PA), Power and Machinery (PM), Postharvest Technology (PH), Structures and Environment (SE), Animal Production Technology (AP), Soil and Water (SW), Rural Development (RD). • Transactions of the ASAE (http://asae.frymulti.com/) Published six times a year (February, April, June, August, October, December) by the American Society of Agricultural Engineers, 2950 Niles Rd, St Joseph, MI 49085–9659, USA. Founded in 1907, ASAE is a professional and technical organization of members dedicated to the advancement of engineering applicable to agricultural, food, and other biological systems including the environment and natural resources, and to associated industries. Topics are broadly classified under five interest fields and each paper is allocated to the most appropriate category: Power and Machinery, Soil and Water, Food and Process Engineering Institute, Structure and Environment and Information and Electrical Technologies.

11.8 Acknowledgement The authors wish to express their appreciation to Woodhead Publishing Ltd for the honor of being asked to write this Chapter 11; to Prof. Paul Chen and Dr Junichi Sugiyama for their contributions of information and the photos of the pear firmness tester; and to the internet database for research journals, abstract databases and reference works: ScienceDirect (http://www.sciencedirect.com), SpringerLink (http://link.springer.de/home.hml), ASAE technical library (http:/ /asae.frymulti.com/), International Conference on Noise & Vibration Engineering (ISMA) at Katholieke Universiteit Leuven (http://www.isma-isaac.be/), BlackwellSynergy list of journals (http://www.blackwell-synergy.com/), Nordic Association of Agricultural Scientists www.njf.dk/njf/NJ-Abstracts/ oversight.htm

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11.9 References and HYDE G M (1998) Instrumented pendulum for impact characterization of whole fruit and vegetable specimens, Trans ASAE, 41(5), 1399–405. BARITELLE A and HYDE G M (2000) Strain rate and size effects on pear tissue failure, Trans ASAE, 43(1), 95–8. BARITELLE A L and HYDE G M (2001) Commodity conditioning to reduce impact bruising, Postharvest Bio and Technol, 21(3), 331–9. BARITELLE A L, HYDE G M and VARITH J (2000) Turgor and temperature effects on pear tissue failure, www.bsyse.wsu.edu/gmhyde/impact-pdfs/pdf.mss-impact-properties/996003pear-dac.pdf/pear-turgor-temp.pdf BARITELLE A L, HYDE G M, FELLMAN J K and VARITH J (2001) Using 1-MCP to inhibit the influence of ripening on impact properties of pear and apple tissue, Postharvest Bio and Technol, 23(2), 153–60. BARTOLOMÉ A P, RUPERÉZ P and FÚSTER C (1995) Pineapple fruit: morphological characteristics, chemical composition and sensory analysis of Red Spanish and Smooth Cayenne cultivars, Food Chemistry, 53(1), 75–9. BARTOLOMÉ A P, RUPERÉZ P and FÚSTER C (1996) Non-volatile organic acids, pH, and titratable acidity changes in pineapple fruit slices during frozen storage, J Sci Food Agric, 70, 475–80. BATISSE C, FILS-LYCAON B and BURET M (1994) Pectin change in ripening cherry fruit, J Food Sci, 59(2), 389–93. BATISSE C, BURET M and COULOMB P J (1996) Biochemical differences in cell wall of cherry fruit between soft and crisp fruit, J Agric Food Chem, 44, 453–7. BEN-ARIE R, SONEGO L and FRANKEL C (1979a) Changes in pectic substances in ripening pears, J Amer Soc Hort Sci, 104(4), 500–5. BEN-ARIE R, KISLEV N and FRENKEL C (1979b) Ultrastructure changes in the cell walls of ripening apple and pear fruit, J Plant Physiol, 64, 197–202. BERTELSEN M (2001) Preliminary results with Aweta acoustic fruit firmness sensor. Measurement methods of soil, water, plant and fruit, in berry and fruit growing, Nordic Association of Agricultural Scientists, NJF seminar no 333, 29–30 November Swedish University of Agricultural Sciences, Alnarp, Sweden. www.njf.dk/njf/NJAbstracts/oversight.htm BOERIU C G, STOLLE-SMITS T and VAN DIJK C (1998) Characterisation of cell wall pectins by near infrared spectroscopy, J Near Infrared Spectroscopy, 6, A299–A301. BOLLEN A F, NGUYEN H X and DELA RUE B T (1999) Comparison of methods for estimating the bruise volume of apples, J Agric Engng Res, 74, 325–30. BROWNLEADER M D, JACKSON P D, MOBASHERI A D, PANTELIDES A T, SUMAR S T, TREVAN M T and DEY P M (1999) Molecular aspects of cell wall modifications during fruit ripening, Critical Reviews in Food Science and Nutrition, 39(2), 149–64. BUDIASTRA W, IKEDA Y and NUSHIZU T (1998) Optical methods for quality evaluation of fruits (part 1) optical properties of selected fruits using the Kubelka-Munk theory and their relationships with fruit maturity and sugar content, J Jpn Soc Agric Mach, 60(2), 117– 28. CANO M P, BEGOÑA DE ANCOS, MATALLANA M C, CÁMARA M, REGLERO G and TABERA J (1997) Differences among Spanish and Latin-America banana cultivars: morphological, chemical and sensory characteristics, Food Chemistry, 59(3), 411–19. CHEN P and SUN Z (1981) Impact parameters related to bruise injury in apples, ASAE Paper No. 81-3041, St Joseph, MI, ASAE. CHEN P and THOMPSON J (project leaders) (1999) Development of a firmness tester for pears, www.calpear.com/ind.html CHEN P and THOMPSON J (project leaders) (2000) Development of a firmness tester for pears, www.calpear.com/ind.html CHEN P, TANG S and CHEN S (1985) Instrument for testing the response of fruits to impact, ASAE Paper No. 85-3537, St Joseph, MI, ASAE. BAJEMA R W

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12 Controlling the texture of fruit and vegetables: the role of oxidising enzymes H. J. Wichers and C. Boeriu, Agrotechnology and Food Innovations, The Netherlands

12.1 Introduction: distribution of polyphenoloxidases (PPOs) and peroxidases (PODs) in plants and plant cells Polyphenol oxidases (PPOs) comprise an enzyme family that is composed of the copper-containing tyrosinases (EC 1.14.18.1) and laccases (EC 1.10.3.2). The denomination polyphenol oxidase resp. tyrosinase is sometimes used to indicate the same type of enzymes; polyphenol oxidase is seldom used to indicate a laccase. In the context of this chapter, when we discuss PPOs, we are referring to the tyrosinases, a group of enzymes that bears structural similarities to the oxygen-transporting hemocyanins from molluscs and arthropods (Decker and Tuczek, 2000; Fujimoto et al., 1995). PPOs are copper-containing enzymes that are ubiquitous in nature. PPOs, meaning tyrosinases, have been found to occur in mammals, many plant species, fungi, insects, molluscs, and arthropods as well as in bacteria (Andersen et al., 1992; Van Gelder et al., 1997). Also, for many PPOs, nucleotide sequences have been described, and analyses on their genetic relationships have been initiated (Wichers et al., 2003). In many organisms, PPOs occur in either latent or active forms that are sometimes mutually convertible (Van Leeuwen and Wichers, 1999). Peroxidases (donor: hydrogen peroxide oxidoreductase, EC 1.11.1.X, where X indicates the specific peroxidase) are enzymes that use hydrogen peroxide to catalyse the oxidation of a wide range of organic and inorganic compounds. Peroxidases (PODs) are widely distributed in nature and are expressed by both prokaryotic and eukaryotic cells. Peroxidases from higher plants, mammals, fungi and bacteria have been isolated and characterised. Most peroxidases contain the iron porphyrin (haem) as prosthetic group, but

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peroxidases with other prosthetic groups such as metal ions (e.g. vanadate, selenium) or flavin have been characterised. Bacterial, non-metal peroxidases which require acetate or propionate buffer for activity have also been reported (Wolfframm et al., 1993; Hofmann et al., 1998). Plant peroxidases (PODs, EC1.1.1.7) belong to the class III peroxidases of the superfamily of plant, fungal and bacterial peroxidases, according to the classification of Welinder (Welinder, 1992). In plants, peroxidases exist in groups of discrete molecular isoforms, the isoperoxidases, that show different electrophoretic patterns and stability (McLellan and Robinson, 1984, 1987 a, b; Moulding et al., 1987; 1989; Ros Barcelo et al., 1987; Boucoiran et al., 2000; Forsyth et al., 1999). For example, horseradish peroxidase (HRP) shows 14 isoenzymes (three acidic, five neutral and six basic), of which the isoenzyme C (HRP-C) is the most abundant (Dunford, 1991a,b). Based on their isoelectric points, isoperoxidases have been classified into acidic, neutral and basic peroxidases. The acidic (anionic) isoperoxidases have isoelectric points in the pH range between 3.5 and 6, while basic (cationic peroxidases) have isoelectric points in the pH range 8.0–10.0. It has been suggested that small differences in the isoelectric point for a group of isoperoxidases can reflect organ specificity, genetic variation and minor post-synthesis modification (Robinson, 1991). Within one plant family peroxidase isoenzymes may show differences in physicochemical and catalytic activity (Lee et al., 2001; Yoshimura et al., 1998). The isoenzyme pattern depends on the developmental stage of the plant and may be affected by environmental stress (Bricage, 1987; Chang et al., 1983; Penel and Greppin, 1979). Season-related changes of the isoperoxidase pattern have also been reported (Baier et al., 1993). The location, transport and biosynthesis of peroxidases have been examined. The enzymes seem to be located in all parts and organelles of the cell, varying in amount and activity with physiological state. Taking into account the biosynthesis and transport mechanisms, and based on cytochemical studies, it has been claimed that peroxidases are located in the cell walls, intracellular free spaces and vacuoles (Gaspar et al., 1986). Location of individual isoperoxidases in plant cells is, however, not fully elucidated. Peroxidase activity has been found in the cell wall of young tissues and of lignifying parts. Cell wall peroxidases are either free or ionically or covalently bound to the cell wall polymers and wall, respectively. Ionic binding of isoperoxidases to cell wall pectin depends on the presence of calcium and on the degree of esterification of pectin (Penel and Greppin, 1994).

12.2 Biochemical and physiological role of PPOs and PODs It is generally assumed, for plants and fungi, that PPOs play a role in pigmentation and in defence against pathogens. Whereas the participation of PPOs in pigmentation seems obvious, considering their role in melanin formation, it is more difficult to provide direct evidence for their involvement

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in defence systems. The induction of a specific PPO in the button mushroom in response to microbial challenge, and the simultaneous activation of latent enzyme, is strongly indicative of a defence-related role (Soler-Rivas et al., 1997, 2001). In mammals, PPOs (tyrosinases) are particularly involved in skin and hair pigmentation (Oetting and King, 1992; Tobin and Paus, 2001). In insects, a role is suggested for PPO in cuticle sclerotisation (Andersen et al., 1992). Due to the crosslinking properties of PPOs, it is also possible to envisage a role in the formation of rigid exoskeletons and similar structures. Below, a more extensive survey of the role of PPOs in network formation will be given. In addition to colour formation and crosslinking, PPOs may be involved in taste perception of food products, also because of crosslinking properties: linking of smaller molecules for instance to peptides with a sensory impact may change the taste of a product. The mechanistic aspects of this phenomenon will be discussed in more detail below. Also the nutritional value of food products, in particular their content of nutraceuticals, may be affected by PPO-action, as many compounds with putative antioxidant properties, such as flavonoids, may be utilised by PPOs as substrates (Martinez and Whitaker, 1995; Van Rensburg et al., 2000). Additionally, the action of PPOs may lead to depletion of natural antioxidants such as vitamin C in food products. Vitamin C serves as a shuttle between catechols and quinones that are formed as a result of PPO-action, and is thus consumed in this process (Fig. 12.1; Espin et al., 2000d). Plant PODs exhibit a range of specificities and functionalities and are involved in various physiological processes associated with plant growth, ripening of fruits, defence against pathogens and senescence. For the various physiological processes in which they are implicated, PODs require H2O2. Temporal and spatial co-localisation of hydrogen peroxide and peroxidase in vivo has been demonstrated (Liu et al., 1999). It has been suggested that cell-wall-located PODs play a role both in cell wall formation and in cell wall reinforcement during regular plant growth (Fry, 1986; Fry et al., 2000; Wallace and Fry, 1995; Schopfer, 1996; Obel et al., 2002) and in response to wounding and infection (Graham and Graham, R

R

R PPO

PPO

POD (+H2O2) OH

Pigments

POD OH (+H2O2) OH

O O

Vitamin C, Other antioxidants

Fig. 12.1

Oxidation reactions catalysed by PPO and POD.

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1991; Ketsa and Atantee, 1998; Ikegawa et al., 1996; Chen et al., 2001). Peroxidases are responsible for several biochemical processes related to cell wall formation and strengthening: (1) the H2O2-mediated crosslinking of phenolic moieties of cell wall polysaccharides and glycoproteins; (2) lignin biosynthesis; (3) generation of free hydroxyl radical. The significance of these biochemical processes catalysed by PODs is detailed below. Peroxidases have been shown to catalyse the formation of diferulic acid crosslinks between cell wall polysacharides (e.g. hemicellulose and pectins) in the non-lignified actively growing primary wall. Crosslinking of wall polysaccharides decreases the wall extensibility and enzymatic degradability (Fry, 1986; Fry et al., 2000; Wallace and Fry, 1995; Schopfer, 1996; Ng et al., 1998; Waldron et al., 1997a; Wende et al., 2000). Recent work on grass cell wall has shown that, during lignification, PODs can mediate the oxidative coupling between lignin units and ferulate esters thus anchoring lignins to polysaccharides and further strengthening the cell wall (Jacquet et al., 1995; Lapierre et al., 2001; Grabber et al., 2000). Recently, it has been shown that PODs are also able to degrade cell wall ˙ polysaccharides via generation of highly reactive hydroxyl radicals (HO), and thus they can play an important role in the cell growth caused by wall loosening (Schweikert et al., 2000, 2002). It has been reported that PODs are responsible for the rapid crosslinking of cell wall glycoproteins, such as extensin, through oxidative coupling of tyrosine residues and formation of iso-dityrosine (IDT) bridges, as response to elicitor infection (Waffenschmidt et al., 1993; Fry, 1982). Besides IDT, the trimeric and tetrameric tyrosine derivatives, di-IDT and pulcherosine may form intra-polipeptide loops and inter-peptide bridges (Brady et al., 1998). Structural proteins like collagen and many non-structural proteins have been crosslinked by the action of POD and H2O2 in vitro. Extensin crosslinking might enhance cell adhesion and confer rigidity to cell walls thus preventing wall penetration by pathogens. It has been proposed that PODs are involved in the dehydrogenative polymerisation of monolignols into lignins (Lagrimini et al., 1987; Nose et al., 1995). Lignin is an abundant heterogeneous phenolic polymer which forms a three-dimensional network in plant cell walls, and thus contributes to the mechanical strength of the cells. Among the many PODs expressed in plants, no studies have shown a particular isozyme to be specifically involved in lignin formation. Although it has been shown that lignification is always accompanied by an increased activity of most anionic isoperoxidases, the significance of the many PODs isozymes remains unclear. Based on in vitro studies, it was hypothesised that lignin formation occurs via the randomcoupling of monolignol radicals. Recently, it has been shown that a dirigent protein assists POD in the lignification process and is responsible for controlling

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the regio- and stereo-selectivity of the phenoxy free radical coupling (Davin and Lewis, 1997; Gang et al., 1999). It is also suggested that a family of dirigent proteins exits and that each member stipulates a distinct coupling mode of monolignol radicals. In potato tubers, an anionic POD has been associated with the oxidative coupling of hydroxycinnamates into the lignin-like polyphenolic domain of suberin (Bernards et al., 1999; Bernards and Razem, 2001). Suberin provides a physical barrier to moisture loss and forms a defensive shield against pathogens. The role of PODs in the defence against stress and pathogens has been clearly substantiated. It has been shown that a subfamily of apoplastic PODs are able to produce hydrogen peroxide through compound III during oxidative burst (Blee et al., 2001; Bolwell, 1999), while other PODs are involved in the removal of H2O2 and cell wall reinforcement through oxidative crosslinking (Bradley et al., 1992) and insolubilisation of hydroxyproline-rich proteins (Otte and Barz, 1996). Besides their role in cell wall formation, PODs are also involved in regulation of the in vivo concentration of indole-3-acetic acid (IAA), a plant phytohormone that retards fruit ripening (Reinecke and Bandurski, 1988). It has been shown that plant PODs are highly specific IAA oxygenases, which oxidise IAA both in the absence and presence of H2O2 (see Section 12.4.2). In post-harvest fruits and vegetables, PODs may be responsible for deterioration of flavour, colour and nutritional value. Loss of vitamin C is related to ascorbic acid peroxidase, but it can also be linked to the cooxidation of ascorbic acid with phenoxyl radicals generated by peroxidase (Galati et al., 2002). Also, the concentration of dietary flavonoids and polyphenols can be lowered as a result of POD activity, since most of these compounds are substrates for PODs. Relationships between POD activity and off-flavour development due to oxidation of lipids and phenolic constituents have been established (Wagenknecht and Lee, 1958; Sessa and Anderson, 1981). Peroxidase activity also results in deterioration of colour due to pigment decay and associated browning reaction. Complementary localisation pattern and synergistic role of PODs and polyphenol oxidases in browning reactions has been suggested (Lopez-Serrano and Barcelo, 2001). Recently, it has been shown that in the vacuoles of Vicia faba, POD can catalyse the formation of melanin-like compounds by oxidation of dihydroxyphenylalanine (DOPA) and that this process is enhanced by cinnamic acid derivatives (Takahama, 1997). Involvement of PODs in chlorophyll degradation during leaf senescence and yellowing of fruit and vegetables has been also suggested (Funamoto et al., 2002). Peroxidase-mediated chlorophyll degradation requires both hydrogen peroxide and a monophenol. It has been suggested that peroxidative-chlorophyll degradation may be a naturally occurring breakdown process as both POD and phenols required for the process are formed within the chloroplasts.

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12.3 PPOs and PODs: structure and mechanisms of action 12.3.1 Molecular structure of PPOs and PODs Homology between various PPOs was shown in a phylogenetic tree that was constructed for 35 PPO sequences from the EMBL and NCBI databases (Wichers et al., 2003). This phylogenetic tree was based on partial sequences, which included the central catalytic domains, flanked by the copper binding domains as defined by Van Gelder et al., (1997). Phylogenetic analysis showed that PPOs clustered in groups for higher plants, vertebrate animals, fungi and bacteria. Homologies within such clusters were considerably higher than between them. The most strongly conserved regions are the copper binding domains (Van Gelder et al., 1997; Wichers et al., 2003). On the basis of sequence analysis and structure similarity, it has been shown that PODs from plants, fungi and bacteria are structurally related and belong to the plant POD superfamily (Welinder et al., 1992). Haem-peroxidases of animal origin such as eosinophil, tyrosyl, lacto- and myelo-peroxidases constitute a separate superfamily (animal POD superfamily) unrelated in structure to the plant superfamily. Haloperoxidases, which catalyse the oxidation of halides by H2O2 resulting in the halogenation of organic compounds, form an apart group of PODs. They may contain haem, vanadium or no heterogroup at the redox active centre, and they show little sequence homology among them or with other known peroxidases (Butler, 1998; Conesa et al., 2002). The superfamily of plant, fungal and bacterial PODs contains three evolutionary lineages. Class I comprises intra-cellular PODs, including cytochrome C peroxidase (CcP), the cytosolic ascorbate peroxidase (APX) and the bacterial and fungal catalase-peroxidase. Class II contains the secretory fungal enzymes, like manganese peroxidase and lignin peroxidase (LiP). Class III consists of the secretory higher plant PODs, of which the horseradish peroxidase (HRP) is the most studied. Structural patterns conserved in the evolution of the plant POD superfamily have been identified, based on available sequences and crystallographic models (Welinder et al., 1992). Iron coordination and most of the residues in the active site are conserved in the PODs sequenced up to now. In the past decade, comprehensive and detailed analysis of the plant, fungal and bacterial genomes for peroxide-degrading enzymes have been reported (Zamocky et al., 2000; Regelsberger et al., 2002; Tognolli et al., 2002). The crystal structures of 12 haemperoxidases have been reported, including PODs from soybean (Henriksen et al., 2001), barley grain (Henriksen et al., 1998), horseradish (Gajhede et al., 1997), peanut (Schuller et al., 1996), arabidopsis (Ostergaard et al., 2000; Mirza et al., 2000) and ascorbate peroxidase from pea (Pisum sativum), (Patterson and Poulos, 1995). All plant PODs contain iron protoporphyrin IX as prosthetic group, generally coordinated by a histidine as proximal ligand, and have conserved arginine and histidine residues on the distal haem-binding site. They have two conserved calcium ions, an N-terminal signal peptide and four-conserved disulphide

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bridges. Plant PODs are glycosylated, but the amount of glycosyl residues varies for each particular enzyme family: eight asparagine glycosylation sites have been reported for horseradish peroxidase (Welinder, 1985), while the turnip TP7 possesses only one site and tobacco anionic isoperoxidases has four potential asparagine-linked glycosylation sites (Lagrimini et al., 1987). The carbohydrate residues are glycans with mannose and glucosamines as the predominant sugars. It has been shown that glycosylation increases the kinetic stability of peroxidases (Tams and Welinder, 1998).

12.3.2 Mechanism of reactions catalysed by PPOs and peroxidases PPOs (tyrosinases) are able to catalyse the ortho-hydroxylation of phenolic moieties into the corresponding catechols, and the subsequent oxidation of these catechols into the corresponding quinones (Fig. 12.1). After the formation of quinones, subsequent reaction is possible if a nucleophilic moiety, potentially intramolecularly, is present (Fig. 12.2). This nucleophilic moiety does not necessarily have to be present intramolecularly, which offers the mechanistic basis for the involvement of PPOs and PODs in a variety of cross-linking reactions, as will be discussed in more detail below (Section 12.4.1). Considerable research efforts have been invested in the Cu-chemistry of PPOs (tyrosinases and catecholoxidases) (Decker et al., 2000; Eicken et al., 1999; García-Borrón and Solano, 2002; Lind et al., 1999) and the co-ordination of copper in PPOs (Klabunde et al., 1998; Lind et al., 1999). The reader is referred to these references for further reading, as well as for further information, i.e. the relationship between PPOs and hemocyanins (Van Gelder et al., 1997; Decker and Tuczek, 2000). For the mono-phenolase activity, there appears to exist a positive relationship between electron density of hydroxyl-carrying carbon atoms in phenolic side chains and Vmax values for such substrates (Espin et al., 2000a,b). Plant PODs catalyse various reactions such as oxidative dehydrogenation, halogenation, oxygen transfer and hydrogen peroxide cleavage. Oxidative CH2

COOH

HO

NH2

HO

O O

Fig. 12.2

CH

CH2

CH2

NH2

HO

CH :NH2

COOH

COOH

CH

HO HO

COOH N H

H

Spontaneous cyclisation reaction following oxidation of phenolic substrates by PPO. Example for tyrosine-oxidation sequence.

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dehydrogenation is the main reaction of PODs and it consists of the conversion of hydrogen peroxide to water in the presence of a wide range of compounds like phenols, arylamines, halides and thiols as hydrogen donor (Krylov and Dunford, 1996). Besides hydrogen peroxide, other organic hydroperoxides, peracids or inorganic oxides such as periodate can serve as electron donor. The reaction (Fig. 12.3a, pathways 1–3) is a three-step cyclic reaction in which the inactive ferric resting enzyme is first oxidised by H2O2 to an oxyferryl-porphyrin cation radical (compound I) and then reduced in two one-electron transfer steps by the reducing substrates, with formation of an intermediate oxyferryl porphyrin (compound II). The radicals generated in the reaction, i.e. the phenolic radicals, can polymerise, with the final product depending on the chemical character of the radical and the environment. In the absence of reducing substrates or when exposed to high concentration of hydrogen peroxide, POD is inactivated (Fig. 12.3b). Hydrogen peroxide is a suicide substrate that converts compound II to a highly reactive peroxyiron(III) porphyrin free radical (compound III) which is further oxidised by hydrogen peroxide or other oxidative species resulting in haem destruction and irreversible enzyme inactivation (Valderrama et al., 2002 and references therein). It has been shown that HRP isoenzymes behave differently during peroxide inactivation, basic isoenzymes being more susceptible to peroxide inactivation than acidic ones (Hiner et al., 1996). The key step in the enzymatic cycle of haem PODs is the formation of the catalytically active compound I by reaction of peroxide with the resting ferric form of the enzyme. Following elucidation of X-ray structures of CcP and HRP-C, a detailed mechanism has been proposed for the formation of compound I in these enzymes, which confirms the original acid-base mechanism proposed by Poulos and Kraut in 1980. This mechanism involves binding of peroxide to the haem iron of the ferric resting enzyme species with concomitant donation of a proton to the distal histidine from the αoxygen atom (Hiner et al., 2002). The positively charged guanidinium side chain of distal arginine stabilises the negative charge on the β-oxygen atom. Heterolytic cleavage of the O–O bond and the formation of H2O and compound I results from a general acid-base catalysis mediated by distal histidine (Hiner et al., 2002). Site-directed mutagenesis studies have been used to establish the mechanism of compound I formation and to elucidate the role of other residues in the distal haem pocket in compound I formation at molecular level (Gajhede, 2001; Smith and Veitch, 1998; Filizola and Loew, 2000; Tanaka et al., 1997). The reader is referred to these references and to recent monographs (Dunford, 1999) and reviews (Hiner et al., 2002; Valderrama et al., 2002) for further reading. Besides the oxidative dehydrogenation reaction discussed above, plant PODs can catalyse a variety of related reactions such as oxygen transfer (pathways 4 and 5, Fig. 12.3a) (Dordick et al., 1986; van Rantwijk and Sheldon, 2000). It has been reported that HRP can catalyse the hydroxylation of a range of aromatic compounds including tyrosine, phenylalanine and p-coumaric acid

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X– (6)

P-Fe(III)OX Compound X (7)

R•

+ RH + H+ P-Fe(IV)= O Compound II

(8)

RH (2)

RX+H2O

(1)

(3)

RX + H2O (9)

H2O

(4)

(10)

RH

+ RH+H

RH

H2O2 –

P-Fe(IV) = O• Compound I

OX

H2O2

R• + H2O

H2O + O2 (5)

P-Fe(III) Native POX

R• P-Fe(IV)OH

ROH (a) Haem destruction/release H2O2

(2)

H2O

P-Fe(IV)=O+ Compound II

RH

P-Fe(III)-•OOH Compound III

R• + H2O2 P–Fe(III) Protein oxidation

(3)

(1) (4) Protein oxidation

•

•

OH

OOH

P-Fe(III)

(b)

Fig. 12.3 (a) Reactions catalysed by peroxidases (adapted from Valderrama et al., 2002); (b) inactivation of peroxidase by hydrogen peroxide (adapted from Valderrama et al., 2002).

in the presence of reducing equivalents of dihydroxyfumaric acid (DHF) and oxygen. NADH, pyridoxal compounds, thiols and indoleacetic acid can act as reducing agents. Dordick et al. (1986) showed that compound III is the key intermediate in this process. In the absence of reducing agents, PODs can show a “catalase” like activity, and decompose H2O2 to oxygen (pathway 10, Fig. 12.3a) (Hiner et al., 2001). Peroxidase-induced generation of hydroxyl radicals from oxygen, in the presence of suitable reductants, such as NADH or dihydroxyfumarate has also been reported (Chen and Schopfer, 1999). Peroxidative halogenation of aromatic compounds (pathways 6–8, Fig. 12.3a) is catalysed by haloperoxidases, but recently it has been shown that plant PODs (e.g. soybean peroxidase) are also able to catalyse bromination and iodination (not chlorination) of phenolic compounds (Munir and Dordick, 2000). A specific reaction catalysed by plant PODs is the oxidation of IAA. The oxidation of IAA proceeds via two different pathways, a conventional H2O2-

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dependent pathway and one that requires oxygen but not peroxide. It has been shown that only those plant PODs that posses the domains with structural similarities with auxin-binding proteins, such as anionic tobacco peroxidase, HRP-C, cationic peanut peroxidase, soybean peroxidase, are able to oxidise IAA via the H2O2-independent pathway. A model for IAA oxidation catalysed by various states of HRP has been proposed (Kawano et al., 2002).

12.4 PPOs, PODs and texture development 12.4.1 Crosslinking of proteins catalysed by PPO Oxidation of tyrosine residues in proteins or in peptides by PPO leads to the formation of, initially, an L-DOPA-structure in the peptide chain. Subsequent oxidation to the corresponding DOPA-quinone results in the formation of a reactive moiety in the peptide chain that can react with nucleophilic amino acid side chains in adjacent proteins or peptides. Similarly, phenolic or catecholic moieties linked to carbohydrate backbones can be oxidised into quinones. Nucleophilic amino acid side chains may be found in lysine, asparagine, arginine, histidine, methionine, or cystein. An example of the role of PPO as crosslinker of gluten was given by Takasaki and Kawakishi (1997). Based on the reaction mechanism that was proposed by the authors, and taking into account the cyclisation mechanism as depicted in Fig. 12.2, a generic reaction scheme can be postulated for crosslinking of proteins by PPOs or PODs (Fig. 12.4). Also the ortho-positions at the phenolic residues possess a partly positive charge due to resonance structures. The postulated scheme is most likely because it is least subject to steric hindrance. Another example of the protein crosslinking properties of PPOs is the crosslinking of thaumatin molecules (Ramsohoye and Kozlov, 1991). The crosslinking of thaumatin by PPO, and the resulting decrease in sweetness, indicate an important potential role of PPOs in the sensory properties, not only in colour but also in taste, of food products. PPOs may play a role in the perceived taste of various protein and peptide containing food products, such as yeast extracts and bouillons, and PPO may be used as a tool to influence the taste of these products. Mechanistically interesting is the role of PPO in the formation of blue mussel adhesive (Burzio et al., 2000; Fant et al., 2002, Haemers et al., 2002), and in insect cuticle sclerotisation (Andersen et al., 1992).

12.4.2 POD and crosslinking of proteins and polysaccharides (in vitro experiments) In vitro oxidative dehydrogenation reactions catalysed by POD have been used in plant phenolic research as a means to study lignification, crosslinking of cell wall polymers and of proteins and for synthesis of new derivatives with potential industrial applications. Gelling of arabinoxylans, linear polymers

Controlling the texture of fruit and vegetables H N

C

305

CO

R NH H

C

X

Tyrosine residue CH2

OH

NH H

CH2

C

CO

O

CO

O

PPO, POD

DOPA residue

NH H

C

CH2

CO

H N

OH

C

CO

R

OH

X: PPO, POD

NH H

C CO

Fig. 12.4

+

DOPA quinone residue CH2

O O

NH H

C

CH2

O

CO O

Mechanism for cross-linking of (poly)peptide chains as a consequence of PPO or POD action.

of xylose substituted with arabinose, which carry ferulic acid molecules, and of feruloylated cell wall pectin polymers by peroxidase-mediated oxidative coupling with hydrogen peroxide has been reported. Crosslinking of these carbohydrate polymers occurs via the formation of diferulic acid bridges (Schooneveld-Bergmans et al., 1999; Oosterveld et al., 1997). Several studies have demonstrated that oxidation of tyrosine residues in proteins and peptides induces intra- and inter-molecular protein crosslinking via dityrosine bonds (Gross and Sizer, 1959; Aeshbach et al., 1976). Dityrosines and higher oligomers resulting from peroxidase-mediated conjugation of tyrosine radicals have been characterised for tyrosine-containing peptides (Michon et al., 1997). Involvement of cysteines and lysines in protein crosslinking has also been suggested, based on reduction of the amount of free –SH and –NH2 groups after reaction (Matheis and Whitaker, 1984, 1987). However, since cysteine is oxidised by hydrogen peroxide in model systems (Vinkx et al., 1991) the published data are not convincing. Nevertheless, since during peroxidase-mediated oxidative dehydrogenation of tyrosyl moieties quinone-like structures could be generated, it can be speculated that cysteine

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and lysine residues may be involved in protein crosslinking via a similar mechanism as described in Fig. 12.4. More research is needed to identify the amino acid residues involved in protein crosslinking and to elucidate the reaction mechanism. In vitro peroxidase-mediated crosslinking of various proteins in the presence of hydrogen peroxide and low molecular weight phenols as hydrogen donors has also been reported (Stahmann et al., 1977; Matheis and Whitaker, 1984; Hurrell et al., 1982; Huizing et al., 1999; Faergemand et al., 1998). In these studies, formation of various oligomers and polymers of proteins has been detected using SDS gel electrophoresis and gel exclusion chromatography. It has been shown that product pattern and protein conversion are determined by the structure of both the protein and phenol substrate and by phenol reactivity (Boeriu et al., unpublished results).

12.4.3 PPO, POD and crosslinking of proteins with polysaccharides; role of endogenous and exogenous phenolics on peroxidase-mediated crosslinking (in vitro experiments) Chen et al. (2002) described the conjugation of gelatine to chitosan catalysed by PPO. Since chitosan (poly N-acetyl glucoseamine) is a polysaccharide that contains nucleophilic amino groups, a similar reaction mechanism as proposed for the crosslinking of proteins may be suggested (Fig. 12.4). The authors described the formation of gel-like structures that differed mechanically from the gels that were obtained by cooling gelatine solutions, and that could be broken down by chitosinase. These mechanistic observations are supported by the work of Lenhart et al. (1998) that describes the PPO-catalysed modification of chitosan by a variety of phenolic compounds such as phenol, catechol, caffeic acid and L-DOPA. The oxidative crosslinking of proteins with carbohydrate polymers containing phenolic residues (e.g. arabinoxylans and pectins) by POD has been suggested (Neukom and Markwalder, 1978; Oosterveld et al., 1997), but only recently confirmed (Oudgenoeg et al., 2000, 2001, 2002). Model studies with a tyrosine containing tripeptide (Gly-Tyr-Gly) and ferulic acid (FA), have shown that kinetically controlled reactions result in the formation of a complex range of isomeric hetero-oligomers of FA linked via oxidative dehydrogenation to the peptidyl tyrosine (Oudgenoeg et al., 2001, 2002). Based on the product pattern obtained, the authors suggest that oligomerisation is governed by radical chemistry. Two mechanisms for the formation of the ferulic acidtyrosine oligomers have been proposed that explain the concentration-dependent behaviour of FA, and their possible role in the regulation of plant cell wall tissue growth has been postulated (Oudgenoeg et al., 2002). Furthermore, βcasein and α-lactalbumin have been crosslinked with arabinoxylans containing ferulic acid residues in the presence of HRP and hydrogen peroxide (Oudgenoeg et al., 2000). Protein-arabinoxylan heteroconjugates containing up to 10% protein and 90% carbohydrate have been isolated and characterised.

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12.5 Controlling PPO and POD activity A number of technological approaches can be considered to control the activity of PPOs and PODs during the food manufacturing process.

12.5.1 Genetic modification of raw material First of all, manipulating the raw materials may control enzyme activity. Examples for this can be found in for instance the development of a potato variety in which PPO was knocked out via antisense technology (Zabeau et al., 1994), and in button mushroom that was transformed with antisense PPO-constructs (Stoop and Mooibroek, 1999).

12.5.2 Modification of PPO-activity via processing Additionally, many options are available via processing to manipulate PPOactivity, and thus its impact on sensory properties. Both physicochemical as well as biochemical approaches are feasible in this respect. It should be thoroughly understood that PPOs usually occur initially in latent forms that may be activated via a variety of mechanisms (Espín et al., 2000c). Some of the physicochemical parameters that affect PPO-activity are outlined below. Ionic strength in the microenvironment of PPO A latent mushroom preparation can be activated when the ionic strength of the buffer is decreased, for instance through dialysis (Fig. 12.5). Decrease of ionic strength stabilises intra-molecular ionic interactions, which may, therefore, be postulated to play a role in this activation process. Other factors that 70

Percent active PPO

60 50 40 30 20 10 0 0.01

Fig. 12.5

0.1

1 10 Phosphate (mM)

100

Activation of PPO through decrease of ionic strength.

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possibly interfere with intra-molecular ionic interactions in relation to PPOactivity are for instance polyamines that bear multiple positive charges (JimenezAtienzar et al., 1991) or poly-negatively charged polyglucans (Jimenez and Garcia-Carmona, 1993), that are able to activate latent grape PPO. In addition, the pH in the micro-environment of PPO determines the activity of PPO. A decrease of the pH from 6.5 to 4.5, via dialysis, considerably activates latent mushroom PPO, measured against a non-dialysed control (Fig. 12.6). Kenten (1957) and Ichishima et al. (1984) described a similar activation for resp. broad bean and Aspergillus-tyrosinase. The latter also postulated the attachment of a protease to the latent PPO at low pH (see below for proteolytic activation of PPO). Pressure Pear-PPO can be activated at pressures of 400 MPa, highest activity being reached at 600 MPa (Asaka and Hayashi, 1991; Asaka et al., 1994). Temperature Mushroom PPO becomes activated before being thermally inactivated during heating of mushrooms (Wichers, unpublished). Viscosity and water activity A decreased PPO-activity with increased viscosity is claimed by some (Gama Terrazas et al., 1990), but questioned by others (Manzocco et al., 1998). Water activity is also claimed to decrease PPO-activity (Gama Terrazas et al., 1990), which may be consistent with the author’s observations (Fig. 12.5). 100

Degree of activation (%)

80

60

40

20

0 0

Fig. 12.6

5 Time (min)

10

Activation of PPO via pH-decrease. 䊊: control; ⵧ: phosphate pH 6.5; ∆: citrate, pH 4.5.

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Hydrophobic compounds The activation of latent PPO by hydrophobic or surface active compounds, such as various detergents, has been well documented for various PPOs (Asada et al., 1993; Escribano et al., 1997; Espín et al., 1999; Espín and Wichers, 1999a,b; Moore and Flurkey, 1990, Yamaguchi et al., 1970). Not only physiologically non-occurring and food-technologically unacceptable compounds such as detergents like SDS, but also endogenously occurring and sensorily valuable hydrophobic flavour compounds such benzyl alcohol (Espín and Wichers, 1999a) or 1-octen-3-ol (Espín and Wichers, unpublished observations) are able to activate latent mushroom PPO. All these physicochemical treatments have in common that they can be considered as leading to partial destabilisation or denaturation of proteins, in this case latent PPO, which may result in activation because the active site becomes more exposed or more readily accessible for substrates. Such hypothesis is in line with the proposed activation mechanism for mushroom PPO as formulated by Espín et al. (1999). Proteolytic activation of PPO The activation of latent PPOs from various organisms by proteases is well documented. Apple (Espín et al., 1995 a,b), pear (Espín et al., 1996), Drosophila (Chosa et al., 1997), broad bean (King and Flurkey, 1987; Robinson and Dry, 1992), frog epidermis (Galindo et al., 1983), mushroom PPO (Espín et al., 1999; Yamaguchi et al., 1970, Fig. 12.7), amongst many others, are proven susceptible to proteolytic activation. Also, a solid mathematical and theoretical description for such process has been published (Vazquez et al., 1993). Figure 12.7 shows that the degree of proteolytic activation depends on the specificity of the combination of protease and PPO, an observation that may

Degree of activation (%)

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15

10

5

0 0

30

60

90 120 Time (min)

150

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Fig. 12.7 Activation of latent mushroom PPO by various proteases. 䊊: subtilisin; ⵧ: clostripain; ∆: trypsin.

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be useful for control of the activation process that may partly be tuned by a proper choice of enzymes. It may be anticipated that proteolysis leads to the removal of a part of the peptide chain, resulting either in conformational change or, directly, in improved accessibility of the active site (Espín et al., 1999). Proteolytic activation is obviously an irreversible process, but may be used, provided the treatment can be controlled sufficiently, to confer desired sensory properties to food products.

12.5.3 Modification of PODs activity via processing Thermal inactivation of PODs during heat treatment of fruit and vegetables has been thoroughly investigated, since the action of these enzymes may cause adverse changes of colour and flavours during food preservation. Moreover, PODs are generally used as indicator enzyme to assess the effectiveness of heat treatment. Thermal inactivation and regeneration of POD activity depends on several factors, such as temperature and time of treatment, pH value and ionic strength (Kermasha et al., 1988; Stolle-Smits et al., 2000; Lemos et al., 2000). It has been shown that most plant PODs have a maximum thermal stability at neutral pH values and are more susceptible to heat inactivation at extreme pH values (Lemos et al., 2000). The authors have suggested that the effect of pH on thermal stability of PODs is due to the equilibrium between several pH-dependent processes involved, such as (1) dissociation of haem from the holoenzyme, (2) conformation changes in the apoprotein and (3) modification or degradation of prosthetic group. Irreversible changes in the apoprotein at acidic pH and denaturation of haem at alkaline pH values (pH = 11 or higher) are responsible for irreversible inactivation of peroxidases. Thermal inactivation of PODs during the short time heating specific for blanching process shows non-linear kinetics (Adams, 1978; Ling and Lund, 1978; McLellan and Robinson, 1984; Moulding et al., 1987; Robinson et al., 1989; Khan and Robinson, 1993). Deviation from linear first-order kinetics has been related to (1) the presence of heat-resistant and heat-labile isoenzymes for crude enzyme preparates (Ling and Lund, 1978), (2) formation of thermostable enzyme aggregates (Lopez et al., 1994), and (3) conformation and chemical changes of protein accompanied by haem dissociation (Adams, 1997). Recent work has shown that purified anionic and cationic isoperoxidases differ significantly in thermal stability and in the ability to regenerate activity after heat inactivation (Forsyth et al., 1999; Boucoiran et al., 2000). A particular property of PODs is regeneration of activity after thermal denaturation, which is pH dependent (see discussion above). Recovery of POD activity has been observed over a period of a few hours after heat treatment of both test enzyme solutions and whole vegetables. Difference in the ability to regenerate activity after thermal inactivation has been observed between isoenzymes and between various PODs. Regeneration of POD activity after thermal inactivation has been associated with recombination of haem with

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apoprotein and with regain of protein conformation (Adams et al., 1996; Forsyth et al., 1999). Peroxidase activity is not significantly affected by high pressure (Krebbers et al., 2002). However, a combined effect of heat, ultrasonic waves and pressure on POD activity has been reported. A synergistic effect, which can substantially reduce enzyme resistance to heat treatment, has been observed (Lopez et al., 1994).

12.6 PPOs and PODs: implications for food texture The exact role of PPOs and PODs in determining texture of fruits and vegetables via their possible involvement in the formation of cell walls in food crops is sometimes hard to assess. An exact evaluation requires the study of the function of single parameters (the enzymes under study) in complex systems with interdependent parameters that affect the eventual outcome, i.e. texture. Yet, the work of Brett et al. (1999) suggests a direct relation between cell growth, cell wall expansion, and oxidative coupling of hydroxycinnamate derivatives in the cell walls to polymers, inducing network and texture formation. It has also been reported that ferulic-acid crosslinks between cell wall polysaccharides increase the thermal stability of cell–cell adhesion and consequently the textural strength of processed vegetables (Parker and Waldron, 1995; Waldron et al., 1997a, b; Ng et al., 1998). High levels of peroxidase activity have been associated with increased firmness of thermally processed green beans, but no information is available about the underlying mechanisms (Stolle-Smits et al., 2000). In processing, however, a variety of options to affect texture is offered by better control of the activity of oxidative enzyme systems such as PPO and POD during processing. A mechanistic basis is provided, that does not differ essentially from the mechanism by which PPOs and PODs may be involved in colour, in particular melanin, formation (Fig. 12.4). A variety of options exists to control PPO-activity during processing. This area has received little attention thus far, which is perhaps not surprising because of the much more prominent role of oxidative enzyme systems in colour of food products. However, research into improved process control of enzymatically catalysed oxidative reactions will offer a plethora of possibilities to fine-tune eventual texture of processed foods, and to develop novel texturedetermining food ingredients based on carbohydrate and peptides or proteins as starting materials.

12.7 Future trends Further research into the option of using natural products or properties such as (endogenous) enzymes would fit extremely well into a trend to decrease

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the use of additives to optimise sensory food quality. A better understanding of the role of oxidative enzyme systems in product texture, but also in other sensory properties such as flavour and colour, would offer various openings to further decrease the use of ingredients. For instance, crops with desirable intrinsic properties could be developed, in particular against the rapidly expanding knowledge in the X-omics area. Enhancement of textural quality of fruits and vegetables by modulating the nature, the location and the degree of phenolic crosslinking in the cell walls is an area of potential interest. Waldron (Waldron et al., 1997b) has suggested the potential for exploiting metabolic pathways that are associated with the response to infection to improve textural characteristics, pest resistance, nutritional quality and sensory (e.g. colour and flavour) properties of crops. The growing market for replacement of animal proteins offers a further market perspective for enzymatic texturisation, including the potential use of oxidative enzyme systems. The need to develop products with a suitable texture is holding up consumer acceptance and market introduction. It seems worth investigating the feasibility of oxidative enzyme systems for such applications, as they may result in textures that could differ subtly from those that can be obtained via other enzymatic crosslinkers such as transglutaminase or via physical processes such as extrusion.

12.8 Sources of further information For further reading on the physical and chemical aspects of texture, the reader is referred to the references in the bibliography that are marked with an asterisk.

12.9

References

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and LEWIS N G (1995) Towards the specification of consecutive steps in macromolecular lignin assembly, Phytochemistry, 39, 71–9. NG A, HARVEY A J, PARKER M L, SMITH A C and WALDRON K W (1998) Effect of oxidative coupling on the thermal stability of texture and cell wall chemistry of Beet root (Beta vulgaris), J Agric Food Chem, 46, 3365–70. OBEL N, PORCHIA A C and SCHELLER H V (2002) Dynamic changes in cell wall polysaccharides during wheat seedling development, Phytochemistry, 60, 603–10. OETTING W S and KING R A (1992) Molecular analysis of type 1-A (tyrosinase negative) oculocutaneous albinism, Human Genetics, 90, 258–62. *OSTERGAARD L, TEILUM K, MIRZA O, MATTSSON O, PETERSEN M, WELINDER K G, MUNDY J, GAJHEDE M and HENDRIKSEN A (2000) Arabidopsis ATP A2 peroxidase. Expression and high resolution structure of a plant peroxidase with implications for lignification, Plant Mol Biol, 44, 231–43. OOSTERVELD O, GRABBER J H, BELDMAN G, RALPH J and VORAGEN A G J (1997) Formation of ferulic acid dehydromers through oxidative cross-linking of sugar-beet pectin, Carbohydr Res, 300, 179–81. OTTE O and BARZ W (1996) The elicitor-induced oxidative burst in cultured chickpea cells drives the rapid insolubilization of two cell wall structural proteins, Planta, 200, 238– 46. OUDGENOEG G, PIERSMA S, BOERIU C, GRUPPEN H and HILHORST R (2000) Peroxidase mediated cross-linking of proteins and carbohydrates, Industrial Proteins, 8(2), 14–16. OUDGENOEG G, HILHORST R, PIERSMA S R, BOERIU C G, GRUPPEN H, HESSING M, VORAGEN A G J and LAANE C (2001) Peroxidase mediated cross-linking of a tyrosine containing peptide with ferulic acid, J Agric Food Chem, 49, 2503–10. *OUDGENOEG G, DIRKSEN E, INGEMANN S, HILHORST R, GRUPPEN H, BOERIU C G, PIERSMA R S, VAN BERKEL W J H, LAANE C and VORAGEN A G J (2002) Horseradish peroxidase-catalyzed oligomerization of ferulic acid on a template of a tyrosine-containing tripeptide, J Biol Chem, 277, 21332–40. PARKER M L and WALDRON K W (1995) Texture of chinese water chestnut: involvement of cell wall phenolics, J Sci Food Agric, 68, 337–46. *PATTERSON W R and POULOS T L (1995) Crystal structure of recombinant cytosolic ascorbate peroxidase, Biochemistry, 34, 4331–41. PENEL C and GREPPIN H (1979) Effect of calcium on subcellular distribution of peroxidases, Phytochemistry, 18(1), 29–33. PENEL C and GREPPIN H (1994) Binding of plant peroxidases to pectin in the presence of calcium, FEBS Letters, 343, 51–5. POULOS T L and KRAUT J (1980) The stereochemistry of peroxidase catalysis, J Biol Chem, 255, 8199–205. RAMSOHOYE P and KOZLOV I A (1991) Isoprotein composition and cross-linking of thaumatins using mushroom tyrosinase and dimethyl suberimidate, Int J Food Sci Technol, 26, 271–82. RANTWIJK VAN F and SHELDON R A (2000) Selective oxygen transfer catalysed by heme peroxidases: synthetic and mechanistic aspects, Curr Opin Biotechnol, 11, 554–64. REINECKE D M and BANDURSKI R S (1988) Oxidation of indole-3-acetic acid to oxindole-3acetic acid by an enzyme preparation from Zea mays, Plant Physiol, 86, 868–72. REGELSBERGER G, JAKOPITSCH C, PLASSER L, SCHWARGER H, FURTMULLER G, PESCHEK G A, ZAMOCKY M and OBINGER C (2002) Occurrence and biochemistry of peroxidases in oxygenic photothropic prokaryotes (cyanobacteria), Plant Physiol Biochem, 40, 479–90. ROBINSON S P (1991) Peroxidases and catalases in foods. In Oxidative Enzymes in Food. Eds D S Robinson and N A M Eskin, London, Elsevier, 1–47. ROBINSON S P and DRY I B (1992) Broad bean leaf polyphenol oxidase is a 60-Kilodalton protein susceptible to proteolytic cleavage, Plant Physiol, 99, 317–23. ROBINSON D S, BRETHERICK M R, DONELLY J K (1989) The heat stability and isoenzyme composition of peroxidases in Ohane grapes, Int J Food Sci Technol, 24, 613–18.

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13 Improving fruit and vegetable texture by genetic transformation G. Tucker, University of Nottingham, UK

13.1

Introduction

Texture of plant material, as described extensively within this book, is often dictated by the structure of the cell wall. Thus any modification to the wall, through either altered synthesis or degradation, may result in modification to texture. Change in texture, i.e. softening, is a major factor in determining the shelf life of fruit. Thus techniques to slow down softening have been extensively sought. Softening may be brought about by a combination of factors, but the most important is likely to be the degradation of the cell wall which accompanies the ripening of most fruit. The structure of a typical plant cell wall has been documented elsewhere in this book and has also been extensively reviewed (McCann and Roberts, 1991; Carpita and Gibeaut, 1993; Carpita et al., 2001). Similarly the changes in the wall accompanying ripening have had extensive coverage (Huber, 1983; Fischer and Bennett, 1991; Tucker, 1993; Brummell and Harpster, 2001; Giovannoni, 2001). The major observable changes in most fruit are those occurring within the pectin. These include a progressive solubilisation and depolymerisation of the galacturonic acid containing polymers; a reduction in the degree of esterification of the galacturonic acid residues and loss of neutral sugars, such as galactose and arabinose, from the side chains of the “hairy region” of the pectin polymer. There are also likely to be modifications of the other cell wall domains, in particular the cellulose/hemicellulose framework. There are many enzymes found in nature capable of degrading cell wall components (Table 13.1). The list in Table 13.1 is not exhaustive but includes enzymes – such as polygalacturonase – which are thought to be primarily targeted at the homogalacturonan or “smooth” region of the pectin and those like – galactosidase – targeted at the “hairy” regions. Some of these enzyme

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Texture in food Table 13.1 Enzymes involved in cell wall degradation Pectin “smooth” region Polygalacturonase Pectate lyase Pectin lyase Pectin esterase Pectin acetylesterase Pectin “hairy” region Rhamnogalacturonase Rhamnogalacturonan lyase Rhamnogalacturonan acetylesterase Galactosidase Arabinosidase Other cell wall domains Glucanases Xyloglucan endotransferase Expansin Mannanase

activities are associated primarily with microbial sources; however, a good many are also found in plant tissues. These enzymes are encoded for by a very large array of genes. Following the entire sequence analysis of the Arabidopsis genome it has been shown that a staggering 1–2% of the genes, an estimated total of 730 open reading frames (ORF), are dedicated to the production of cell wall hydrolases or glycosyltransferases. A large number of the latter are presumably involved with the biosynthesis of the wall. The genes associated with hydrolytic enzymes have been grouped into classes (Table 13.2). Again this list is not exhaustive. For more detail see the review by Henrissat et al. (2001). Within this huge array of genes there seems to be a preponderance of genes, at least 170 ORFs, dedicated to pectin degradation. With the increase in knowledge of the enzymes and genes involved in wall degradation has come the ability to genetically manipulate these activities with a view to modifying the softening process and hence the texture of the fruit. Table 13.2 Putative Arabidopsis genes involved in wall degradation Enzyme

Class

Number of ORF

Hydrolases Pectin esterases Pectin acetylesterases Rhamnogalacturonan lyases β-galactosidase Cellulase

GH28 CE8 CE13 PL1 GH35 GH9

66 58 13 28 20 27

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13.2 Tools of genetic modification Genetic engineering has been used to alter a range of functions in plants. However, the majority of these genetic manipulations are directed at: • insertion of a “foreign” gene into a host cell such that a protein product not normally associated with that cell is produced; • enhancing the activity of an endogenous gene; • switching off a deleterious gene so that it can no longer produce the undesirable protein. Although these three objectives are widely different, the basic genetic engineering techniques used to achieve them are remarkably similar. Indeed much of genetic engineering relies on only a few basic techniques which, whilst often difficult to execute, are nonetheless relatively simple to understand. There are many excellent texts and reviews on the methodology of genetic engineering, but a good starting point is the text by Brown (1995). All genetic modifications require the transfer of new “DNA” into the plant in such a way that it is expressed and performs its desired function. Once a suitable piece of DNA has been obtained, the major problem is how to engineer this into the plant. There are several methods available for the transfer of the “foreign” DNA into a plant cell. In some cases the DNA can be inserted directly into the host cell. This inserted DNA may then be physically incorporated into the host’s DNA. There are several methods of direct implantation of the DNA, and these include electroporation, microinjection and the “DNA gun”. However, there are limitations to this methodology. The use of such direct methods often fails to result in the stable integration of the DNA into the host genome. Thus the new gene is not passed onto offspring as the cell divides or indeed may even be destroyed within the original cell. More efficient mechanisms employ the use of a vector or carrier to get the DNA stably inserted into a host cell. 13.2.1 Ti plasmid There are many vectors available for genetic engineering. Several viruses naturally incorporate their DNA into that of plant cells, and this property can be used to transfer new genes. A second, more commonly used vector is the Ti plasmid from Agrobacterium tumefaciens. Many plants are susceptible to infection by this bacterium which is responsible for the formation of crown galls on infected plants. The natural mechanism of infection of this particular bacterium involves gene transfer. The bacteria contain a plasmid – the Ti plasmid – which upon infection of a plant is passed from the bacteria into the plant cells where it inserts part of itself into the host’s genome and becomes functional. The genes on the plasmid direct the production of proteins which produce food for the bacteria and cause the plant tissue to differentiate into the gall associated with this disease. The properties of this natural genetic engineer have been harnessed to manipulate the plant genome. The virulence

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genes can be deleted from the plasmid without impairing its ability to integrate into the host DNA. These are replaced with antibiotic resistance genes and any “foreign” DNA as required. When this recombinant Ti plasmid is used to transform plant cells the “foreign” DNA is stably inserted into the plant DNA, but no gall or other symptoms of disease develop. There is an extra problem encountered with the transformation of plants, and this is that they are multi-cellular organisms. The Ti plasmid is used to transform a single plant cell. However, this cell contains all the DNA needed to produce an entire plant. The original cell can be treated in such a way that it continually divides and eventually forms an entire plant. Since at each division the stably inserted “foreign” DNA is passed onto each of the progeny, all the cells in the resultant plant have a copy of the newly inserted gene. Transformed plant cells are selected during this process by exposure to antibiotic, and only those with the Ti plasmid insert would be able to survive and divide to produce plants. The use of antibiotic resistance genes in this technology has given rise to some concern about the possible spread of resistance to non-host organisms. The “foreign” DNA could in theory encode for any protein. This could be a novel enzyme not normally associated with the host. Alternatively it may represent a second copy of a gene already present in the host but under the control of a more efficient promoter in order to enhance the levels of the specific enzyme. Such genetic modification techniques have been, or could be, used to study the role of wall hydrolases during ripening. However, the majority of reports have involved the “switching off ” of endogenous host genes and examination of the subsequent effect on wall degradation and fruit texture. Conceptually this is the most difficult technique to comprehend (for a general coverage see Lycett et al., 1996). However, it is basically the same as that used for the insertion of a novel gene, or a second copy of an endogenous gene, into a host, except that in this case this new gene is what is known as an antisense gene. The first stage is to isolate the coding DNA sequence from the target gene, i.e. the gene that needs to be switched off. Under normal conditions it is the antisense strand of this gene which is copied to give sense mRNA (Fig. 13.1). However, for this technique a new type of gene is constructed in which the sense strand will be copied to give antisense mRNA (Fig. 13.1). This new gene is then introduced back into the cell. Now the cell has two genes, the normal gene making sense mRNA and the newly introduced gene which makes antisense mRNA. Exactly how these two interact is not known, but the end result is that the presence of both sense and antisense mRNAs in some way prevents any of the sense mRNA from being used to make protein, effectively switching the target gene off. There are other methods which can be employed for gene silencing. These include cosupression and transposon inactivation. However, the mechanisms of these are beyond the scope of this review. Given the complex structure of the plant cell wall it is unlikely that simply silencing the gene for a single hydrolytic enzyme would have any major

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325

Antisense

DNA

RNA Antisense RNA

Protein

Fig. 13.1 In the normal gene on the left the top strand of DNA ( ) is copied to produce mRNA ( ). A copy of this gene is transformed into the plant to produce an antisense gene. This antisense gene is constructed such that in this case the other ). Since the antisense RNA is strand ( ) is copied to give an antisense RNA( complementary to the normal mRNA these may hybridise and thus protein synthesis is blocked.

effect on texture. Indeed, this is the conclusion from many of the gene silencing experiments that will be discussed later in this chapter. Plants in which two or more genes are silenced can be produced by means of conventional breeding between the individually genetically modified lines. However, this is both labour-intensive and time-consuming. An alternative approach has been demonstrated through the application of “chimeric gene constructs”. This was f irst exemplif ied by the simultaneous silencing of genes for polygalacturonase and pectinesterase in tomato plants (Seymour et al., 1993). The technique involved is illustrated in Fig. 13.2. Parts of the coding sequence Polygalacturonase

Pectinesterase

Chimeric construct

Fig. 13.2 cDNA clones for the two endogenous genes – polygalacturonase and pectinesterase – are isolated. These are cut with restriction endonucleases and the products are ligated together to form a single chimeric construct. Such that the 5’ end represents part of the coding sequence for one gene and the 3’ end that from the other. This chimeric construct can then be used as an antisense gene in a single transformation event to simultaneously silence both polygalacturonase and pectinesterase genes.

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from each of the target genes are taken and ligated together to form a chimeric antisense construct. When this is used to transform plants, the resultant transformants exhibit simultaneous silencing of the two endogenous genes. The orientation of these two fragments with respect to each other does not seem to be important. Indeed it has been shown that a chimeric construct containing both sense and antisense oriented fragments will still result in the silencing of both target genes (Jones et al., 1998).

13.2.2 GM approaches–QTL There is a potential problem in the commercial application of genetic modification due to the strong consumer resistance to this technology. An alternative “genetic”-based approach might be through the identification of quantitative trait loci (QTL) responsible for texture determination. This technology involves making a large number of crosses between two parental lines of a species with a relatively intensive genetic map. In the fruit area tomato is one such candidate. QTL are then identified by co-segregation, within the progeny, of the “trait” (in this case texture) with specific genetic markers which in turn indicate that gene(s) within the loci associated with those markers are having a determinant effect on the trait being measured. This concept is illustrated, in a very simplistic manner, in Fig. 13.3. Once a A

F

S

F

F

S

S

F

B S

F

F

Fig. 13.3 On the left the two boxes represent the genetic map of two parental lines A and B which show firm (F) or soft (S) fruit, respectively. For simplicity, this map shows just three loci when in reality there are many thousands. These two parents are crossed to generate a large number of progeny as depicted on the right hand side. Each progeny is tested for either the “trait” soft (S) or the “trait” firm (F) fruit. In addition each progeny is genotyped to determine the parental contribution at each of the loci. Correlations are then sought to determine co-segregation of specific loci with the trait. In this simple example the firm trait can clearly be seen to be associated with the inheritance of the middle loci ( ) of parent A. This would thus represent a QTL for texture.

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QTL, or more specifically its associated genetic marker, has been identified then this can be used in marker-assisted breeding programmes to rapidly generate lines with improved texture. One classic example of how this technology has been applied to the improvement of plants can be seen in the breeding of high glucosinolate lines of broccoli (Faulkner et al., 1998).

13.3 Approaches to the manipulation of texture: the tomato 13.3.1 Introduction Tomato has been used by many groups as a model to study fruit ripening. This is because the tomato plant is readily and easily transformed, has a short lifespan and has a good genetic map. The fruit are also easy to work with at a biochemical level as they contain little protease activity. As stated above, the vast majority of the approaches to manipulating fruit texture specifically using genetic modification have targeted cell wall degrading enzymes, and this has been recently reviewed (Brummell and Harpster, 2001). It can be seen from Table 13.1 that there are a significant number of these. Unfortunately little information is available regarding the relative load-bearing qualities of the various polymers or bonds within the wall. Since these are likely to be the most significant in terms of texture they would represent good targets for manipulation. In the absence of such information early targets for genetic modification were those enzymes whose activity increased dramatically during tomato fruit ripening, the classic example being polygalacturonase (PG).

13.3.2 Polygalacturonase Green tomato fruit exhibit very low levels of PG activity (Hobson, 1964), and that activity which is present is thought to reside in an exo-acting PG isoform that is expressed constitutively throughout development and ripening. Polygalacturonase activity increases exponentially during fruit ripening as a result of the de novo synthesis of endo-acting PG isoforms (Tucker and Grierson, 1982). Polygalacturonase is a hydrolytic enzyme that cleaves the α (1–4) bonds between adjacent galacturonic acids within the homogalacturonic acid backbone of pectin. This endo-acting PG was thus thought to be responsible for the progressive depolymerisation and perhaps solubilisation of pectin that accompanies tomato fruit ripening, and perhaps represented a key enzyme in the softening process. It was thus the first enzyme to be targeted for silencing. Two groups have independently down-regulated PG activity in tomato fruit (Sheehy et al., 1988; Smith et al., 1988). This resulted in a reduction in PG activity to below 1% of that normally detected in ripe tomato fruit. The effect on the degradation of the pectin during ripening was clearly seen. The depolymerisation of the pectin occurring during normal ripening was markedly

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inhibited (Smith et al., 1990). Thus in normal ripening the chelator soluble pectin undergoes depolymerisation from an average molecular weight of around 300 000 in green fruit down to about 100 000 in red ripe fruit. In transgenic tomato fruit with reduced PG activity, the size of the chelatorsoluble pectin from green fruit was unaltered, but the reduction in molecular weight was largely eliminated, the chelator-soluble pectin from red ripe fruit in this case being in the order of 250 000. This modification to the depolymerisation of the pectin was as expected given the mode of action of PG. It was anticipated that the reduction in PG activity might also result in a reduction in the increased solubility of the pectin associated with ripening. This was not the case if pectins were solubilised with chelators such as 1, 2cyclohexanediamine tetraacetic acid (CDTA). Indeed in some reports this was actually found to increase in the transgenic fruit (Carrington et al., 1993; Brummell and Labovitch, 1997). However, solubility in water was found to be reduced in these transgenic fruit (Carrington et al., 1993). The degree of methyl esterification of the pectin throughout ripening was largely unaffected (Smith et al., 1990) showing that reduction in PG activity does not influence the action of the other major cell wall degrading enzyme in tomato fruit namely pectinesterase. These modifications to the pectin have also been shown to have an effect on the polymer in situ. During ripening a proportion of the polyuronide in the wall appears to increase in mobility as measured by NMR. This mobility is slightly reduced in the transgenic fruit with reduced PG activity (Fenwick et al., 1996). Transgenic fruit were only slightly firmer (Kramer et al., 1992; Langley et al., 1994; Errington et al., 1998; Tucker et al., 1999) and handled better with less cracking (Schuch et al., 1991). A major commercial advantage, however, was that this genetic modification resulted in an improved quality of the puree made from the transformed fruit (Schuch et al., 1991; Kramer et al., 1992; Brummell and Labovitch, 1997). The lack of any significant effect on texture in these transgenic fruit could have been explained by the fact that whilst PG activity had been reduced to around 1% of normal this still represents a significant level of activity given the unusually high level of this enzyme found in tomato fruit. More recently Cooley and Yoder (1998) have produced a line in which the PG gene has been disrupted by a transposon insertion. This resulted in the almost complete elimination of PG activity, but does not appear to have resulted in any further reduction in firmness. In addition Giovannoni et al. (1989) transformed a tomato ripening mutant – rin – with a copy of the PG gene under the control of another promoter. The rin is a natural pleiotrophic ripening mutant which does not soften and does not normally synthesise any endo-PG activity. Expression of the new PG gene in the transgenic rin fruit resulted in the synthesis of endo-PG activity in the ripe fruit and demonstratable depolymerisation of the pectin, but did not restore any softening phenotype. These results would seem to demonstrate that PG, whilst responsible for pectin depolymerisation and to some extent solubility, is not causal, on its own, for softening.

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Endo-polygalacturonase activity in tomato fruit results from the action of a single gene (Bird et al., 1988). However, the enzyme as extracted occurs in several isoforms – PG1, PG2A and PG2B (Ali and Brady, 1982; Tucker et al., 1980). It is thought that PG2 represents the primary gene product and is the catalytic subunit of the enzyme. This polypeptide is differentially glycosylated which accounts for isoforms PG2A and PG2B (Ali and Brady, 1982). The PG2 polypeptide is also thought to react post-transcriptionally with a second polypeptide – the β-subunit – and this results in the formation of PG1 (Tucker et al., 1981; Pressey, 1986). The β-subunit – unlike the catalytic subunit – is synthesised throughout fruit development and ripening and has been localised to the middle lamella of the tomato cell walls (Pogson et al., 1991). It is thought that this β-subunit is somehow involved in sequestering the catalytic subunit within the wall and thus limiting and controlling its activity. Expression of the β-subunit has been silenced in tomato fruit by the introduction of an antisense gene (Watson et al., 1994). This has resulted in transformed fruit in which expression of the β-subunit has been reduced to almost undetectable levels. In normal wild type fruit the early expression of endo-PG activity in ripening fruit is associated almost entirely with the presence of the PG1 isoform (Tucker et al., 1980). In contrast in these antisense β-subunit fruit almost all PG activity was associated with the PG2 isoform. The amount of chelator-soluble polyuronide was found to be about 50% greater from the ripe antisense fruit compared to normal fruit (Watson et al., 1994). More interestingly, there was also a 90% increase in chelatorsoluble polyuronide in mature green fruit (Chun and Huber, 1997) indicating a possible effect of reduced β-subunit which is independent of endo-PG activity. Analysis of the molecular weight distributions of the chelator-soluble polyuronides from normal and antisense β-subunit fruit showed little or no difference (Watson et al., 1994; Chun and Huber, 1997). There was no real difference in the firmness of normal or transgenic fruit at the mature green stage. However, ripe fruit from the β-subunit antisense plants showed a 22% reduction in firmness compared to controls (Chun and Huber, 2000). Pericarp discs cut from mature green β-subunit antisense fruit also exhibited a greater reduction in firmness, compared to controls, following treatment with exogenous PG2 enzyme (Chun and Huber, 2000). This apparent increase in the susceptibility of cell walls to digestion by PG2 in the absence of the βsubunit supports a role for the latter in controlling the activity of the former. 13.3.3 Pectinesterase A second major pecteolytic activity associated with fruit is that of pectinesterase (PE). This enzyme catalyses the removal of the methyl group from the C6 of methyl galacturonic acid to form the free acid. The enzyme is found in most, if not all, plant tissues. Its function, if any, in the determination of texture is not at all clear. In order for PG to degrade pectin it requires the presence of blocks of demethylated galacturonic acid residues. These can be produced by

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the action of PE, and in this respect the enzyme may act in a synergistic fashion with PG to bring about a weakening of the wall structure. Alternatively, long regions of deesterified galacturonic acid residues on adjacent pectin polymers can interact via calcium chelation to generate the so called “egg box” junction zones (Grant et al., 1973) and thus strengthen the wall. In tomato fruit PE activity can be separated into at least three isoforms and, unlike endo-PG, these seem to be derived from the action of three separate genes or gene families. Some of these isoforms are fruit specific whilst others appear to occur in all tissues including fruit (Gaffe et al., 1994). Also unlike endo-PG, PE activity is present throughout the development and ripening of the fruit, although the ratio of the isoforms changes (Tucker et al., 1982). The major tomato fruit isoform – PE2 – accounts for about 80% of the activity in a ripe fruit and was also an early target for gene silencing technology. Genes encoding the PE2 isoform have been identified (Ray et al., 1988; Harriman et al., 1991; Hall et al., 1994), and two groups have independently down-regulated the expression of PE activity (Tieman et al., 1992; Hall et al., 1993). In both cases, expression of PE activity was reduced to around 10% of that in normal fruit. The expression of PE2 in these transgenics was found to be almost completely blocked, the residual 10% enzyme activity being associated with the continued expression of the other two isoforms – PE1 and PE3 – of this enzyme (Tucker and Zhang, 1996). This demonstrates the extreme selectivity of these gene silencing techniques. Presumably in this instance the genes encoding the PE1 and PE3 isoforms have insufficient sequence homology to those encoding PE2 for the antisense message to hybridise and thus inhibit expression. Little is known about the genes encoding these other PE isoforms although a tomato leaf PE gene (PMEU1) has been described which is also expressed in green fruit (Gaffe et al., 1997). During the ripening of normal tomato fruit the degree of esterification of the pectin drops, from around 90% in green to 35% in ripe fruit (Koch and Nevins, 1989). This degree of esterification was higher throughout ripening by between 15 and 40% in the transgenic fruit (Tieman et al., 1992; Hall et al., 1993), and the increase in the retention of methyl groups was accompanied by a reduced depolymerisation of the polyuronides in ripe fruit and a decrease in the amount of chelator-soluble pectin of around 20–30% (Tieman et al., 1992). These changes in pectin structure had little or no effect on fruit firmness during normal ripening (Tieman and Handa, 1994). However, they were associated with a complete loss of tissue integrity in over-ripe fruit (Tieman and Handa, 1994) and with an increase in quality of processed juice made from the transgenic fruit, these having higher soluble solids and viscosity (Thakur et al., 1996). 13.3.4 β-galactosidase The third pectin modifying enzyme to be targeted by genetic modification was β-galactosidase (β-gal). This enzyme is normally assayed using an artificial

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substrate–p–nitrophenyl galactoside. When this is used β-gal has been shown to occur in tomato fruit in at least three isoforms but only one of these – βgal II – has been shown, using lupin galactan as a substrate, to act as an exogalactanase (Pressey, 1983; Carey et al., 1995). This enzyme is thus presumably capable of cleaving off the terminal non-reducing galactose residues from the pectin sidechains. Whilst total β-gal activity does not change significantly during tomato fruit ripening, the activity specifically associated with the βgal II isoform has been shown to increase by about seven fold during this period (Carey et al., 1995). Thus isoform β-gal II has been the target for gene silencing. At least seven putative β-galactosidase genes (TBG1–7) have been identified in tomato fruit (Smith et al., 1998; Smith and Gross, 2000). TBG1, 3, 4 and 5 have all been shown to have ripening-related expression (Smith et al., 1998; Smith and Gross, 2000). The tomato ripening mutant rin has been shown to be deficient in β-gal II activity (Carey et al., 1995) and in the specific expression of TBG4 (Smith et al., 1998; Smith and Gross, 2000). The coding sequence of TBG4 also corresponds to the N-terminal amino acid sequence of the purified β-gal II enzyme from tomato fruit (Smith et al., 1998). This evidence suggests that TBG4 may be the most significant gene for the expression of exo-galactanase activity associated with tomato fruit ripening. The TBG4 gene has been shown to encode for an exo-galactanase by expression in yeast (Smith and Gross, (2000), and antisense suppression of this gene has resulted in a 90% reduction in exo-galactanase activity in transgenic fruit (Smith and Gross, 2000). It has also been shown that if this suppression of activity can be brought about very early in ripening then there is a 40% increase in the firmness of the ripe fruit (Smith et al., 2002). The antisense gene used in these experiments contained a very large proportion of the coding sequence of the TBG4 gene. This has high homology with other gene family members, in particular TBG1 and 3, and it is possible that these too had been silenced. The TBG1 gene has also been shown to encode for an exo-galactanase by expression in yeast (Carey et al., 2001), and sense suppression of this gene, whilst reducing TBG1 mRNA levels to less than 10% of normal, had little effect on either total exo-galactanase activity or fruit firmness (Carey et al., 2001). Suppression of TBG3 by antisense technology has resulted in a 75% reduction in total exo-galactanase activity (de Silva and Verhoeyen, 1998). In this case TBG1 and 4 may also have been affected, and whilst there was a demonstratable reduction of galactose loss from the wall there was no effect on texture.

13.3.5 Endo-glucanase Enzymes thought to be involved in the degradation of the other major cell wall domain – the cellulose/hemicellulose framework – have also been targeted by gene silencing techniques. The matrix glycans in particular have been shown to undergo depolymerisation in a range of fruit, including tomato,

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during ripening (Huber, 1983; Brummell et al., 1999a). An early target in this area was endo-glucanase (EGase). This enzyme cleaves the β-(1-4) bond between adjacent glucose residues. It is thought that these bonds probably exist within the hemicellulases such as xyloglucan since these enzymes would be expected to have little if any activity against native crystalline cellulose (Brummell et al., 1994). The EGase activity in tomato plants is thought to arise from at least eight separate genes LeCel1–8. Of these LeCel1 and 2 show ripening related accumulation of mRNA transcripts (Lashbrook et al., 1994; Gonzalez-Bosch et al., 1996) and LeCel5 is also expressed in ripe fruit (Kalaitzis et al., 1999). The expression of LeCel1 is maximal early in ripening and then declines whilst that of LeCel2 continues to increase, and it is this gene transcript that predominates in ripe fruit. These two genes have both been a target for silencing – LeCel1 (Lashbrook et al., 1998) and LeCel2 (Brummell et al., 1999a). In each case silencing was highly specific in that whilst expression of the target gene was reduced by as much as 90% that of the other gene was unaffected. In both cases there was no detectable effect of the transformation on fruit texture. This suggests either that there is a degree of redundancy in the action of these EGases, i.e. that the LeCel1 (or LeCel5) product can compensate for loss of LeCel2 and vice versa, or that this enzyme alone does not play a significant role in texture development. Tomato fruit express low levels of EGase activity, the enzyme often being more predominant in other fruit. Two genes – FaCel1 and FaCel2 – have been identified in ripening strawberry fruit, and these have again been the target for silencing. In this case down, regulation of the FaCel1 gene had no effect on expression of the FaCel2 gene. This transformation had no effect on either total extractable EGase activity or fruit texture (Woolley et al., 2001). Sweet pepper has also provided a means to investigate the potential role of EGase in the development of texture. The depolymerisatin of the matrix glucans is a predominant feature in this fruit. The silencing of EGase, by sense suppression, to non-detectable levels, however, had no effect on this depolymerisation (Harpster et al., 2002a). Similarly expression of the pepper EGase gene in transgenic tomato fruit did not result in any increase in xyloglycan depolymerisation nor fruit softening (Harpster et al., 2002b).

13.3.6 Xyloglucan endotransglycosylase The EGase described above may be capable of the irreversible cleavage of matrix glycans such as xyloglucan. There is another enzyme found in plant tissues, however, that is thought to be more specifically involved in modification of the hemicellulosic polymer xyloglucan. This enzyme is called xyloglucan endotransglycosylase (XET). This enzyme catalyses the breakage of an internal bond, within a xyloglucan backbone, to form a free reducing group. This reducing group then reacts with the C4 group of a terminal non-reducing glucose associated with another xyloglucan polymer or oligosaccharide. As with many of the other wall hydrolases, XET activity in plant tissues is

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encoded for by a large multi-gene family and exists as several isoforms. In tomato fruit there are at least two genes involved in the production of XET activity – LeEXGT is expressed in expanding green fruit (Catala et al., 2000) and LeXETB1 shows maximal expression in pink ripening fruit (Arrowsmith and deSilva, 1995). Expression of both of these two genes has been the target for genetic modification. Asada et al. (1999) silenced the LeEXGT1 and deSilva et al. (1994) the LeXETB1 gene. In both cases antisense technology was employed. In neither case was an effect on the texture of the ripe fruit reported. Although it was shown that reduction or over-expression of LeEXGT1 resulted in smaller or larger fruit, respectively.

13.3.7 Expansin The last wall modifying protein to be discussed with respect to tomato fruit is expansin. This is an unusual protein in that it does not have any detectable hydrolase activity, yet it has clearly been shown to influence the structure of cellulose/hemicellulose frameworks (McQueen-Mason and Cosgrove, 1995). The protein has been shown to bind strongly to cellulose which is coated with matrix glycans, and it is thought to act by the disruption of the hydrogen bonds, thus allowing loosening of the wall and turgor-driven slippage (McQueen-Mason and Cosgrove, 1995; Whitney et al., 2000). This protein is again synthesised by several genes and occurs as several isoforms. In tomato fruit at least six genes have been identified and these are expressed differentially during ripening (Brummell et al., 1999c; Catala et al., 2000). The most abundant mRNA found in the ripe fruit is derived from the LeExp1 gene and this has been the target for genetic modification. Both down-regulation and over-expression of this gene resulted in modification of the softening process (Brummell et al., 1999b). Suppression of activity resulted in fruit with a 15–20% increase in firmness and with increased shelf life and processing quality (Brummell et al., 2002). Conversely, a three fold increase in expression resulted in fruit which softened more extensively. These changes in texture were accompanied by changes in the depolymerisation of cell wall polymers. Suppression of expansin gene expression prevented the depolymerisation of polyuronides normally associated with late ripening, whilst having no effect on the depolymerisation of the matrix glycans. Overexpression of this protein had no effect on polyuronide depolymerisation which proceeded as normal, but it was shown to enhance the depolymerisation of the matrix glucans, this presumably as a result of increased accessibility of the latter to wall hydrolases.

13.4 Other approaches to the manipulation of texture Whilst most of the work has been carried out in tomato, this fruit is by no means characteristic of all types of fruit ripening. In fact in some ways it is

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actually atypical. This is particularly the case for PG activity, which is relatively high in tomato fruit but is low or even non-detectable in many other fruit such as strawberry, banana and melon. These fruit, in many cases, nonetheless still undergo extensive depolymerisation of their pectin backbones during ripening. There is another pectin degrading enzyme, pectate lyase (PEL). This enzyme can bring about the depolymerisation of polyuronides, not via a hydrolytic reaction as with PG, but through a β-elimination reaction targeted at the same chemical linkage, i.e. the α(1–4) link between galacturonic acid residues in the backbone of the pectin polymer. This enzyme has in the past been commonly associated with microorganisms, but it is becoming clear that it is also expressed in plant tissues. Pectate lyase in strawberry fruit has been inferred by the identification of at least two ripening related genes (Medina-Escobar et al., 1997; Benitez-Burraco et al., 2003). Two putative cDNA clones for PEL have also been identified in banana (DominguezPuigjaner, 1997; Pua et al., 2001; Marin-Rodriguez et al., 2003) one of which when expressed in yeast has been shown to encode a PEL enzyme (Marin-Rodriguez et al., 2003). This group, and others, have also demonstrated PEL activity in the fruit directly (Payasi and Sanwal, 2003). Antisense technology has been used to reduce the expression of a PEL gene in ripe strawberry, and this was accompanied by an increased firmness of the fruit (Jimenez-Bermudez et al., 2002) It is perhaps significant that in none of the cases described above has a major alteration to the texture of the intact fruit been achieved. This is perhaps not surprising given the complex nature of the cell wall and the fact that several hydrolytic activities are likely to be required to be modified in order to get a major effect. It is possible that the best approach would be to simultaneously target enzymes involved in the degradation of the two major cell wall domains, namely the pectin matrix and the hemicellulose /cellulose framework. This could be achieved by crossing plants in which individual enzymes have been down-regulated. An alternative approach would be to employ chimeric constructs aimed at two or more target endogenous genes. This was first achieved by Seymour et al., (1993) using PG and PE2 as the two target genes and has been described previously in this chapter. A single construct was made by ligating the 5’ region of the PG cDNA to the 3 region of the PE2 cDNA. When this single construct was used to transform fruit the resultant fruit were down-regulated for both enzyme activities. There was still no significant effect on fruit softening though. Recently, suppression of both PG and expansin together has resulted in significantly firmer tomato fruit (Powell et al., 2003). More recently a different approach seems to have been successful. All these degradative enzymes require to be targeted to the cell wall during ripening. It is thus possible that any disruption of this trafficking mechanism would mean that the cell wall hydrolases will not reach the wall and thus the fruit will remain firm. The process of trafficking within a plant cell is very complex and is outwith the scope of this review. However, this involves the

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complex interaction of many protein families including the so called rab GTPases (Rutherford and Moore, 2002). Early studies in this area identified a rab protein – rab11 – that appeared to be expressed in a ripening specific manner in mango fruit (Zainal et al., 1996). A close homologue to this mango protein was found in tomato fruit – although in this case expression was not entirely ripening specific (Lu et al., 2001). Silencing of this tomato rab11 protein resulted in fruit which whilst normal in all other respects, i.e. colour, shape, etc., retained their firmness for nearly twice as long as controls (Lu et al., 2001).

13.5 Future trends Although many degradative enzymes have been investigated for their effect on fruit texture using gene knock out techniques, they have by no means all been studied by this approach. Thus there is still scope in this area for examining the roles of arabinosidases and mannosidases for instance. Similarly, the role of isoforms has not really been addressed. Of particular interest may be the individual isoforms of pectinesterase. This enzyme is ubiquitous in that it is expressed in almost all plants and tissues. Its function is presently ambiguous in that it may be implicated in both the strengthening or weakening of the wall. To date only the fruit specific isoform of pectinesterase seems to have been targeted for silencing. It would be interesting to examine the relative roles of the fruit specific and constitutive isoforms. Finally there are several structural proteins associated with the plant cell wall (Cassab, 1998). The role of these, if any, in the determination of texture is not at all clear, and these might also be a target for gene silencing technology. It is unlikely that the reduction of any one enzyme activity would result in a significant change in texture. More progress may be made by using a combination of knockouts, and indeed, if these are targeted to different domains within the wall, a significant change in texture may well result. The approach of modifying the targeting of enzymes to the wall is also worthy of further investigation. Homologues of the rab11 from tomato fruit may exist in other fruit, and the effect of silencing these would be interesting. Protein engineering techniques may be employed in the near future to make subtle changes to the mode of action of these cell wall hydrolases. Information is accumulating on the domain structure and function of these hydrolases, in particular the microbial pectinases (Benen et al., 2002) and this could be used to modify the substrate specificity and functionality of these enzymes in genetically modified plants. One key example may again be pectinesterase – in this instance its functionality with respect to the size of the deesterified blocks formed may be a target for modification. Study of these enzymes is also important since, as well as determining texture, these cell wall components have many other useful functions (Thakur et al., 1997),

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and it is likely that modification of these polymers, whilst perhaps not significant for texture within the whole plant, may have definite benefits in other areas. To date manipulation of cell wall polymers has focussed on modification of cell wall hydrolases. However, recent advances in genomics are highlighting many genes potentially encoding for enzymes involved in the biosynthesis of carbohydrate polymers including those of the cell wall (Henrissat et al., 2001). Whilst starch synthesis has been most intensively studied to date, significant progress is being made in the elucidation of the biochemical mechanisms involved in wall synthesis (Bolwell, 2000; Reiter, 2002). Most progress at present seems to be in the elucidation of genes involved with cellulose biosynthesis (Doblin et al., 2002), but those involved with the synthesis of other matrix polymers can also be sought using genetic-based techniques (Perrin et al., 2001; Ridley et al., 2001). This is providing a new set of gene targets for manipulation. Modification of these by gene silencing, or indeed by expression of new or over-expression of existing enzymes, may lead to some interesting polymers and may influence texture. Studies in this area have already started with, for example, the identification of transposon “knock out” mutants of several putative “cellulase synthase” genes in Arabidopsis (Bonetta et al., 2002). The QTL approach to defining the genetic basis of texture is likely to continue, but a complex trait like firmness may be difficult to study. Tomato QTL linked to texture attributes have been reported (Causse et al., 2001, 2002; Doganlar et al., 2002). This has already led to the production of a near isogenic line of tomato with good firmness (Frary et al., 2003). The technology is also being applied to other fruit such as apple (King et al., 2001), and QTL associated with texture attributes have been identified. Finally there is a need to integrate these genetic-based studies with those on a macro scale. Texture is likely to be determined not only at the molecular but at the supra molecular scale as well. Thus how modification to individual polymers translates into large scale modification of the tissue needs to be examined.

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HALL L H, TUCKER G A, SMITH C J, WATSON C F, SEYMOUR G B, BUNDICK Y, BONIWELL J M, FLETCHER J D, RAY J A, SCHUCH W, BIRD C R

and GRIERSON D (1993) Antisense inhibition of pectin esterase gene expression in transgenic tomatoes, The Plant J, 3(1), 121–9. HALL L N, BIRD C R, PICTON S P, TUCKER G A, SEYMOUR G B and GRIERSON D (1994) Molecular characterisation of cDNA clones representing pectin esterase isozymes from tomato, Plant Mol Biol, 25(2), 313–18. HARPSTER M H, BRUMMELL D A and DUNSMUIR P (2002a) Suppression of a ripening-related endo-1,-beta-glucanase in transgenic pepper fruit does not prevent depolymerisation of cell wall polysaccharides during ripening, Plant Mol Biol, 50(3), 345–55. HARPSTER M H, DAWSON D M, NEVINS D J, DUNSMUIR P and BRUMMELL D A (2002b) Constitutive expression of a ripening-related pepper endo-1,-beta-glucanase in transgenic tomato fruit does not increase xyloglucan depolymerisation or fruit softening, Plant Mol Biol, 50(3), 357–69. HARRIMAN R W , TIEMAN D M and HANDA A K (1991) Molecular cloning of tomato pectinmethylesterase gene and its expression in Rutgers, ripening inhibitor, nonripening and never ripe tomato fruits, Plant Physiol, 97(1), 80–87. HENRISSAT B, COUTINHO P M and DAVIES G J (2001) A census of carbohydrate-active enzymes in the genome of Arabidopsis thaliana, Plant Mol Biol, 47(1), 55–72. HOBSON G E (1964) Polygalacturonase in normal and abnormal tomato fruit, Biochem J, 92, 324–32. HUBER D J (1983) Polyuronise degradation and hemicellulose modifications in ripening tomato fruit, J Am Soc Hort Sci, 108(3), 405–9. JIMENEZ-BERMUDEZ S, MUNOZ-BLANCO J, CABALLERO J L, LOPEZ-ARANDA J M, VALPUESTA V, PLIEGOALFARO F, QUESADA M A and MERCADO J A (2002) Manipulation of strawberry fruit softening by antisense expression of a pectate lyase gene, Plant Physiol, 128(2), 751– 9. JONES C G, SCOTHERN G P, LYCETT G W and TUCKER G A (1998) The effect of chimeric architecture on co-ordinated gene silencing, Planta, 204(4), 499–505. KALAITZIS P, HONG S-B, SOLOMOS T and TUCKER M L (1999) Molecular characterization of a tomato endo-β-1,4-glucanase gene expressed in mature pistils, abscission zones and fruit, Plant Cell Physiol, 40(8), 905–8. KING G J, LYNN J R, DOVER C J, EVANS K M and SEYMOUR G B (2001) Resolution of quantitative trait loci for mechanical measures accounting for genetic variation in fruit texture of apple (Malu pumila Mill.), Theor Appl Genet, 102(8), 1227–35. KOCH J L and NEVINS D J (1989) Tomato fruit cell wall. I. Use of purified tomato polygalacturonase and pectinmethylesterase to identify developmental changes in pectins, Plant Physiol, 91(3), 816–22. KRAMER M, SANDERS R, BOLKAN H, WATERS C, SHEEHY R E and HIATT W R (1992) Postharvest evaluation of transgenic tomatoes with reduced levels of polygalacturonase: processing, firmness and disease resistance, Postharvest Bio Technol, 1(3), 241–55. LANGLEY K R, MARTIN A, STENNING R, MURRAY A J, HOBSON G E, SCHUCH W and BIRD C R (1994) Mechanical and optical assessment of the ripening of tomato fruit with reduced polygalacturonase activity, J Sci Food Agric, 66(4), 547–54. LASHBROOK C C, GONZALEZ-BOSCH C and BENNETT A B (1994) Two divergent tomato endo-1,4β glucanase genes exhibit overlapping expression in ripening fruit and abscising flowers, Plant Cell, 6(10), 1485–3. LASHBROOK C C, GIOVANNONI J J, HALL B D, FISCHER R L and BENNETT A B (1998) Transgenic analysis of tomato endo-1,4-β glucanase gene function. Role of Cel1 in floral abscission, Plant J, 13(3), 303–10. LU C, ZAINAL Z, TUCKER G and LYCETT G (2001) Developmental abnormalities and reduced fruit softening in tomato plants expressing an antisense Rab11 GTPase gene, The Plant Cell, 13(1), 1–15. LYCETT G W, GRIERSON D and TUCKER G A (1996) Mechanisms and Applications of Gene Silencing. Nottingham, Nottingham University Press.

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MARIN-RODRIGUEZ M C, SMITH D L, MANNING K, ORCHARD J

and SEYMOUR G B (2003) Pectate lyase gene expression and enzyme activity in ripening banana fruit, Plant Mol Biol, 51(6) 851–7. MCCANN M C and ROBERTS K (1991) Architecture of the primary cell wall. In The Cytoskeletal Basis of Plant Growth and Form. Ed. C W Lloyd, London, Academic Press, 109–29. MCQUEEN-MASON S J and COSGROVE D J (1995) Expansin mode of action on cell walls. Analysis of wall hydrolysis, stress relaxation and binding, Plant Physiol, 107(1), 87– 100. MEDINA-ESCOBAR N, CARDENAS J, MOYANO E, CABALLERO J L and MUNOZ-BLANCO J (1997) Cloning, molecular characterisation and expression pattern of a strawberry ripening-specific cDNA with sequence homology to pectate lyase from higher plants, Plant Mol Biol, 34(6), 867–77. PAYASI A and SANWAL G G (2003) Pectate lyase activity during ripening of banana fruit, Phytochem, 63(3), 243–8. PERRIN R, WILKERSON C and KEEGSTRA K (2001) Golgi enzymes that synthesize plant cell wall polysaccharides: finding and evaluating candidates in the genomic era, Plant Mol Biol, 47(1–2), 115–30. POGSON B J, BRADY C J and ORR G R (1991) On the occurrence and structure of subunits of endo-polygalacturonase isoforms in mature-green and ripening tomato fruit, Aust J of Plant Physiol, 18(1), 65–79. POWELL ALT, KALAMAKI MS, KUTIEN PA, GARRIEN S AND BENNETT AB (2003) Simultaneous transgenic suppression of LePG and LeEXCPI influences fruit texture and juice viscosity in a fresh market tomato variety, J Agric food chem 51(25), 7450–7455. PRESSEY R (1983) ß-galactosidases in ripening tomatoes, Plant Physiol, 71(1), 132–5. PRESSEY R (1986) Changes in polygalacturonase isoenzymes and converter in tomatoes during ripening, HortScience, 21(5), 1183–5. PUA E C, ONG C K, LIU P and LUI J Z (2001) Isolation and expression of two pectate lyase genes during fruit ripening of banana(Musa acuminata), Physiol Plant, 113(1), 92–9. RAY J, KNAPP J, GRIERSON D, BIRD C and SCHUCH W (1988) Identification and sequence determination of a cDNA clone for tomato pectinesterase, European J of Biochem, 174(1), 119–24. REITER W D (2002) Biosynthesis and properties of the plant cell wall, Curr Opin Plant Biol, 5(6) 536–42. RIDLEY B L, O’NEILL M A and MOHNEN D A (2001) Pectins : structure, biosynthesis, and oligogalacturonide-related signalling, Phytochem, 57(6), 929–67. RUTHERFORD S and MOORE I (2002) The Arabidopsis rab GTPase family: another enigma variation, Curr Opin Plant Biol, 5(6), 518–28. SCHUCH W, KANCZLER J, ROBERTSON D, HOBSON G E, TUCKER G A, GRIERSON D, BRIGHT S and BIRD C (1991) Fruit quality characteristics of transgenic tomato fruit with altered polygalacturonase activity, Hort Sci, 26(12), 1517–20. SEYMOUR G B, FRAY R G, HILL P and TUCKER G A (1993) Down regulation of two nonhomologous endogenous tomato genes with a single chimearic gene construct, Plant Mol Biol, 23(1), 1–9. SHEEHY R E, KRAMER M and HIATT W R (1988) Reduction of polygalacturonase activity in tomato fruit by antisense RNA, Proc Nat Acad Sci USA, 85(23), 8805–9. SMITH D L and GROSS K C (2000) A family of at least seven β-galactosidase genes is expressed during tomato fruit development, Plant Physiol, 123(3), 1173–83. SMITH C J S, WATSON C F, RAY J, BIRD C J , MORRIS P C, SCHUCH W and GRIERSON D (1988) Antisense RNA inhibition of polygalacturonase gene expression in transgenic tomatoes, Nature, 334(6184), 724–6. SMITH C J S, WATSON C F, MORRIS P C, BIRD C R, SEYMOUR G B, GRAY J E, ARNOLD C, TUCKER G A, SCHUCH W, HARDING S E and GRIERSON D (1990) Inheritance and effects on ripening of antisence polygalacturonase genes in transgenic tomatoes, Plant Mol Biol, 14(3), 369–79.

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and GROSS K C (1998) A gene coding for tomato fruit betagalactosidase II is expressed during fruit ripening – Cloning, characterization, and expression pattern’, Plant Physiol, 117(2), 417–23. SMITH D L, ABBOTT J A and GROSS K C (2002) Down regulation of tomato beta-galactosidase 4 results in decreased fruit softening, Plant Physiol, 129(4), 1755–62. THAKUR B R, SINGH R K, TIEMAN D M and HANDA A K (1996) Tomato product quality from transgenic fruits with reduced pectin methylesterase, J Food Sci, 61(1), 85–9. THAKUR B R, SINGH R K and HANDA A K (1997) Chemistry and uses of pectin - a review, Crit Rev Food Sci Nutr, 37(1), 47–73. TIEMAN D M and HANDA A K (1994) Reduction in pectin methylesterase activity modifies tissue integrity and cation levels in ripening tomato (Lycopersicon esculentum) fruits, Plant Physiol, 106(2), 429–36. TIEMAN D M, HARRIMAN R W, RAMAMOHAN G and HANDA A K (1992) An antisense pectin methylesterase gene alters pectin chemistry and soluble solids in tomato fruit, The Plant Cell, 4(6), 667–79. TUCKER G A (1993) Fruit ripening, In The Biochemistry of Fruit Ripening Eds G B Seymour, J E Taylor and G A Tucker, London, Chapman and Hall, 1–51. TUCKER G A and GRIERSON D (1982) Synthesis of polygalacturonase during tomato fruit ripening, Planta, 155(1), 64–7. TUCKER G A and ZHANG J (1996) Expression of polygalacturonase and pectinesterase in normal and transgenic tomatoes, In Pectins and Pectinases Eds. J Visser and A G J Voragen, Amsterdam, Elsevier, 347–54. TUCKER G A, ROBERTSON N G and GRIERSON D (1980) Changes in polygalacturonase isoenzymes during the ripening of normal and mutant tomato fruit, Eur J Biochem, 112(1), 119– 24. TUCKER G A, ROBERTSON N G and GRIERSON D (1981) The conversion of tomato fruit polygalacturonase isoenzyme 2 into isoenzyme 1 in vitro, Eur J Biochem, 115(1), 87– 90. TUCKER G A, ROBERTSON N G and GRIERSON D (1982) Purification and changes in activities of tomato pectinesterase isoenzyme, J Sci Food and Agric, 33(4), 396–400. TUCKER G A, SIMON H and ERRINGTON N (1999) Enzymic modification of pectin in pastes from transgenic tomatoes, Biotech and Genet Engin Rev, 16, 293–308. WATSON C F, ZHENG L and DELLA PENNA D (1994) Reduction of tomato polygalacturonase β subunit expression affects pectin solubilisation and degradation during fruit ripening, Plant Cell, 6(11), 1623–34. WHITNEY S E C, GIDLEY M J and MCQUEEN-MASON S J (2000) Probing expansin action using cellulose/hemicellulose composites, Plant J, 22(4), 327–34. WOOLLEY L C, JAMES D J and MANNING K (2001) Purification and properties of a beta-1,4glucanase from strawberry and down-regulation of the corresponding gene, Planta, 214(1), 11–21. ZAINAL Z, TUCKER G and LYCETT G (1996) A rab11 like gene is developmentally regulated in ripening mango (Mangifera indica.L) fruit, Biochem et Biophy Acta, 1314(3), 187–90.

14 Raw materials quality and the texture of processed vegetables J. B. Adams, formerly of CCFRA, UK

14.1

Introduction

Texture is a vital component of the organoleptically-perceived quality of heat-processed vegetables. Consumers expect the final product, even after an extensive heat process, to have a similar firmness to that of the freshly cooked vegetable with little or no significant breakdown of the tissue. The extent to which this can be achieved depends crucially on the quality of the raw material, as well as on processing factors such as the time–temperature profile of the heat treatment applied. The initial textural impact experienced on first biting into raw vegetables is determined by the natural turgor pressure in the cells of the living plant and by the mechanical strength of the tissue being consumed. On destruction of cell membranes during heat processing, the turgor is lost and the strength of the tissue structure then plays a dominant role in this immediate assessment of texture. As the tissue is broken down during mastication, the overall feeling of the food in the mouth becomes more important. The composition of the raw material has a major role to play in the textural acceptability of the processed product and will therefore be the main emphasis of this chapter.

14.2 Vegetable texture determined by starch Starch contributes to the texture of heat-processed vegetable products due to its presence in various crystalline and amorphous forms within the granule, and the extent to which the crystalline regions are disrupted and the starch gelatinised during heat treatments. The degree of starch gelatinisation has a

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major impact on the flow of the product in the mouth during mastication (mouthfeel), and therefore on the perception of texture. Whilst environmental conditions are known to have an effect on the physical properties of starch from various botanical sources (Tester and Karkalas, 2001), gelatinisation in processed vegetables is also influenced by factors such as the presence of starch-degrading amylase enzymes in the raw material and the chemical degradation of the starch during the heat treatment. The chemical state of water in cooked vegetables can also have an effect on certain mouthfeel variables (Thygesen et al., 2001).

14.2.1 Pea Peas (Pisum sativum) mature rapidly during growth at high temperatures and are at optimum quality as the garden type for only a day or two. As the seed matures, the cotyledons increase in firmness and the skin thickens and becomes tough. In order to minimise these textural changes after harvesting and vining, it is important to chill the peas to as near 0 °C as possible. Peas maintain a higher quality if kept in their pods. Within a cultivar, large peas are usually less tender than small ones, but between cultivars size is not an indication of tenderness. During the maturation of actively metabolising peas, a net conversion of sucrose to starch occurs. The mouthfeel of heat-processed peas depends greatly on the starch gelatinisation occurring during processing and also on the extent to which the starch has chemically degraded. The starch in peas hydrolyses during high-temperature processing to an extent that depends on the amylose/amylopectin ratio (Skrabanja et al., 1999). In wrinkle-seeded pea cultivars, the gene encoding the starch-branching enzyme isoform I has mutated, leading to reduced levels of starch and amylopectin and an enhanced level of amylose (around 70%). The starch is more resistant to hydrolysis than in round-seeded peas that contain only about 30% starch. During maturation of wrinkled-seeded peas, the structural and thermodynamic properties of the starch granules change (Kozhevnikov et al., 2001) and the peas take on a wrinkled appearance due to the relatively greater loss of water that occurs in the cotyledons compared to the skins. When dried for use as a legume vegetable, they absorb higher quantities of water on rehydration than the round-seeded type. The starch fully gelatinises in the cooked legumes well before the hardness reaches a minimum value (Klamczynska et al., 2001), indicating that other constituents could contribute to the heat-processed pea texture in addition to starch. As alcohol insoluble solids (AIS) correlate well with many pea textural attributes, particularly mealiness and skin toughness, and negatively with juiciness (Table 14.1), other polymers, such as the pectins, could be influential. This is supported by evidence of pecticgalactan at the plasma membrane face of the cell wall causing a significant increase in the firmness of pea cotyledons (McCartney et al., 2000). Pecticgalactan appears in cell walls at a defined stage late in the developing cotyledon,

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Table 14.1 Pearson correlation between the sensory texture of peas and alcohol insoluble solids. (Adapted from Edelenbos et al., 2001) Textural attribute

Crispness Juiciness Seed Hardness Mealiness Testa Toughness

Season 1994

1996

1998

–0.64 –0.89 0.77 0.88 0.82

–0.68 –0.85 –0.46 0.82 0.85

–0.85 –0.94 n.s. 0.92 0.95

n.s.= not significant

whereas the pectic polysaccharides, homogalacturonan and pectic-arabinan, are present throughout development. The edible quality of garden peas for canning or freezing is frequently determined objectively using a tenderometer. This instrument, first employed in 1937, measures the resistance of a pea sample to compression and shearing forces; the greater the resistance the less tender are the peas. Tenderometer readings, when adjusted for temperature, correlate well with sensory assessments of sweetness, tenderness and starchiness. In order that the tenderometers in use give mutually comparable data, much effort has been put into standardisation of these instruments (Visscher and Lovink, 1999). The difficulties involved in establishing traceable calibration standards have led to research into alternative methods of determining pea maturity. For example, near-infrared (NIR) spectral transmittance of whole peas poured loose into a specially designed sample holder has been found to correlate well with AIS, dry matter content and firmness (Chalucova et al., 2000). Mathematical models for calibration and prediction of some indices of maturity were proposed that compensated for instrumental noise and other factors such as variations in light path length. The results could lead to the development of a portable NIR analyser suitable for field application. 14.2.2 Potato Starch makes an important contribution to the texture of cooked and processed potatoes (Solanum tuberosum) (Martens and Thybo, 2000). The dry matter content reflects the amount of starch present and is often taken as an indication of the likely textural behaviour of potatoes on cooking (Table 14.2). In general, tubers with a high dry matter content, high amylose to amylopectin ratio, small cell size, and low sugar content are preferred for processes employing baking and frying. The texture of cooked potatoes of this type is described as ‘mealy’. Mealy behaviour in potatoes after cooking is associated with complete engorgement of cells with gelatinised starch and with a thickened cell wallmiddle lamella in the raw material. Potatoes with a low dry matter tend to have a ‘soggy’ texture on cooking, whilst those with intermediate dry matter

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Table 14.2 Relationship of specific gravity and total solids to cooked potato texture and use. (Adapted from Rubatzky and Yamaguchi, 1997) Specific gravity

Percentage total solids

Texture

Optimum usage

1.06–1.07 1.07–1.08

16–18 18–20

Soggy Waxy

1.08–1.09

20–22

Mealy

Canning, boiling, potato salads Boiling, mashing, fair to good for crisps or canning Good for baking, crisps and french fries

are described as ‘waxy’. The latter types are preferred for boiling or for canning. A non-destructive method of determining dry matter, using NIR, has been tested that could be used for assessing the potential of potato tubers to produce processed products having good textural attributes (Scanlon et al., 1999). Cultivar effects The contribution of cultivar to cooked potato textural properties is unclear at present. Studies in the USA have suggested that the Russet Burbank cultivar typifies mealiness, whilst Norchip was only sometimes mealy, and Pontiac and La Soda were waxy (McComber et al.,1994). In contrast, research in the Netherlands using Nicola, Irene and Bintje has concluded that cultivar has little effect on the sensory-perceived texture of steam-cooked potatoes compared with dry matter content (Van Dijk et al., 2002a). Environmental influences The physicochemical properties of the starch in potatoes are influenced by both cultivar and growing conditions (Tester et al., 1999; Morrison et al., 2001). Starches of potato tubers (cv.Maris Piper) grown under controlled conditions at 10, 16, 20 and 25 °C showed an increase in gelatinisation temperature as a function of growth temperature. This indicated an enhanced registration of the amylopectin double helices in the starch crystallites which limited hydration and hence caused elevation of the gelatinisation temperature. The consequence was that swelling during gelatinisation was restricted (see Fig. 14.1). Similarly, it has been demonstrated that starch obtained from potatoes with a shorter growth period (and thus presumably a higher growth temperature for equal potato maturity) had a higher gelatinisation temperature (Liu et al., 2003). Soil treatment with different organic fertilisers has been shown to have an effect on dry matter, starch, and textural attributes of cooked potatoes (Thybo et al., 2002). Of six treatments applied, mealiness was highest when composted deep litter was ploughed in and lowest when deep litter and straw were used together. However, the dry matter content of the organically treated potatoes was slightly lower, on average, than that of conventionally grown material.

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Texture in food 180

16 °C

10 °C

Swelling factor (amylopectin basis)

160 140 20 °C

120 100

25 °C 80 60 40 20 0 50

60

70

80

90

100

Temperature (°C)

Fig. 14.1 Swelling factors of starches from potato tubers grown at different temperatures. Arrows indicate the gelatinisation temperatures. Coefficients of variation are shown. (䊊 – 10 °C, 䉬 – 16 °C, 䉱 – 20 °C, ⵧ – 25 °C) (From Tester et al., 1999).

Starch breakdown in raw potatoes Following harvest, potatoes intended for storage are cured by holding at 15– 20 ºC and at high relative humidity (RH) for around 10 days to enhance periderm formation and to heal harvest wounds. After curing, the temperature is lowered by an amount that depends on the length of storage and expected use. Potatoes for table use are often stored at around 4 °C, and at high RH, in order to reduce sprouting, weight loss and general deterioration. Potatoes for processing tend to be stored at somewhat higher temperatures, typically 10–16 ºC. Chill storage causes conversion of some of the starch to sucrose and reducing sugars that cause sweetening and, in baked and fried products, can lead to excessive non-enzymatic browning. This breakdown of the starch can be reversed by ‘reconditioning’ the cold-stored material for several weeks at around 18–21 °C at 85–90% relative humidity. Some potato cultivars can resist cold-induced starch breakdown, and research is currently being carried out in several countries to develop varieties that can be processed directly from chill storage. In low dry matter material, the starch breakdown during chilling may be less of a colour and flavour issue as the potatoes will probably be boiled or canned. The prevailing aqueous conditions would lead to diffusion of the sugars and tend to discourage the browning reactions that occur optimally under the low water activity conditions existing at high baking or frying temperatures. However, an excessive loss of starch in material that is already low in dry matter may have a serious impact on texture, leading to an unacceptably ‘soggy’ product.

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Starch-to-sugar conversion in chill-stored potatoes can be slowed down by employing controlled atmospheres containing less than 3% oxygen, although such treatments may increase sprouting (Burton, 1982). Chemical sprouting inhibitors appear to be satisfactory for long-term storage of potatoes at around 10 ºC, as long as good storage practices are employed. However, most chemicals suppress the development of wound periderm, and this may lead to higher levels of tuber rot during storage (Thomas, 1984). Moreover, there is increasing public concern about the safety of chemical inhibitor residues. Ionising radiation, where permitted, can inhibit sprouting, although the development of wound periderm is suppressed in this case also. A rise in sugar levels may occur, though these often return to normal on further storage.

14.2.3 Cassava Approximately two-thirds of cassava (Manihot esculenta) production is used for human consumption in either fresh or processed form. The major value of cassava is its high-caloric contribution with more than 300 million people in tropical regions depending on it as an energy source. Fresh roots contain 35–40% dry matter, of which about 90% is carbohydrate. Cassava requires immediate utilisation because of its rapid physiological and microbial deterioration after harvest. A large proportion of the crop is therefore processed in order to stabilise it, and also to reduce the levels of the toxic cyanogenic glucosides. Processing methods include drying, roasting, frying, steaming and boiling. One of the main obstacles in processing cassava is that it tends to be too hard. Little is known about how raw material can affect cassava starch or the link between the starch properties and the texture of processed products. Research on the effect of pre-harvest pruning of the shrub prior to harvesting the storage roots has shown that this treatment has no influence on the pasting properties of starches from a number of cultivars (van Oirschot et al., 2000). Other studies have found that cultivar has an effect on the gelatinisation profile of cassava starch and flour obtained by solvent extraction (Moorthy et al., 1996). In the latter case, there was no apparent relationship with the size and amylose content of the starch granules. Cultivar has been shown to have a significant effect on the friability of cassava crisps (Grizotto and De Menezes, 2002), although changes in starch properties were not studied.

14.2.4 Sweet potato Sweet potato (Ipomoea batatas) is a storage root that is consumed for its high energy value by many millions of people, mainly in Asia. There are three major sweet potato types which are characterised after cooking as: (1) flesh that is firm, dry and mealy; (2) flesh that is soft, moist and gelatinous; (3) flesh that is coarse and fibrous.

348

Texture in food

Only the first two types are used for human consumption, of which the first is generally preferred. In the USA, cultivars having a deep orange flesh and a soft and moist texture after cooking are erroneously referred to as ‘yams’. Besides freshly cooked uses, sweet potatoes are processed by canning and used to make crisps, noodles, flour and candy. The average dry matter of sweet potatoes is around 30 %, of which 50–70% is starch. Sweet potato starch is composed of about one-third amylose and two-thirds amylopectin. During curing at 27–30 ºC at high RH for 4–7 days, a loss in dry matter occurs with an increase in sugars. Because of yearround supply in many tropical regions, there is little incentive to store roots. In temperate regions, temperatures are reduced to 13–16 ºC following curing and RH is kept high. Under optimal conditions, sweet potatoes can be stored for six months or longer, during which time starch levels can show a small decrease and sugar levels increase for many cultivars (Acedo et al., 1996; Zhang et al., 2002). Storage at temperatures below approximately 13 ºC results in chilling injury and internal tissue pithiness. Starch is a major contributor to sweet potato dry matter and, as in potatoes, mealiness of the cooked product is associated with cells filled with gelatinised starch. However, the precise role of starch in cooked sweet potato texture remains unclear (Walter et al., 2000). It has been suggested that β-amylase is rapidly inactivated on cooking sweet potatoes at 100 ºC and that this allows the starch to distend and cause cell separation (Binner et al., 2000). The firm, brittle texture obtained on cooking at 70 ºC could have been due to amylase activity causing breakdown of the starch to lower-molecularweight oligosaccharides that escaped from the cell. Evidence was presented that pectinmethylesterase was not involved in the firming of the sweet potato tissue.

14.2.5 Yam Yam (Dioscorea spp.) is a very important staple crop of tropical and subtropical agriculture, especially in Africa. When first harvested, the dormant tubers are stored at 15–16 ºC and 70% RH. Under favourable conditions, some species can be stored for around three months. As storage at 10 ºC or less to reduce sprouting can cause chilling injury to occur, sprout-suppressing chemicals are employed and, infrequently, gamma irradiation is used. About 25% of yam fresh weight is starch, mainly in the form of amylopectin. The physicochemical properties (granule size and morphology, amylose content, crystal form and gelatinisation and pasting behaviour) of isolated starch have been shown to depend strongly on the Dioscorea species (Farhat et al., 1999). During the first few days of storage of the trifoliate yam (Dioscorea dumetorum), it has been found that starch levels decline slightly and the pasting characteristics of extracted starch decrease, apparently due to enhanced amylase activity (Afoakwa and Sefa-Dedeh, 2002a).

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14.3 Vegetable texture determined by cell wall polysaccharides Vegetable tissues are made up of cells composed mainly of cellulose microfibrils, with hemicelluloses and hydroxyproline-rich glycoproteins occupying the interstices between the fibrils. The boundary between cells, the middle lamella, consists mainly of the pectic polysaccharides that cement the cells together. Cell enlargement during the growth of plant tissues is considered to depend primarily on the expansin group of proteins (Cosgrove, 1997). These proteins appear to disrupt the non-covalent bonding of hemicelluloses to the cellulose microfibril, thereby allowing the wall to yield to the mechanical forces generated by cell turgor. Certain types of peroxidase can generate hydroxyl radicals that cause scission of polysaccharides (Schweikert et al., 2000), and these may make the cell wall more responsive to expansin-mediated creep.

14.3.1 Tomato For whole tomatoes (Lycopersicon esculentum), texture depends on the firmness of the flesh, the proportion of locular gel, and the toughness of the skin. The cells located in the flesh are generally large with thin cell walls, whilst the skin is composed of a thin layer of heavily cutinised epidermal cells and several layers of relatively small, flattened cells. The strength of the skin is determined by the depth of penetration of the cutinised outer layer. Removal of the peel, using high-pressure steam and mechanical peel eliminators, is one of the first unit operations involved in whole tomato production. Optimisation of the peeling operation to achieve adequate peel loosening without excessive yield loss depends mainly on the tomato cultivar, maturity and fruit size. The production-related factors that affect the textural properties of processing tomatoes include cultivar, maturity/degree of ripeness, cultural practices, environmental stresses and treatments with chemical firming agents (Barrett et al., 1998). Due to the scarcity of data reported in the literature on processing cultivars of tomatoes, much of the following discussion refers to fresh market varieties. Effects of cultivar and maturity Studies in the USA have suggested that both the cultivar and the stage of maturity at which the fruit is picked can have important effects on the firmness of tomatoes (see Fig. 14.2). Firm fruit having thick pericarp walls, much pericarp tissue and few locules is required for processing. Small fruit with these features are less subject to impact and compression damage. Processors normally harvest the fruit while it is under-ripe (i.e. mature green) and can then treat with ethylene to ripen it. This approach ensures that the tomatoes remain firm during transportation and storage, minimising softening and

350

Texture in food 700

Force (g) at 30% strain

600 500 400 300 200 100 0 Halley 3155

La Rossa

H 8892

Brigade

FM 9208

N 512

H 3044

Variety and maturity stage

Fig. 14.2 Firmness of seven varieties of processing tomatoes harvested at under-ripe, red-ripe and over-mature stages. ⵧ – under-ripe, – red-ripe and – over-mature. (From Barrett et al., 1998).

loss of product. Once red-ripe, the tomato fruit colour remains constant whereas the firmness decreases (Barrett et al., 1998). Cultural practices and environmental conditions Tomato fruit firmness is strongly influenced by environmental growth conditions (Davies and Hobson, 1981). The levels of some constituents potentially related to texture, such as ethylene and polygalacturonase (PG), are diminished at temperatures in the range 30–40 ºC whilst other constituents, such as the phenolic compounds, are increased (Rivero et al., 2001). Tomatoes are sensitive to low temperatures post-harvest. Red fruit can tolerate lower temperatures than mature green fruit and can be stored at 7–10 ºC for several days without significant quality loss. Loss in firmness occurs below 7 ºC. If stored at 0–1.5 ºC, red fruit can be stored for up to three weeks, but should be used within a day or two after removal from storage as flavour and textural quality become unacceptable. Mature green fruit should be stored at 13–18 ºC, and 85–90% RH, and ripened at 18–21 ºC. Chilling at temperatures below 13 ºC can lead to irregular ripening and premature softening when the fruit is transferred to ripening temperatures. Chilling can also give rise to mealiness in tomatoes, a dry, soft texture that occurs even though the moisture content is the same as non-chilled fruit of the same age (Jackman and Stanley, 1995). Mealiness is considered to be due to an increase in cytosol membrane damage and an increase in the activity of pectin degrading enzymes. It is unlikely that starch plays a role in the perception of chilled tomato mealiness, analogous to that in cooked potato, as starch levels are very low in ripe tomato fruit. Rather, the effect appears to be related to the loss of cellular turgor and the reduction in release of internal fluids that are normally associated with the perception of juiciness. Freezing

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(below –1 ºC) causes the fruit to appear water-soaked, and leads to softening, and drying of the locular gel. Controlled-atmosphere storage of under-ripe tomatoes in 3% oxygen or 20% carbon dioxide can cause less ethylene formation and a reduction in firmness (Sozzi et al., 1999) (see Fig. 14.3). When the fruit was transferred to air, flesh firmness decreased but at a lower rate than for fruit held continually in air. These results suggested an antagonistic relationship between carbon dioxide/oxygen and ethylene that may determine most of the ripening behaviour under controlled-atmosphere storage, though a direct regulatory mechanism by oxygen and/or carbon dioxide could not be excluded. The most important nutrients in tomato development and therefore in textural integrity are nitrogen, phosphorus and potassium. Calcium is known to cross-link pectins and may thereby be involved in maintaining tissue firmness (see the discussion below on calcium addition to canned whole tomatoes). It also inhibits ethylene biosynthesis (Wills and Tirmazi, 1979; Njoroge et al., 1998). However, it does not appear to be useful in fertiliser applications (Barrett et al., 1998). Ripening related softening The control of softening during the ripening of tomatoes is not clearly understood. It had previously been hypothesised that, during ripening, the 40

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Fig. 14.3 Effect of controlled atmosphere storage on tomato firmness. Single fruits were sampled on days 0, 3, 6, 12 and 18. Arrow indicates that fruits kept in controlled atmospheres were returned to air after 150 h storage. Data represent the means of eight replicates. Least significant ranges are shown. (䊊 – control; ∇ – 3% O2; 䉲 – 3% O2 + 100 ppm C2H4; ⵧ – 20% CO2; 䊏 – 20% CO2 + 100 ppm C2H4) (From Sozzi et al., 1999).

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endo-polygalacturonase enzyme (endo-PG) acted in conjunction with pectinmethylesterase to degrade the flesh cell wall and middle lamella pectin. The evidence for this was the dramatic increase in endo-PG activity at the onset of ripening and the simultaneous increase in soluble polyuronides. Also, reduced-ripening strains of tomato expressed only very low levels of the gene for PG. This hypothesis could not be sustained, however, as some softening occurs in green tomatoes and early in ripening, prior to detectable PG activity (Hall, 1987). In addition, very low PG fruit from transgenic plants showed a smaller increase in raw fruit firmness than expected over non-transgenic controls (Schuch et al., 1991; Kramer et al., 1992). Other softening mechanisms must therefore be involved. Breaking down the bridges between the hemicelluloses and cellulose microfibrils, perhaps by hydrolysis of the hemicelluloses (Barrett et al., 1998), or by disruption of the non-covalent bonding between the hemicelluloses and the cellulose, could make an important contribution to ripening-associated softening. The identification of a ripening-regulated expansin gene in tomato and other fruit suggests that, in addition to their role in facilitating the expansion of plant cells, expansins may also contribute to cell wall disassembly in nongrowing tissues, possibly by enhancing the accessibility of non-covalently bound polymers to endogenous enzymic action (Rose et al.,1997). Recent studies have shown that suppression of the expansin gene in tomatoes leads to an improved shelf life (at 13 ºC) and an enhanced paste viscosity ( Brummell et al., 2002). In addition, 1-methylcyclopropene, a potent inhibitor of ethylene action, has been shown to delay ripening, even at very advanced stages, and reduce the level of expansin mRNA formed (Hoeberichts et al., 2002). The studies on tomato expansin are being complemented by others that cover a broad range of structural and architectural alterations in plant cell walls that occur as a consequence of developmental regulation, environmental adaptation or genetic modification (McCann et al., 2001). Calcium salt addition to canned tomatoes Dipping peeled tomatoes in solutions of calcium chloride causes the formation of a calcium pectate gel that supports the tissues and minimises softening during heat processing. In order to cause a desirable increase in firmness, the calcium content must be increased by approximately 100–300 ppm. After an evaluation of the effect on textural properties, cost and ease of use, it was recommended that dipping should be in 2% solutions for two to three minutes (Kertesz et al., 1940). It is common practice to add calcium chloride or sodium chloride–calcium chloride tablets when filling into individual cans and to dissolve tablets into tomato juice in which tomato products are dipped when packing in drums. The addition of a firming agent must be declared on the label.

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14.3.2 Potato texture and pectins The main cause of mealy potatoes tending to slough or break down on boiling is evidently the excessive hydration of the pectic substances in the thickened cell wall (Warren and Woodman, 1974). The higher degree of pectin solubilisation on cooking mealy cultivars (van Marle et al., 1997) suggests that starch dominates the mouthfeel in these cases. Sloughing is probably due to an increase in the thickness of the cell wall matrix and a reduction in its viscosity, eventually leading to spontaneous cell separation. Less sloughing in dehydrated mashed potatoes and firmer canned and frozen products are obtained when potatoes are pre-heated at 60–70 °C. Several theories have been presented to account for this, including starch retrogradation, amylose leaching, pectinesterase activity and calcium release from gelatinised starch causing crosslinking of pectate in the cell wall (Andersson et al., 1994). Recently, evidence was presented that the effect of pectinesterase activity is to increase the yield of cell wall material of cooked potatoes and that this is linked to the firming effect (Van Dijk et al., 2002b). However, the contribution of starch-based degradation products could not be excluded.

14.3.3 Green beans Green beans (Phaseolus vulgaris), often referred to as snap beans in the USA from the sound of fresh pods being broken, are processed by canning or freezing the whole, cut or sliced pods containing the seeds. Beans should be harvested when the pod is fleshy and seeds are small and green. After that, seed development reduces quality and the pod becomes pithy and tough. Optimum conditions for maintaining post-harvest quality are 5–7.5 ºC and 95–100% RH. A shelf life of 8–12 days can then be expected. Very good quality appears to be obtained at 4 ºC, but chilling injury is induced that is evident on transfer to 20 ºC. Water loss is also a common post-harvest problem in green beans with the pods shrivelling and becoming limp after about 5% weight loss. Green beans have tissues at different stages of maturity when harvested, and this has implications for the texture of the cooked product (Reeve and Brown, 1968). Fibrous tissues are at a very early stage of development in the stringless cultivars used in commercial processing and have thin cell walls that are susceptible to damage, whereas the parenchyma tissues are much more developed with relatively thick cell walls. High growth temperatures lead to over-mature fibrous tissue giving rise to stringiness as a textural fault. The same growing conditions tend to result in sloughing of beans on cooking. Evidence has been presented that cooking causes fracture along the middle lamellae of outer parenchyma tissue, whereas inner parenchyma fractures across the cell walls (Stolle-Smits et al., 1998). It is feasible therefore that breakdown of the middle lamellae in the over-mature, outer parenchyma causes aggregates of whole cells to be shed during cooking (sloughing).

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The importance of pectin for the texture of green beans has been known for many years (Culpepper, 1936). The significance of the pectin-modifying enzyme, pectinmethylesterase (PME), coupled with the application of calcium salts to correct textural quality defects has also been appreciated for some time (van Buren et al., 1960; Sistrunk et al., 1989). As with potatoes, a firmer texture is obtained in processed products when the green beans are pre-heated at 60–70 ºC, apparently due to the PME activity and the increased capacity of the demethylated pectin to form calcium bridges. The effect has also been demonstrated in different cultivars (Stolle-Smits et al., 2000). Blanching in this temperature range has been employed as a means of firming canned green beans, and has also been proposed as method of improving the texture of frozen green bean products (Steinbuch, 1976). In the latter case, the low-temperature blanch is followed by a short blanch at higher temperature (90–95 ºC) to inactivate residual PME and other enzymes that could affect quality in frozen storage. Unfortunately, the firming obtained on lowtemperature treatment is accompanied by a significant loss of green colour that would probably be a serious drawback to commercial exploitation in the frozen vegetable sector (Adams et al., 1983).

14.3.4 Mushroom Mushrooms are eaten primarily for their flavour and textural features and are used fresh, dried, or processed by canning, freezing or pickling. However, the understanding of the causes of textural changes in mushroom tissue is incomplete and, consequentially, little information is available on the effect of raw material factors on the texture of processed mushrooms. Fresh mushrooms are easily bruised and rapidly lose weight through dehydration. Post-harvest refrigeration at 0 ºC and 95% RH will maintain mushrooms in good condition for five to six days. Storage for nine days at 12 ºC causes mushrooms (Agaricus bisporus) to become spongy and tough (Zivanovic et al., 2000). Sponginess paralleled expansion of the intercellular space at the pilei surface, hyphae shrinkage, central vacuole disruption, and loss of proteins and polysaccharides, while toughening was associated with increased chitin content in the hyphal walls.

14.4 Vegetable texture affected by phenolic reactions In vegetables, the main phenolic reactions identified as having an influence on texture are (1) lignification reactions, and (2) cross-linking reactions with polysaccharides. Simple phenolic compounds can be oxidised and polymerised in the presence of the enzymes peroxidase and polyphenol oxidase leading to the formation of lignin, a high molecular weight, relatively hydrophobic polymer that is the basic unit of xylem- and strengthening elements in wood. Edible plant tissue contains primary cell walls with lignin only at very low

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levels. However, in cut vegetables lignin deposition can begin, mainly in the secondary cell wall and to a lesser extent in the primary cell wall and middle lamella. Tissues with lignified cells have diminished swelling power and increased compressive strength, which leads to an undesirably tough and fibrous texture. Phenolic compounds have also been found to have a major effect on stabilising the textural properties of vegetables by crosslinking polysaccharides within the cell wall and between cells. In particular, ferulic acid is involved in an apparently similar reaction to that which occurs in the primary cell walls of cereals. Here the arabinoxylans are found complexed with ferulic acid esters which, after oxidative coupling in vivo, mediated by hydrogen peroxide and peroxidases, or even by photochemical means, give crosslinked diferuloyl derivatives that confer strength and extensibility to the cell wall.

14.4.1

Lignin forming vegetables

Yam Yam tubers begin to harden a few days after harvesting. Hardening becomes so pronounced that two or three weeks later the tubers are inedible, even after extended cooking. This is probably due to lignification reactions. The parenchyma cell walls rapidly lignify, starting from the corners of the cells near intercellular spaces and proceeding along the walls (Sealy et al., 1985). Increased levels of acid and neutral detergent fibres have been shown to characterise the post-harvest hardening of white cultivars of Dioscorea dumetorum tubers (Afoakwa and Sefa-Dedeh, 2002b). Legume beans Textural variation in cooked reconstituted beans (Phaseolus vulgaris), and other legumes, has been known for many centuries. Defects have been classified as ‘hardshell’ when the seeds do not absorb sufficient water on soaking and fail to soften during cooking, and ‘hard-to-cook’ when enough water is absorbed but softening still does not occur (Stanley and Aguilera, 1985). Extended storage of dry beans, and storage at above 25 ºC and at high RH (> 65%) increases the ‘hard-to-cook’ problem. Also, the level of divalent cations in the cooking water and phytic acid content of the beans appear to be crucial factors (Crean and Haisman, 1963; Kyriakidis et al., 1997). Although solutions to the problem have been proffered, the underlying chemistry is not completely understood (Liu, 1995). A number of reactions have been implicated, including cross-linking of calcium with cell wall pectin, and the formation of lignins. The distribution of calcium within the bean is dependent on the presence of complexing agents, such as the naturally occurring phytic acid. On breakdown of the phytic acid by the enzyme phytase, the calcium is potentially available to bridge the pectin and to restrict cell separation on cooking. Lignin accumulates in the cell walls and middle lamella of beans

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stored under high-temperature and high-humidity conditions (Hincks and Stanley, 1987). As a result of its hydrophobic nature, it may enhance the restraining effect of calcium on pectin by leading to a reduction in the hydration of the cell wall polysaccharides. Asparagus Texture is the most important parameter for evaluating the quality of fresh and canned asparagus (Asparagus officinalis). Post-harvest cutting, handling and storage leads to an undesirable increase in toughness, within hours of harvesting the spears, caused by lignification of the fibrovascular bundles. Toughening occurs mainly in the lower portion of the stem, particularly in the outer tissues (Rodriguez-Arcos et al., 2002) (see Fig. 14.4). The asparagus loses textural acceptability whether it is exposed to sunlight or held in the shade, although the rate of toughening is higher in spears held in the sun or cut ‘all green’ (Powers and Drake, 1980). Low-temperature storage, at about 4 ºC, greatly reduces the lignification rate.

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Fig. 14.4 Transverse puncture strength of fresh (ⵧ), cooked (䊏), stored ( ), and stored and cooked ( ) asparagus sections determined over two consecutive seasons. (Standard deviations are shown.) (From Rodriguez-Arcos et al., 2002).

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NIR spectroscopy is an accurate (and non-invasive) method of determining the amount of neutral and acid detergent fibre that forms during asparagus storage (Garrido et al., 2001). However, a link between the fibre formation and perceived texture deterioration has yet to be established. 14.4.2

Phenolic cross-link forming vegetables

Chinese waterchestnut The Chinese waterchestnut (Eleocharis dulcis) is the corm of a sedge that grows in water and is commonly used in Asian foods. The edible part consists of starch-rich, non-lignified storage parenchyma interspersed with vascular strands. In the raw state, it has a very crisp texture, and this is retained on cooking and even after extended heat treatments, such as in canning. The reason for this textural stability is that the water chestnut cells do not separate on cooking. Evidence has been presented that this is due to ferulic acid and its dimer, diferulate, forming thermally stable cross-links between polysaccharides within the wall and between cells (Parr et al., 1996). Chufa Chufa (Cyperus esculentus) is a perennial plant with grass-like stems and leaves, found throughout the tropics and in warm, temperate areas. It is valued for its small, fleshy underground tubers. These are very crisp in the raw state and, like the Chinese waterchestnut, retain their crisp texture on cooking. Ferulic acid cross-links between structural polysaccharides have been suggested as being the cause of the textural stability (Parker et al., 2000). Sugarbeet A similar texture stabilising mechanism appears to apply in the case of the sugarbeet (Beta vulgaris) which does not completely soften after heating at 100 ºC for several hours (Waldron et al., 1997). This is in contrast to the behaviour of beetroot that softens rapidly due to cell separation. In the case of sugarbeet, it has been proposed that the texture retention is related to the degree of ferulic acid cross-linking between pectic polysaccharides. However, borate ester crosslinks (Ishii and Matsunaga, 2001) may also be involved. Sweet potato Holding sweet potatoes under chill conditions and then at higher temperatures can cause a textural disorder known as ‘hardcore’ that is strongly cultivar dependent (Buescher and Balmoori, 1982). It appears, after cooking, as hard tissue that is not softened by extending the cooking time. Phenylpropanoid metabolism is stimulated during cold-temperature treatments of sweet potato (Rhodes et al., 1981), suggesting that ferulic acid cross-linking reactions may occur and particularly rapidly when the temperature is raised. The observation that sweet potato peroxidase has a high substrate specificity for ferulic acid (Leon et al., 2002) is in agreement with this hypothesis.

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Carrot Phenolic compounds bound to cell walls in carrots (Daucus carota) have been shown to be mainly hydroxybenzoic acid with only a minor contribution from ferulic acid (Beveridge and Harrison, 2001). The cell wall phenolics varied widely between cultivars and increased on storage. However, these elevated levels were not correlated with increased carrot toughness. As the normal behaviour of carrot is to soften on cooking, it may be suggested that texture stabilisation due to ferulic acid cross-linking between cells does not occur. Lignin is formed in cut carrots (Murakami and Takeyama, 1978), but no link with the texture of heat-processed products appears to have been studied.

14.5 Future trends 14.5.1 Vegetable texture improvements through breeding and genetic engineering Conventional breeding is based on crossing two cultivars with complementary traits and selecting within the segregating population. The development of improved hybrids using this process is very time-consuming and breeding has therefore concentrated on key agronomic traits. Breeding of vegetables specifically to obtain improved textural properties on processing has not been carried out to any significant extent. Although lead-in times can be dramatically reduced using genetic engineering approaches, the controversy surrounding genetically engineered foods needs to be resolved first. This controversy led to the removal of GM tomato puree from the UK market despite it being of better flavour and consistency, as well as being cheaper, than the non-GM puree. However, GM studies directed towards the development of more precise techniques for the manipulation and expression of transgenes should lead to new cultivars that have enhanced textural attributes being grown under controlled conditions. Genetic studies are currently being employed to help uncover the genes that encode some of the cell wall-related biosynthetic and hydrolytic enzymes, and structural proteins. The latter have tended to be overlooked in the efforts to understand the functionality of the starches, polysaccharides and phenolic components of plants. For example, the hydroxyproline-rich glycoproteins, or extensins, a family of structural proteins present in plant cell walls, have long been considered to contribute to fruit and vegetable texture. They are essential and integral components of the macromolecular cell wall complex whose close association with sclerenchyma, lignin and fibres suggests that they play a structural role. Research is required to determine whether extensin synthesis leads to a stronger wall, and to see if an alteration of the amount in the plant cell wall affects its strength and rigidity. If it does, it may be possible to alter the properties of plant cell walls by genetically, or otherwise, manipulating the amount and type of extensin that is synthesised in the wall,

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thereby regulating processes in ripening/maturation in order to improve postharvest textural characteristics. The finding of a ferulic acid-specific peroxidase in sweet potato (Leon et al., 2002) could lead to studies with other vegetables designed to enhance the formation of stable diferulate crosslinks. Essential to such studies would be the knowledge gained on the phenylpropanoid pathway in plants.

14.5.2 Texture changes in organically grown vegetables As well as cultivar, the textural characteristics of vegetables can depend on the fertiliser type and method of application, climatic factors and soil type. It is expected that organic treatments with their variation in available nutrients will therefore have a significant effect on vegetable dry matter and consequently on texture. With few exceptions, such as the work on potatoes described in Section 14.2.2, little research has been published on the effect of organic treatments on the textural quality of processed vegetables. Further studies are therefore required before recommendations can be made on optimal organic treatments.

14.5.3 Vegetable texture maintenance through treatment of the raw material Heat processing in water or brine leads to hydration of starch and cell wall polysaccharides and results in a soft textured vegetable that easily forms a paste during mastication. If the heat process could be reduced significantly or eliminated, a texture more resembling the raw material could feasibly be achieved. Non-thermal processing techniques, such as those based on highintensity electric field pulses or high-pressure treatments, are therefore receiving considerable attention. Studies on low heat input processes are likely to continue in the future. Further developments are likely in modified atmosphere storage of raw vegetables that could have significant effects on the texture of the processed products. In particular, the use of ethylene inhibitors is expected to have a considerable impact on current techniques for controlling rate of ripening/maturation and, if applied to vegetables for processing, could have a beneficial impact on final product texture.

14.6 Sources of further information and advice The following books are suggested as supplementary reading to this chapter: • Dennis C and Arthey V D (1999) Vegetable Processing, New York, VCH Publishers Inc. • Eskin N A M (1989) Quality and Preservation of Vegetables, Boca Raton, FL, CRC Press.

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• Hägg M, Ahvenainen R, Evers A M and Tiilikkala K (1999) Agri-Food Quality II: Quality Management of Fruits and Vegetables, Cambridge, The Royal Society of Chemistry. • Rubatzky V E and Yamaguchi M (1997) World Vegetables: Principles, Production and Nutritive Values, Second Edition, New York, Chapman & Hall. • Springett M B (2001) Raw Ingredient Quality in Processed Foods: The Influence of Agricultural Principles and Practice, Gaithersburg, MD, Aspen Publishers Inc.

14.7

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ACEDO JR A L, DATA E S

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MOORTHY S N, WENHAM J E

and BLANSHARD J M V (1996) Effect of solvent extraction on the gelatinisation properties of flour and starch of five cassava varieties, J Sci Food Agric, 72(3), 329–36. MORRISON I M, COCHRANE M P, COOPER A M, DALE M F B, DUFFUS C M, ELLIS R P, LYNN A, MACKAY G R, PATERSON L J , PRENTICE R D M, SWANSTON J S and TILLER S A (2001) Potato starches: variation in composition and properties between three genotypes grown at two different sites and in two different years, J Sci Food Agric, 81(3), 319–28. MURAKAMI H and TAKEYAMA S (1978) Accumulation of lignin in cut edible parts of vegetables, J Japan Soc Food Nutrn, 31(1), 91–4. NJOROGE C K, KERBEL E L and BRISKIN D P (1998) Effect of calcium and calmodulin antagonists on ethylene biosynthesis in tomato fruits, J Sci Food Agric, 76(2), 209–14. PARKER M L, NG A, SMITH A C and WALDRON K W (2000) Esterified phenolics of the cell walls of Chufa (Cyperus esculentus L.) tubers and their role in texture, J Agric Food Chem, 48(12), 6284–91. PARR A J, WALDRON K W, NG A and PARKER M L (1996) The wall-bound phenolics of Chinese waterchestnut (Eleocharis dulcis), J Sci Food Agric, 71(4), 501–7. POWERS J R and DRAKE S R (1980) Effect of cut and field-holding conditions on activity of phenylalanine ammonia-lyase and texture in fresh asparagus spears, J Food Sci, 45(3), 509–10. REEVE R M and BROWN M S (1968) Histological development of the green bean pod as related to culinary texture: 2. Structure and composition at edible maturity, J Food Sci, 33(3), 327–31. RHODES M J C, WOOLTORTON L S C and HILL A C (1981) Changes in phenolic metabolism in fruit and vegetable tissues under stress. In Recent Advances in the Biochemistry of Fruits and Vegetables. Eds J Friend and M J C Rhodes, London, Academic Press, 191– 220. RIVERO R M, RUIZ J M, GARCIA P C, LOPEZ-LEFEBRE L R, SANCHEZ E and ROMERO R (2001) Resistance to cold and heat stress: accumulation of phenolic compounds in tomato and watermelon plants, Plant Sci, 160(2), 315–21. RODRIGUEZ-ARCOS R C, SMITH A C and WALDRON K W (2002) Mechanical properties of green asparagus, J Sci Food Agric, 82(3), 293–300. ROSE J K C, LEE H H and BENNETT A B (1997) Expression of a divergent expansin gene is fruitspecific and ripening-regulated, Proc Natl Acad Sci USA, 94(11), 5955–60. RUBATZKY V E and YAMAGUCHI M (1997) World Vegetables: Principles, Production and Nutritive Values (Second Edition). New York, Chapman and Hall. SCANLON M G, PRITCHARD M K and ADAM L R (1999) Quality evaluation of processing potatoes by near infrared reflectance, J Sci Food Agric, 79(5), 763–71. SCHUCH W, KANCZLER J, ROBERTSON D, HOBSON G, TUCKER G, GRIERSON D, BRIGHT S and BIRD C (1991) Fruit quality characteristics of transgenic tomato fruit with altered polygalacturonase activity, HortSci, 26(12), 1517–20. SCHWEIKERT C, LISZKAY A and SCHOPFER P (2000) Scission of polysaccharides by peroxidasegenerated hydroxyl radicals, Phytochem, 53(5), 565–70. SEALY L, RENAUDIN S, GALLANT D J, BOUCHET B and BRILLOUET J M (1985) Ultrastructural study of yam tuber as related to postharvest hardness, Food Microstruc, 4(1), 173–81. SISTRUNK W A, GONZALEZ A R and MOORE K J (1989) Green beans. In Quality and Preservation of Vegetables. Ed. NAM Eskin, Boca Raton, FL, CRC Press, 185–215. SKRABANJA V, LILJEBERG H G, HEDLEY C L, KREFT I and BJÖRCK I M (1999) Influence of genotype and processing on the in vitro rate of starch hydrolysis and resistant starch formation in peas (Pisum sativum L.), J Agric Food Chem, 47(5), 2033–9. SOZZI G O, TRINCHERO G D and FRASCHINA A A (1999) Controlled-atmosphere storage of tomato fruit: low oxygen or elevated carbon dioxide levels alter galactosidase activity and inhibit exogenous ethylene action, J Sci Food Agric, 79(8), 1065–70. STANLEY D W and AGUILERA J M (1985) A review of textural defects in cooked reconstituted legumes: the influence of structure and composition, J Food Biochem, 9(4), 277–323.

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(1976) Technical note: improvement of texture of frozen vegetables by stepwise blanching treatments, J Food Technol, 11(3), 313–16. STOLLE-SMITS T, DONKERS J, DIJK C VAN, DERKSEN J and SASSEN M M A (1998) An electron microscopy study on the texture of fresh, blanched and sterilised green bean pods (Phaseolus vulgaris L.), Lebens Wiss u Technol, 31(3), 237–44. STOLLE-SMITS T, BEEKHUIZEN J G, RECOURT K, VORAGEN A G J and DIJK C VAN (2000) Preheating effects on the textural strength of canned green beans I. Cell wall chemistry, J Agric Food Chem, 48(11), 5269–77. TESTER R F and KARKALAS J (2001) The effects of environmental conditions on the structural features and physico-chemical properties of starches, Starch-Stärke, 53(10), 513–19. TESTER R F, DEBON S J J, DAVIES H V and GIDLEY M J (1999) Effect of temperature on the synthesis, composition and physical properties of potato starch, J Sci Food Agric, 79(14), 2045–51. THOMAS P (1984) Radiation preservation of foods of plant origin. Part I. Potatoes and other tuber crops, CRC Crit Rev Food Sci Nutrn, 19(4), 327–79. THYBO A K, MØLGAARD J P and KIDMOSE U (2002) Effect of organic growing conditions on quality of cooked potatoes, J Sci Food Agric, 82(1), 12–18. THYGESEN L G, THYBO A K and ENGELSEN S B (2001) Prediction of sensory texture quality of boiled potatoes from low-field 1H-NMR of raw potatoes. The role of chemical constituents, Lebensm-Wiss u-Technol, 34(7), 469–77. VAN BUREN J P, MOYER J C, WILSON W B and HAND D B (1960) Influence of blanching conditions on sloughing, splitting and firmness of canned snap beans, Food Technol, 14(5), 233– 6. VAN DIJK C, FISCHER M, HOLM J, BEEKHUIZEN J-G, STOLLE-SMITS T and BOERIU C (2002a) Texture of cooked potatoes (Solanum tuberosum). 1. Relationships between dry matter content, sensory-perceived texture, and near-infrared spectroscopy, J Agric Food Chem, 50(18), 5082–8. VAN DIJK C, FISCHER M, BEEKHUIZEN J-G, BOERIU C and STOLLE-SMITS T (2002b) Texture of cooked potatoes (Solanum tuberosum). 3. Pre-heating and the consequences for the texture and cell wall chemistry, J Agric Food Chem, 50(18), 5098–106. VAN MARLE J T, STOLLE-SMITS T, DONKERS J, VAN DIJK C, VORAGEN A G J and RECOURT K (1997) Chemical and microscopic characterisation of potato (Solanum tuberosum L.) cell walls during cooking, J Agric Food Chem, 45(1), 50–58. VAN OIRSCHOT Q E A, O’BRIEN G M, DUFOUR D, EL-SHARKAWY M A and MESA E (2000) The effect of pre-harvest pruning of cassava upon root deterioration and quality characteristics, J Sci Food Agric, 80(13), 1866–73. VISSCHER G J W and LOVINK E (1999) Pea tenderometers and their calibration, Lebensm-Wiss u-Technol, 32(7), 455–9. WALDRON K W, NG A, PARKER M L and PARR A J (1997) Ferulic acid dehydrodimers in the cell walls of Beta vulgaris and their possible role in texture, J Sci Food Agric, 74(2), 221– 8. WALTER W M , TRUONG V D , WIESENBORN D P and CARVAJAL P (2000) Rheological and physicochemical properties of starches from moist- and dry-type sweet potatoes, J Agric Food Chem, 48(7), 2937–42. WARREN D S and WOODMAN J S (1974) The texture of cooked potatoes. A review, J Sci Food Agric, 25(2), 129–38. WILLS R B H and TIRMAZI S I H (1979) Effect of calcium and other minerals on ripening of tomatoes, Aust J Plant Physiol, 6(2), 221–7. ZHANG Z, WHEATLEY C C and CORKE H (2002) Biochemical changes during storage of sweet potato roots differing in dry matter content, Postharvest Biol and Technol, 24(3), 317– 25. ZIVANOVIC S, BUESCHER R W and KIM K S (2000) Textural changes in mushrooms (Agaricus bisporus) associated with tissue ultrastructure and composition, J Food Sci, 65(8), 1404–8. STEINBUCH E

15 Improving the texture of processed vegetables by vacuum infusion R. Saurel, University of Lyon, France

15.1

Introduction

Vacuum technology, which is also called ‘vacuum infusion’ or ‘vacuum impregnation’ (VI), is considered to be a pre-treatment for processed fruit or vegetables which improves their quality by incorporating functional ingredients such as acids, preservatives, water activity depressors or firming agents in the product structure (Saurel, 2002). Vacuum infusion technology is based upon hydrodynamic mass transfers promoted by pressure change, and it consists of putting the food product in the impregnation solution under vacuum before restoring the atmospheric pressure. This allows the occluded gas initially occupying the fruit or vegetable pores to be replaced by the impregnation solution in a quick and simple way. The treatment seems generally to adapt well to porous products and can be applied to whole or cut fruits and vegetables. Particular attention has been given to the use of this treatment to minimise the problems of post-harvest or processing-related deterioration of vegetable products. It is well established that processing treatments for fruits and vegetables that are designed to preserve them in various forms (fresh, frozen, pasteurised or dried) affect the initial organoleptic qualities such as texture, colour or flavour. With regard to the stability of vegetable texture, firmness improvement is the most common objective of vacuum technology application. For this purpose, the solutes used are firming agents such as calcium salts, gelling hydrocolloids or certain enzymes. The effect of these texture agents is indeed optimal, thanks to their large penetration into the internal porous structure of fruit or vegetable pieces. In addition, the diversity of ingredients used as infusion aqueous solutions makes it possible to generate novel structural effects and thus design new processed products.

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The aim of this chapter is first to give the description and the model of transfer which occurs during vacuum treatment and the consequent modification of the structural properties of products. Secondly, the different mechanisms involved in texture modification are discussed in the light of current or proposed future applications in the fruit and vegetables sector.

15.2 Vacuum infusion technology 15.2.1 Mass transfer phenomena Comprehension and control of the mass transfer resulting from the application of VI technology are particularly important with respect to the structural effect required for the food product. Indeed, the nature of the mass transfer determines the quantity of the reactive substance which penetrates into the product, its distribution in the plant structure and the possible structural modifications generated by the vacuum step. Compared to a classical diffusion process (candying, salting, soaking, osmotic dehydration…) which is carried out by simple dipping or prolonged immersion of the product in the solution for several hours or days, VI treatment has the advantage of a fast penetration – only a few minutes – of the active substance directly into the internal structure of the product. The mass transfer occurring during the vacuum treatment is mainly governed by a hydrodynamic phenomenon which is due to pressure changes. When a vacuum pulse is applied, trapped gases are expanded and partially removed from the food matrix during the vacuum application. After atmospheric pressure is restored, a positive pressure differential results which allows penetration of the liquid into the free voids in the structure until internal and external pressure equilibrium is reached. The time taken to reach a vacuum usually depends on the efficiency of the vacuum system (pump, closed volume of apparatus...) and lasts at best for only a few seconds. In most cases, products have to be maintained under vacuum for a few minutes to ensure good extraction of internal gases, but this step could be unnecessary if degassing is completed during the pressure drop. At the end of the treatment, vacuum release is generally obtained instantly. Fito and Pastor (1994) and Fito (1994) gave a clear description and model of the mass transfer phenomena – referred to as the ‘hydrodynamic mechanism’ (HDM) – observed in vacuum technology. Intercellular spaces in plant products are described as a set of elementary cylindrical pores being occupied by an ideal gas undergoing isothermal compression (Fig. 15.1). The penetration of solution into the ideal rigid pores breaks down into two stages. In the first part of the treatment, corresponding to atmospheric immersion and vacuum application, the pores fill by capillary action. Secondly, when restoring normal pressure, the resulting driving force induces liquid flow in the pores. The quantity of external liquid transferred can be almost as great as the available void space in the food structure. The impregnated sample volume fraction

366

Texture in food

Gas

Liquid

Solid (a)

(b)

Residual gas

(c)

(d)

Fig. 15.1 Main stages during vacuum infusion of porous food immersed in a liquid. The situation in an elementary ideal pore (adapted from Fito, 1994): (a) the capillary effect under normal pressure; (b) degassing under vacuum conditions; (c) capillary effect under reduced pressure; (d) HDM at restored normal pressure.

(X) (m3 impregnated solution/m3 initial sample), usually measured by a gravimetric method, has been modelled on the basis of the HDM and the Hagen-Poiseuille equation. At its simplest X is a function of the product effective porosity (εe) and the compression rate (r = (P2 + Pc)/P1; P1 is the applied vacuum pressure, P2 is the restored atmospheric pressure, Pc is the capillary pressure). Thus, Fito and colleagues established that the simple

Improving the texture of processed vegetables by vacuum infusion

367

expression for the volume fraction occupied by the liquid in the fruit or vegetable product after vacuum infusion is: X = εe · (1 – 1/r)

[15.1]

Capillary pressure appears to be negligible with respect to the driving force imposed on the system when the work is carried out at sufficiently low pressure (lower than 600 mbar according to Fito, 1994). Effective porosity is thus determined from an experimental procedure involving calculating the slope of the linear function obtained by adjusting the X versus 1 – 1/r curve (Fito, 1994; Del Valle et al., 1998a), and it is close to the percentage of sample volume initially occupied by the gases (Calbo and Sommer, 1987). In the case of fruit and vegetables, the porosity values were found to be extremely variable depending on the raw materials: for example average εe values are 0.20 for apple and 0.05 for apricot. Moreover, the effective porosity will depend not only on the type of fruits or vegetables, but also on their variety and their maturity (Del Valle et al., 1998a). Several authors (Fito et al., 1996; Salvatori et al., 1998) reported that the HDM mechanism is accompanied by deformation of the food matrix which influences the final liquid uptake and affects the mechanical properties of the product after treatment. The deformation phenomenon corresponds firstly to an extension of the internal occluded air volume inside the product when degassing at the time the vacuum is created, and secondly to a partial retraction in pore volume caused by structure relaxation at the time of return to atmospheric pressure. The total structure relaxation in the fruit pores may not be instantaneous, but it does not exceed a period of five minutes. As a function of the viscoelastic properties of the internal structure and the cohesive forces in plant cellular tissue, the deformation–relaxation phenomenon correlated with the pressure driving forces, resulting in variation of the effective porosity value and of the quantity of infused liquid in the product. A more complete and accurate equation was proposed by Fito et al. (1996) to take account of this phenomenon as follows: X = εe · (1 – 1/r) + γ + (1/r) · γ1

[15.2]

where γ1 and γ are the relative deformations of sample volume measured respectively at the end of the vacuum step (Fig. 15.1a) at the end of the process (Fig. 15.1d). At relatively low pressure, r is high and it may be accepted that: X ≅ εe . (1 – 1/r) + γ

[15.3]

Experimental values of the different parameters of the model are available for different fruit and vegetable varieties (Table 15.1). The data indicate that some poorly porous products like mango, peach or carrot could be impregnated with a significant quantity of liquid owing to the deformation phenomena and to the loss of native liquid – as indicated in Table 15.1 by negative X1 value – that is replaced by the solution. In other cases, the deformation

Peach (Miraflores) Pear (Passa Crassana) Pineapple (Espanola Roja)

0.9 ± 0.2 –4 ± 0.3 –6 ± 0.02

5.4 ± 0.5 2 ± 0.3 2 ± 0.02

Slice (t = 1 cm)

Dice (t = 2.5 cm) Slice (t = 1 cm) –6.5 ± 0.6

-0.2 ± 0.2

6.8 ± 0.6

1.8 ± 0.4

–

–

–1.3 ± 0.2

–

–

2.8 ± 0.2

–2.7 ± 0.3

2.8 ± 0.2

–2.29 ± 0.13

–5.0 ± 0.4

2.1 ± 0.4

2.0 ± 0.3

–4.2 ± 0.3

1.7 ± 0.3

Slice (d = 2 cm, t = 2 cm) Rectangle (2 × 7 cm) –

–

–

Slice (d = 2 cm, t = 3 cm) Slice (d = 2 cm, t = 2 cm) Slice (d = 2 cm, t = 2 cm) Slice (d = 2 cm, t = 2 cm) Half (d = 5–6 cm) Slice (d = 2 cm, t = 3 cm) Quarter 13.5 ± 1.3

5.3 ± 1.2

2.3 ± 0.4

2.2 ± 0.7

2.1 ± 0.4

14 ± 0.03

–0.4 ± 0.2

8.9 ± 0.4

5.7 ± 0.8

5.3 ± 0.9

5.6 ± 1.0

40 ± 0.05

5 ± 0.2

14.2 ± 0.5

0.89 ± 0.14

6.0 ± 0.7

1.4 ± 0.8

0.8 ± 0.5

8.9 ± 1.2

12.2 ± 1.8

15 ± 2

–6.0 ± 0.5

–2.4 ± 1.0

–0.6 ± 1.2

18.7 ± 0.7

1.2 ± 0.6

3.7 ± 1.3

3.4 ± 0.5

2.9 ± 0.4

21 ± 0.04

6 ± 0.3

5.9 ± 0.4

0.5 ± 0.5

8.6 ± 1.2

5.6 ± 0.5

15.9 ± 1.2

15.5 ± 1.1

16.6 ± 1.2

18.4 ± 1.8

εe

–

–

2.6 ± 0.5

–

–

9.9 ± 1.3

2.3 ± 0.8

–

–

25.4 ± 1.4

21.6 ± 1.0

23.8 ± 1.0

–

Real porosity

Fito et al. 1996) Salvatori et al. 1998) Salvatori et al. (1998) Salvatori et al. (1998) Fito et al. (1996) Fito et al. (1996) Salvatori et al. (1998) Salvatori et al. (1998) Fito et al. (2001a) Fito et al. (2001a) Salvatori et al. (1998) Fito et al. (2001a) Fito et al. (2001a)

References

Apple (G. Smith) Apple (G. Smith) Apple (Red Chief) Apple (Golden) Apricot (Canino) Banana (G. Cavendish) Kiwi (Hayward) Mango (T. Atkins) Melon (Inodorus) Orange peel

X*

Shape (d = diameter, t = thickness)

Product γ

Table 15.1 Some Fito model parameters and vacuum impregnation responses of some fruits and vegetables. Vacuum pressure: 50 mbar

X1 *

Texture in food

γ1

368

Whole (d = 5–6 cm) Whole (d = 2–2.5 cm) Dice (t = 2.5 cm) Dice (t = 1.5 cm) Slice (d = 4.3 cm, t = 1 cm) Dice (t = 2.5 cm) Slice (d = 4.3 cm, t = 1 cm) Whole (d = 3.3 ± 0.2 cm) Whole (d = 3–4 cm) Square piece (2.5 cm side) Square piece (2 cm side, t = 1 cm) Dice (t = 2.5 cm) Slice (d = 4.3 cm, t = 1 cm)

–2.1 ± 0.2 – –4.6 ± 0.8 –18 ± 3 –5.79 ± 0.08 –9.9 ± 0.9 –8.9 ± 0.5 –3 ± 2 – –21 ± 5 –48 ± 4 –3.8 ± 0.6 –3.43 ± 0.03

2.9 ± 0.4 – 1.3 ± 0.6 1.2 ± 1.1 1.7 ± 0.1 –1.8 ± 0.7 0.7 ± 0.2 1.9 ± 0.5 – 7±4 3±1 2.3 ± 0.6 3.2 ± 0.8

3±2

5±2

9±1

8±4

20 ± 3

49 ± 2

30 ± 4

25.7 ± 2.6

–4.8 ± 1.7 10 ± 5

34 ± 2

52 ± 1

24 ± 3

5.8 ± 0.3

16 ± 3

0.0 ± 1.1

1.6 ± 0.5

–37 ± 5

3.3 ± 0.4

3.0 ± 0.6

7±2

4.2 ± 1.1

1.4 ± 3.6 3.1 ± 1.3

0.2 ± 0.7

–4.0 ± 0.6

5±2

16 ± 3

41 ± 2

20.0 ± 0.5

34.4 ± 1.8

37 ± 3

54 ± 1

64 ± 4

2.6 ± 0.9

13 ± 3

4.47 ± 0.03

3.0 ± 3.4

4.8 ± 0.3

18 ± 2

18 ± 2

–

10.3 ± 0.5

–

37 ± 2

–

59.6 ± 0.5

–

0.3 ± 0.8

6±2

–

6.3 ± 1.6

*X1 and X are volume fractions of initial sample impregnated at the end of the vacuum step and at the end of the atmospheric step.

Zucchini

Mushroom (Agaricus b.) Mushroom (Gurelan 55) Oyster mushroom Oyster mushroom Zucchini

Eggplant

Eggplant

Carrot

Carrot

Strawberry (Chandler) Strawberry (Chandler) Beetroot

Salvatori et al (1998) Fito et al. (1996) Gras et al. (2002) Gras et al. (2002) Gras et al. (2003) Gras et al. (2002) Gras et al. (2003) Gras et al. (2002) Fito et al. (1996) Gras et al. (2002) Gras et al. (2003) Gras et al. (2002) Gras et al. (2002)

Improving the texture of processed vegetables by vacuum infusion 369

370

Texture in food

phenomena tend to reduce the product porosity. Taking account of these variations, the porosity estimated by the previous models is defined more precisely as the sample volume fraction available for the HDM mechanism and depends on the vacuum conditions (Mujica-Paz et al., 2003). Following the calculation of X by the Eqs (15.2) and (15.3), the content of the active substance in the product after the infusion step is determined from the concentration of the substance in the solution and the density of this solution (Fito et al., 2001a, b; Roa et al., 2001). The limitation of the previously proposed Eq. (15.2) is that the model was strictly derived from capillary flow theory and established for isotonic solutions. This gives rise to two problems. The first is that this approach is not adapted in the case of the infiltration of non-Newtonian liquids or high-viscosity solutions which induce significant flow pressure drop. Moreover, the compressed structure may generally relax following the vacuum step, thus promoting suction of the external solution. This liquid intake may be inhibited in high-viscosity solutions because of the equilibrium between the relaxation force of the matrix and the friction force, thus resulting in sample deformation instead of impregnation. In this case, product volume relaxation may occur when the sample is removed from the viscous solution, thus gaining ambient air instead of liquid (Barat et al., 2001). The influence of viscosity is particularly important because the dissolution of solutes such as gelling agents leads to very viscous liquid whose flow into pores appears limited. Figure 15.2, reporting results obtained in our laboratory, shows the effect of the solution viscosity – adjusted with low methylated (LM) pectin – on the X values of 30

25

X (%)

Effective porosity εe = 0.3 X = εe . (1 – 1/r) · [1 – 0.078 Ln (µ)] 20

15

10

0

100

200

300

400

500

600

700

µ, viscosity (mPa.s)

Fig. 15.2 Effect of viscosity on the impregnated sample volume fraction X of apple slices (diameter 20 mm, thickness 8 mm) after vacuum infusion at 20 °C in water and different pectin solutions. Vacuum treatment conditions are 50 mbar for 1 min 15 s.

Improving the texture of processed vegetables by vacuum infusion

371

apple cylinders chosen as a model fruit during vacuum treatment. The decrease of X values with viscosity indicates a significant effect on the hydrodynamic mass transfer which could not be predicted from the previous model. We propose to take account of the viscous effect during HDM by correcting the driving force with a viscosity-dependent term. Thus, neglecting the final deformation value γ in the case of apple, the expression for X becomes: X = εe . (1 – 1/r) . [1 – k Ln(µ)]

[15.4]

where µ is the solution viscosity (mPa.s) and k is a specific empirical factor for the product and the solute considered. The second problem is that the Fito model is not able to represent directly the mass transfer phenomena when hypertonic solutions are used. This is due to osmotic phenomena occurring simultaneously with the hydrodynamic mechanism which could affect the penetration of external liquid. The osmotic phenomena decrease the mass gain of the product measured after vacuum impregnation because of the water loss due to cell plamolysis, and seem to block partially the liquid flow in the pores due to structure collapse (Barat et al., 2001). With vacuum pressure, the composition and concentration of the aqueous solutions represent the main governing factors that modify the liquid intake in porous fruits or vegetables. The other variables upon which the vacuum process depends, i.e. temperature of impregnation solution, time to achieve vacuum, time maintained under vacuum, time to restore atmospheric pressure, have not received a great deal of systematic study. After restoring normal pressure, the time during which the product is maintained in the solution is also non-negligible with respect to the relaxation time in the pores and the flow kinetic of liquid. However, in most cases no evolution was reported after a five minutes period (see above). The few existing data concerning the effect of temperature showed that a slight variation in mass transfer rate was induced (Hoover and Miller, 1975). In practice, temperature conditions are limited when nearing the liquid boiling point under vacuum, for example close to 46 °C for water at 100 mbar. Finally, it can be suggested that the temperature effect on liquid viscosity or food matrix elasticity certainly has a role in vacuum technology.

15.2.2 Structural modifications As mentioned before, some fruits or vegetables tend to lose native liquid during processing at very low pressure, thus revealing cell structure damage due to mechanical strain. Thus, the deformation–relaxation phenomenon could induce irreversible effects, involving in some cases rigidity loss due to embrittlement or rupture in the cell walls junctions, as pointed out by several authors. From microscopic observations of kiwi fruit before and after vacuum treatment with glucose solutions, Muntada et al. (1998) noticed that the size of the cells in the infused plant tissue and their arrangement were preserved

372

Texture in food

even if ruptures in the cellular walls were observed. This was in agreement with the previous work of Bolin and Huxsoll (1987) on apple, which showed that vacuum impregnation causes the rupture of a non-negligible number of cells and structure collapse. When isotonic solutions are used as impregnation media, the intercellular spaces are filled with the liquid and cell turgor is maintained. With hypertonic solutions, as generally used in the osmotic dehydration (OD) process, microscopic observations show plasmolysis of the cells in all cases. Nevertheless, the structural change, which influences the final mechanical properties of osmo-dehydrated products, develops differently according to whether OD is preceded by a vacuum step (pulsed vacuum OD, PVOD) or is carried out at atmospheric pressure. In the classical OD process, water loss mainly occurs cell to cell by osmotic mechanisms and during the cell shrinkage cell wall significantly deforms while remaining bonded to the plamalemma. On the other hand, in PVOD treatment, plamalemma separates from cell wall throughout shrinkage and the liquid phase flows into the cell cavity from the intercellular spaces through the permeable cell wall. In this case, cell cohesiveness and cell wall integrity are enhanced and a less deformed shape of cells can be distinguished (Muntada et al., 1998; Salvatori et al., 1998; Barat et al. 1999). All of the phenomena involved in vacuum application (pores filling, pores deformation, cell damage) have consequences for the rheological properties of the pre-treated products. Del Valle et al. (1998b) noticed some undesirable structure changes as a result of vacuum infiltration of water, as indicated by a reduction in the textural attributes of cylindrical apple samples measured by compression-to-failure tests. As the absolute pressure level decreased from 59.9 to 9.3 kPa, fracture-point (the minimal required force to cause failure) values decreased from 46 to 32% of the value measured for an untreated control sample. The relative ‘fragility’ of impregnated samples, which was related to cell de-bonding or cell fracture, was estimated to be proportional to the vacuum pressure and the infiltrated liquid quantity. Vacuum infusion at 50 mbar of cylindrical apple samples with hypotonic or isotonic solution does not significantly change the maximum stress value, assessed by stress/relaxation tests, compared with that obtained for fresh apple (Martinez-Monzo et al., 1998). Nevertheless, the use of an isotonic pectin solution produces a significant decay in the maximum force. The high viscosity of this solution made the flow of the liquid into the apple pores difficult when atmospheric pressure was restored. Thus the change in pressure resulted in a deformation of the cellular structure of the sample, affecting mechanical behaviour. Moreover vacuum impregnation with hypertonic solutions strongly decreases the maximum stress value due to both the loss of cell turgor that causes cell de-bonding and the reduction of the mechanical modulus of vegetal tissue. After VI treatment, samples became all the more ‘less elastic–more viscous’ because the solutions are hypertonic. The ‘more viscous’ feature observed for VI samples must be partially explained by the

Improving the texture of processed vegetables by vacuum infusion

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out-flow of the impregnated liquid through the fruit pores during the compression/relaxation test. The structural modifications observed in the VI process appear relatively complex and mainly depend on the following factors: vacuum pressure, osmolarity and viscosity of the solution, and mechanical properties of the vegetable products. In order to minimise the deterioration of cellular tissue, we would thus recommend working as far as possible with an isotonic solution which is not very viscous and at moderate vacuum pressure (absolute pressure between 50 and 100 mbar). This is a compromise solution between the uptake of a pre-determined quantity of solute and the possible denaturation of the fresh food structure. As will be emphasised hereafter, the damage to food structure at the infusion stage may be masked by reinforcing the cell wall structure by calcium or by strengthening the wall with gelling agents or other solutes, which could improve the texture of the products at the processing stage still more. The paradox of the vacuum technique becomes clear when considering the negative effect of the deformation–relaxation phenomenon: the moderate loss of integrity which is a consequence of the vacuum treatment can be compensated to a large extent by the active role of the transferred solutes.

15.3 Applications to improve texture The use of vacuum technology has been proposed as a pre-treatment in many processing and product applications: post-harvest storage, frozen fruits or vegetables, blanched, canned, cooked, osmo-dehydrated products, and so on. The major way in which vacuum technology has been exploited is the modification of food structure in order to improve the strength and firmness of products after physical treatment for preservation and/or during storage. Different ingredients or additives are used which have specific effects on food texture.

15.3.1 Structuration mechanisms involved and advantages The main changes affecting the mechanical behaviour of plant tissues during thermal treatments (freezing, blanching, cooking, pasteurisation, sterilisation) are generally attributed to the following phenomena: • cell membrane alteration resulting in loss of cell turgor, juice loss and softening; and • de-bonding or rupture of cell wall decreasing cell wall resistance. It is possible to partially compensate for these types of damage by infusing specific solutes to strengthen the structure of vegetable products. Thus, we can distinguish four possible actions to improve texture, which gain advantage from the vacuum technology application.

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• The protective action of solutes through a physicochemical mechanism. The work of Martinez-Monzo et al. (1998) seems to be a particularly representative example of this possibility, offering promising prospects for the development of a pre-treatment that will modify the initial composition of porous fruit, making it more resistant to damage caused by the freezing–thawing process. The infusion of concentrated cryoprotectant solutions (low molecular weight solutes) into apple pieces before freezing significantly reduced the freezable water content. This may contribute to a decrease in the damage produced by ice crystals because of the reduction in their volume fraction. After impregnation with modified grape must as the chosen cryoprotectant, cryo-scanning electron microscopy observations of the cellular structure of the apple showed that the formation of ice crystal was similar in intercellular space and inside the vacuole, but no apparent disturbances in the cell (size, shape and intracellular arrangement) were detected. • The role of physiologically active solutes in the case of fresh and postharvested fruits or vegetables. Solutes such as cations or plant hormones act as membrane regulators, but they are of limited interest in the case of living plant tissue. • The strengthening of the cell wall structure by calcium addition which may be amplif ied by the action of endogenous or exogenous pectinemethylesterase. This appears to be a more efficient mode for thermally processed products and will be largely described hereafter. • The formation of an additional gel network in the pores or in the intracellular spaces filled with thickening or gelling hydrocolloids. The efficiency of this process depends strongly on the porosity of the product since significant penetration rates of gelling agent solution are required to generate texture modification. As the different actions directed at structure protection and texture improvement described above are often linked or combined, we will specify the preservation mechanisms of solutes for the different applications presented in the following sections.

15.3.2 Preserving fruit firmness by calcium or polyamines infusion Post-harvested products The process of dipping whole fruits in aqueous preservative solutions, which is improved by vacuum application, has been used to prolong the postharvest shelf life of many products: apples (Scott and Wills, 1977, 1979; Lidster et al., 1986), lemons (Valero et al., 1998a, b), avocados (Wills and Sirivatanapa, 1988), mangoes (Tirmazi and Wills, 1981), tomatoes (Wills and Tirmazi, 1979), strawberries (Ponappa et al., 1993). The compounds

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used in the impregnation solution are usually calcium salts (mostly calcium chloride) and many plant hormones (polyamines). Vacuum infusion seems to be used as an alternative to the pressure infiltration process (Poovaiah, 1986; Wang et al., 1993). The benefit of calcium application is generally related to the ability of the cation to interact with cell membranes and walls, as well as to its regulatory role at the metabolic level. The beneficial effects of calcium enrichment of whole fruit after harvest have multiple causes (Poovaiah, 1986; Stow, 1989; Glenn and Poovaiah, 1990; Picchioni et al., 1998). First, calcium plays a special role in maintaining the middle lamella and the cell wall rigidity in fruits and other storage organs by interacting with the pectic acid in the structure to form calcium pectate. Extensive crosslinking may facilitate packing of pectic polymers and form a cell wall network that increases mechanical strength and restricts access to hydrolytic enzymes such as polygalacturonase. Second, calcium interacts with the cellular membrane by modifying its structure and it exerts a regulating role on its permeability and on the transport of some substances involved in product ripening and senescence. Thirdly, many enzymatic reactions (e.g. polypeptides phosphorilation by protein kinase) would be calcium-dependent. The presence of impregnated calcium thus allows the cell wall resistance and cell turgor that are mainly responsible for fruit firmness during storage and/ or ripening to be maintained. Through this multiple action, calcium acts favourably to delay senescence and to control physiological disorders during fruit or vegetables storage. Vacuum infiltration of calcium applied to various apple varieties (Gravenstein, Cox’s Orange Pippin) and harvests made it possible to decrease the physiological disorders after three weeks, storage at ambient temperature (around 20 °C) while puncture testing indicated a significant gain in firmness (Scott and Wills, 1977). Poovaiah (1986) found that the firmness of Golden Delicious apples stored for 15 weeks at 0 °C was improved after vacuum infusion in a 3–4% calcium chloride solution. At the same time, the ascorbic acid content was enhanced up to two-fold, while carbon dioxide production and ethylene evolution appeared to be significantly reduced. After six months, storage at 2 °C, Golden Delicious apples infiltrated with CaCl2 (4% v/w) solution using a combination of vacuum (–28 kPa) and pressure (28 kPa) were firmer and had greater tensile strength than untreated fruit (Glenn and Poovaiah, 1990). Tissue firmness values from puncture testing and tensile strength values were correlated with the calcium content of fruit samples, thus indicating an uneven distribution of calcium within the fruit. Although the infiltration was not uniform, firmness improvement was mainly explained by the increased structural integrity of the middle lamellar region maintaining cell cohesiveness. Vacuum infiltration of polyamines, which are positively charged molecules, could play the same role as calcium in delaying softening and senescence of plant products, due to their ability to bind the cell wall or to stabilise the membrane, and to their implication in physiological processes. The similar action of these compounds is illustrated by the work of Valero et al. (1998a,

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b) who compared the effect of different impregnation runs with calcium and with polyamines on the preservation of lemons during the ripening stage. It was shown that vacuum infiltration of putrescine or gibberellin increased the firmness of whole lemons preserved for 21 days at 15 °C up to 50% at the same time as it delayed the colour changes in unripe-picked fruits. Finally, the work of Lidster et al. (1986) displayed the potential of postharvest vacuum infusion in solutions containing flavonoid glycosides (quercetin, rutin) and phenolic acid (chlorogenic acid) to retard fruit softening of ‘Spartan’ and ‘Golden Delicious’ apples held at 20 °C and 0 °C. This effect was mainly explained by the inhibitory properties of these compounds on β-galactosidase. The delay in ripening and senescence would thus make it possible for many products to be maintained for longer in the distribution chain even at ambient temperature, which is particularly interesting for most developing countries where little or no refrigeration is available. Minimally and thermally processed products Ponappa et al. (1993) compared the effect of vacuum impregnation with calcium and with different polyamines on the preservation of fresh strawberry slices during four and nine days’ storage at 20 °C and 1 °C, respectively. Among the polyamines studied in this work, spermidine and spermine had a greater effect than putrescine but appeared to be less effective than calcium in maintaining the firmness of the fruits especially at the highest temperature studied. Recently, Fito et al. (2001a) used vacuum infusion in order to design fresh vegetal food fortified with nutrients or physiogically active compounds such as calcium. Following this approach, Gras et al. (2003) have studied the interactions of the cation with the cellular matrix when isotonic sucrose and calcium lactate solution were impregnated in different vegetables pieces (carrot, eggplant and oyster mushroom). The stress–strain curves registered during compression tests indicated that the calcium-infused samples of carrot and eggplant presented a less viscoelastic, harder texture response compared to the VI control without calcium lactate. In addition, sample volume recovery after compression was greatly reduced by the Ca presence thus indicating irreversible fractures in the cell network due to its higher rigidity and fragility. In contrast, the calcium infusion did not modify the oyster mushroom microstructure and mechanical response since mushroom cellular wall does not contain pectin. Del Valle et al. (1998b) have studied the effect of different pre-treatments (blanching and calcium infiltration) on the texture of apple cylinders osmodehydrated in sucrose solutions. The authors reported that the infiltration of calcium solutions partially compensated for the undesirable textural changes caused by the vacuum treatment (see Section 15.2.2). Samples subjected to a vacuum treatment with 2% CaCl2 retained texture during osmotic dehydration better than any other sample (untreated control or blanched apple with or without calcium) which was explained by a more extensive crosslinking of

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demethoxylated pectin by a large excess of calcium present in the intercellular spaces. Moreover, it was qualitatively assessed that vacuum treated apple cylinders retained their external geometry and colour better than any other. Calcium lactate infusion in fresh whole or sliced strawberries improved their texture and reduced their weight loss measured after canning (Main et al., 1986) owing to the presence of calcium which reinforces the cell wall structure by forming pectates. In addition, Main et al. (1986) showed that calcium impregnation on whole or sliced strawberries prior to freezing only slightly improved the resistance of thawed fruit to shear. The low effectiveness of calcium in improving firmness was explained by insufficient demethylation of endogenous pectins in the fruit for the purpose of pectate formation. When the freezing/defrosting cycle was followed by heat treatment, the effect on texture was greater owing to increased enzymatic demethylation activated during temperature rise. The improvement of texture by calcium infusion was also observed by French et al. (1989) with canned apricot – Patterson cultivar fruits – even if the chelator effect of exogenous or endogenous citrate tended to limit calcium effectiveness, especially on low maturity fruits because of their stronger acidity.

15.3.3 Modifying texture by enzyme addition The vacuum infusion of enzymes in the structure of fruits and vegetables has been mentioned in connection with designing enzymatically modified food (McArdle and Culver, 1994; Baker and Wicker, 1996; Culver et al., 2000). Enzymatic modification of the internal characteristics of intact fruit or vegetables by vacuum infusion leads to an interesting transfer/reaction process in food matrix engineering. The applications of enzyme vacuum infusion appear to be numerous, depending on the specific activity and function of the enzyme: peeling, firming or softening, generating volatile aroma from glycosidic precursors, removal of off-flavours, degradation of non-digestible or toxic components, and so on. A more advanced application now in commercial use is the use of infused pectinases and cellulases for easier peeling of citrus fruits (Rouhana and Mannheim, 1994; Pretel et al., 1997). The applications involving structure modification have been studied successfully and the vacuum infusion of exogenous PME in fruit was found to be effective in increasing firmness in thermally processed foods. PME is a cell wall-bound enzyme in fruits and vegetables, which deesterifies pectin. In post-harvest ripening of fruits, PME activity precedes depolymerization by polygalacturonase, resulting in fruits softening. However, the PME is postulated to increase firmness of fruits and vegetables by demethylation of endogenous pectin and subsequent chelation of divalent cations by ionised carboxylic acid groups on adjacent pectic acid chains. In the presence of calcium, the firming effect is proportional to the natural PME activity preceding the thermal treatment, and it can be reinforced by vacuum–assisted infusion of exogenous PME. As reported by Javeri et al.

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(1991) for blanched (95 °C, 30 s) or blanched-canned (104 °C, 12 minutes) peaches, vacuum-infused citrus PME and calcium increased the firmness of these thermally-processed products up to a value nearly four times that of un-infused controls (Javeri et al., 1991). Vacuum treatment combining exogenous citrus PME and calcium chloride infusion was applied successfully to whole strawberries prior to freezing (Suutarinen et al., 2000, 2002a; b). Texture tests assessing compression force and area of deformation curve were performed in an Ottawa cell system to compare the firmness improvement of enzymatically treated berries notably with that of fruits treated with calcium only or untreated. The pre-freezing vacuum treatments with PME and calcium had the most significant influence on the firmness of frozen strawberries. Furthermore, jams made from the frozen strawberries vacuum treated with CaCl2 and PME had significantly higher firmness values than the reference samples (approximately twice as great). Fourier transform infrared microscopy and bright-field microscopy studies proved that the pectin, protein and structural carbohydrates components of the strawberries treated with PME were more stable than those of the controls. Vacuum treatment with CaCl2 and PME seemed also to result in interesting improvements in sensory attributes such as wholeness of berries and redness of the colour of the jam. Recently, vacuum impregnation with a commercial fungal PME preparation (Rapidase® FP Super) and calcium was proposed as a means of increasing the firmness of pasteurised 1 cm apple cubes, strawberry halves and whole raspberries (Degraeve et al., 2003). In order to check the effectiveness of the VI pre-treatment, the fresh fruits were infused following an atmospheric dipping procedure or vacuum-treated with solutions containing water, calcium alone, or calcium and PME. The classical atmospheric procedure consisted of putting the fruits in a 35°Brix sucrose syrup with or without the firming agents during the pasteurisation cycle. Classically-prepared and vacuumimpregnated samples were both pasteurised in a 35°Brix sucrose syrup representing a simplified standard recipe for fruit preparation. The heat treatment was carried out by maintaining the blends during 20 minutes at 40 °C to eventually activate PME followed by holding the fruit preparation at 85 °C for about 15 minutes to both pasteurise the fruit preparation and inactivate the enzyme. The compression force on the pasteurised fruits separated from the syrup was measured in an Ottawa cell system after 48 hours storage at 4 °C. The firmness of the fruits treated with PME and calcium was always found to be higher than that of the controls. The results (Fig. 15.3) indicated that the effectiveness of vacuum-assisted impregnation strongly depends on the plant tissue considered. Raspberry, having a polydrupe structure, is poorly porous and very fragile, and a high degree of softening of the fruit structure was observed after vacuum application at 50 mm Hg; this observation points to atmospheric treatment being more advantageous in improving firmness in raspberries. The two types of pretreatment when applied to Granny Smith apples resulted in similar firmness;

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40 35

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Fig. 15.3 Firmness of pasteurised strawberry halves, apple cubes and whole raspberries treated by a classical (a) or a vacuum-assisted (b) infusion procedure. (Reprinted from Journal of Food Science, 68(2), 715–21© Institute of Food Technologists, Chicago, Ill, 2003).

the mechanical strength and good thermal resistance of apple and the small size of the fruit pieces used were mentioned to explain this behaviour. Vacuum infusion was more efficient for strawberry halves, which have fragile and intermediary porous structure, because it favoured the penetration of the firming agents in the fruit pores. The authors, who also studied the influence of infusion solution composition on strawberry firmness, proved that thresholds exist in the solution concentration of both PME and CaCl2 at which maximum firmness is reached. These correspond to respective thresholds in PME and calcium contents in the fruit. The PME content necessary for maximum firmness varied logically inversely with the reduction in activation time applied during the heating operation.

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The use of exogenous PME to modify cell wall structure of fruits or vegetables is a promising tool for thermally-processed products since the enzyme has to be activated and inactivated by heating. In this case, the processed food does not require any specific reference on the label (i.e. as food ingredient or additive) to enzyme use. Furthermore, the use of enzyme with vacuum technology is adapted to vegetable products with low porosity since the enzymatic firming process generally requires a low level of PME activity. To reach a definitive conclusion on this advantage, the factors limiting PME penetration in plant tissue under vacuum application are being further studied at the University of Lyon (Guillemin, 2003).

15.3.4 Processed fruit firming by infusion of gelling agents The infusion of gelling agents in the fruit or vegetable structure allows texture improvement either by generating intercellular bridges in the pores complementing the plant cell wall network or by forming bonds between the added hydrocolloids and the cell wall components. However, the intercellular gel can modify the texture response only where the porous structure is high and the solute gain is significant, especially as mass transfer phenomena are generally limited by the high viscosity of gelling agent solutions. In addition, gel formation for many hydrocolloids (pectin or alginate) works with calcium, and the use of the cation is usually needed. Because of this the combination of the two reagents in the same solution is not appropriate due to the risk of causing thickening or gel formation before infusion. Thus two vacuum infusion cycles are generally necessary: the first with the solution containing the gelling agent to fill a large fraction of pores and the second with the calcium to complement the residual free spaces. Otherwise, infused gelling agents could react with the endogenous calcium or with the calcium present in the food medium – e.g. a sauce or a syrup – where the fruit or vegetable pieces would be incorporated. Other medium conditions, such as concentration, temperature or pH, exist to make the gelling effective. Different mechanisms are thus involved depending on the hydrocolloid type. This complexity makes the control of the application more difficult, as was the case for the firming agents proposed in the previous sections. Among the well-known applications, the vacuum treatment of button mushroom with xanthan gum before blanching and canning has been shown to improve the weight yield and the organoleptic quality of the final product (Gormley and Walshe, 1986). Xanthan impregnation tended to decrease the shrinkage of mushroom during the blanching/canning cycle and thus to reduce the product weight loss. The pre-treatment with xanthan led to a more acceptable and less tough texture of canned mushrooms. This ‘softening’ effect of the vacuum treatment on canned mushroom is a desirable feature since canned mushrooms often have a ‘hard’ texture. The benefit is due presumably to the thickening property of the xanthan gum solution (0.5–1% w/w) which occupies the wide-open hyphae structure of mushroom and prevents expulsion during

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blanching and retorting. Some of the xanthan molecules might also be bound by the mushroom proteins. Demeaux et al. (1988) indicated that in terms of weight loss reduction of canned mushroom, the use of gelling agents such as egg white proteins is much more effective than xanthan gum which does not gel. The freezing/thawing cycle applied to fruits or vegetables causes substantial damage to the cellular structure, that is denaturation of the membranes and rupture of the cell walls by ice crystals, leading to loss of turgor and rigidity. This generally results in a strong juice exudation when defrosting the product. With the aim of limiting these problems, Barton (1951) attested that fresh fruits mixed with sugar and gelling agents and consequently submitted to a vacuum step give frozen/defrosted products with better organoleptic quality. In the case of strawberry slices, as proposed by Barton, the use of pectin and alginate before freezing made it possible to maintain the shape, weight and colour of the fruit to a greater degree than was the case for untreated fruit this was particularly so when high methylated (HM) pectin was used. Preliminary vacuum impregnation of strawberries in solutions containing gelling agents was proposed by Cierco (1994) as a new method for improving the quality of frozen fruits. Using this process, frozen/thawed strawberries were obtained which maintained the features and the taste of fresh ones even after several years’ storage at –20 °C and that are usable for traditional pastry-making. More recently, Matringe et al. (1999) showed the possibility of introducing various gelling hydrocolloids (gelatine, pectin, alginate and starch) through the application of vacuum on fresh apple pieces before freezing. Texture measurements by compression test on apple samples infused in that way showed firmness improvement with certain gelling agents just after vacuum treatment (Fig. 15.4). If the gelling agent uptake is sufficient, a structuring 140

Firmness (N)

120 100 80 60 40 20 0

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ro

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+

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Cl

2

lat

in

+

Ca

Cl

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Fig. 15.4 Firmness of apple slices (1 cm thickness, 2 cm diameter) after vacuum impregnation with water and different texture agents. Compression speed: 0.5 mm/s. Relative deformation: 15%. *double impregnation

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effect is also observed on the frozen-defrosted product. An example of this texture modification is presented in Fig. 15.5. The ‘cuttability’ – defined as the force to cut a 1 cm thick apple cube measured by a texture analyser equipped with a blade – of samples impregnated with gelatine appeared to exhibit behaviour similar to that with a simple hydrocolloid gel. Indeed, apple dices treated with gelatine before freezing definitely showed higher gel strength (the slope of the curve is steeper). Then, the frozen impregnated sample showed a tendency to be cut like a gel (there is a breaking point before the end of the measurement), which was completely different from the control case for which the gel strength value corresponded only to a continuous crushing. The measurement of gel strength under compression gave similar results (Fig. 15.5). These phenomena were explained by the formation of gel-filled intercellular spaces predominating over the softened structure of defrosted apple. Thereafter, work carried out on pasteurised fruit preparations for dairy products described in the FAIR European programme, referenced in Section 15.5, showed significant texture improvement for products containing pear or strawberry pieces enriched with pectin or alginate. The improved organoleptic qualities of the processed products were validated by sensory analysis (Cattaneo et al., 2000; Avitabile Leva et al., 2000). 300

300 Vacuum infused Non-infused

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250

200

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50

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0 0.0

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Fig. 15.5 Texture analysis profiles of frozen-defrosted 1 cm3 apple cubes, vacuum infused with gelatine and non-infused, representing shearing force (‘cuttability’) or strength force under compression versus distance.

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15.4 Future trends At the time of writing, industrial application of vacuum impregnation is to the best of our knowledge still being developed. This can be explained by the great variety in the response of fruits and vegetables to the vacuum process, which is due to their structure properties being extremely variable. Indeed, no ready-made formula can be applied to a raw material without acquiring, as a preliminary, data relating to the porosity, mass transfer, and resistance of the plant tissue to mechanical strains exerted during pressure changes, and without having checked the effectiveness of the numerous firming agents available. In addition, the qualitative and commercial added value which results from any texture improvement has to be balanced against the additional cost generated by the pre-treatment application, which includes investment, lengthening of operating time and consumption of energy and ingredients. Some new lines of research might develop which could widen the scope of the innovative applications offered by the vacuum technique with respect to the texture improvement of foodstuffs. It could be interesting to investigate the co-impregnation of two or several ingredients which act differently and synergistically on the product structure as a means of enhancing the firming effect. For example, we might propose to combine the impregnation of PME, which acts to reinforce the cell wall structure, with the infusion of a gelling agent which stabilises the solution occupying the fruit pores. Another interesting subject is the combination of vacuum treatment with an emerging technology (high pressure, microwave, electric f ields, ultrasound…) with a view to enhancing synergistically the effectiveness of these procedures. A well-known example is the use of vacuum infusion before the osmotic dehydration treatment of fruits in concentrated syrup. As already indicated in our chapter, the pre-treatment induces new structure effects on the osmo-dehydrated products in addition to the modification of the mass transfer kinetics. Other applications have been reported, such as the vacuum impregnation of vegetable pieces with salt solutions prior to ohmic treatment in order to modify their electric conductivity and thus optimise the heating conditions (Wang and Sastry, 1993). Lastly, we should mention a few works dealing with meat or cheese processing (Pavia et al., 1999; Chiralt et al., 2001) which mention the beneficial effect on product structure of vacuum brining techniques.

15.5 Sources of further information and advice Department of Food Technology Director: Pr P Fito Universidad Politecnica de Valencia PO Box 22012 46071 Valencia, Spain

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Tel: +34 96 387 7360 Fax: +34 96 387 7369 Research Laboratory in Food Engineering Contact: Dr R Saurel IUT A - University of Lyon 1 Rue Henri de Boissieu 01060 Bourg-en-Bresse, France Tel: +33 (0)4 74 45 52 52 Fax: +33 (0)4 74 45 52 53 European AAIR project F-FE 253/97 ‘Texture of heat processed fruits’ Contact: Dr S A Jones Leatherhead Food Research Association Randalls Road, Leatherhead Surrey KT227RY, United Kingdom Tel: +44 1372 376761 Fax: +44 1372 386228 European FAIR demonstration project CT 98-3814 ‘Improvement of processed fruit and vegetable texture by using a new technology: vacuum infusion’ TMI International 20, Bd Eugene Deruelle 69432 Lyon cedex 03, France Tel: +33 (0)4 72 84 04 82 Fax: +33 (0)4 72 84 04 85

15.6 References AVITABILE LEVA L, MARABOLI A, CATTANEO T M P

and TORREGGIANI D (2000) Improvement of processed pear ingredients using vacuum infusion: influence on the quality characteristics of pear yoghurt. In Book of Abstracts (edited by Dipartimiento di Colture Arboree): 8th International Pear Symposium, September 4–9, Ferrara, Bologna, Italy. BAKER R A and WICKER L (1996) Current and potential applications of enzyme infusion in the food industry, Trends Food Sci Technol, 7(9), 279–84. BARAT J M, ALBORS A, CHIRALT A and FITO P (1999) Equilibrium of apple tissue in osmotic dehydration: microstructural changes, Drying Technol, 17(7&8), 1375–86. BARAT J M, FITO P and CHIRALT A (2001) Modeling of simultaneous mass transfer and structural changes in fruit tissues, J Food Eng, 49, 77–85. BARTON R R (1951) Improving the quality of frozen premier strawberries, J Amer Soc Hort Sci, 58, 95–8. BOLIN H R and HUXSOLL C C (1987) Scanning electron microscope/image analyser determination of dimensional postharvest changes in fruit cells, J Food Sci, 52(6), 1649–50. CALBO A G and SOMMER N F (1987) Intercellular volume and resistance to air flow of fruits and vegetables, J Amer Soc Hort Sci, 112(1), 131–4. CATTANEO T M P, AVITABILE LEVA A and TORREGGIANI D (2000) Improvement of processed strawberry ingredients using vacuum infusion: influence on the quality characteristics of strawberry yoghurt, Acta Horticulturae, 567(2), 787–90.

Improving the texture of processed vegetables by vacuum infusion CHIRALT A, FITO P, BARAT J M, ANDRES A, GONZALES-MARTINEZ C, ESRICHE I

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and CAMACHO M M (2001a) Use of vacuum impregnation in food salting process, J Food Eng, 49(2&3), 141–51. CIERCO M (1994) Pre-freezing treatment of strawberries and their use as fresh strawberry, French patent application (in French), FR 94-13864. CULVER C A, BJURLIN M A and FULSHER R G (2000) Visualizing Enzyme Infusion into apple tissue, J Agric Food Chem, 48(12), 5933–5. DEGRAEVE P, SAUREL R and COUTEL Y (2003) Vacuum impregnation pretreatment with pectinmethylesterase to improve firmness of pasteurized fruits, J Food Sci, 68(2), 716–21. DEL VALLE J M, ARANGUIZ V and DIAZ L (1998a) Volumetric procedure to assess infiltration kinetics and porosity of fruits by applying a vacuum pulse, J Food Eng, 38(2), 207– 21. DEL VALLE J M, ARANGUIZ V and LEON H (1998b) Effects of blanching and calcium infiltration on PPO activity, texture, microstructure and kinetics of osmotic dehydration of apple tissue, Food Res Int, 31(8), 557–69. DEMEAUX M, SONNERAT P and LORIENT D (1988) Localization and behaviour study of egg white proteins incorporated in cultivated mushrooms (in French), Sciences des Aliments, 8, 269–83. FITO P (1994) Modelling of vacuum osmotic dehydration of food, J Food Eng, 22(1–4), 313–28. FITO P and PASTOR R (1994) Non-diffusional mechanisms occurring during vacuum osmotic dehydration, J Food Eng, 21(4), 513–19. FITO P, ANDRES A, CHIRALT A and PARDO P (1996) Coupling of hydrodynamic mechanism and deformation-relaxation phenomena during vacuum treatments in solid porous foodliquid systems, J Food Eng, 27(3), 229–40. FITO P, CHIRALT A, BETORET N, GRAS M, CHAFER M, MARTINEZ-MONZO J , ANDRES A and VIDAL D (2001a) Vacuum impregnation and osmotic dehydration in matrix engineering: Application in functional fresh food development, J Food Eng, 49(2&3), 175–83. FITO P, CHIRALT A, BARAT J M, ANDRES A, MARTINEZ-MONZO J and MARTINEZ-NAVARRETE N (2001b) Vacuum impregnation for development of new dehydrated products, J Food Eng, 49(4), 297–302. FRENCH D A, KADER A A and LABAVITCH J M (1989) Softening of canned apricots: a chelation hypothesis, J Food Sci, 54(1), 86–9. GLENN M G and POOVAIAH B W (1990) Calcium-mediated postharvest changes in texture and cell wall structure and composition in Golden Delicious apples, J Amer Soc Hort Sci, 115(6), 962–8. GORMLEY T R and WALSHE P E (1986) Shrinkage in canned mushrooms treated with xanthan gum as a pre-blanch soak treatment, J Food Tech, 21, 67–74. GRAS M, VIDAL-BROTONS D, BETORET N, CHIRALT A and FITO P (2002) The response of some vegetables to vacuum impregnation, Innov Food Sci Emerg Tech, 3(3), 263–9. GRAS M, VIDAL D, BETORET N, CHIRALT A and FITO P (2003) Calcium fortification of vegetables by vacuum impregnation. Interactions with cellular matrix, J Food Eng, 56(2&3), 279–84. GUILLEMIN A (2003) PhD in progress, Université Claude Bernard LYON 1, France. HOOVER M W and MILLER N C (1975) Factors influencing impregnation of apple slices and development of a continuous process, J Food Sci, 40, 698–700. JAVERI H, TOLEDO R and WICKER L (1991) Vacuum infusion of citrus pectinmethylesterase and calcium effects on firmness of peaches, J Food Sci, 56(3), 739–42. LIDSTER P D, DICK A J, DEMARCO A and MCRAE K B (1986) Application of flavonoid glycosides and phenolic acid to suppress firmness loss in apples, J Amer Soc Hort Sci, 111(6), 892–6. MAIN G L, MORRIS J R and WEHUNT E J (1986) Effects of pre-processing treatments on the firmness and quality characteristics of whole and sliced strawberries after freezing and thermal processing, J Food Sci, 51(2), 391–4.

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MARTINEZ-MONZO J, MARTINEZ-NAVARETTE N, CHIRALT A

and FITO P (1998) Mechanical and structural changes in apple (var. Granny Smith) due to vacuum impregnation with cryoprotectants, J Food Sci, 63(3), 499–503. MATRINGE E, CHATELLIER J and SAUREL R (1999) Improvement of processed fruit and vegetable texture by using a new technology «vacuum infusion», Proceedings of the International Congress “Improved traditional foods for the next century”, 28–29 October, Valencia, Spain, 164–7. MCARDLE R N and CULVER C A (1994) Enzyme Infusion: a developing Technology, Food Tech, November, 85–9. MUJICA- PAZ H, VALDEZ-FRAGOSO A, LOPEZ-MALO A, PALOU E and WELTI -CHANES J (2003) Impregnation properties of some fruits at vacuum pressure, J Food Eng, 57(4), 305– 14. MUNTADA V, GERSCHENSON L N, ALZAMORA S M and CASTRO M A (1998) Solute infusion effects on texture of minimally processed kiwifruit, J Food Sci, 63(4), 616–20. PAVIA M, TRUJILLO A J, GUAMIS B, CAPELLAS M and FERRAGUT V (1999) Changes in microstructural, textural and color characteristics during ripening of manchego type cheese salted by brine vacuum impregnation, Int Dairy J, 9(2), 91–8. PICCHIONI G A, WATADA A E, CONWAY W S, WHITAKER B D and SAMS C E (1998) Postharvest calcium infiltration delays membrane lipid catabolism in apple fruit, J Agric Food Chem, 46(7), 2452–7. PONAPPA T, SCHEERENS J C and MILLER A R (1993) Vacuum infiltration of polyamines increases firmness of strawberry slices under various storage conditions, J Food Sci, 58(2), 361–4. POOVAIAH B W (1986) Role of calcium in prolonging storage life of fruits and vegetables, Food Tech, May, 86–9. PRETEL M T, LOZANO P, RIQUELME F and ROMOJARO F (1997) Pectic enzymes in fresh fruit processing: optimisation of enzymatic peeling of oranges, Proc Bioch, 32(1), 43–9. ROA V, TAPIA M S and MILLAN F (2001) Mass balances in porous foods impregnation, J Food Sci, 66(9), 1332–6. ROUHANA A and MANNHEIM C H (1994) Optimisation of enzymatic peeling of grapefruit, Lebensm Wiss und Technol, 27(2), 103–7. SALVATORI D, ANDRES A, CHIRALT A and FITO P (1998) The response of some properties of fruits to vacuum impregnation, J Food Proc Eng, 21, 59–73. SAUREL R (2002) The use of vacuum technology to improve processed fruit and vegetables, In Fruit and Vegetable Processing Ed. W Jongen Cambridge, Woodhead, 363–80. SCOTT K J and WILLS R B H (1977) Vacuum infiltration of calcium chloride: a method for reducing bitter pit and senescence of apples during storage at ambient temperatures, Hort Sci, 12(1), 71–2. SCOTT K J and WILLS R B H (1979) Effects of vacuum and pressure infiltration of calcium chloride and storage temperature on the incidence of bitter pit and low temperature breakdown of apples, Aust J Agric Res, 30, 917–28. STOW J (1989) The involvement of calcium ions in maintenance of apple fruit tissue structure, J Exp Bot, 40(218), 1053–7. SUUTARINEN J, HONKAPÄÄ K, HEINIÖ R L, AUTIO K and MOKKILA M (2000) The effect of different prefreezing treatments on the structure of strawberries before and after jam making, Lebensm Wiss und Technol, 33(3), 188–201. SUUTARINEN J, HONKAPÄÄ K, HEINIÖ R L, AUTIO K, MUSTRANTA A, KARPPINEN S, KIUTAMO T, LIUKKONEN-LILIA H and MOKKILA M (2002a) Effects of calcium chloride-based prefreezing treatments on the quality factors of strawberry jams, J Food Sci, 67(2), 884–94. SUUTARINEN J, HONKAPÄÄ K, HEINIÖ R L, MUSTRANTA A, LIUKKONEN-LILIA H and MOKKILA M (2002b) Modeling of calcium chloride and pectin methylesterase prefreezing treatments of strawberries and jams, J Food Sci, 67(3), 1240–48. TIRMAZI S I H and WILLS R B H (1981) Retardation of ripening of mangoes by postharvest application of calcium, Trop Agric, 58(2), 137–41.

Improving the texture of processed vegetables by vacuum infusion VALERO D, MARTINEZ-ROMERO D, SERRANO M

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and RIQUELME F (1998a) Influence of postharvest treatment with putrescine and calcium on endogenous polyamines, firmness, and abscissic acid in lemon (Citrus lemon L. Burm Cv. Verna), J Agric Food Chem, 46(6), 2102–9. VALERO D, MARTINEZ-ROMERO D, SERRANO M and RIQUELME F (1998b) Postharvest gibberellin and heat treatment effects on polyamines, abscisic acid and firmness in lemons, J Food Sci, 63(4), 611–15. WANG W C and SASTRY S K (1993) Salt diffusion into vegetable tissue as a pretreatment for ohmic heating: electrical conductivity profiles and vacuum infusion studies, J Food Eng, 20(4), 299–309. WANG C Y, CONWAY W S, ABOTT J A, KRAMER G F and SAMS C E (1993) Postharvest infiltration of polyamines and calcium influences ethylene production and texture changes in Golden Delicious apples, J Amer Soc Hort Sci, 118(6), 801–6. WILLS R B H and SIRIVATANAPA S (1988) Evaluation of postharvest infiltration of calcium to delay the ripening of avocados, Aust J Exp Agric, 28, 801–4. WILLS R B H and TIRMAZI S I H (1979) Effect of calcium and other minerals on ripening of tomatoes, Aust J Plant Physiol, 6, 221–7.

16 Improving the texture of frozen fruit: the case of berries M. Suutarinen and K. Autio, VTT Biotechnology, Finland

16.1 Introduction: the effects of freezing and thawing on berry texture Freezing and subsequent frozen storage causes excessive softening of berries, and frozen strawberries are characteristically very soft and moist. The great susceptibility of strawberries to textural damage is due to their low solids content, large cells and thin cell walls. During freezing the ice crystals formed rupture the parenchyma cell walls and induce loss of turgor. These structural characteristics account for the loss of instrumental firmness. The right choice of cultivar is important in producing a high-quality frozen product (Oswin, 1979) and so grading involving possible defects, size and texture is necessary before freezing. The rate of freezing is a critical issue with regard to tissue damage. The fast freezing method allows better retention of texture, and higher texture values can be obtained with freezing in liquid nitrogen and plate freezing. However, berries immersed in liquid nitrogen may crack due to ultra-rapid freezing. Slow freezing results in formation of large ice crystals and significant damage to cell walls. Disruption of the intercellular structure will also release enzymes and substrates which will cause further problems with the flavor and color. High pressure offers new opportunities to treat samples at temperatures below zero. By using high pressure, the freezing point of water can be lowered to –22 ºC allowing storage of foods in liquid state without freezing. The cost-effectiveness can be greatly increased by using pressure-shift freezing and pressure-induced thawing. In the latter case the high-pressure treatment is used only for freezing and thawing.

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16.1.1 Sensory perception of texture The sensory quality of fresh strawberries is influenced by cultivar, maturity, site, season and agronomic practice. The texture profiling technique has been used to assess sensory perception of the texture of strawberries (Szczesniak and Smith, 1969). Fresh berries are firm, plump, low in cohesiveness and moderately juicy. Sensory firmness, cohesiveness and degree of fibrousness decrease with increased ripeness. Not all berries have good freezing characteristics and berries with good quality should be chosen for freezing. Strawberries retain much of their natural flavor and color under freezing, frozen storage and thawing but suffer serious softening in texture and release more juice than the fresh product. This loss in sensory firmness can be characterized by a moist, soft and limp appearance, poor shape retention and a tough texture of the interior fibers. 16.1.2 Microstructure Figure 16.1 shows the cross-section of a strawberry. Strawberry is composed of different tissues which differ in terms of chemical structure and microstructure. Vascular tissue composed of long fibers and pith forms the skeleton of the strawberry structure. Epidermal cells form the outer layer. Vascular bundles beginning from the achenes and connecting to the pith play a very important role in the texture of strawberries. The second layer is composed of hypodermal cells and the third layer of cortical cells (cortex). In cortical cells the pectins in the middle lamella have an important role in cementing the cells together. In plant tissue, the maintainance of shape is based on turgor pressure within individual cells. Freezing and thawing has a highly detrimental effect on cortical parenchyma cells which have large cells Epidermis Hypodermis

Vascular tissue Cortex

Achenes

Pith

Fig. 16.1 Cross-section of strawberry demonstrating the location of different tissues. (Reprinted with permission from Suutarinen et al., 2002b).

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and thin cell walls. The degree of cortical cell wall rupture during freezing determines the extent of textural change. Frozen berries also showed plasmolyzed cells. 16.1.3 Mechanical properties Measuring the mechanical properties of strawberries by normal compression is difficult, since the shape and size of strawberries vary a lot. It is also impossible to take a cylindrical sample from strawberry, as the structure is very unhomogenous. Puncture testing has most often been used in strawberry texture measurement (Bourne, 1979). By utilizing a puncture-penetration method it has been possible to measure both the skin toughness and flesh firmness of different strawberry varieties before freezing and after freezing and thawing and to grade species according to their suitability for freezing (Lacroix et al., 1985). The skin toughness of fresh samples for 25 different strawberry varieties varied from 3.19–6.19 N, and after freezing (–50 °C/20– 25 min), frozen storage (three months at –30 °C) and thawing (at 3 °C for 48 h) from 1.32–2.92 N. In the same way, flesh firmness of fresh berries was in the range 5.34–15.28 N. After freezing and thawing it was 1.75–6.28 N indicating significant softening of the berry skin and flesh due to freezing, frozen storage and thawing. The flesh firmness of fresh berry correlates with that of thawed sample (0.88) suggesting that berries most suitable for freezing can be graded according to firmness. Fresh strawberries cultivated in the field gave firmness values of 30 N, whereas for fresh strawberries cultivated in the greenhouse the corresponding value was 10 N (Agnelli and Mascheroni, 2002). Strawberries with high initial firmness showed a better texture when they were frozen in liquid nitrogen following storage in an air-blast freezer. 16.1.4 Changes in water binding properties The juice loss from strawberries has been measured by quantifying the amount of moisture released to filter paper during mechnical mastication by the Texturometer (Szczesniak and Smith, 1969). Frozen berry releases juice in great quantity. It has been demonstrated in one study that the texture of fresh samples is correlated with the drip loss during thawing (Lacroix et al., 1985). Freezing method and field (F)/greenhouse (G) cultivation had very significant influence on water drip (Agnelli and Mascheroni, 2002). Up to 60% reduction in drip loss was obtained by freezing the field cultivated strawberries in liquid nitrogen following storage in an air-blast freezer.

16.2 Maintaining texture: conventional pre-freezing treatments The two most common problems in frozen fruits and vegetables are: chemical reactions that cause changes in flavor, color, texture, and nutrition; and physical

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damage that causes loss of turgor and texture changes (Haard, 1997). Loss of tissue firmness, disruption of the cell membrane, and excessive softness are the major consequences which need to be avoided (Rahman, 1999). Loss of membrane semipermeability and disruption of cellular compartments can be minimized by rapid freezing rate, low storage temperature, and slow thawing (Haard, 1997). The use of pre-freeze treatments can help to reduce (or avoid) detrimental phenomena, either by inactivating the deterioration reactions, or by reducing the water content in the material (Torregiani, 2000). Prior to freezing, the fresh fruit is normally washed to improve the microbiological standard and physical appearance. A fast continuous wash will minimize the leaching of color and flavor. A close inspection should be carried out to remove any foreign material that might still be adhering to the fruit, together with any blemished pieces. After washing, fruits can be individually quick frozen (IQF) or they can be packed in sugar or syrup or they can be puréed before freezing (Burrows, 1996).

16.2.1 Blanching Most fruits do not require blanching, although those susceptible to enzymatic browning benefit from inactivation of polyphenoloxidase (Haard, 1997). This is achieved by denaturating the proteins that would otherwise take part in reactions leading to deterioration (Torregiani, 2000). Blanching at 70–105 °C is associated with destruction of enzyme activity. Blanching can be achieved by immersion or steaming, and high-pressure steam blanching is more energy efficient than water blanching. Wrolstad et al. (1980) found a positive effect of microwave blanching on the color and composition of strawberry concentrate. It is important that cooling is carried out shortly after blanching for products which are to be frozen. The advantages of blanching may be slightly offset by the loss of nutritional values that occurs during the operation. Therefore, blanching times should be kept as short as possible (Burrows, 1996). The activities of most enzymes are greatly dependent on pH of the tissue or the blanching water. Additives, such as citric acid, sodium chloride, and carbonates, can be used in water, depending on the purpose (Rahman, 1999). According to the structural changes recorded by a scanning electron microscope (SEM), in blanched strawberry tissues cellular membranes are broken, decreasing the cell turgor pressure. Hot water blanching results in a very severe ultrastructural disorganization of the cell walls: fibrillar organization is lost, the cell wall appears perforated, and the cells are difficult to identify because of the structural disruption of the walls. Cell walls exhibit a severe loss of material and extreme swelling. Steam blanching, on the contrary, maintains the original arrangements of cells, although cell walls appear more porous. In the steam-treated sample examination of tissues with a transmission electron microscope (TEM) indicates electron-dense cell walls, a network of microfibrils, and a pectin matrix very similar to those of the fresh fruit, although the middle lamella has been partially lost (Alzamora et al., 2000).

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16.2.2 Dipping Softening caused by freezing–thawing can sometimes be minimized by dipping the tissue in different solutions before freezing. Much of the softening results from degradation of the middle lamella of the walls of cortical cells with increased release of pectins from the cell walls. In many fruit, such as tomato, endo-polygalacturonase begins water solubilization of pectins while exopolygalacturonase completes hydrolysis (Perkins-Veazie, 1995). Firmness changes in strawberries during senescence and in the absence of polygalacturonase have been linked to changes in ionic stability of the middle lamella. Divalent calcium ions (Ca2+) normally occur between the cells, where they form crosslinks between the carboxyl groups of adjacent polyuronide chains (Van Buren, 1979; Main et al., 1986). The structure of the pectin “gel” in the middle lamella is generally described by the “egg box” model in which pectins have few “hairy regions” and a low methoxyl rate. For calcium to be an effective firming agent, a low degree of methoxylation must be present. As a result of the formation of calcium pectate in the cell wall, calcium may decrease softening by cell-wall macerating enzymes produced by plant pathogens. If the activity of pectinesterase is enhanced only during heating, the exogenously supplied calcium will be able to form calcium pectate, which will increase resistance to decay. Addition of calcium actually decreases pectinesterase activity (Sams et al., 1993). Examination of the cell wall of the strawberry fruit has indicated very low levels of PME (pectin methylesterase) (Jones, 1996). In addition, to enhance the activity of pectinesterase naturally present in fruit, commercially available PME preparations can be used. PME catalyzes cleavage of the ester bonds between the methyl and the carboxyl groups of pectic substances, thus forming anionic COO– groups with which calcium ions can form salt bridge crosslinks. Calcium pectate is thus formed, which is assumed to anchor the pectic substances, resulting in an overall increase in firmness (Baker and Wicker, 1996). Pre-treatments could be combined with heat treatment. Heat allows demethylation of pectin by PME (Sams et al., 1993). The treatment of strawberries at 45 °C for 3 h delayed ripening and fruit decay under ambient conditions (20 °C for two to four days). The most dramatic effects under refrigeration conditions were delayed anthocyanin degradation and reduced viable fungi counts (Vicente et al., 2002). Alonso et al. (1997) found that thermal and calcium pre-treatment affected texture, pectinesterase and pectic substances of frozen sweet cherries. Thermal pretreatment at 50 °C for 10 min followed by immersion in 100 mM CaCl2 prevented freezing-induced loss of firmness. Thermal pre-treatments reduced the degree of pectin esterification and increased both the concentration of divalent cations in the cell wall and the pectinesterase activity bounded to the cell wall. Immersion in CaCl2 increased concentration of Ca2+ cations in the cell wall and enhanced the effect of thermal pre-treatments on pectinesterase activity.

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In many cases the frozen product is protected by a suitable glazing compound. A glaze acts as a protective coating against the two main causes of deterioration during storage: dehydration and oxidation. It protects against dehydration by preventing moisture from leaving the product and against oxidation by physically preventing air contact with the product. Oxidation can also be minimized if the glaze carries a suitable antioxidant. The different glazes available include inorganic salts of sodium acid phosphate, sodium carbonate, and calcium lactate, alginate solution, ascorbic and citric acids, glutamic acid, and monosodium glutamate, and other edible coatings, such as corn syrup solids (Rahman, 1999). El Ghaouth et al. (1991) applied a chitosan-based coating to strawberries. The coating modified the internal fruit atmosphere and decreased water evaporation, resulting in delayed strawberry ripening. Starch-based coatings were applied to extend storage life of strawberries in a cold chamber at 0 °C. Coatings made with starches with a higher amylose content decreased water vapor permeability (WVP) and weight losses and retained fruit firmness for longer periods than coatings formulated from medium amylose content starches. Coatings with sorbitol showed lower WVPs than glycerol ones (García et al., 1998). Other studies have been carried out treating strawberries with pectin, alginate, starch, gelatine or polyamine (Ponappa et al., 1993; Cierco, 1994; Pontes et al., 1996), sugar and soybean flour (Nobutsugu, 1984), as well as dry sucrose, sucrose solution, and sucrose or glucose syrup (Armbruster, 1967; Garrote and Bertone, 1975; Deng and Ueda, 1993; Sormani et al., 1999), before freezing and thawing or before refrigeration. These pre-treatments and calcium chloride pre-treatments (Polesello and Crivelli, 1971; García et al., 1996), calcium chloride pre-treatments with PME (Grassin and Fauquembergue, 1994), calcium chloride pre-treatments with PME in a vacuum (Suutarinen et al., 2000, 2002a), or of calcium lactate or other calcium salt pre-treatments in a vacuum before freezing and thawing (Main et al., 1986; Garcia-Berbari et al., 1998), enhanced firmness independent of the species and variety of fruits and also increased their soluble solids content. Pretreatments proved to be effective in reducing liquid and ascorbic acid loss in thawed strawberries. The treatments did not affect the sensory quality of the fruits.

16.3 Maintaining texture: alternative pre-freezing treatments 16.3.1 Microwave blanching Microwave processing can offer several advantages when compared to conventional heating methods. These include speed of operation, energy savings, precise control, and faster start-up and shut-down times. Microwave blanching can fulfil one or more of several purposes:

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1. inactivation of enzymes prevents discoloration or development of unpleasent taste during storage; 2. improved texture due to liberation of water; 3. expulsion of air, which is confined to plant tissues, and reduction of oxidation during frozen storage; 4. microbial status is improved because vegetative cells, yeast and mold are killed; 5. cooking time of the finished products is shortened. When water or steam is used for heating, leaching of vitamins, flavors, colors, carbohydrates, and other water-soluble components takes place. If products are going to be frozen after blanching, a chilling step will generally take place before transporting the product into the freezer. If this cooling is done with cold water, additional leaching takes place. The ripening level of fruit to be processed is critical in microwave treatment. More information about the effects of microwave energy on quality indicators is still needed (Cano, 1994). Microwave blanching of strawberry concentrate (fruits were heated to an internal temperature of 82–88 °C for 3–4 min) resulted in improved color stability and had a protective effect on anthocyanin pigment, reactive phenolics, and ascorbic acid (Wrolstad et al., 1980).

16.3.2 Partial air drying Partial dehydration is generally achieved by air drying. The resulting process is termed dehydrofreezing. The advantages over conventional freezing include: (1) energy savings, since the water load to the freezer is reduced, as well as reduced transport, storage and wrapping costs; (2) improved quality and stability (color, flavor), as well as better thawing behavior (lower drip loss). When using partial air drying, food ingredients of high water activity (aw > 0.96) are generally obtained, since water removal is limited to 50–60% of the original content. To avoid browning during air drying, blanching or other treatments, such as dipping in antioxidant solutions (ascorbic or citric acid, sulphur dioxide), can be used (Torregiani, 2000). Microwave-assisted air drying methods such as microwave vacuum drying, microwave freeze-drying and microwave atmospheric pressure drying for dehydration of fruits can also be used (Funebo, 1997). Partial water removal from the food prior to the freezing process leads to the concentration of components in the cytoplasm of cells, the decrease of the freezing point, and promoted supercooling. Thus, there are relatively fewer large ice crystals, and a lower ratio of ice crystals to unfrozen phase, with a consequent reduction in textural changes (Torregiani, 2000). Partial removal of water through air dehydration of strawberry slices maintains better tissue organization compared to strawberry frozen without pre-treatment. A good agreement was obtained between structural and textural changes observed after both pre-dehydration and freeze-thawing of strawberry slices (Sormani et al., 1999). Changes in mechanical properties due to freezing–

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thawing in fresh and pre-dried strawberries were studied in order to quantify the possible cryoprotective effect of osmotic treatment with sucrose solutions. Good mechanical properties were retained to a higher degree by air drying than osmotic drying; however, after freezing–thawing these differences were not significant (Chiralt et al., 2001).

16.3.3 Osmotic dehydration Air drying can be substituted by (or combined with) osmotic dehydration as a pre-freeze treatment (Torregiani, 2000). This process involves placing the solid food (whole or in pieces) into hypertonic solution, i.e. a high-concentration sugar or salt solution. Osmotic dehydration can be applied either as a separate process or as a processing step in alternative processing schemes leading to a variety of end products (Lazarides, 1994). The selection of the solute for the osmotic solution is based on the sensory characteristics of the product; the cost of the solute or solutes; and the molecular weight of the solute. The most common solutes used for osmotic dehydration are sodium chloride, sucrose, lactose, high-fructose corn syrup, and glycerol (Barbosa-Cánovas and Vega-Mercado, 1996). In osmotic dehydration, operating temperatures range from 30 to 80 °C. In practical conditions, with anosmotic treatment of 1–2 h at ambient temperature, a solid gain of up to 5–10% can be attained. This gain corresponds to a 50–100% increase, if referred to an initial soluble solid content of 10% (Torregiani, 2000). In the process of osmosis, water diffuses through the membrane from the dilute to the concentrated solution until an equilibrium concentration is reached. The solute is unable to diffuse through the membrane in the reverse direction, or can do so only very slowly, so that the major result of this process is a transfer of water to the concentrated solution. Transfer of water by osmosis is applicable to fruit pieces, since they contain sugars and other solutes in dilute solution, and their cellular surface structure acts as an effective semipermeable membrane (Ponting et al., 1966). Application of ultrasound during treatments, or of a high-intensity electrical field pulse or an ultrahigh hydrostatic pressure to the material prior to osmotic treatment have been used to improve mass transfer rate (Rastogi et al., 1999). Sucrose, corn starch syrup at various fructose/glucose ratios, concentrated fruit juices and other mono- and di-saccharides have been used as osmotic solutions (Torregiani, 2000). By immersing fruit pieces in a concentrated sugar solution, water can be removed to the extent of over 50% of the initial weight of the fruit. The added sucrose acts as a dehydrating agent in order to decrease the water content of strawberries. Water is transferred out of the fruit into the syrup matrix and sucrose diffuses into the berries (Ponting et al., 1966). Sodium chloride (NaCl) was employed to assess the possibility of increasing the process rate without affecting the sensory acceptability of osmotically treated apple samples. Salt concentration improved water loss at equilibrium but showed a negative interaction effect with sucrose concentration. Salt and

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sucrose concentrations had a synergistic effect on soluble solid impregnation; on the other hand, sugar concentration was shown to reduce salt gain in the fruit samples (Sacchetti et al., 2001). The same phenomenon was found with strawberries that had been treated with CaCl2 and sucrose solution (Suutarinen et al., 2002a). This fact could be explained by the existence of instantaneous interactions at the food–solution interface. The addition of an NaCl level (0.5%) caused a reduction in product acceptability very close to that determined by the addition of a sucrose level (5%). Salt gain was not sufficient to balance the sweetness of the product, but an addition of NaCl may help to attenuate the excessive sweetness of products processed with high sucrose concentration (Sacchetti et al., 2001). Reduced water content will decrease structural collapse of berries during freezing. Lower drip losses ensure better fruit appearance, flavor, aroma and rheological behavior in cooking. Owing to the soluble solid intake, the overall effect of osmosis is a decrease in water activity, with only a limited increase in texture. Fruit texture is partly associated with the plasticizing and swelling effect of water on the pectic and cellulosic matrix of the fruit tissues. Hence, texture depends primarily on the insoluble matter and water content, rather than on the soluble solids and water activity. In this way, low water activities may be achieved while maintaining an acceptable consistency (Torregiani, 2000). Viberg et al., (1998) investigated volume and density changes during osmotic processing of strawberries in aqueous sucrose solutions (20–85% w/w) and granulated sucrose. Best results were obtained by pre-treatment in 60% (w/w) sucrose solution; this resulted in increased density with only a small decrease in volume. Shrinkage was greatest when osmotic pre-treatment with granulated sucrose was used. The volume of pre-treated strawberries was not altered significantly by subsequent thermal processing. Early penetration studies showed that the rate of solute penetration is directly related to the solution concentration and inversely related to the size of the sugar molecule (Lazarides, 1994). Air drying alone, or in combination with osmotic drying, resulted in the greatest texture improvement of the strawberry slices after thawing (Sormani et al., 1999). Application of selective coatings to fruits could result in a desirable reduction in solute uptake rate during osmotic drying. Strawberries were coated with 4% aqueous solutions of either low methylated pectin, potato starch, or a mixture of pectin and potato starch and osmotically dried in 61.5% saccharose solution at 30–80 °C. Lowest water loss and solid gain was observed in strawberries coated with potato starch solution. This coating showed the best potential for use on frozen stawberries prior to osmotic drying (Ogonek and Lenart, 2001). Vacuum osmotic dehydration When pressures lower than atmospheric pressure are used, vacuum osmotic dehydration (VOD) occurs (Fito et al., 1994). The air content of some fruit tissues adversely affects the processability as well as the colour and flavor of

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the final product (Escriche et al., 2000), and strawberries contain 18–22 vol. % air. Vacuum techniques have been used to increase the incorporation of sugar and firming agents in strawberries (Kolev et al., 1983). Fito (1994) described a rapid mass transfer phenomenon which occurs when porous structures are immersed in a liquid phase under vacuum (hydrodynamic mechanism). This involves the in-flow of the external liquid through the capillary pores. Temperature and pressure changes also impose in the system. Reduced pressure is imposed in a solid–liquid system, followed by restoration of atmospheric pressure. During the vacuum step, the internal air in the fruit pores expands and partially flows out. In the atmospheric step, the residual air is compressed and the external liquid flows into the pores. Several advantages are observed for VOD: a faster kinetic for water loss, principally during the first period of drying; and a sugar gain similar to that obtained for osmotic drying. In fruits, the product processed by VOD showed better sensory perception of texture than could be achieved at the same temperature by osmotic drying. More stable products with a lower level of browning and oxidation can be produced by VOD (Fito et al., 1994), Neither the duration under vacuum nor that at atmospheric pressure has an influence on the deformation and impregnation levels of the fruits in the examined time scales of 5–15 min (Salvatori et al., 1998). However, pressure changes can promote deformation of the fruit because of the viscoelastic properties of its solid matrix. A high-pressure variation is due to rapid vacuum release which can reduce the effectiveness of the process by crushing some tissues. Even when the vacuum is released gradually, tissues can be seen to compress. Ideally, vacuum release should be sufficiently slow to permit the porous tissue to recover its original shape while absorbing the solution (Baker and Wicker, 1996). The use of a vacuum impairs the overall appearance of the fruit somewhat, but this may not be noticeable in a jam-type product (Main et al., 1986), Limited cell wall porosity and high molecular weight of macromolecules limit the effectiveness of osmotic diffusion. Treatment of soybean cells with pectin esterase enlarges the trans-wall channels without affecting cellular viability, indicating that cell wall pectins are responsible for the sieving effect (Baron-Epel et al., 1988). A vacuum infusion of PME is not successful in all applications. An impermeable skin or lack of interior voids may minimize the effects of infusion (Baker and Wicker, 1996). Pulsed vacuum osmotic dehydration The main advantage of vacuum osmotic drying compared to osmotic drying lies in the mass transfer due to the hydrodynamic mechanism and to the corresponding increase produced in the solid–liquid interface surface. In view of the fact that the most important hydrodynamic mechanism effect is very quick and it occurs just when the system is returned to atmospheric pressure, a new procedure – pulsed vacuum osmotic dehydration (PVOD) – was designed to carry out vacuum osmotic drying. Through this procedure,

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short periods (e.g. 5 min) of vacuum treatment were applied to apple slices, while they were immersed in the osmotic solution (sucrose solution in water at 65% (w/w)). In this way, the filling of the food pores with the same osmotic solution was induced at the beginning of the treatment. During the pulsed vacuum osmotic drying a great proportion of the volume of pores was occupied by osmotic solution, the residual volume of air being small. When shrinking of pores occurs, the large liquid plug at the entry to the pore prevents the air from escaping (Fito et al., 1994). Pulsed vacuum osmotic dehydration with 65 °Brix sucrose solution following steam blanching was found to be the most effective treatment of strawberries in decreasing water activity, due to the maximum sucrose gain achieved. Firmness and color were adversely affected, although they remained within reasonable values, and the microbiological quality of these strawberries was optimal (Moreno et al., 1998).

16.3.4 Immersion chilling and freezing Immersion chilling and freezing (ICF) consists of dipping food materials in a chilled aqueous liquid (< 5 °C), also called aqueous freezant (AF) (Lucas et al., 1999). ICF is quite similar to osmotic dehydration in that both involve direct contact between food pieces and a concentrated solution. However, ICF is carried out at lower temperatures ranging from –20 °C to 0 °C whereas operating temperatures range from 30 °C–80 °C in the case of osmotic dehydration (Torregiani, 2000). During immersion, simultaneous mass and heat transfer take place at the solid/liquid interface and within the material. Mass transfer includes cross-solute gain and water loss, and it proceeds in two stages. While freezing (primary stage) mass transfer rate is high, of the same order of magnitude as observed when working at temperatures superior to 0 °C (osmotic dehydration). Once freezing is completed the product may be stored in the AF (secondary stage) for several hours or days. Mass transfer rate is lower than in the primary stage, but long-term storage leads to subsequent impregnation and dehydration levels (2–10% for solute gain, 2–39% for water loss) (Lucas et al., 1999). The concentration, molecular mass and combinations of the dissolved solutes added to the solution determine the temperature range which will keep the solution in the liquid state. Binary solutions comprising 23% sodium chloride or 40% ethanol allow the use of temperatures as low as –20 °C and –30 °C respectively. Because of the low operating temperatures and the freezing process occuring inside food during ICF, mass transfer rates are much lower than in osmotic dehydration, ranging from 1–7% w/w water loss, and 0.5–1% w/w solute gain. ICF should be considered as a quick pre-cooling state, associated with a light surface formulation effect. Coating the frozen product with the remaining ICF solution can also help to improve food color, slow down food deterioration during cold storage, or bring new properties to food (Torregiani, 2000).

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Evolution of mass transfer during the secondary stage was assessed against time (6 h, 21 days). Apple cylinders were soaked in four different aqueous freezing agents (binary monophasic NaCl-water liquid; biphasic binary NaClwater solution mixture; two ternary NaCl-sucrose-water liquids) (–17.8, –17.4, –5 °C). Results demonstrated that over the whole process (primary and secondary), mass transfer was lower when the AF temperatures were lower (–17.8 °C). The addition of sucrose in the AF led to a subsequent increase in water loss (as high as 29% initial material (i.m.) after 21 days), but did not significantly affect salt gain levels over 21 days’ storage. Only the use of a biphasic mixture could limit mass transfer (less than 3.5% i.m. after 21 days) (Lucas et al., 1999).

16.3.5 High pressure High isostatic pressure (HP) ranges between 50 and 1000 MPa. The benefit of using HP instead of heat for preservation is that aroma, color and nutritional value of the fresh product are better maintained, if the pressure treatment can be carried out at a reduced temperature. HP also offers opportunities in food processing because it influences the texture of foods containing cell structures. The presence of water in foods to be used in high-pressure processing is essential (at least 40% w/v water) for the process to be effective. HP treatment can also simplify the preparation of jams (400–600 MPa at room temperature for 10–30 minutes, refrigerate for a two-month shelf life) (Williams, 1994). An integrated approach is necessary to study the effect of HP on food. For example, the inactivation kinetic of enzymes depends on the medium in which the enzymes are treated. In addition the optimal pressure–time– temperature combinations can also differ with respect to different product characteristics. The preservation of porous fruits and vegetables is often problematic, since they contain significant air voids within the tissue. During pressure-processing the air is not removed from the product. In systems subjected to pressure, any air present in a porous material will be almost completely compressed, which can cause an irreversible collapse of the tissue. The oxygen dissolved in the product causes trouble also for keeping quality. Various oxidative reactions (enzymatic or non-enzymatic), by which the quality of the product deteriorates, can take place in the product during storage. Examples of reactions affecting product quality are discoloration, formation of off-flavours and decomposition of vitamin C (Matser et al., 1998). Strawberry slices were treated in a liquid medium containing the PME enzyme (200 µL/100 g fresh strawberries; PME activity 100 000 nkat/ml) and calcium ions (1% CaCl2 solution) under vacuum (13.3 kPa for 10 min) prior to high-pressure processing. It was possible to improve the firmness of the foodstuff while simultaneously deaerating the product. It has been reported that high-pressure treatment (50 MPa/min up to 500 MPa; temperature 25 °C and holding time 15 min) may even increase the activity of PME (Stute et al.,

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1996). A slight increase in firmness of berries was found when berries were soaked in a liquid containing either enzyme or calcium ions. The best result was obtained when both enzyme and calcium ions were present in the soaking liquid, giving a firmness that was about nine times higher than that for the berries that were not pre-treated at all (Stolt et al., 2001).

16.4 Application: frozen berries and jams 16.4.1 Potential techniques for berries Berries can be frozen in syrup or as individual berries. As individual berries, they may be frozen on a tray or individual quick frozen (IQF) on a belt in an air-blast or cryogenic freezer. Depending on the final product, different freezing procedures might be appropriate (Reid, 1996). Freezing berries by fluidization yields products of the same quality as does liquid nitrogen freezing, but requires less time (Miller and Butcher, 2000). Strawberries for commercial preserves, jam, and jelly manufacture are usually washed and capped, frozen with either 5:1 or 4:1 fruit:sugar ratio, and then thawed before processing (Sistrunk et al., 1982). The appearance of the frozen berries is influenced by the method of mixing the sweetener with berries rather than the sweetener itself (Aref et al., 1956). One possible way to pre-treat berries is to use calcium, which fortifies the fruit by changing the pectin structure (Poovaiah, 1986; García et al., 1996). The use of PME enzyme has proved to be an efficient way to produce fruit jams and jellies without, or with greatly reduced, sugar and pectin addition (Grassin and Fauquembergue, 1994). When fruits or fruit pieces are pretreated with calcium and PME before heat treatment the integrity and firmness of fruits are preserved as much as possible (Coutel and Dale, 1998). According to microscopical studies the pre-treatments with calcium chloride and crystallized sucrose as well as with CaCl2 and PME in a vacuum influence the microstructure of strawberry tissues. These pre-treatments especially affect pectin, protein, lignin and structural carbohydrates in the vascular tissue and cortex when compared to the untreated reference samples. The use of a vacuum appears to make the cortex and pitch absorb the pre-treatment solutions more efficiently thus improving the stabilization particularly of pectin and structural carbohydrates (Suutarinen, 2002). Micrographs of the strawberry cortical and vascular tissues of the untreated reference and the CaCl2– and PME-treated strawberries in a vacuum after pectin staining, are shown in Fig. 16.2 (Suutarinen et al., 2000). Firmness of thawed strawberries pretreated with CaCl2 and PME in a vacuum is more than twice as high as that of other pre-treated or untreated berries (Fig. 16.3) (Suutarinen et al., 2002a). High hydrostatic pressure thawing (600 MPa, 25 and 50 °C, 15 min) of frozen strawberries increases sucrose uptake in strawberry slices (21%) as well as in whole fruit (140%), reaching maximum sucrose contents of 45.6

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Fig. 16.2 Micrographs of strawberry (a) cortical and (b) vascular tissues. Pectin (marked with an arrow) appears pink. Bar is 50 µm (Suutarinen et al., 2000).

and 34.7 °Brix respectively. This process can be compared with thawing of strawberries at atmospheric pressure prior to thermal processing (92 °C, 20° min) (Eshtiaghi and Knorr, 1996).

16.4.2 Potential techniques for jams The four essential ingredients in manufacturing jams are fruit, pectin, sugar, and acid. Optional ingredients include spice, buffering agents, preservatives, and anti-foaming agents. The exact process selected will depend upon the type of product to be manufactured, the raw materials available, and the scale of production. Traditionally, all of the ingredients are blended together as the first stage of processing; however, with modern demands for a high degree of consistency in quality, it has become common to add some critical ingredients such as citric acid and volatile flavoring at later stages in the process. Most modern plants are based on low temperature or vacuum evaporation, which may necessitate the addition of an extra pasteurizing stage to give a product of suitable microbiological quality to allow prolonged storage (Baker et al., 1996b). The pre-freezing treatments of berries are particularly interesting for the jam-making industry because fresh berries are not available for jam-making in all seasons. Furthermore, harvesting conditions and the size and chemical structure of berries selected for jam-making vary. For industrial jam-making

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Fig. 16.3 Firmness of pretreated thawed strawberries. Error bars are shown as means ± SD (n = 6). a-e Means with the same letter are not significantly different as determined by Tukey’s test (p < 0.05), 1a. Reference, 1b. Water in a vacuum, 2a. CaCl2, 2b. CaCl2 in a vacuum, 3a. CaCl2 + PME, 3b. CaCl2 + PME in a vacuum, 4a. CaCl2 + sucrose, 4b. CaCl2 + sucrose in a vacuum, 5a. CaCl2 + PME + sucrose, 5b. CaCl2 + PME + sucrose in a vacuum, 6. CaCl2 + PME + sucrose-solution before freezing (Reprinted with permission from Suutarinen et al. (2002a).

it is of primary importance to ensure both consistent jam-making conditions and the integrity of berries (Suutarinen, 2002). Firmness of jam strawberries pre-treated with CaCl2 and PME in a vacuum was higher than that of untreated berries or berries pre-treated with different methods (Suutarinen, 2002 b)/(Fig. 16.4). Dipping of strawberries into a CaCl2 solution with PME in a vacuum resulted in a significantly different sensory profile compared to that found in the other jams. The sensory attributes such as wholeness of the berries (p < 0.001), firmness, clarity and evenness of the jam medium (p < 0.001), softness of the berries (p < 0.001) and faultlessness of odour and flavor (p < 0.001) in particular showed statistically significant differences among the strawberry jams. Sensory quality was perceived to decrease during four months of storage, even though the shapes of the sensory profiles of the studied jams did not change significantly from those evaluated after two weeks’ storage (Suutarinen et al., 2002a). For achieving highquality jams, the pre-treatment time should be short (about 5–15 min), the temperature low (below 20 °C), the vacuum level high (pressure less than 10 kPa), the CaCl2 concentration moderate (about 1%) and the dosage of PME comparatively low (about 50–100 µkat/kg strawberries). The pre-treatment studies allows several different strawberry varieties to be used in industrial production rather than its being dependent on a single variety. A further advantage is the enrichment with calcium ions. This is advantageous for

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Fig. 16.4 Firmness of jam strawberries and jam medium after two weeks of storage. Error bars are shown as means ± SD (n = 6), a-e Means with the same letter are not significantly different as determined by Tukey’s test (p < 0.05). 1a, Reference. 1b. Water in a vacuum. 2a, CaCl2. 2b, CaCl2 in a vacuum. 3a CaCl2 + PME. 3b, CaCl2 + PME in a vacuum. 4a, CaCl2 + sucrose. 4b, CaCl2 + sucrose in a vacuum. 5a, CaCl2 + PME + sucrose. 5b, CaCl2 + PME + sucrose in a vacuum. 6, CaCl2 + PME + sucrosesolution before freezing. 7, CaCl2 + PME + sucrose-solution before jam-making. (Reprinted with permission from Suutarinen et al., 2002a).

many people requiring dietary calcium, e.g. people having lactose intolerance. Further, it appears that the vacuum reduces the air content in the strawberries, so that there is less air which may cause foaming in jam-making (Suutarinen, 2002). In addition to strawberries, the studied pre-treatment could also be applied to other soft berries such as raspberries and cloudberries. It is suitable for use before freezing or for application to frozen berries before jam-making (Suutarinen, et al., 2001). The pre-treatment is suitable for exported berries, which can be treated before freezing and transport to foreign countries (Suutarinen 2002). The vacuum equipment includes a pump, an air-pressure chamber and pipes. Costs of the pre-treatment equipment and chemicals will be reasonable. A pump which can be used to decrease the pressure of a 200 L chamber to 7 kPa with a suction effect of 100 m3/h would cost about 1300–2000 EUR (Busch Vakuumteknik Oy, Finland, year 2001). Equipment can be designed for batch or continuous processing. Chemicals including CaCl2 (1%) and PME enzyme (50 µkat/kg strawberries) would cost about 45 cent per 100 kg strawberries. The cost of food-grade CaCl2 is about 3 EUR per kg (Telko Oy, Finland) and that of PME (50 µkat/kg strawberries) (Novo Shape) is about 54 EUR per L (S.O. Strömberg Ky, Finland), The amount of pre-treatment solution needed for 100 kg strawberries is 120 L. The solution must be changed every 2 h, which means that the total

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amount of solution per 8 h day will be 500 L. One pretreatment will take about 15 min. About 30 batches will be treated per day, which means about 3000 kg of strawberries per day. About 8.5 L extra pre-treatment solution must be added into the chamber after each treatment to compensate for solution absorbed into the berries and for losses during pouring. From this it can be concluded that chemical costs for the treatment of 3000 kg strawberries would be about 27 EUR per day (Suutarinen, 2002). High-pressure treated fruit preparations have been commercially available in Japan since 1990, but limited information regarding the processing of such products remains limited. Horie et al. (1991) reported on a strawberry jam developed by pressurization at 400–600 MPa, which maintains its original fresh fruit color and flavor and sustains 95% of the vitamin C content of the fresh fruits. The pressure-processed jam is preferrable to a heat-processed jam. Dissolved oxygen and residual enzyme activities in pressure-treated preparations are given as the reason for color and flavor changes during storage. Consequently, chilled storage is suggested by the authors. Arai (1992) reports the production of high-pressure jam in which frozen strawberries or raspberries are mixed with 45–65% saccharides (sucrose or glucose), 0.1– 0.5% pectin and 0.02–0.1% sour agent. The mixture is then kept at over 200 MPa (normally 300–800 MPa) to afford a high-quality jam. Pre-treatment in the form of CaCl2 and PME solution in a vacuum can be used for strawberries and similar berries having a sensitive and soft texture before subjecting them to HP treatment (Stolt et al., 2001). In this case, the berries do not need to be drained at all, or are only partially drained after the pre-treatment. Sugar, salt or other agents including gelling and/or thickening agents, optionally in liquid medium, are then added to the pre-treatment medium. These agents may also be added before the actual pre-treatment. The berries are then packaged in a flexible container and subjected to a pressure of 400–700 MPa at 0–40 °C for 1–60 min. Firmness measurements of HP-treated strawberry slices soaked in a liquid containing CaCl2 and PME gave a result that was about nine times higher than that of berries which were not pre-treated at all. Watanabe et al. (1991) described a process in which strawberry paste is freeze concentrated, using ice nucleation active bacteria suspended in the juice previously separated from the pulp, and subsequently pressurized at 400 MPa for 5 min at room temperature. The resulting product is superior in brightness and red colour as well as in maintaining the original flavor compared to the heat-processed controls. Quality of the pressure-treated jam is higher immediately after processing as compared to the heat-treated one and could be maintained under lowtemperature conditions for two to three months. Storage at room temperature results in discoloration, off-flavor formation and decomposition of sucrose and vitamin C. Dissolved oxygen and residual enzyme activities are the causative factors for quality changes in pressure-treated jams (Kimura et al., 1994). Optimal pressure for freeze concentration and high hydrostatic pressure-

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treated strawberry jams is 200 MPa for preservation of anthocyanins in jams (Gimenz et al., 2001). Dervisi et al. (2001) studied the rheological properties and color of strawberry jams with various concentrations of pectin (0.1–10% weight basis) using high-pressure processing at 400 MPa for 5 min. Results suggested that optimum pectin concentration for colour retention and texture quality was between 2.5 and 5%. Shi et al. (1996) introduced a new jam manufacturing process using osmotic drying of strawberries (in 80 °Brix sucrose solution at 35–40 °C for 2 h) prior to addition at room temperature to sugar-pectin solution, potassium sorbate and citric acid. The jam was stored for up to three months in the dark at 5, 25 or 35 °C. Color, flavor, and taste scores of jam made in this way were significantly higher than those of commercial jam.

16.5 Future trends High pressure offers new opportunities for food processing and preservation, not only at temperatures above zero but also at those below zero (Denys et al., 2002). The advantage obtained by using high pressure is the prevention or retardation of the damages caused by ice crystals. By using high pressure, the freezing point can be lowered to –22 °C allowing sub-zero storage of foods in the liquid state without freezing. Studies with strawberries have shown that their fresh taste, texture and color can be maintained for weeks (Deuchi and Hayashi, 1991). Sudden pressure release of a product initially kept at sub-zero conditions in liquid state promotes rapid ice nucleation and formation of smaller ice crystals (Denys et al, 2002). This type of process is called pressure-shift freezing. In the same way, a frozen product can be forced to the liquid state by applying high pressure which allows faster thawing, and this process is called pressure-induced thawing (Deuchi and Hayashi, 1992). The quality changes during storage are, however, also dependent on subsequent frozen storage conditions. Usually pressure-shift freezing allows uniform and rapid nucleation and smaller ice crystal size distribution. The main advantages of pressure-induced thawing generally include reduction of the time required to thaw and smaller drip losses. The high-pressure equipment is used only for freezing and thawing. This is more cost-effective than using high-pressure throughout the entire freezing period.

16.6 References and MASCHERONI R H (2002) Quality evaluation of foodstuffs frozen in a cryomechanical freezer, J Food Engineering, 52(3), 257–63.

AGNELLI M E

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ALONSO J, CANET W

and RODRIQUEZ T (1997) Thermal and calcium pretreatment affects texture, pectinesterase and pectic substances of frozen sweet cherries, J Food Sci, 62(3), 511–15. ALZAMORA S M, CASTRO M A, VIDALES S L, NIETO A B and SALVATORI D (2000) The role of microstructure in the textural characteristics of minimally processed fruits. In Minimally Processed Fruits and Vegetables. Fundamental Aspects and Applications. Eds S M Alzamore, M S Tapia and A López-Malo, Gaithersburg, Aspen Publishers, 153–71. ARAI S (1992) Production of jam. Int. patent appl. JP patent 900202236 900730. AREF M, SIDWELL A P and LITWILLER E M (1956) The effect of various sweetening agents on frozen strawberries for preserve manufacture, Food Tech, 10(7), 293–7. ARMBRUSTER G (1967) Cellular and textural changes in three varieties of strawberries as a result of pre-freezing treatments, J Amer Soc Hortic Sci, 91, 876–80. BAKER R A and WICKER L (1996) Current and potential applications of enzyme infusion in the food industry, Trends in Food Sci and Techn, 7(9), 279–84. BARBOSA-CÄNOVAS G V and VEGA-MERCADO H (Eds) (1996) Dehydration of Foods, New York, Chapman and Hall. BARON-EPEL O, GHARYAL P K and SCHINDLER M (1988) Pectins as mediators of wall porosity in soybean cells, Planta, 175, 389–95. BOURNE M C (1979) Texture of temperate fruits. J Text Studies, 10(1), 25–44. BURROWS G (1996) Production of thermally processed and frozen fruit. In Fruit Processing. Ed. D Arthey and P R Ashurst, London, Chapman & Hall, 135–64. CANO M P (1994) Combined microwave/freezing methods to improve preserved fruit quality. In Minimal Processing of Foods and Process Optimization – an Interface. Eds R P Singh and F A R Oliveira, London, CRC Press, 135–52. CHIRALT A, MARTINEZ-NAVARRETE N, MARTINEZ-MONZO J, TALENS P, MORAGA G, AYALA A and FITO P (2001) Changes in mechanical properties throughout osmotic processes. Cryoprotectant effect, J Food Eng, 49(2–3), 129–35. CIERCO M (1994) Process for freezing of strawberries, their use in a similar manner to fresh strawberries. Int. patent appl. FR 94 13864, Institut National de la Propriété Industrielle. COUTEL Y A G and DALE R H S (1998) A method for fruit processing, Int. patent appl. Patent 98/52423, Gist-Brocades Co. DENG H and UEDA Y (1993) Effects of freezing methods and storage temperature on flavor stability and ester contents of frozen strawberries, Engei Gakkai Zasshi (in Japanese), 62, 633–9. DENYS S, SCHLÜTER O, HENDRICKX M E G and KNORR D (2002) Effects of high pressure on water-ice transitions in foods. In Ultra High Pressure Treatments of Foods. Eds M E G Hendrickx and D Knorr, New York, Kluwer Academic/Plenum Publishers, 215–48. DERVISI P, LAMB J and ZABETAKIS I (2001) High pressure processing in jam manufacture: effects on textural and colour properties, Food Chem, 73(1), 85–91. DEUCHI T and HAYASHI R (1991) Pressure application to thawing of frozen foods and to food preservation under subzero temperature. In High Pressure Science for Food. Ed. R Hayashi, Kyoto, San-Ei Suppan, 101–10. DEUCHI T and HAYASHI R (1992) High pressure treatments at subzero temperature: Application to preservation, rapid freezing and rapid thawing of foods. In High Pressure and Biotechnology. Eds. C Balny, R Hayashi, K Heremans and P Mason, Montrouge, John Libbey Eurotaxt, 353–5. EL GHAOUTH A, ARUL R, PONNAMPALAM R and BOULET M (1991) Chitosan coating effect on storability and quality of fresh strawberries, J Food Sci, 56(6), 1618–31. ESCRICHE I, CHIRALT A, MORENO J and SERRA J A (2000) Influence on blanching-osmotic dehydration treatments on volatile fraction of strawberries, J Food Sci 65(7), 1107–11. ESHTIAGHI M N and KNORR D (1996) High hydrostatic thawing for the processing of fruit preparations from frozen strawberries, Food Biotech, 10(2), 143–8. FITO P (1994) Modelling of vacuum osmotic dehydration of foods, J Food Eng, 22(1–4), 313–18.

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and CHIRALR A (1994) Vacuum osmotic dehydration of fruits. In Minimal Processing of Foods and Process Optimization – an Interface. Eds R P Singh and F A R Oliveira, London, CRC Press, 107–21. FUNEBO T (1997) Microwave assisted air dehydration of fruits and vegetables – a literature review, SIK-Rapport. GARCÍA J M, HERERA S and MORILLA A (1996) Effects of postharvest dips in calcium chloride on strawberry, J Agric Food Chem, 44(1), 30–33. GARCÍA M A, MARTINO M N and ZARITZKY N E (1998) Plasticized starch-based coatings to improve strawberry (Fragaria x Ananassa) quality and stability, J Agric Food Chem, 46(9), 3758–67. GARCIA-BERBARI S A, NUNES-NOGUEIRA J N and SILVA-CAMPOS S D (1998) Effect of different pre-freezing treatments on the quality of frozen strawberry variety Chandler, Ciénc Tecnol Aliment, 18(1), 82–6. GARROTE R L and BERTONE R (1975) Chemical evaluation and suitability for freezing of strawberry varieties, Revista del ITA (1), 81–99. GIMENEZ J, KAJDA P, MARGOMENOU L, PIGGOTT J R and ZABETAKIS I (2001) A study on the colour and sensory attributes of high hydrostatic pressure jams as compared with traditional jams, J Food Sci Agric, 81(13), 1228–34. GRASSIN C M T and FAUQUEMBERGUE P C L (1994) Use of pectinesterase in the treatment of fruit and vegetables. Int. patent appl. WO patent 94/12055, Gist-Brocades Co. HAARD N F (1997) Product composition and the quality of frozen foods. In Quality in Frozen Food. Eds M C Erickson and Y-C Hung, New York, Chapman & Hall, 275– 95. HORIE Y, KIMURA K, IDA M, YOSHIDA Y and OHKI K (1991) Jam preparation by pressurization, Nippon Nogeikagaku Kaishi (in Japanese), 65(6), 975–80. JONES S A (1996) Optimisation of texture in heat processed fruits – Flair-Flow seminar, VTT Biotechnology and Food Research, Espoo, Finland, 24th of May 1996. KIMURA K, IDA M, YOSIDA Y, OHKI K, FUKUMOTO T and SAKUI N (1994) Comparison of keeping quality between pressure-processed jam and heat-processed jam: Changes in flavor components, hue, and nutrients during storage, Biosci Biotech Biochem, 58(8), 1386– 91. KOLEV D, KAFEDZHIEV I and ATANASOV I (1983) Technology of vacuum degassing of apples, Konservna Promishlenost (in Bulgarian), 12, 26–7. LACROIX C, CASTAIGNE F and ROUTHIER B (1985) Evaluation du comportement textural à la congélation de diverses espèces de fraises, Lebensm-Wiss und –Technol, 18(1), 35–42. LAZARIDES H N (1994) Osmotic preconcentration: developments and prospects. In Minimal Processing of Foods and Process Optimization – an Interface. Ed. Singh R P and F A R Oliveira, London, CRC Press, 73–85. LUCAS T, FRANCOIS J, BOHUON P and RAOULT-WACK A L (1999) Factors influencing mass transfer during immersion cold storage of apples in NaCl/sucrose solutions, LebensmWiss und –Technol, 32(6), 327–32. MAIN G L, MORRIS J R and WEHUNT E J (1986) Effect of preprocessing treatments on the firmness and quality characteristics of whole and sliced strawberries after freezing and thermal processing, J Food Sci, 51(2), 391–4. MATSER A M, KNOTT E R and BARTELS P V (1998) High pressure preservation of mushrooms. In Fresh Novel Foods by High Pressure. Ed. K Autio, VTT Symposium 186; Helsinki 21–22 September 1998. Espoo: Technical Research Centre of Finland, 123–30. MILLER J P and BUTCHER C (2000) Freezer technology. In Managing Frozen Foods. Ed. C J Kennedy, Cambridge, CRC Press, 159–93. MORENO J, CHIRALT A, ESCHRICHE I and SERRA J (1998) Stabilizing effect of combined enzyme inactivation/osmotic dehydration methods minimally processed strawberries, Alimentaria (in Spanish) (295), 49–52. NOBUTSUGU K (1984) Improved jam. Int. patent appl. JP patent 59106258.

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OGONEK A and LENART A (2001) Influence of selective edible coatings on osmotic dehydration

of strawberries, Zywsnosc, 8(3), 62–74. (1979) Quick frozen fruit. Quality assessment of raw material and frozen product, Technical-Memorandum-Campden-Food-Preservation-Research-Association, 221, 1–20. PERKINS-VEAZIE P (1995) Growth and ripening of strawberry fruit. In Horticultural Reviews. Ed J Janick, London, John Wiley and Sons, 267–97. POLESELLO A and CRIVELLI G (1971) Addition of calcium salts to fruits as pretreatment for quick freezing, Ind Agr (in Italian), 9, 413–22. PONAPPA T, SCHEERENS J C and MILLER A R (1993) Vacuum infiltration of polyamines increases firmness of strawberry slices under various storage conditions J Food Sci, 58(2), 361– 4. PONTES J, MC NICHOL R J and KERBY N (1996) Treating organic tissue. Int. patent appl. WO patent 96/41542, Mylnefield Research Services Ltd. PONTING J D, WATTERS G G, FORREY R R, JACKSON R and STANLEY W L (1966) Osmotic dehydration of fruits, Food Tech, 20(10), 1365–8. POOVAIAH B W (1986) Role of calcium in prolonging storage life of fruits and vegetables, Food Tech, 40(5), 86–9. RAHMAN M S (1999) Food preservation by freezing. In Handbook of Food Preservation. Ed M S Rahman, New York, Marcel Dekker Inc., 259–84. RASTOGI N K, ANGERSBACH A and KNORR D (1999) Mechanism of mass transfer during osmotic removal of water from plant materials. In Improved Traditional Foods for the Next Century. Eds F Toldrà, D Ramó and J L Navarro, Proceedings of the International Congress organised by D G XII- European Comission and the Instituto de Agroquímica y Tecnología de Alimentos (C.S.I.C.), Valencia 28–29 October, 366–71. REID D S (1996) Fruit processing. In Processing Fruits: Science and Technology. Vol 1. Biology, Principles, and Applications. Eds L P Somogyi, H S Ramaswamy and Y H Hui, Lancaster, Technomic Inc, 169–83. SACCHETTI G, GIANOTTI A and DALLA ROSA M (2001) Sucrose-salt combined effects on mass transfer kinetics and product acceptability. Study on apple osmotic treatments, J Food Eng, 49(2–3), 163–73. SALVATORI D, ANDRÉS A, CHIRALT A and FITO P (1998) The response of some properties of fruits to vacuum impregnation, J Food Proc Eng, 21(1), 59–73. SAMS C E, CONWAY W S, ABBOTT J A, LEWIS R J and BEN-SHALOM N (1993) Firmness and decay of apples following postharvest pressure infiltration of calcium and heat treatment, J Amer Soc Hortic Sci, 118(5), 623–7. SHI X Q, CHIRALT A, FITO P, SERRA J, ESCOIN C and GAQUE L (1996) Application of osmotic dehydration technology on jam processing, Drying Tech, 14(3–4), 841–57. SISTRUNK W A, MORRIS J R and KOZUP J (1982) The effect of chemical treatments and heat on color stability of frozen machine-harvested strawberries for jam, J Amer Soc Hort Sci, 107(4), 693–7. SORMANI A, MAFFI D, BERTOLO G and TORREGIANI D (1999) Textural and structural changes of dehydrofreeze-thawed strawberry slices: effects of different dehydration pretreatments, Food Sci Tech Int, 5(6), 479–85. STOLT M, SUUTARINEN M and AUTIO K (2001) A process for preserving foodstuff. Int. patent appl. WO patent 01/58286, Technical Research Centre of Finland. STUTE R, ESHTIAGHI M N, BOGUSLAWSKI S and KNORR D (1996) High pressure treatment of vegetables. In High Pressure Chemical Engineering. Eds Rudolf von Rohr and Ch. Trepp, London, Elsevier Science, 271–6. SUUTARINEN M (2002) Effects of prefreezing treatments on the structure of strawberries and jams (Doctor Thesis, VTT Publications 462, Espoo, Finland, Edita Prima Oy Ltd). SUUTARINEN M, HEISKA K, AUTIO K and MOKKILA M (2000) The effect of CaCl2 and PME prefreezing treatment in a vacuum on the structure of strawberries (abstract), In: 4th International Strawberry Symposium Book of Abstracts. Eds T Hietaranta, M–M Linna, July 9–14, Tampere, Finland, Kaarinan Tasopaino Oy Ltd, 28. OSWIN P M

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and MOKKILA M (2001) A process for preparing jam, WO patent 01/30178, Technical Research Centre of Finland. SUUTARINEN M, HONKAPÄÄ K, HEINIÖ R–L, AUTIO K, MUSTRANTA A, KARPPINEN S, KIUTAMO T, LIUKKONEN–LILJA H and MOKKILA M (2002a) Effects of calcium chloride-based prefreezing treatments on the quality factors of strawberry jams, J Food Sci, 67(2), 884–94. SUUTARINEN M, HONKAPÄÄ K, HEINIÖ R–L, MUSTRANTA A, LIUKKONEN–LILJA H and MOKKILA M. (2002b) Modeling of calcium chloride and pectin methylesterase prefreezing treatments of strawberries and jams, J Food Sci, 67(3), 1240–48. SZCZESNIAK A S and SMITH B J (1969) Observations on strawberry texture a three-pronged approach, J Text Studies, 1(1), 65–89. TORREGIANI D, LUCAS T and RAOULT-WACK A–L (2000) The pre-treatment of fruits and vegetables. In Managing Frozen Foods. Ed. C J Kennedy, Cambridge, CRC Press. VAN BUREN J P (1979) The chemistry of texture in fruits and vegetables, J Text Studies, 10(1), 1–23. VIBERG U, FREULER S, GEKAS V and SJÖHOLM I (1998) Osmotic pretreatment of strawberries and shrinkage effects, J Food Eng, 35(2), 135–45. VICENTE A R, MARTINEZ G A, CIVELLO P M and CHAVES A R (2002) Quality of heat-treated strawberry fruit during refrigerated storage, Postharvest Biol Technol, 25(1), 59–71. WATANABE M, ARAI E, KUMENO K and HONMA K (1991) A new method for producing a nonheated jam sample: the use of freeze concentration and high-pressure sterilization, Agric Biol Chem, 55(8), 2175–6. WILLIAMS A (1994) New technologies in food preservation and processing: part II, Nutrition & Food Sci, (1), 20–23. WROLSTAD R E, LEE D D and POEI M S (1980) Effect of microwave blanching on the color and composition of strawberry concentrate, J Food Sci, 45(6), 1573–7.

17 Improving the texture of processed fruit: the case of olives I. Mafra, University of Porto and M. A. Coimbra, University of Aveiro, Portugal

17.1 Introduction: the texture of table olives The table olive is the fruit of varieties of the cultivated olive tree, Olea europaea. The origins of cultivation of the olive tree lie rooted in legend and tradition. It probably started about 5000–6000 years ago within a wide strip of land by the eastern Mediterranean Sea and in the adjacent zones comprising Asia Minor, part of India, Africa and Europe (Fernández Díez, 1971). World production of table olives is about 1.3 million tonnes, of which 43% are produced in America, 36% in Europe, and 7% in Arab countries (http:// www.asemesa.es). In relation to the 12 million tonnes of olives for olive oil production, table olives represent about one tenth of the world olive production. The olive fruit is a drupe, similar to other drupes or stoned fruits. However, the olive differs from all other drupes in its chemical composition due to its relatively low sugar content, 2–5% versus around 12%, high oil content, 20–30% versus 1–2%, and its characteristic strong bitter taste which is caused by oleuropein (Table 17.1) (Garrido Fernández et al., 1997). The length of the fruit is usually between 2 and 3 cm and its transverse diameter between 1 and 2 cm. Its total weight may vary between 0.5 and 20 g but generally falls within the range 3–10 g (Fernández Díez, 1983). The texture of the edible flesh is very variable, depending upon variety, oil content, stage of maturity, type of culture, soil, climate, and other factors which influence the chemical composition of the fruit. The component parts of olive are the epicarp layer which has a continuous well-developed cuticle, the mesocarp layer which constitutes between 70 and 90% of the fruit, and the woody endocarp enclosing the embryo. The mesocarp and epicarp are composed of parenchymatous cells involved in a thin cell wall and associated by the middle lamella. In the epicarp, the cells are

Improving the texture of processed fruit: the case of olives 411 Table 17.1 Composition of the olive fruit mesocarp (Garrido Fernández et al., 1997) Component

Proportion (%)

Moisture Lipids Reducing sugars, soluble Non-reducing sugars Mannitol Raw fibre Proteins (N × 6.25) Ash Organic acids and their salts Phenolic compounds Pectic substances Other compounds

60–75 10–25 3–6 ≤ 0.3 0.5–1.0 1–4 1–2 < 1.0 0.5–1.0 2–3 ≤ 0.6 3–7

closely packed without the empty spaces apparent between the mesocarp cells. Changes in the structural arrangement and chemical composition of the epicarp and mesocarp cell walls determine the physical properties of olive tissue. A study of the mechanical properties of Hojiblanca and Douro varieties showed that in a tensile test the epicarp (skin) is stronger and stiffer than the mesocarp (flesh) (Georget et al., 2001). This was also reflected in the strain at failure of the two tissues, the epicarp being less deformable than the mesocarp. Table olives are included in pickled products, which are defined as those products whose preparation and preservation are carried out by a combination of salting, fermentation and/or acidification (Garrido Fernández et al., 1997). This system of processing allows the preservation of an otherwise perishable raw material over extended periods of time. The combined effects of the salt, low pH and organic acidity often allow the preservation to be carried out without heat treatment, depending on the type of processing. The natural bitterness of the fruit can be eliminated, or at least reduced, by processing to make it acceptable for human consumption. The processing is also responsible for softening of the tissue, which is desirable if the raw fruit is too hard, but is a problem when the olives become too soft. The type and extent of processing should then be suited to the characteristics of the raw fruits, in order to obtain a final product with an appropriate sensory profile, in which the texture characteristics are of great importance. Olives are among the fruits with an appreciable fibre content (Fernández Díez, 1985). The dietary fibre content, that is the total polysaccharide and lignin which, when eaten, is not digested by endogenous secretions of the digestive tract, represents around 12% of the weight of some processed varieties, although it can reach 20% in dried olives (Jiménez et al., 2000). On a fruit basis, the amount of dietary fibre is approximately constant, depending on the size of the fruit and the flesh to pit ratio. However, on a pulp basis, the relative content of dietary fibre might change due to variation

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in the moisture and fat contents caused by the type of processing and stage of ripening of the raw fruits.

17.2 Factors affecting the texture quality of raw olives The chemical composition and physical properties of the olive are important factors in determining the quality of the final product, and these are strongly influenced by both the variety and the time of harvest. In the following subsections, some relevant parameters related to the texture of raw olives intended for the production of table olives are discussed with regard to the effect of ripening and the variety.

17.2.1 Ripening During ripening, the fruit surface colour changes progressively from green to pale-green, straw yellow, pink, purple–pink (cherry) and black. Normally the fruits reach their maximum size when they change their surface colour from slightly pink to purple–pink or black. During development and ripening, other chemical changes take place, such as increase in oil content, decrease in water and reduction in sugars. Changes in cell wall polysaccharides play a major role in bringing about alterations in olive fruit texture during ripening. Olive pulp cell walls are composed mainly of pectic polysaccharides rich in arabinose, glucuronoxylans and cellulose, while xyloglucans, mannans and glycoproteins occur as minor components (Coimbra et al., 1994, 1995) (Fig. 17.1). Studies carried out on olives of Douro variety (Mafra et al., 2001), a Portuguese variety suitable for black oxidised olives, showed that, on a dry pulp basis, an overall decrease in pectic polysaccharides occurs between the green and the cherry stages due to the degradation of the galacturonan moiety as well as the arabinan side chains (Fig. 17.1a). The change in pectic polysaccharides was followed by a decrease in their degree of methylesterification. The amounts of glucuronoxylans and cellulose were also reduced. Higher solubilisation was observed in all polymers between the cherry and black stages. The same study, but on a fruit basis (Fig. 17.1b) (Mafra, 2002), also indicated the decrease in pectic polysaccharides between green and cherry; however, between cherry and black, a considerable increase in pectic polysaccharides was observed. This increase was related to the synthesis of new polysaccharides to match the increase in cellular volume and amount of fruit pulp, which is observed with ripening (John and Dey, 1986). The increase in xyloglucan content corresponded to the decrease in cellulose, which indicated an increase in the solubility of glucans with ripening, rather than an increase in xyloglucan content per fruit. Also, per fruit, the amount of glucuronoxylans tends to remain constant with ripening.

Improving the texture of processed fruit: the case of olives 413

The study of olive pulp cell wall polysaccharides from the two different harvests showed that the changes with ripening are not always so pronounced from green to cherry and to black stages. One great difference between the

g/kg dry pulp

50

g/kg dry pulp

40 30

25 20 15 10 5 0 Galacturonans Arabinans

20 10 0

Pectic Glucuronoxylans Xyloglucans polysaccharides

Mannans

Cellulose

(a) 30 mg/fruit

25

mg/fruit

20 15

12 10 8 6 4 2 0 Galacturonans Arabinans

10 5 0 Pectic Glucuronoxylans polysaccharides

Xyloglucans

Mannans

Cellulose

(b) 40 20

mg/fruit

35

mg/fruit

30 25 20

15 10 5 0 Galacturonans Arabinans

15 10 5 0 Pectic Glucuronoxylans polysaccharides

Xyloglucans

Mannans

Cellulose

(c) Green

Cherry

Black

Fig. 17.1 Composition of olive pulp cell wall polysaccharides of Douro variety in three stages of ripening, 1997 harvest, expressed as (a) g.kg–1 of dry pulp and (b) as mg per fruit, (c) 1998 harvest, expressed as mg per fruit (Mafra, 2002).

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two harvests was the fact that the extension of the changes and/or degradation of polysaccharides in one harvest (Fig. 17.1b) was higher than in the other one (Fig. 17.1c). In green olives of Douro variety (Mafra et al., 2001), the thin-walled parenchyma cells are uniform and tightly packed (Fig. 17.2b). Tissue fracture involves cell walls breaking, both in epicarp and mesocarp (Fig. 17.2a), but cell separation at the middle lamella level is not observed. The fracture surface of cherry olives consists of a mixture of broken and separated cells and intact cells. In black olives, the region of broken cells is smaller and involves only the epicarp and the first layers of the mesocarp (Fig. 17.2c), with cell separation (Fig. 17.2d). The study of the mechanical properties of olives of Douro variety (Mafra et al., 2001) showed that green olives are the most difficult to penetrate (Fig. 17.3a) and the least deformable (Fig. 17.3b), whereas black olives are the easiest to penetrate and the most deformable. Both puncture and compression tests showed tissue softening induced by ripening. The study of the effect of ripening on Hojiblanca olives, a Spanish variety also used to produce black oxidised olives, indicated that pectic polysaccharides were widely affected (Jiménez et al., 2001a). These authors reported the

(a)

(c)

(b)

(d)

Fig. 17.2 SEM photographs of broken surface olives, adapted from Mafra et al. (2001): (a) green olives, overview, showing tissues fracturing through the cells; (b) details of green olive parenchyma cells tightly packed (cell adhesion); (c) black olives, overview, showing tissues mainly fracturing along the middle lamellae; (d) details of the black olive parenchyma showing the dissolution of pectic polysaccharides (cell separation).

Improving the texture of processed fruit: the case of olives 415 10

Deformation (mm)

5

Force (N)

4 3 2 1 0

8 6 4 2 0

Green

Cherry (a)

Black

Green

Cherry (b)

Black

Fig. 17.3 (a) Puncture test force (penetrometer) and (b) compression test deformation (durometer) of green, cherry and black olives of Douro variety, adapted from Mafra et al. (2001).

release of galacturonans as ripening progresses together with the debranching of the rhamnogalacturonan between cherry and black stages. Between green and cherry there was also a decrease in the yield of tightly bound hemicellulosic polysaccharides together with a general decrease in molecular weight of hemicellulosic polysaccharides (Jiménez et al., 2001b). Between cherry and black fruits, the most significant modifications were those that took place in hemicellulosic polysaccharides and cellulose. The study of the mechanical properties of olives of Hojiblanca and Douro varieties in different stages of ripening showed that the epicarp of green olives of both varieties is stronger than the mesocarp for 25–30 orders of magnitude (Fig. 17.4a, b), whereas for the cherry and black stages, the epicarp became stronger than the mesocarp by 100 orders of magnitude (Georget et al., 2001). The ripening affects the strength of the mesocarp more than that of the epicarp. The decrease of tissue strength occurs mainly in the mesocarp of the olive when the fruit turns from green to cherry. Figure 17.4 (c, d) shows also that the strain at failure, measured by the ratio of the change in length to the original length, is higher in the mesocarp tissues than in epicarp, but is not significantly influenced by the stage of ripening of the olive. These data show that the flexibility for elongation of the mesocarp is higher than that of the epicarp. Another mechanical characteristic measured in the olive was the stiffness (Fig. 17.4e, f). The stiffness is 100–200 orders of magnitude higher in epicarp than in mesocarp, and was shown to decrease with ripening in both tissues (Georget et al., 2001). The dissolution and breakdown of polysaccharides of middle lamella and cell walls during ripening of fruits is caused by the action of a diversity of cell wall enzymes. A series of enzymatic activities has been described during the growth and ripening of olive fruits (Fernández-Bolaños et al., 1995). The activity of polygalacturonase is often considered to be responsible for fruit softening and to be many times inversely related to fruit firmness (Brett and Waldron, 1996). Polygalacturonase catalyses the hydrolysis of galacturonans main chain of pectic polysaccharides and is more active in de-esterified pectic polysaccharides than in methylesterified ones. Pectinmethylesterase

416

Texture in food 0.12

Strength (MN m–2)

Strength (MN m–2)

4.0 3.0 2.0 1.0 0.0 Green

Cherry (a) Epicarp

Strain at failure

Strain at failure

0.03

Green

Cherry (b) Mesocarp

Black

Green

Cherry (d) Mesocarp

Black

Green

Cherry (f) Mesocarp

Black

0.20

0.15 0.10 0.05 0.00

0.15 0.10 0.05 0.00

Cherry (c) Epicarp

Black

60 50 40 30 20 10 Green

Cherry (e) Epicarp

Black

Hojiblanca

Estimated modulus (MN m–2)

Green

Estimated modulus (MN m–2)

0.06

0.00

Black

0.20

0

0.09

0.5 0.4 0.3 0.2 0.1 0.0

Douro

Fig. 17.4 Mechanical properties of olives of Hojiblanca and Douro varieties in different stages of ripening, adapted from Georget et al. (2001). (a) Strength of the epicarp; (b) strength of the mesocarp; (c) strain at failure of the epicarp; (d) strain at failure of the mesocarp; (e) stiffness of the epicarp; (f) stiffness of the mesocarp.

catalyses the hydrolysis of esterified galacturonic acid (Fischer and Bennet, 1991). The concerted action of both enzymes causes an extensive degradation of cell wall pectic polysaccharides. Cellulase activity is also present in ripening fruits. The decrease in organisation of the microcrystalline cellulose fibrils allows the access of cellulases, causing a decrease in the molecular weight of these polysaccharides (O’Donoghue et al., 1994). The reported changes in cell wall polysaccharides of olive pulp during ripening may be attributed to the increases of the activities of polygalacturonase, pectinmethylesterase and cellulose, which were found to occur with ripening of olives of Douro variety (Mafra et al., 2000).

Improving the texture of processed fruit: the case of olives 417

17.2.2 Variety The olive oil content, as well as the general composition of the pulp, is highly variable and dependent on a multiplicity of factors, such as climate, soil, and type of culture. According to the principal use of the olive fruit, varieties can be classified in three groups (Garrido Fernández et al., 1997): • fruits for table olive production; • fruits for olive oil extraction; • fruits for both purposes. Within the group of fruits suitable for table olive production, depending on their chemical and physical properties, different types of processing are traditionally used. Characteristics such as the size of the fruits, the flesh to pit ratio, the colour of the epicarp, the texture of the epicarp and mesocarp, the susceptibility to gaseous spoilage and to shrivelling during brine storage and the sensory properties of the final product are factors which determine the choice of the variety. In OLITEXT Project, FAIR CT97 3053 – Improvement of Texture Characteristics of Some European Olive-Fruit Varieties Suitable for TableOlive Purposes (1997–2000), six olive varieties were studied: Douro (D), from Portugal; Hojiblanca (H), from Spain; Conservolia (C) and Thasos (Th), from Greece; Taggiasca (T) and Cassanese (Ca), from Italy. They were processed as black oxidised (D and H), naturally black (C and T), dry-salted (Th), and boiled, salted and oven-dried (Ca) table olives. This European Union funded project aimed: 1. to increase the understanding of the chemical and biochemical processes that occur during the production of processed olives in relation to product quality (texture) as modulated by the stage of ripening and agronomic factors; 2. to investigate the effects of debittering treatments on final olive texture; 3. to use the knowledge gained to develop and/or improve production methods and thereby optimise table-olive texture. This chapter focusses on the results obtained in this project. The study of cell wall polysaccharides of raw olive pulp of different varieties at a ripe to very ripe black stage showed that Douro and Taggiasca had a very similar polysaccharide composition (Table 17.2). They were rich in pectic polysaccharides (45–50%) and contained 25–29% cellulose, 12–13 % glucuronoxylans, and 9–10 % xyloglucans. Cassanese was also rich in pectic polysaccharides (46%), and contained 25% cellulose, 16% xyloglucans, and 12% glucuronoxylans. Conservolia and Thasos contained 34–36% pectic polysaccharides, 26–27% cellulose, and 18–20% xyloglucans. Glucuronoxylans accounted for 20% in Conservolia and 14% in Thasos.

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Table 17.2 Relative amount (%) of olive pulp cell wall polysaccharides of raw black olives of different varieties

Pectic polysaccharides Glucuronoxylans (Xylo)glucans Mannans Ara-rich glycoproteins Cellulose

Douro (D 97)

Douro (D 98)

Taggiasca (T)

Conservolia Cassanese Thasos (C) (Ca) (Th)

49 13 9 1 1 27

45 12 10 3 1 29

50 13 10 1 1 25

34 20 18 2 0 26

46 12 16 1 0 25

36 14 20 2 1 27

17.3 Influence of processing on table olives The three main methods used for preparation of table olives are: • green pickled olives in brine, produced according to the Sevillian style; • black oxidised olives in brine, produced according to the Californian style; • naturally black olives in brine, produced according to the Greek style. According to the different regions and traditions, other types of table olives processing are used on a small scale.

17.3.1 Green pickled olives The processing of pickled green olives according to the Sevillian Style is widely used in Portugal and Spain. In this procedure, the fruits are collected at a mature green stage (prior to ripening) and are treated anaerobically with a sodium hydroxide solution (lye) for several hours to remove the bitter glucoside oleuropein. The treatment is likely to inactivate all biochemical activity within the fruits. The strength of the alkali (approximately 2% w/w) depends on the fruit size, temperature and stage of ripening. Alkali treatment is terminated when two thirds of the pulp have been penetrated. The fruits are then washed several times with water and subjected to a lactic fermentation in a brine solution (7–10% NaCl) for between two and four months. After fermentation, the green olives are packed for sale (Fernández-Díez, 1985). According to Coimbra et al. (1996), the major structural modification in the cellular matrix with processing to pickled green olives according to the Sevillian Style was due to the degradation of pectic polysaccharides. This modification was reflected both in pectic polysaccharides associated with Ca2+ and in pectic polysaccharides solubilised with alkali solutions, and could be the principal factor in the alteration of olive pulp texture with processing. A study carried out on processing of Hojiblanca variety (Jiménez et al., 1995) reported that the lye treatment had a significant effect on pectic polysaccharides, altering their solubility in aqueous solutions. Fermentation produced a marked degradation of hemicellulosic polysaccharides and cellulose,

Improving the texture of processed fruit: the case of olives 419

and probably an interchange in solubility. These authors also reported that the greatest change in texture measured by a texturometer was observed after lye treatment and, to a smaller extent, after fermentation. They suggested that the loss of texture during the lye treatment, washing and soaking in brine could be due to the variations in polysaccharide solubility, disorganisation of the cell walls, attributed to the breakdown of the different kinds of bonds, and the decrease in cellular turgidity caused by the disorganisation of the plasma membrane. After equilibrium in brine, there was an increase in firmness attributed to the neutralisation of pectic polysaccharides, negatively charged because of the lye treatment. Storage in brine solution caused changes neither in polysaccharide composition nor in texture. The alkali reagent dissolves the epicuticular wax coating and decreases the thickness of the cuticle. This was shown by Marsilio et al. (1996), who also observed changes in pectic polysaccharides of cell walls and intercellular cohesion. The effect depends on the degree of ripening but, above all, on the alkali concentration. The alkali dissolves the pectic polysaccharides leaving the cellular microfibrils as an empty network with a weak structure, which in turn results in loss of firmness and often in collapse. More pronounced changes were noted in soft pulp of Ascolana and Caroleo varieties due to their thinner cell walls and smaller amount of pectic polysaccharides in the middle lamella. The cell structure of the variety Intosso, which has a higher amount of pectic polysaccharides, was less damaged by the lye treatment.

17.3.2 Black oxidised olives In black oxidised processing in the Californian style, the olives, mostly green and cherry, are normally stored in brine with 5–10% NaCl for between two and six months, depending on the needs of production. The brine may be acidified to pH 4 with lactic acid and acetic acid and kept in anaerobic or aerobic conditions. To improve texture, calcium chloride could be added during this period. Once the fresh or stored fruits are sorted and occasionally graded, they are treated with a series of dilute sodium hydroxide solutions and exposed to air between treatments. In the USA, three to five lyes are usually used, while in Portugal and Spain two to three are considered enough. The lye treatments are normally adjusted since the first one penetrates the skin while the remaining lyes are permitted to penetrate the pulp progressively until the last one reaches the stone. The lye concentration varies generally from 1.5 to 2.0%, and the first lye normally has the highest concentration. The duration and concentration of lye should be adjusted to the temperature conditions and fruits used, i.e. previous storage in brine, size and stage of ripening. After each alkaline treatment the fruits are placed in water and oxidised by injecting air under pressure into the immersed olives. After the lye treatments and oxidation, the olives are washed several times with water to remove most of the residual lye to reach a final pH around 7. Generally, 0.1% (w/w) of ferrous gluconate is added to the last wash to stabilise the

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colour produced by oxidation of polyphenols during air injection. The olives are then placed in 3–5% brine during one to three days to equilibrate the sodium chloride content in the flesh. A bulk pasteurisation to minimise the action of some aerobic bacteria during brining is optional. The olives are then packed and sterilised (Garrido Fernández et al., 1997). The effect of the black oxidising process on cell wall polysaccharides and texture has recently been a subject of study in olives of Douro variety. The results of the study in green olives showed that brine storage increased, on a fruit basis, the amount of pectic polysaccharides, glucuronoxylans and cellulose (Fig. 17.5a) (Mafra, 2002). The maintenance of cell wall polysaccharides in NaCl solutions containing CaCl2 might be the result of charge stabilisation conferred by Na+ and Ca2+, and mainly of the ability of Ca2+ to form complexes with pectins (Jiménez et al., 1997), allowing them to form gels (Cardoso et al., 2003). The increase in polysaccharides was attributed (probably) to 45 40 mg/fruit

35

mg/fruit

30 25 20

25 20 15 10 5 0 Galacturonans Arabinans

15 10 5 0

Pectic Glucuronoxylans Xyloglucans polysaccharides (a) Green olives

Mannans

Cellulose

45 40 mg/fruit

35

mg/fruit

30 25 20

25 20 15 10 5 0 Galacturonans Arabinans

15 10 5 0 Pectic Glucuronoxylans polysaccharides

Xyloglucans

Mannans

Cellulose

(b) Black olives Raw

Brine

Lye

Final product

Fig. 17.5 Composition of olive pulp cell wall polysaccharides of (a) green and (b) black olives of Douro variety, expressed as mg per fruit, along the black oxidising process (Mafra, 2002).

Improving the texture of processed fruit: the case of olives 421

biosynthesis of new polysaccharides during the long storage period. The alkali treatment had two effects: it caused degradation and loss of polysaccharides and, on the other hand, it increased their retention in the cell walls. This retention could be due to the ionisation of the hydroxyl groups of cellulose which could prevent the diffusion of negatively charged pectic polysaccharides enmeshed within the swollen cellulose matrix, attenuating their loss. The final thermal treatment by sterilisation caused mainly the loss of hemicellulosic polysaccharides and cellulose. The combined effects of black oxidising processing and ripening on olives of Douro variety were also reported (Mafra, 2002). The storage in brine also contributed to stabilisation of pectic polysaccharides in cherry and black olives; however, there was a decrease in cellulose, especially in black olives, in contrast to the increase observed in green olives. This fact might be the result of a higher biosynthetic activity in green olives and/or a higher hydrolytic activity in black ones. The lye treatment had more effect on the cell wall polysaccharides of black olives as they all decreased, indicating that the degradation caused by processing depended on the stage of ripening of raw fruit. The black oxidised processing increased the differences observed in cell wall polysaccharides of fresh olives at different stages of ripening (Fig. 17.5b). The study of the compression test of olives at different stages of ripening and processing indicated an increase in the deformation during ripening as mentioned above, mainly between green and cherry (Fig. 17.6). After brine storage, it seems that the increase in polysaccharides referred above does not correspond to a great improvement in texture. The lye treatment seemed to decrease the deformation of green and black olives, probably due to the 7

Deformation (mm)

6 5 4 3 2 1 0 Green Raw

Cherry Brine

Lye

Black Final product

Fig. 17.6 Compression test deformation (durometer) of green, cherry and black olives of Douro variety in the different stages of processing to black oxidised olives. (Results from Istituto Sperimentale per la Elaiotecnica, Pescara, OLITEXT Report).

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Texture in food

insolubilisation of polysaccharides. The deformation of the final products of green and cherry olives increased, probably due to the increase in the solubilisation of cell wall polysaccharides after equilibrium of charges in brine and due to sterilisation. The mechanical properties of black oxidised olives of Hojiblanca variety processed in different stages of ripening have been studied by Georget et al. (2003). These authors used the approach of separating the epicarp from the mesocarp and demonstrated that the strength of the epicarp decreased significantly in black oxidising processing, whereas the flesh became stronger after successive treatments with brine, lye, and brine and heat. Most treatments resulted in reduced strength and stiffness of the skin. However, brine storage enhanced the strength of the flesh. Treatment in alkali enhanced the stiffness and the strength of the flesh, which is consistent with the higher retention of cell wall polysaccharides mentioned above in Douro variety (Mafra, 2002). The final heat treatment step resulted in a decreased strength, which is consistent with the increased cell wall polysaccharides solubility. According to the study of Georget et al. (2003), the stage of maturity of the fruit seemed to play a more significant role in governing the mechanical properties of the flesh than those of the skin.

17.3.3 Naturally black olives in brine Naturally black olives in brine according to the Greek style are obtained from fruits harvested when fully ripe or slightly before full ripeness is reached. The olives are placed in NaCl brine with a concentration varying normally between 8 and 14%, allowing a spontaneous and slow fermentation as the diffusion of components through the skin is very slow. As no lye treatment is used in the preparation of this product, oleuropein and other fruit components are only partially and slowly leached into the brine. The initial flora is normally composed of gram-negative bacteria, yeasts, moulds and, sometimes, lactic acid bacteria. During the first days of fermentation the gram-negative bacteria disappear, while yeasts reach their maximum levels (Garrido–Fernández et al., 1997). Depending on the variety, temperature and salt concentration a lactic fermentation might occur (Tassou et al., 2002). The process is considered to be over when fermentable substrates are exhausted, which in most regions of Greece happens after eight months or later. However, this period might change depending on several factors, such as the variety and olive size, the salt concentration and the temperature. After fermentation the olives are exposed to the air to improve colour. Conservolia and Taggiasca are varieties which can be used to produce naturally black olives in brine. Naturally black olives of Conservolia variety originate in Greece, are large in size (6.1 g/fruit), have a high flesh to pit ratio, but are sensitive to softening during processing and storage. Olives of Taggiasca are cultivated in the western side of Liguria (Northwest of Italy), are small in size (2.2 g/fruit) and used for both olive oil extraction and table

Improving the texture of processed fruit: the case of olives 423

olive production. These two varieties allow a lactic fermentation if the conditions of temperature and NaCl concentration are favourable. The study of the effect of fermentation on cell wall polysaccharides of Conservolia indicated an increase, on a fruit basis, in pectic polysaccharides (galacturonan moiety) and xyloglucans, with a slight decrease in glucuronoxylans (Fig. 17.7a). The changes in cell wall polysaccharides of naturally black olives of Taggiasca variety (Fig. 17.7b) indicated a slight increase in pectic polysaccharides, an increase in xyloglucans and a decrease in glucuronoxylans and cellulose. Pectic polysaccharides of Taggiasca variety became more soluble in aqueous solutions as a result of processing, while pectic polysaccharides of Conservolia did not show a shift in solubility after processing. The mechanical properties of Taggiasca and Conservolia indicate that the epicarp is stronger and stiffer than the mesocarp, as observed in Douro and 40 mg/fruit

20

mg/fruit

30

15 10 5 0

20

Galacturonans Arabinans

10

0 Pectic Glucuronoxylans Xyloglucans polysaccharides (a) Conservolia

Mannans

Cellulose

15 mg/fruit

6

mg/fruit

10

4 2 0 Galacturonans Arabinans

5

0 Pectic Glucuronoxylans polysaccharides

Xyloglucans

Mannans

Cellulose

(b) Taggiasca Unprocessed

Processed

Fig. 17.7 Composition of olive pulp cell wall polysaccharides of (a) Conservolia and (b) Taggiasca varieties raw and after natural fermentation. (Results from Universidade de Aveiro, OLITEXT Report).

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Hojiblanca varieties. The compression test of olives of Conservolia variety indicated that with processing the tissues became softer. The same values for Taggiasca variety do not indicate much change. These findings suggest that, as with the changes observed during ripening, an increase in cell wall polysaccharides does not necessarily correspond to an improvement in texture. The activities of cell wall degrading enzymes, such as polygalacturonase, pectinmethylesterase, cellulase and proteolytic, were higher in Taggiasca than in Conservolia, which is in accordance with the higher degree of solubilisation of cell wall polysaccharides in Taggiasca than in Conservolia (Universidade de Aveiro, Portugal, OLITEXT final report). The naturally black processing of olives in brine decreased the activity of all these enzymes in both varieties, especially in Taggiasca.

17.3.4 Other types of processing Other types of processing include naturally black olives dried in salt which are generally obtained from raw black olives or from olives partially ovendried. The olives are arranged in alternating layers of fruits and dry salt, or dry salt can be sprinkled over them. They retain a degree of bitterness and a characteristic fruity flavour and are generally packed in plastic bags, sometimes with potassium sorbate. A small amount of olive oil may be added to cover the surface of the fruits (Garrido Fernández et al., 1997). Dry-salted olives of Thasos are a traditional preparation of naturally black olives cultivated mainly on the island of Thasos in Northern Greece (Panagou et al., 2002). The traditional processing method consists of placing the olives in concrete tanks in layers with coarse salt in a proportion of 40%. Due to the high osmotic pressure exerted by the salt, olives lose water and other watersoluble substances and become shrivelled. Much of the oleuropein is removed from the product while the remaining bitterness is masked by the salt present in the flesh. The longer the fruits remain in the tanks, the saltier they become, obtaining olives with a low water activity. The amount of cell wall polysaccharides of dry-salted olives of Thasos variety did not change much, indicating only slight increases in the amounts of pectic polysaccharides, glucuronoxylans and cellulose per fruit (Fig. 17.8). However, the pectic polysaccharides were affected as the amount of galacturonans increased while arabinan side chains decreased. The solubility of pectic polysaccharides in aqueous solutions was strongly affected, increasing after processing. The study of the mechanical properties of dry-salted olives indicates an increase in the firmness of processed olives (Institute of Food Research, Norwich, UK, OLITEXT final report). That was verified by the measurement of the strength, strain at failure and stiffness of the mesocarp and epicarp. With processing, the strength, the strain at failure, and the stiffness increased both in the mesocarp and epicarp. The changes observed in texture might be the result of the stabilisation of the galacturonan moiety by the Na+, as was

Improving the texture of processed fruit: the case of olives 425 30 20 15 10 5 0

mg/fruit

25

mg/fruit

20 15

Galacturonans Arabinans

10 5 0 Pectic Glucuronoxylans polysaccharides

Xyloglucans

Mannans

Cellulose

Thasos Unprocessed

Processed

Fig. 17.8 Composition of olive pulp cell wall polysaccharides of Thasos variety in raw and dry salted. (Results from Universidade de Aveiro, OLITEXT Report).

the case with the textural changes observed in green olives after equilibrium in brine (Jiménez et al., 1997).

17.4 Improving texture Calcium is known to have a very strong texture-increasing effect by retarding softening during storage. Pectic polysaccharides of middle lamella are crosslinked with calcium ions forming structures called the “egg-box” model (Brett and Waldron, 1996). Jiménez et al. (1997) studied the combined effects of Ca2+, Na+ and pH on the texture of olives processed according to the Sevillian style. These authors reported that the increase in Ca2+ at pH 11 showed a logarithmic increase of firmness, which changed to linear at pH 3. Na+ increased firmness linearly at pH 11 but was ineffective at pH 3. The high concentration of Na+ at pH 11 changed the increase in firmness caused by Ca2+ to a linear correlation. The presence of Na+ caused a minimal displacement of Ca2+ while Na+ was displaced by Ca2+. The ability of Ca2+ to form complexes with pectic polysaccharides allowed a more effective stabilisation of cell walls than that conferred by Na+, as this was only able to stabilise charges. The effect of Ca2+ on naturally black olives in brine of Conservolia variety according to the Greek style was also studied during the OLITEXT project. CaCl2 treatment resulted in a significant Ca2+ uptake in the flesh of olives, and this had a positive effect on texture improvement which was detected mainly by objective measurements (Fig. 17.9). Another parameter studied was the effect of temperature during fermentation of naturally black olives. Increasing the temperature to 25 ºC accelerated the debittering process, allowing smaller periods of fermentation.

426

Texture in food 160

Force (kg)

120

80

40

0 Olive with skin Reference

Olive without skin Calcium

Fig. 17.9 Effect of CaCl2 treatments on olive pulp mechanical properties, Conservolia variety. Maximum forces required to shear 100 g of depitted olives in a Kramer Cell. (Results from Institute of Technology of Agricultural Products – ITAP, Athens, OLITEXT Report).

The degradation of cell wall polysaccharides caused by ripening seems to be very relevant to the textural properties of table olives. The choice of the exact ripening stage for olive processing is a key factor in texture improvement.

17.5 Future trends The increasing interest of the consumer in “natural” products with minimal processing and loss of soluble constituents and nutrients involves changes in the technologies which can offer the guarantee of preservation, hygiene and genuineness of food products. This may arouse interest in preparing ovendried table olives variously seasoned with olive oil and natural aromatic herbs. In this view of the continuing consumer interest, these products may be successful into meeting the consumer demands in terms of dietetic, gustative, social and cultural expectations. The effect of oven-drying processing on the textural properties of Cassanese variety olives according to the Ferrandina method was a subject of study by the OLITEXT Project (Marsilio et al., 2000). The oven-drying processing of raw olives or those stored in brine consists of a quick immersion into boiling water (blanching) for 6 min, salting of the fruit with NaCl for three days, and finally drying the fruits on a wooden trellis in an air oven at approximately 50 ºC in order to reduce the water content of the final product. The effect of oven-drying processing according to the Ferrandina method is reflected in the decrease in uronic acids (Ur.Ac.) and arabinose (Ara), the major sugar components of pectic polysaccharides (Fig. 17.10). No significant differences were observed in hemicellulosic sugar residues, such as xylose (Xyl) or glucose (Glc).

Improving the texture of processed fruit: the case of olives 427 20

mg/fruit

200 150 100 50 0

mg/fruit

15

10

Polymeric material

Polysaccharides

5

0 Rha

Ara Unprocessed

Xyl

Man Cassanese CWM Ferrandina

Gal

Glc

Ur.Ac

Industrial

Fig. 17.10 Sugar composition of cell wall material (CWM) of Cassanese variety, raw and oven-dried according to Ferrandina and industrial methods. (Results from Universidade de Aveiro, OLITEXT Report).

The microstructural analysis of parenchyma cells of raw olives of Cassanese revealed uniform and tightly packed thin-walled cells (Fig. 17.11a, b) (Marsilio et al., 2000). Heat treatment caused gross changes in the parenchyma tissue structure of the fruit as a result of changes in chemical constitution of the middle lamella, which affected its adhesive properties, leading to cell wall separation (Fig. 17.11c, d). Examination of fracture surfaces showed that most cells remained intact, suggesting that tissue failure occurred by cell to cell rupture and that the resulting decrease in firmness of olive fruit was related to the formation of soluble pectic polysaccharides. Oven-dried olive tissues clearly showed cell wall disruption and the presence of some cavities (Fig. 17.11e, f), explained as a gelation process of the network of cellulose microfibrils, hemicelluloses and pectic polysaccharides (Marsilio et al., 2000). The study of mechanical properties indicated that heat treatment induced loss of firmness in comparison with raw samples, while during salting there was a slight increase of firmness (Fig. 17.12). The oven-drying step brought about further firming of the tissues (Marsilio et al., 2000). Based on the Ferrandina method, an alternative procedure was tried on an industrial scale. This industrial scale processing was a major modification compared to the traditional method: the first stage comprised three cuts for each olive, followed by immersion in water for 15–20 days. In the second stage, salt (8–10%, w/w) was added to the olive fruits and then left for about three days for salting-debittering. After these treatments, the product was dried in an oven at around 45 °C. This product showed no significant degradation of cell wall polysaccharides (Figure 17.10), although loss of cellular content was noticed, possibly due to the rupture of olive epicarp and soaking in water and salt. It was classified organoleptically as a slightly firm and crispless product, without flavour defects, which was considered satisfactory by the sensory panel (Figure 17.13).

428

Texture in food

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 17.11 SEM images of Cassanese variety, adapted from Marsilio et al. (2000). (a) unprocessed olives, overview, showing tissues fracturing through the cells; (b) details of green olive parenchyma cells tightly packed (cell adhesion); (c) heated olives, overview, showing tissues fracturing through the middle lamellae; (d) details of parenchyma showing the cell separation, (e) oven-dried olives, overview, showing tissue damaging, (f) details of parenchyma, showing cell disruption.

These are only a few examples of the first attempts to produce table olives that can safeguard olive oil quality and its antioxidant potential in table olives, as well as olive dietary fibre characteristics. These are major challenges for food chemists and food technologists in the near future.

17.6 Sources of further information and advice Fernández-Díez M J (1985) Table Olive Biotechnology (Spanish), Consejo Superior de Investigaciones Cientificas (CSIC), Instituto de la Grasa e seus Derivados, Madrid-Seville, Spain.

Improving the texture of processed fruit: the case of olives 429

Firmness (% deformation)

40

30

20

10

0 Fresh

Heated

Salted

Oven-dried

Fig. 17.12 Compression test deformation (durometer) of Cassanese variety in the different steps of processing according to the Ferrandina method, adapted from Marsilio et al. (2000). Colour 10 Overall judgement

8

Odour

6 4

Detach p/s

Metallic

2 0 Woody

Rancid

Crisp

Fusty Firm

Fig. 17.13 Sensory profile of industrially processed Cassanese variety based on Ferrandina method. (Results from Istituto Sperimentale per la Elaiotecnica, Pescara, OLITEXT Report).

Garrido Fernández A, Fernández-Díez M J and Adams M R (1997), Table Olives, London, Chapman & Hall. International Olive Oil Council, Madrid. OLITEXT Participants Prof. Manuel António COIMBRA, Universidade de Aveiro, PT – 3810-193 AVEIRO – Chemistry of polysaccharides. Dr Keith W. WALDRON and Dr Andrew SMITH, Institute of Food Research, Norwich Research Park, GB – NORWICH NR4 7UA – Texture, tissue mechanics. Prof. Giorgio BIANCHI, Dr Vincenzo MARSILIO and Dr Barbara LANZA, Istituto Sperimentale per la Elaiotecnica, Contrada Fonte Umano, Città S. Ângelo, IT – 65013 CITTÁ S.ANGELO (Pe) – Olive science, phenolics, microstructure, mechanics, sensory analysis.

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Dr Antónia HERÉDIA and Dr Rafael GUILLÉN, Instituto de la Grasa, Av. Padre Garcia Tejero, 4, ES - 41012 SEVILLA – Chemistry of polysaccharides. Dr Konstantinos KATSABOXAKIS, Institute of Technology of Agricultural Products. S Venizelou 1, GR - 14123 ATHENS – LYKOVRISSI – Olive microbiology, olive processing. Prof. Nicola UCCELLA, CIRASAIA - Università della Calabria, Arcavacata di Rende, IT - 87030 RENDE/COSENZA – Phenolic compounds, nutraceuticals. Mr Gil MAÇARICO, Maçarico Lda, Av. Cidade de Coimbra, Praia de Mira, PT - 3070 MIRA – Table olives producer. Mr Nelos GEORGOUDIS, D.E. Georgoudis Co., Riga Fereou 119 PO Box 1169, GR - 381.10 VOLOS – Table olives producer. Dr Mauro AMELIO, Fratelli Carli S.p.A., Via Garessio 11 – 13, IT - 18100 IMPERIA – ONEGLIA – Olive oil and table olives producer. Mr Luis REJANO ZAPATA, Agro-Sevilla Aceitunas Soc. Coop. Andaluza, Carretera Sevilla-Malaga km 124.3, La Roda de Andalucia, ES - 41590 SEVILLA – Table olives producer.

17.7

References

and WALDRON K W (1996) Physiology and Biochemistry of Plant Cell Wall, London, Chapman & Hall. CARDOSO S M, COIMBRA M A and LOPES DA SILVA J A (2003) Calcium-mediated gelation of an olive pomace pectic extract, Carbohydrate Polymers, 52, 125–33. COIMBRA M A, WALDRON K W and SELVENDRAN R R (1994) Isolation and characterisation of cell wall polymers from olive pulp (Olea europaea L.), Carbohydrate Research, 252, 245–62. COIMBRA M A, RIGBY N M, SELVENDRAN R R and WALDRON K W (1995) Investigation of the occurrence of xylan-xyloglucan complexes in the cell walls of olive pulp (Olea europaea), Carbohydrate Polymers, 27, 277–84. COIMBRA M A, WALDRON K W, DELGADILLO I and SELVENDRAN R R (1996) Effect of processing on cell wall polysaccharides of green table olives, Journal of Agricultural and Food Chemistry, 44, 2394–401. FERNÁNDEZ-BOLÃNOS J, RODRÍGUEZ R, GUILLÉN R, JIMÉNEZ A and HEREDIA A (1995) Activity of cell wall-associated enzymes in ripening olive fruit, Physiologia Plantarum, 93, 651– 8. FERNÁNDEZ-DÍEZ M J (1971) The olive. In The Biochemistry of Fruits and their Products. Ed. A C Hulmed, London and New York, Academic Press, 255–79. FERNÁNDEZ-DÍEZ M J (1983) Olives, In Biotechnology – A Comprehensive Treatise in 8 Volumes. Eds H-J Rehm and G Reed, Weimheim, Florida and Basileia, Verlag Chemie, 379–97. FERNÁNDEZ-DÍEZ M J (1985) Table Olive Biotechnology (Spanish), Consejo Superior de Investigaciones Cientificas (CSIC), Instituto de la Grasa e seus Derivados, MadridSeville. FISCHER R L and BENNET A B (1991) Role of cell wall hydrolases in fruit ripening, Annual Review of Plant Physiology and Plant Molecular Biology, 42, 675–703. GARRIDO FERNÁNDEZ A, FERNÁNDEZ DÍEZ M J and ADAMS M R (1997) Table Olives, London, Chapman & Hall. BRETT C T

Improving the texture of processed fruit: the case of olives 431 GEORGET D M R, SMITH A C

and WALDRON K W (2001) Effect of ripening on the mechanical properties of Portuguese and Spanish varieties of olives (Olea europaea L.), Journal of the Science of Food and Agriculture, 81, 448–54. GEORGET D M R, SMITH A C, WALDRON K W and REJANO L (2003) Effect of ‘Californian’ process on the texture of Hojiblanca olive (Olea europaea L) harvested at different ripening stages, Journal of the Science of Food and Agriculture, 83, 574–9. JIMÉNEZ A, GUILLÉN R, SÁNCHEZ C, FERNÁNDEZ-BOLÃNOS J and HEREDIA A (1995) Changes in the texture and cell wall polysaccharides of olive fruit during “Spanish green olive” processing, Journal of Agricultural and Food Chemistry, 43, 2240–46. JIMÉNEZ A, HEREDIA A, GUILLÉN R and FERNÁNDEZ-BOLÃNOS J (1997) Correlation between soaking conditions, cation content of cell wall, and olive firmness during “Spanish green olive” processing, Journal of Agricultural and Food Chemistry, 45, 1653–8. JIMÉNEZ A, RODRÍGUEZ R, FERNÁNDEZ-CARO I, GUILLÉN R, FERNÁNDEZ-BOLÃNOS J and HEREDIA A (2000) Dietary fibre content of table olives processed under different European styles: study of physico-chemical characteristics, Journal of the Science of Food and Agriculture, 80, 1–6. JIMÉNEZ A, RODRÍGUEZ R, FERNÁNDEZ-CARO I, GUILLÉN R, FERNÁNDEZ-BOLÃNOS J and HEREDIA A (2001a) Olive fruit cell wall: degradation of pectic polysaccharides during ripening, Journal of Agricultural and Food Chemistry, 49, 409–15. JIMÉNEZ A, RODRÍGUEZ R, FERNÁNDEZ-CARO I, GUILLÉN R, FERNÁNDEZ-BOLÃNOS J and HEREDIA A (2001b) Olive fruit cell wall: degradation of cellulosic and hemicellulosic polysaccharides during ripening, Journal of Agricultural and Food Chemistry, 49, 2008–13. JOHN M A and DEY P M (1986) Post harvest changes in fruit cell wall, Advances in Food Research, 30, 139–93. MAFRA I, REIS A, BARROS A S, NUNES C, GUEDES S, VITORINO R, SARAIVA J and COIMBRA M A (2000) Ripening-related changes of olive fruit cell wall polysaccharides and associated enzymes in two consecutive harvests, 10th International Symposium in Plant Polysaccharides, 23–26 August, Wageningen, Wageningen University. MAFRA I, LANZA B, REIS A, MARSILIO V, CAMPESTRE C, DE ANGELIS M and COIMBRA M A (2001) Effect of ripening on texture, microstructure and cell wall polysaccharide composition of olive fruit (Olea europaea), Physiologia Plantarum, 111, 439–47. MAFRA I (2002) Effect of ripening and processing in cell wall polysaccharides of olive pulp (Portuguese) (PhD Thesis, Aveiro, Universidade de Aveiro). MARSILIO V, LANZA B and DE ANGELIS M (1996) Olive cell wall components: physical and biochemical changes during processing, Journal of the Science of Food and Agriculture, 70, 35–43. MARSILIO V, LANZA B, CAMPESTRE C and DE ANGELIS M (2000) Oven-dried table olives: textural properties as related to pectic composition, Journal of the Science of Food and Agriculture, 80, 1271–6. O’DONOGHUE E M, HUBER D J, TIMPA J D, ERDOS G W and BRECHT J K (1994) Influence of avocado (Persea americana) Cx-cellulase on the structural features of avocado cellulose, Planta, 194, 573–84. PANAGOU E Z, TASSOU C C and KATSABOXAKIS K Z (2002) Microbiological, physicochemical and organoleptic changes in dry-salted olives of Thassos variety stored under different modified atmospheres at 4 and 20 °C, International Journal of Food Science and Technology, 37, 635–41. TASSOU C C, PANAGOU E Z and KATSABOXAKIS KZ (2002) Microbiological and physicochemical changes of naturally black olives fermented at different temperatures and NaCl levels in the brines, Food Microbiology, 19, 605–15.

18 Improving the texture of bread S. P. Cauvain, CCFRA, UK

18.1

Introduction

Bread is the product of complex interactions between the main raw materials (wheat flour, water, yeast, salt and other functional ingredients) and different processing methods to convert the dough mass into loaf sized units. An overview of the most common breadmaking processes (Cauvain, 2000) and details of the technology involved in modern bread production (Cauvain and Young, 1998) can be obtained elsewhere. There is no such thing as a ‘standard’ bread product with many shapes and sizes of products having evolved throughout the long history of breadmaking. Along with variations in shape, size, environment and cultural inputs have come differences in texture. Interest in bread texture arises because of its direct link with shelf life, eating qualities and flavour. Thus, an understanding of what contributes to bread texture has a direct impact on the sensory pleasures that we can derive from eating bread and other related bread products. The textural variation that one sees with bread and other related products is considerable, and it arises from a number of sources. In order to understand how we measure, adjust and can improve bread textural properties we must first decide which ones are the most important to us. This is not an easy task because of the great variety that we see in such products. One way in which we may attempt to classify the family of bakery products is illustrated in Fig. 18.1. The classification is based on two key formulation issues: flour to sugar and flour to fat ratios. These have been chosen for the illustration because of their role in the inhibition of gluten protein formation in the manufacture of baked products. Gluten formation in baked products occurs when flour and water are mixed together. Gluten is hydrated wheat protein, and its properties have the

100 × ratio fat to flour

20

40

60

80

0

Bread

Cream crackers

Butter puffs

Puff pastry

Buns

20

Short pastry

60 100 × ratio sugar to flour

80

100

HR cake (Fruit)

Ginger snaps

LR cake (Fruit)

Fig. 18.1 Family of baked products.

40

Semi-sweet

Short sweet

Shortbread

Cookies

LR cake

Sponge

HR cake

120

Improving the texture of bread 433

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Texture in food

special role of being able to trap air bubbles during mixing and carbon dioxide gas from yeast fermentation or baking powder reaction in baked products. The function of the gases is critical in creating an expanded structure, thereby modifying product eating qualities and digestibility. In the case of sugar and fat additions higher levels will restrict gluten formation, hence the inclusion of their relationship with flour as defining characters in baked product quality in Fig. 18.1. Baking always involves the loss of water from the ‘raw’ to the baked product (Cauvain and Young, 2000). The baking process therefore has a direct impact on the final moisture content of the product and, in turn, on its eating character. This aspect of baked product character is also illustrated in Fig. 18.1 by the use of solid and broken lines to define the boundaries of the various product types. Those baked products with moisture contents of less than 15% are indicated with broken lines, while those which are above this moisture content are indicated by the solid lines. In general, bakery products with below 15% moisture can be considered as hard and crisp, with those above 15% being soft and tender. Fat too will directly contribute to soft and tender eating characters independent of moisture content.

18.2 Textural characteristics of bread and other cereal-based foods All baked products comprise an outer zone – the crust – and an inner zone – the crumb. The formation of the crust occurs in the oven and it is characterised by being darker in colour and lower in moisture content than the inner crumb. These differences result in very different textural characteristics in the baked product and are part of what determines whether the final product has the ‘right’ quality. In addition to the obvious colour and less obvious moisture content differences, there may be differences in the physical structure between the crust and the crumb. To better understand what characterises bread crust and crumb we can assess the differences by examining cross-sections of typical breads. The darker colour of the crust zone has already been commented on. If the products are freshly baked then we will also observe that the crust is hard to the touch but breaks readily if we try to bend it. Looking very closely just underneath the coloured crust area we are likely to see a zone of dense structure extending 1 or 2 mm towards the product centre. The inner crumb, by contrast, is lightly coloured and has a distinct aerated and cellular structure not unlike that of a bath sponge. In fact cereal scientists often refer to baked products as being ‘sponges’ in the sense that all of the cells which comprise the crumb are interconnected (Cauvain and Young, 2000). When the inner crumb is pressed it feels soft, and when the pressure is released the fresh bread crumb will spring back to it original shape before compression.

Improving the texture of bread

435

Two types of bread products are illustrated in Fig. 18.2 to show how the key characteristics may vary with product type. At the top is the representation of a ‘sandwich’-type loaf characterised by a thin crust and a relatively fine (small average cell size) and uniform crumb structure. On the bottom is the representation of a baguette characterised by a relatively thick crust, a more open (larger average cell size) and random cell structure. All of the differences between the two types of loaf arise from differences in dough processing and baking conditions designed to contribute the relevant product character rather than from the ingredients or formulation used. The main objective attributes which characterise fresh bread are summarised for sandwich and baguette-type products in Table 18.1. There are many types

Fig. 18.2 Sandwich and baguette-type breads.

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Table 18.1 Key properties of sandwich and crusty-type breads

Crust thickness (mm) Crust moisture (%) Crumb cell size (mm)* Crumb moisture (%)

Sandwich-type

Crusty-type

1.0 15.0 1.0–2.5 40.5–41.5

1.5–2.5 13.0 2.0–6.0 29.5–30.5

* Data obtained using C-Cell imaging equipment (Calibre International Ltd, Warrington, UK)

of fermented bread, cake, pastry and biscuit products in the ‘world’ of bakery products and all may be characterised to a greater or lesser degree by the properties listed in Table 18.1.

18.3 Definitions of texture Some of the key textural properties relevant to cereal-based foods have already been introduced. Collectively they may be summarised as follows.

18.3.1 Moistness This is directly related to product moisture content, but absolute levels are determined by the ‘traditional’ properties sought in the bakery food concerned. Thus, breadcrumb is expected to be moist eating but the crust is expected to be dry. Loss of moisture from the crumb renders the product stale on the taste buds of the consumer, and yet the same descriptor would be used for a high moisture content (soft) crust on a baguette. The shelf life of baguette can be measured in hours as the crust gains moisture from the crumb, but the same judgement may not be applied to sandwich-type bread where the shelf life is measured in days. Moistness is seen as a positive character in cakes.

18.3.2 Firmness or hardness These descriptors are generally used to describe a loss of softness in the breadcrumb. This loss of softness may arise from two main sources; a loss of moisture from the crumb or the underlying retrogradation of starch (Pateras, 1998). Hardness may also be considered as a negative attribute for the crust of most bakery products and should not be confused with crispness. While crisp crusts are expected to be hard they are also expected to break or shatter readily. Hard crusts on the other hand tend to require considerable chewing force or time. Hardness is most commonly the positive character sought in low moisture content products such as biscuits and pastries and is seen as a negative character in cakes.

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18.3.3 Softness While this descriptor may be seen as a positive attribute in breadcrumb it will be seen as a negative attribute for crusty bread products, such as baguette, or as a positive attribute for the crust of sandwich bread. Softness is seen as a positive property in cakes but as a negative character for biscuits and pastries.

18.3.4 Cohesiveness The crumb of all bread types is expected to form a ball readily in the mouth and to require some effort in chewing. This character is controlled in part by moisture content and in part by the strength of the network surrounding the holes (cells) in the crumb. Loss of moisture contributes to crumbliness, as do the underlying staling processes. Cohesiveness is generally seen as a positive character in all types of baked products though chewiness should be avoided.

18.3.5 Springiness Fresh bread crumb is expected to be springy following removal of any compressing force. Crumb springiness is related to the strength of the crumb cell wall network. During storage breadcrumb loses its springiness as a result of the underlying staling processes.

18.3.6 Staleness An all-embracing term which describes the collective changes to bread texture as it loses its ‘fresh-baked’ character. Staleness covers many different aspects of changes in bread character with time after baking. While there are some underlying common changes which contribute to staleness, many others are bakery product specific. This means that a statement of the product type being considered should always be used to qualify any comments on bread texture.

18.4 Measuring texture As with most foods, the textural characteristics of bread and other cereal products are most commonly described in terms of their sensory properties. While meaningful to individuals or, on occasions, trained panels, sensory descriptions are of limited value to technologists and scientists involved in quality assessment. In order to be able to measure improvements in bread quality we must first be able to define the baseline character and find some means of measuring both the magnitude and the direction of quality change as the result of our attempts at improvement.

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Subjective descriptions of sensory properties can be used (Hansen and Setser, 1990), but more commonly some form of objective measurement is required. It is useful, though not always necessary, if the subjective sensory and objective measurements can be linked or capable of cross-referencing. The work of Szczesniak et al. (1963a,b) is an early example of how mechanical parameters and sensory methods of texture evaluation can be correlated. Two key bread characteristics associated with freshness are the softness of the crumb and its ability to recover after the deforming force has been removed. These are most readily assessed with the fingers as shown by the common ‘squeeze’ test applied to packaged bread by consumers. Experience, and sub-conscious training by others, leads individuals to reject packaged bread which is firm to the touch or remains ‘squashed’ after the squeeze test. The sensory character experienced through the fingers and with the eyes can be readily confirmed in the mouth. It is not surprising, therefore, that many of the objective tests which are employed to evaluate bread and other cerealbased foods are based on some form of compression or deformation test.

18.4.1 Compression/deformation tests Objective compression or deformation testing may be carried out in one of two ways; either the food is subjected to a standard compression force and the distance through which it is compressed is measured, or the food is compressed through a standard distance and the force required to achieve this is measured. Both methods have been used in the assessment of cerealbased foods. Owing to its cellular structure bread crumb does not obey Hooke’s Law, but it does have a stress/strain relationship similar to that shown in Fig. 18.3. This means that Young’s modulus (stress/strain) varies with the amount of strain, the latter being measured as fractional compression. Compression testing of bread became common because of the potential to express data in terms of fundamental measurements.

18.4.2 Compressimeter and Cone Indenter Compression tests for bread take a number of forms. Early forms of objective measurement compressed a sample of bread crumb of known thickness between two flat, parallel plates using a standard weight applied for a fixed period of time and recording the distance travelled by the upper plate. The apparatus used (known as a Compressimeter) was designed to have little friction, often moving under the weight of < 2 g on the upper plate (Platt and Powers, 1940). A second form, commonly used with compression testing of bread, was a cone with a defined angle. The mechanism of operation was similar to that of the Compressimeter, but in this case the compressing weight was carried on a pan suspended below the bread sample with the cone pressing downwards through the bread crumb. The apparatus used was known as a Cone Indenter (Cornford, 1969).

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Stress–strain curve for bread crumb

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Stress–strain curve for bread crumb (Cornford, 1969).

In the case of both the Compressimeter and the Cone Indenter, additional qualitative information could be obtained by measuring the recovery or springiness of the sample. Carefully removing the compressing force and measuring the height to which the sample recovered within a fixed period of time, typically 1 min, achieved this. Thus, a combination of compression and recovery data provided information relevant to the sensory compression recovery tests commonly applied by consumers. Indeed some companies considered such data to be valuable enough to develop ‘Squeezometers’, capable of compressing whole loaves, as a more direct mimic of consumer requirements. The early forms of bread compression equipment have now largely been superseded by the development of motorised equipment linked with data acquisition and analysis by computer program. The advantage of the motorised equipment is that the compressive force is applied at a fixed rate until the required degree of compression has been achieved. Using the approach it has become common to refer to softness or firmness as the force (in Newtons) required to compress a sample to a given degree. Commonly used degrees of compression with bread are 25 or 40% of sample thickness (AACC, 1987). 18.4.3 Multiple compression tests Multiple compression tests may also be used to determine a range of bread crumb properties. Texture Profile Analysis (TPA) is a common multicompression technique used with bread crumb. In TPA the sample is subjected

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to two compressions in quick succession with withdrawal of the compressing force after each compression. An example of a TPA trace for bread and cake crumb is illustrated in Fig. 18.4. In essence TPA was designed to simulate the processes of biting and chewing in the mouth. The textural parameters of TPA were first defined by Szczesniak (1963a, b). Later Bourne (1978) defined the same parameters with an Instron Universal Texture Machine. Modern instrumental measurements of TPA are based on their pioneering work. 18.4.4 Puncture test The hardness or crispness of bread crusts may be assessed using some form of puncture test. The shape of the probe used for such tests is in the form of either a needle or a small diameter cylinder. Cauvain (1991) used a needle probe to examine the changes in hardness of all of apple pie components with storage time. Measurements of lid pastry crispness, filling firmness and base pastry softness were made in a single pass though the sample. The loss of crispness in the pastry arose from moisture migration from the filling to the lid and base pastry. 18.4.5 Other product parameters Bread texture is directly related to a number of other product parameters, and attempts to measure textural changes should take these into account. Indeed in many cases it is necessary to correct measured texture values so that the contributions of individual ingredients or processes changes can be truly evaluated. The main properties which should be considered in this context are moisture content, density and porosity. Moisture content The common method for measuring sample moisture content is by a form of oven drying method (Cauvain and Young, 2000). In general, the higher the 6

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moisture content of a baked product the lower will be its hardness value (i.e. it will be softer). Density In the case of a whole loaf it is relatively easy to measure loaf weight and volume, the latter being carried out using some form of seed displacement apparatus (Cornford, 1969). In general the greater the product volume the lower will be its hardness value (i.e. it will be softer). However, in the case of many breads the crumb density across a given cross-section is not uniform. For such samples it would be ideal to know the density at the point of testing. This may be achieved using a coring technique such as that described by Cauvain (1991). Using a known bread slice thickness and a cylindrical cutter of known diameter it is a simple matter to weigh the core and calculate its density from its dimensions. Cellular structure Until recently this has not been a bread property which could be readily and objectively measured. The development of the image analysis equipment known as C-Cell (developed by CCFRA and available from Calibre Control International Ltd, Warrington, UK) has provided a tool with the capacity to measure many features of cell size and distribution and cell-wall thickness. Such data can be used to provide a more complete picture of how bread texture properties may be changed or controlled.

18.4.6 Influence of raw materials The textural properties of all cereal-based products are strongly influenced by the quality of the ingredients used and how they are combined in the formulation. In bread the key textural features come from the development of a wheat protein (gluten) network in the dough. The gluten network traps small air bubbles and retains them in the dough where they will later be expanded by the carbon dioxide gas produced from bakers’ yeast fermentation (Cauvain, 1998a). Bakers refer to the formation of the gluten structure from wheat flour, water, yeast and other functional ingredients as ‘development’ and they commonly refer to the ‘gas retention’ properties of the dough. Improvements in dough gas retention yield larger volume and therefore softer loaves which are seen as fresher by the consumer. The formation of a suitable gluten structure with good gas retention properties is then essential to improving bread texture. Many ingredients can and do contribute to the necessary improvements. They include the following. Flour properties Gas retention and therefore loaf volume are improved by increasing the flour protein content. In this case the improvements in texture, usually crumb softness, come from the increase in volume rather than the flour protein per

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se. The protein used may be indigenous (that is derived from the wheat) or it may be added in the form of dried gluten. Cauvain and Mitchell (1986) showed that additions of dried gluten yielded softer bread crumb; however, they did not adjust their data for crumb density. The quality of flour proteins and their ability to retain gas also varies and may have an influence on bread texture. Other flour properties likely to influence bread texture through their volume effect include the bran content of white flours and the level of enzymic activity present (usually cereal alpha-amylase). Bread improvers A wide range of functional ingredients may be added to dough in order to improve its breadmaking qualities (Williams and Pullen, 1998). Since many of the ingredients have a direct effect on dough gas retention they will by implication affect product texture. In a few cases, changes from the effects of functional ingredients on bread texture may occur independently of other product character changes but most commonly texture changes are the result of changes in product moisture content, density or crumb porosity, or any combination of the three. The most common texture change observed by using bread improvers is an increase in the initial softness of the bread. Such changes are seen with the addition of the oxidant ascorbic acid, fat, emulsifiers, such as di-acetyl tartaric esters of monoglycerides, sodium steoryl-2-lactylate, and a range of active enzymes preparations, such as fungal alpha-amylase (Cauvain and Mitchell, 1986).

18.5 Influence of processing and storage 18.5.1 Textural changes in bread during processing Since bread texture is strongly influenced by product density and crumb porosity it is reasonable to assume that those aspects of dough processing which change bread volume and cell structure are important influences. Dough mixing, and in particular the level of energy imparted, is an integral part of dough development. It is well known that increasing the level of energy transferred to the dough during mixing increases bread volume and crumb softness. This is an especially important relationship in no-time dough making processes, such as the Chorleywood Bread Process (CBP) (Cauvain, 1998b). Indeed by using the CBP it is possible to get the same bread volume and crumb softness with a lower protein flour than would be required with other breadmaking processes. The combination of the size of the holes (cells) in the crumb and the thickness of the crumb (cell walls) is a key factor in determining the texture of bread. In no-time dough processes like the CBP the final cell structure in the crumb is largely determined by the sizes of the gas bubbles incorporated

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during dough mixing. The distribution of gas bubbles sizes in the dough leaving the mixer is a function of the mixer design and the conditions under which it is operated. In the case of CBP it is possible to control gas bubble populations in the dough during mixing by varying the pressure in the mixer headspace. Fine, uniform cell structures are encouraged by using pressure below atmospheric while open, more random structures can be obtained using pressures greater than atmospheric (Cauvain et al., 1999). In the socalled Pressure-Vacuum mixers it is possible to vary pressure during mixing and thus directly affect bread cell structure (Cauvain, 1995). Variations in cell size in the loaf crumb influence texture. In general, a fine uniform crumb structure is accompanied with thin cells walls which confer softness to the crumb. If the dough has been suitably developed then the crumb is strong enough to recover from modest deformations, whether sensory or mechanical. On the other hand, larger cell sizes with thick cell walls tend to give the crumb a firmer character. Changes may occur to gas bubble populations during processing which have an adverse effect on bread texture. The processing stage with the greatest potential for introducing textural defects is when the dough pieces are finally shaped (moulded). At this time the rheological properties of the dough are critical in ensuring that the gas bubbles created during the mixing stage are retained in their required form. If the dough rheological properties are unsuitable then the gluten membranes may be damaged during moulding which may lead to loss of gas bubbles or coalescence of smaller ones to form undesirable larger ones (Cauvain and Young, 2000). Dough damage during moulding is most commonly manifested as dark coloured and firm patches in the crumb. The discolouration and firmness of the crumb are a direct result of the thicker cells walls which have formed. Cells of larger size characterise many crusty products such as baguette. In this case the openness of the crumb structure encourages the formation of a crisp or hard outer crust. The open cell structure required may be achieved by changing the dough processing conditions. The most common approach is to encourage some yeast fermentation before the dough enters the final shaping or moulding stage. It is important with such processing conditions that the large gas cells which have formed are retained in the dough, otherwise product quality will be lost. The formation of a crisp crust on baguette and other crusty product is also encouraged by the application of steam in the early stages of the baking process (Cauvain and Young, 2000). The application of moisture and heat encourage enzymic activity which converts the starch into dextrins, confers gloss and contributes to the brittleness of the crust.

18.5.2 Textural changes in bread during storage Freshly baked bread has very different characteristics to that which has been stored for short periods of time. The nature and magnitude of the changes depends on the conditions under which the product has been kept. If held

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unwrapped then the products in most cases will dry out as moisture evaporates from the product to the surrounding atmosphere. The rate at which moisture is lost from the product depends in part on the differential in moisture content between product and atmosphere, and it proceeds faster when the moisture content of the atmosphere is lower. A further factor controlling moisture loss from baked products is the water activity (aw); the lower the aw the lower the rate at which the product will lose moisture (Cauvain and Young, 2000). Wrapping bread will cause it to lose moisture more slowly; however, in this case the shelf life of the product will be limited by the occurrence of mould growth (Pateras, 1998). The appearance of mould on the surface of the bread product is possible because the aw is high enough to permit its growth, typically 0.90–0.98 (Cauvain and Young, 2000). At the end of baking the moisture content and aw of bread crust is usually too low to permit mould growth. During storage, moisture moves from the moist crumb zone to the drier crust. In unwrapped bread the moisture evaporates to the atmosphere, but for wrapped bread an equilibrium is reached between the crumb, crust and atmosphere in the wrapper surrounding the bread. Collectively the changes result in a reduction of the crumb moisture content and an increase in that of the crust. In addition to creating the potential for mould growth, the absorption of moisture by the bread crust causes it to lose its crispness and go ‘soft’. This change reduces the sensory pleasure experienced by the consumer, especially if the expectation is that the crust should be hard (e.g. as with baguette), and the product is seen as being ‘stale’. It is common practice to reduce the loss of crispness of bread crust by wrapping the product in a perforated film. The small holes in the wrapper allow some of the moisture that migrates from the moist crumb to evaporate from the crust which allows the latter to remain hard and crisp. However, the overall effect of the moisture loss is for the crumb to quickly dry out and become hard. In composite products there is considerable potential for moisture migration to and from the bread crumb and the other materials which may be used. There are few components of sandwiches which have a higher water activity than bread. One well-known example is lettuce, and sandwiches made with lettuce frequently go ‘soggy’ during storage, that is the bread crumb moisture content increases. In many other cases the high water activity of bread crumb means that the moisture may be lost to the other sandwich components and the bread will go hard. The application of butter or margarine to the surface of the bread represents an attempt to ‘waterproof ’ the surface of the bread crumb and minimise the migration of water to or from the bread component. Bread staling may be described as the loss of ‘oven-freshness’. It encompasses a number of different changes: • loss of crumb and crust moisture, especially if the product is unwrapped; • loss of crust crispness, more likely to occur if the product is wrapped; • increases in product crumbliness, commonly related to moisture content;

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• increases in crumb firmness; • changes in taste, usually a loss of; • changes in aroma, usually a loss of. Even when bread products are wrapped to prevent moisture losses during storage there is progressive increase in the firmness of the crumb with increasing storage time. This intrinsic firming is the change most commonly referred to as ‘staling’ in the scientific literature and arises because of changes in the crystalline structures of the starch component of the product (Pateras, 1998). Starch in wheat flour undergoes a number of changes during the breadmaking process and during storage. The re-crystallisation process which occurs during storage is known as ‘retrogradation’ and is responsible for the moistureindependent firming of bread crumb. The magnitude and rate of the retrogradation process depend on a number of formulation factors and the conditions of storage. While there are a number of potential ameliorating measures which may be applied, they merely slow down the retrogradation process. Refrigeration and deep freezing may be used to delay both the microbial spoilage and staling processes. The rate of bread staling increases as the storage temperature decreases with the maximum staling rate occurring at around 4 °C. This is unfortunate because refrigerated temperatures are used to restrict microbial growth in sandwiches, and this is the very temperature at which the bread component will stale most rapidly. This effect explains, in part, why bread flavour is not always distinct when white bread is used and may go someway to explaining the popularity of wholemeal or other ‘enriched’ breads. While deep freezing will prevent microbial activity and bring bread staling to a halt, the very act of freezing and thawing is the equivalent of 24 h of bread staling (Pence and Standridge, 1955) because the product must pass twice through the temperature of optimum staling, once on cooling and once on thawing.

18.6 Improving texture The opportunities for improving bread texture can be considered under three main headings: • changing bread structure; • ameliorating storage changes; • using alternative production technologies. The profound influence of changes in bread volume, density and cell structure on the textural properties of bread have already been discussed above and a number of potential ingredient effects introduced. In most cases improvements are aimed at produced a softer bread crumb at the start of shelf life so that, even if the staling rate remains constant during storage, the impression for

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the consumer is that the product is ‘fresher’ later in life. Examples of such effects may be seen for the use of oxidants and emulsifiers. One aspect of bread technology not often considered in understanding how bread texture may be improved relates to the distribution of density in the cross-section of a slice of bread. Careful sampling of crumb density using a coring technique (Cauvain, 1991) will reveal that the crumb is most dense towards the crust and least dense in the centre. The higher density towards the crust arises because the expanding inner crumb has been compressed against the rigid crust formed in the early stages of baking, especially if the product is baked in a pan (Wiggins, 1998). The low centre crumb density provides low resistance to compression, whether sensory or mechanical. The variation in density in the cross-section of a loaf is now a key aspect of bread texture, and those bread processing technologies which do not achieve this aspect have not remained commercially viable. Uniform cross-section density yields a bread which is firm to the touch and considered to be stale. Some bread processing techniques deliberately set out to achieve variations in cross-section density. One such processing technique is known as ‘fourpiecing’ in which the bubbles in the dough are re-orientated to provide a different orientation in the final product (see Fig. 18.5). This four-piecing technique is most commonly seen in the production of bread for sandwich making. Bread staling cannot be prevented though the rate at which it occurs can be lowered. The mechanism by which this can be achieved involves changing the rate at which starch retrogrades during storage. This reduction in the firming rate can achieved through the optimisation of moisture levels in the baked product (Zelesnak and Hoseney, 1986), the addition of ‘antistaling’ emulsifiers, such as glycerol mono-stearate (Russell, 1983), intermediate thermal stable alpha-amylase enzymes (Si, 1997), lipase enzymes (Leon et al., 2002) treatment with alcohol (Pateras, 1998) or the addition of sugars (I’Anson et al., 1990; Cairnes et al., 1991). The significant effect

Fig. 18.5 Four-piecing of bread dough.

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of sugars is shown by the fact that the maximum staling rate for cakes is around 21 °C. A large number of ‘bakeries’ as seen in convenience stores, garages, restaurants and hotels base their production of ‘fresh’ bread on an interrupted baking process. In the baking industry this is referred to as ‘part-’ or ‘parbaking’. In essence the bread product is set but not coloured significantly during the first bake. This is achieved through the adjustment of baking conditions. The part-baked product is stable and may be stored subject to the usual microbial spoilage conditions. It will stale, that is crustiness will be lost and the crumb will go firm. However, in order to serve or sell the product it must be re-heated and this second baking stage largely reverses the adverse changes which have occurred during storage. The crumb will certainly soften as the starch retrogradation process is reversed. To some extent product crustiness returns and the surface colour will darken so that the product appears similar to that of a freshly baked product. After the second bake staling will once again begin and the rate of crumb firming will occur at a much faster rate than before. This means that the shelf life of part-baked products after second baking may be measured in hours rather than days.

18.7 Future trends Bread has existed as a ‘processed’ food for several thousands of years and evolved into a wide variety of products, but many opportunities for developing new products, textures and eating characters remain. In part these opportunities arise because the cellular structure which characterises bread and other cerealbased foods yields products which may be eaten and enjoyed on their own or as key elements of multi-component foods. Cereal-based foods, and bread in particular, provide a suitable ‘wrapper’ or ‘carrier’ for other food components and this has placed them in a key position in the development of modern convenience foods. In modern breadmaking the cell structure of bread is effectively created in the mixer through the incorporation of small air bubbles in the developing dough. In the later processing stages, the bubble populations will be modified. In the dough which leaves the mixer the air bubbles are at their smallest and greatest in number. During processing, gas bubbles become larger in size and less numerous. The key to creating new bread textures lies in understanding these transitions. Some of the key questions which remain to be answered include: • what is the mechanism by which air bubbles are incorporated into the dough during mixing? • how are they stabilised? • why do their numbers decrease?

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• what are the relative contributions of cell wall material and cells (the holes) to texture? • how can bread be prevented from losing its freshness? A number of factors which control the development of bread texture have been discussed above. However, while there is a significant corpus of knowledge related to bread manufacture there are still significant gaps. The key to bread eating qualities lies in the manipulation of the ingredients and processing methods which are used in bread production. The numbers of potential interactions are very large, and it remains difficult to predict the end result with confidence. Continued research and development into breadmaking is beginning to provide the building blocks of the required knowledge base. The arrival of the Pressure-Vacuum mixer, developed by the Flour Milling and Baking Research Association (FMBRA) at Chorleywood, provides bakers with unique opportunities for creating new cell structures in bread products because of the greatly improved possibilities for manipulating gas bubble populations in the mixed dough. Nor are the opportunities limited to the incorporation of air during dough mixing. As long ago as the 1970s FMBRA showed that the modification of the gas composition of the mixer headspace also contributed to bread crumb structure (Chamberlain and Collins, 1979). The advent of considerably increased computing power has enabled cereal scientists to harness the power of image analysis and to form a better understanding of the foam to sponge transition in bread. To conventional light microscopy of frozen dough sections (Whitworth and Alava, 1999) has been added imaging of bread doughs using X-ray scanning (Cauvain, 1997; Whitworth and Alava, 1999) so that a more complete picture of what bubbles are formed in the dough and how they are changed during processing is emerging. Loss of bread freshness (staling) remains a major concern to bread manufacturers and, while much is known about the mechanisms involved, preventing it remains a significant challenge. The advent of enzyme-based solutions appears to offer new opportunities for solving this age-old problem, but there is a long way to go. On the one hand the opportunities for developing new bread texture appear limitless, but on the other they appear limited by our (still) imperfect knowledge of how to make bread textures. Given the long tradition of breadmaking this may seem surprising. This is not the case to those involved in the study of cereal-based foods. The processes which go to make-up the baking remain a mixture of part science, part technology, part craft and part ‘art’.

18.8 Sources of further information and advice The following texts provide useful sources.

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• Technology of Breadmaking (1998) Eds S P Cauvain and L S Young, Blackie Academic & Professional, London, UK. Fabricacion de Pan (Spanish language version) Acribbia S A, Zaragoza, Spain. Provides a comprehensive review of modern breadmaking practices. • Bakery Food Manufacture & Quality: Water Control & Effects (2000) S P Cauvain and L S Young, Blackwell Science, Oxford, UK. Provides a comprehensive review of the role of water in the manufacture of bakery products and discusses many aspects of bakery product texture formation and modification during storage. • Dough Rheology and Baked Product Texture (1990) Eds H Faridi and J M Fabion, Van Nostrand Reinhold, New York, USA. Provides discussion of the principles involved in the measurement of food texture and dough rheology for baked products. • Breadmaking: Improving Quality (2003) Ed. S P Cauvain, Woodhead Publishing, Cambridge, UK. Reviews key recent research on the ingredient and process factors which determine bread quality. • Baked Goods Freshness (1996) Eds R F Hebeda and H F Zobel, Marcel Dekker, Inc., New York, USA. Considers the technology, evaluation and inhibition of staling in baked products. • Bubbles in Food (1999) Eds G M Campbell, C Webb S S Pandiella and K Niranjan, AACC, St Paul, MN, USA. A compendium of papers presented at an international conference, this book provides an in-depth consideration of the contribution of bubbles to food structure and texture.

18.9 References AACC (1987) Approved Methods of the American Association of Cereal Chemists, St Paul, MN, AACC. BOURNE M C (1978) Texture profile analysis, Food Technology, July, 62–6, 72. CAIRNES P, MILES M J and MORRIS V J (1991) Studies of the effect of the sugars ribose, xylose and fructose on the retrogradation of wheat starch gels by X-ray diffraction, Carbohydrate Polymers, 16(4), 355–65. CAUVAIN S P (1991) Evaluating the texture of baked products, The South African Journal of Food Science & Nutrition, 3(4), 81–6. CAUVAIN S P (1995) Controlling the structure: the key to quality, South African Food Review, 22(2), April/May, 51, 53. CAUVAIN S P (1997) Controlling the structure is the key to quality, Proceedings of the Fiftieth Anniversary Meeting of the Australian Society of Baking, Sydney, Australia, October 16, 6–11. CAUVAIN S P (1998a) Bread – the product. In Technology of Breadmaking. Eds S P Cauvain and L S Young, London, Blackie Academic & Professional, 1–17. CAUVAIN S P (1998b) Breadmaking processes. In Technology of Breadmaking. Eds S P Cauvain and L S Young, London, Blackie Academic & Professional, 18–44.

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(2000) Breadmaking. In Cereals Processing Technology. Ed. G Owens, Cambridge, Woodhead Publishing. CAUVAIN S P and MITCHELL T J (1986) Effects of gluten and fungal alpha-amylase on CBP bread crumb properties, FMBRA Report No. 134, Chipping Campden, UK, CCFRA. CAUVAIN S P and YOUNG L S (1998) Technology of Breadmaking, London, Blackie Academic and Professional. CAUVAIN S P and YOUNG L S (2000) Bakery Food Manufacture & Quality: Water Control & Effects, Oxford, Blackwell Science. CAUVAIN S P, WHITWORTH M B and ALAVA J M (1999) The evolution of bubbles structure in bread doughs and its effects on bread structure. In Bubbles in Food. Eds G M Campbell, C Webb, S S Pandiella and K Niranjan, St Paul, MN, Eagan Press, 85–8. CHAMBERLAIN N and COLLINS T H (1979) The Chorleywood Bread Process: the role of oxygen and nitrogen, Baker’s Digest, 53(February), 18–24. CORNFORD S J (1969) Volume and crumb firmness measurements in bread and cake, FMBRA Report No. 25, Chipping Campden, UK, CCFRA. HANSEN L S and SETSER C S (1990) Texture evaluation of baked products using Descriptive Sensory Analysis. In Dough Rheology and Baked Product Texture. Eds H Farid and J M Faubion, New York, Van Nostrand Reinhold, 573–96. I’ANSON K J, MILES M J, MORRIS V J et al., (1990) The effects of added sugars on the retrogradation of wheat starch gels, Journal of Cereal Science, 11(3), 243–8. LEON A E, DURAN E and BENEDITO DE BARBER C (2002) Utilization of enzyme mixtures to retard bread crumb staling, Journal of Agricultural and Food Chemistry, 50(6), 1416– 19. PATERAS I (1998) Bread Spoilage and Staling. In Technology of Breadmaking. Eds S P Cauvain and L S Young, London, Blackie Academic & Professional, 240–61. PENCE J W and STANDRIDGE N N (1955) Effect of storage temperature and freezing on the firming of a commercial bread, Cereal Chemistry, 32(November), 519–26. PLATT W and POWERS R (1940) Compressibility of bread crumb, Cereal Chemistry, 17, 601– 21. RUSSELL P L (1983) A kinetic study of bread staling by differential scanning calorimetry, Starch/Starke, 35, 277–81. SI J Q (1997) Synergistic effect of enzymes for bread baking, Cereal Foods World, 42(10), 802–3. SZCZESNIAK A S (1963a) Classification of textural characteristics, J Food Science, 28(4), 385–9. SZCZESNIAK A S (1963b) Objective measurement of food texture, J Food Science, 28(4), 410–20. SZCZESNIAK A S, BRANDT M A and FRIEDMAN H H (1963) Development of standing rating scales for mechanical parameters of texture and correlation between the objective and sensory methods of texture evaluation, J Food Science, 28(4), 397–403. WHITWORTH M B and ALAVA J M (1999) The imaging and measurement of bubbles in bread doughs. In Bubbles in Food. Eds G M Campbell, C Webb, S S Pandiella and K Niranjan, St Paul, MN, Eagan Press, 221–32. WIGGINS C (1998) Proving, baking and cooling. In Technology of Breadmaking. Eds S P Cauvain and L S Young, London, Blackie Academic & Professional, 120–48. WILLIAMS T and PULLEN G (1998) Functional ingredients. In Technology of Breadmaking. Eds S P Cauvain and L S Young, London, Blackie Academic & Professional, 45–80. ZELESNAK K J and HOSENEY R C (1986) The role of water in the retrogradation of wheat starch gels and bread crumb, Cereal Chemistry, 63(5), 407–11. CAUVAIN S P

19 Analysing and improving the texture of cooked rice S. K. Kim, Dankook University and C. O. Rhee, Chonnam National University, Korea

19.1 Introduction Cultivated rice at present sustains two-thirds of the world’s population. Asian countries produce 91.1% and consume 90.2% of the world production of milled rice (International Rice Commission, 2000). Although there are a number of different markets, rice has traditionally been thinly traded with a market less than 6% of the world production of 397.2 million tonnes of milled rice. Most of this is consumed as cooked rice, and only a small portion (0.86% in the world and 0.67% in Asia) is utilized for processing, such as rice cake in Korea (Kim et al., 2001) and some other products (Juliano, 1998; Kohlwey et al., 1995). Grain quality denotes different properties to different sectors of the rice industry – farmers, processors and millers, retailers, shoppers who buy the milled rice in the market, the consumers themselves, and nutritionists and policymakers (Juliano, 1972). Criteria for price and market quality of milled rice, however, are not directly related to those for cooking and eating quality and for the nutritional quality of the cooked rice. Aging or storage changes should also be considered in the effective implementation of tests for rice quality. The freshly harvested grain undergoes texture changes during the first three months of harvest; hence, it is preferable to carry out milling tests and tests for cooked rice texture on aged rice (Juliano, 1985a). Aspects of aging of rice have been reviewed in depth by Juliano (1985a), Chrastil (1994), and Zhou et al. (2002). The mechanism of aging involving lipids, protein, and starch was first proposed by Moritaka and Yasumatsu (1972). Zhou et al. (2002) modified the mechanism and proposed that alteration of integrity of the cell wall by the release of phenolic acids should

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also be considered in the aging process. Sowbhagya and Bhattacharya (2001) discussed the aging process in relation to pasting properties. The rice produced in Asia is indica and japonica type with a wide range of amylose contents, and preferences for rice type and its amylose content differ from region to region (Juliano, 2001). Moreover, some markets (e.g. India) have a preference for stored rice, while others (e.g. Korea, Japan, China) favor fresh rice. Freshness is considered so highly in the Japanese market that tests are devised for its measurement (Matsukura et al., 2000). Texture of cooked rice has been shown to govern its acceptance by consumers when eaten as the whole grain (Okabe, 1977). Texture can be defined as a multi-dimensional characteristic that humans can perceive, define, and measure (Szczesniak, 1987). Therefore, sensory evaluation is critical, although instrumental measurement of textural properties is also common practice. This chapter gives an overview of rice quality evaluation methods and recent developments in the cooking and eating quality of rice. Studies on criteria and tests for rice grain qualities up to the early 1980s are well documented (Juliano, 1985a). In many countries the cooking process involves pre-soaking the rice in cold or hot water. Although considerable information about the hydration of rice is available in the literature, this has not been highlighted in the reviews on rice quality (Juliano, 1985a; Zhou et al., 2002). The hydration process has therefore been included in this chapter.

19.2 Criteria for evaluating rice quality A recent survey on the criteria and methods used by Asian countries to evaluate the cooking and eating quality of rice in rice-breeding programs is summarized in Table 19.1 (Juliano, 2001). The criteria include physicochemical properties, cooking test, and properties of cooked rice. 19.2.1 Physicochemical properties Measurement of physicochemical properties is an indirect method of estimating eating quality based on chemical composition and gelatinization properties. Among the chemical components of rice, amylose and protein contents are the most important factors influencing eating quality. Amylose content is determined by a simplified colorimetric method (Juliano, 1971), or by nearinfrared reflectance spectroscopy (NIRS) (Delwiche et al., 1996). For the determination of protein content, the Kjeldahl method is most popular, but recently NIRS has also been used (Delwiche et al., 1996). The authentication of rice has been attempted using NIRS (Kim et al., 2003). Okadome et al. (2002) proposed chemometric formulae for predicting amylose and protein contents based on the surface and overall physical properties of single-cooked milled rice as predictive variables. Amylose content is considered to be the single critical factor affecting eating quality (Noda et al., 2003). Preferred

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rice grain quality based on amylose content varies between countries (Juliano, 2001). It was reported that protein content of Japanese milled rice was inversely related to eating quality as determined by sensory evaluation (Ishima et al., 1974). However, Champagne et al. (1999) found that protein content of short- , medium- , and long-grain rice produced in the USA had a poor correlation with sensory attributes. Alkali spreading value, which is related to the gelatinization temperature of raw starch granules, involves incubating six grains of milled rice in 100 mL of 1.7% KOH at 30 °C for 23 h (Little et al., 1958). It measures the degree of spreading using a seven point scale (1 = intact, 7 = greatly dispersed) and corresponds to gelatinization temperature as follows: 1–2, high (74.5– 80 °C); 3, high-intermediate; 4–5, intermediate (70–74 °C); and 6–7, low (< 70 °C). Gel consistency was originally developed to differentiate high-amylose rice with contrasting amylograph pasting viscosities (Cagampang et al., 1973). The method consists of dispersing 100 mg of milled rice flour in 2.0 mL of 0.2 N KOH, heating for 8 min in vigorously boiling water, cooling in an icewater bath, and measuring gel length. The gel consistency values are classified as soft (61–100 mm), medium (41–60 mm) or hard (26–40 mm), which are positively correlated with gelatinization temperature of high amylose rice, and negatively correlated with protein content (Juliano, 1998) and amylograph consistency (Perez, 1979).

19.2.2 Cooking test Batcher et al. (1963) classified the various methods in the world of cooking milled rice into groups: • • • • • •

oven cooking method; cooking in a small amount of water; cooking in a medium amount of water; cooking in a large amount of water; steaming; and cooking in water or steaming with oil added.

However, in rice-breeding programs, rice is cooked by being dropped in boiling water until the center is gelatinized or, as in most of Asia, by being washed, possibly pre-soaked, and then cooked at a fixed water–rice ratio, which varies between countries depending on the amylose content of the rice (Juliano, 2001). Aspects of the cooking properties of milled rice have been reviewed by Juliano (1985b). The methods commonly used for evaluation of rice cooking tests are aroma and measurement of pasting viscosities by either Rapid Visco Analyser (RVA, Newport Scientific Pty Ltd, Australia) or Brabender viscoamylograph (Table 19.1). Viscosity properties measured on a Brabender viscoamylograph

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Table 19.1 Major methods used in Asian rice-breeding programs to evaluate the cooking and eating quality of milled rice (Adopted in part from Juliano, 2001 and modified) Rice property

Frequencya

Methodology

Physicochemical Amylose content

11 (I)

Colorimetric (Juliano, 1971); NIRS (Delwiche et al., 1996) Kjeldahl; NIRS (Delwiche et al., 1996) Little et al. (1958) Cagampang et al. (1973)

Protein content Alkalispreding value Gel consistency Cooking Aroma RVA viscosity Amylograph viscosity Cooked rice Appearance/gloss Hardness

5 (I) 9 (M) 9 (M) 12 (S) 6 (I) 3 (I) 8 (S)/ 2 (I) 7 (S)/ 2 (I)

Texture

6 (S)/ 5 (I)

Stickiness/cohesiveness Taste/flavor Aroma

7 (S) 7 (S) 5 (S)

IRRI (1971) AACC method 61-02; Japanese method (Ohtsubo et al, 1998) Juliano et al. (1985) Toyo Midometer for gloss (Yoon, 2002) Instron (OTMS cell) (Perez and Juliano, 1979) Texture profile analysis (Champagne et al., 1998, 1999; Perdon et al., 1999)

a

Number out of 13 respondents from 11 countries. Symbols in parentheses, I, M and S, are instrumental, manual and sensory methods, respectively.

are similar to those measured with RVA (Blankeney et al., 1991). The Japanese RVA method and the American Association of Cereal Chemists method (AACC, 2000) differ in that the former uses 3.5 g instead of 3.0 g of flour and heats only to 93 °C rather than to 95 °C, but it cooks for longer by prolonging the holding time at 93 °C from 4 to 7 min (Ohtsubo et al., 1998). Since a main component of rice grain is starch, gelatinization properties of rice or rice starch are closely related to eating quality (Ohtsubo et al., 1998). During heating in water, starch granules swell and amylose leaches out. The increase in viscosity observed during heating of starch in water in an amylograph or RVA is mainly contributed by the swollen granules. Breakdown of viscosity is caused by breakdown of gelatinized starch granules, of which the degree of breakdown is dependent on rigidity of the swollen granules. The breakdown of the amylograph is positively correlated with the overall eating quality of Japanese rice (Chikubu et al., 1985). Bhattacharya and Sowbhagya (1978, 1979) recommended running the amylograph at a fixed-peak viscosity rather than fixed-concentration (i.e. 10% solid), since at fixed-peak viscosity the breakdown was the primary index in evaluation of rice varieties differing in amylose contents, and of the change in pasting behavior of rice during aging (Sowbhagya and Bhattacharya, 2001).

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Amylose content is inversely proportional to the degree of granule swelling during pasting (Lii et al., 1966; Sasaki and Matsuki, 1998). Noda et al. (2003) reported that amylose content (3.5–17.2%) of rice starch was significantly positively correlated with RVA peak viscosity, breakdown, set-back, and pasting temperature. Han and Hamaker (2001) reported that the paste breakdown of rice starches with a fairly narrow range of amylose content (15.1–17.9%), but a wide variation in RVA pasting curve, was affected by the fine structure of amylopectin. Proportion of long chains (DPn > 100), which represents the long B chain of amylopectin, was negatively correlated with breakdown and proportion of short chains (DPn = 17), which would be mostly A chains of amylopectin, was positively correlated with breakdown. Martin and Fitzgerald (2002) reported that proteins in rice grain influenced RVA viscosity curves both through binding water, which increased the concentration of the dispersed and viscous phase of gelatinized starch, and through the agency of a network linked by disulfide bonds.

19.2.3 Properties of cooked rice The properties of freshly cooked rice that are important to consumers include intact grains, appearance, gloss, softness, stickiness, taste, and aroma (Table 19.1). These properties are commonly evaluated by sensory test. Instruments are also used to complement sensory scores (Champagne et al., 1999; Ohtsubo et al., 1998). Gloss of cooked rice is measured by a Midometer (Toyo Rice Cleaning Machine Co. Ltd, Japan) in Japan and Korea. Rice (33 g) in a measuring device is cooked in a water bath at 80 °C for 10 min, rested for 2 min at room temperature, and measured for gloss, which is converted to eating quality score (100 point scale). The eating quality score of Korean rice indicates that the score varies by variety and degree of milling as shown in Fig. 19.1 (Yoon, 2002).

19.3 Hydration of rice Soaking of rice in water is one of the unit operations during cooking, parboiling or production of quick cooked rice. Soaking is also an essential step in wetmilling of rice flour (Chiang and Yeh, 2002). During the soaking process water diffuses into the rice and is absorbed by the starch. The absorption of liquid water by rice grain proceeds by a heterogeneous mechanism. Mathematical analysis of non-stationary-state diffusion in solids of arbitrary shape can be simply expressed as follows (Becker, 1960):

m – m0 = k 0 t and

k 0 = 2 ( ms – m0 ) S V π

[19.1]

D

[19.2]

456

Texture in food 84 82 C 80 B 78

Eating quality score

76 74 72 A 70 68 66 64 62 60 10

11

12 13 14 15 16 Degree of milliing (%)

17

18

Fig. 19.1 Relationship between degree of milling and eating quality score measured with Toyo Midometer. A = Odaebyeo, B = Hwasungbyeo, and C = Ilpumbyeo. (Adopted from Yoon, 2002).

where m is the average moisture content (g/g) at a given absorption time (t), m0 is the initial moisture content (g/g), ms is the effective surface moisture content (g/g), S is the surface area, V is the volume, and D is the diffusion coefficient. Equation 19.1 indicates that the moisture gain of a sample immersed in water should be approximately proportional to the square root of the absorption time.

19.3.1 Water uptake rate In general, the absorption of water by a given rice is a function of time, temperature, and initial moisture content. In practice, variety, degree of milling, and storage conditions should be considered in evaluating the hydration rate of rice. Varietal differences in hydration rate of japonica type milled rice have been reported (Kim et al., 1984b; Kim et al., 1985; Lee et al., 1983). The equilibrium moisture content (EMC) of milled rice at room temperature is 30–32% for japonica type (Cho et al., 1980; Lee et al., 1983; Suzuki et al., 1977; Takeuchi et al., 1997a), 28–29% for indica type, (Bhattacharya et al., 1972) and 30.4 % (Chiang and Yeh, 2002) and 34–37% for waxy rice (Bhattacharya et al., 1982). It was reported that EMC of milled rice was very highly significantly related inversely to amylose content and directly to kernel chalkiness index (Bhattacharya et al., 1982).

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The diffusion coefficients of milled rice reported in the literature are summarized in Table 19.2. Steff and Singh (1980) and Suzuki et al. (1977) assumed that the rice grain is spherical, and the others assumed it to be prolate spheroid. This gives a difference in the calculations of S and V of rice grain, hence affecting the calculated value of diffusion coefficient. Degree of milling (Yoon, 2002) and defatting (Kim et al., 1986) of milled rice also affect the hydration rate. Zhang et al. (1984) developed a computer-aided model using finite elements to analyze non-linear diffusion in milled rice during soaking at 60 °C. The contour plots of the moisture distribution showed that variation in moisture content was between 35 and 50% at 30 min of soaking, whereas at 60 min the variation was between 40 and 51%. The average mass diffusivity changed from an initial value of 6.4 × 10–7 m2/hr to 4.2 × 10–7 m2/h in 10 min of soaking. This period corresponded to intervals of maximum water uptake, after which the water uptake slowed down as did the change in mass diffusivity. Average mass diffusivity decreased from 6.4 × 10–7 m2/h to 3.0 × 10–7 m2/h as the moisture content increased from 13 to 50%, which indicates that mass diffusivity is a function of concentration. The diffusion coefficients of brown rice at soaking temperature of 30 °C are 1.78–3.68 × 10–6 cm2/min (Song et al., 1988), 3.51 × 10–6 cm2/min (Kim et al., 1984a), 5.52 × 10–6 cm2/min (Steff and Singh, 1980), and 2.31 × 10–6 cm2/min (Han et al., 1996). These results suggest that the diffusion coefficient of brown rice is fairly constant regardless of the variety. The hydration rate of brown rice at 60 °C was reported to be 0.0394–0.0552 min–1/2 (Lee and Kim, 1994), that at 100 °C being 0.0743–0.1419 min–1/2 (Kim and Suh, 1990). Lee and Kim (1994) reported that the water absorption rate of brown rice had a positive correlation with numbers and thickness of aleurone layers of the kernel, implying that the differences in water absorption among brown rice are due to the differences in bran structure of the kernel. Yoon (2002) demonstrated that moisture gain of brown and milled rice soaked in water for 20 min at 30 °C showed a linear relationship with degree of milling and the slope was essentially the same among three rice cultivars. An increase of degree of milling by 1% resulted in an increase of moisture gain by 1.06 times. Moisture gain was highly significantly related inversely to protein and fat contents. Table 19.2 Diffusion coefficients of milled rice at soaking temperature of 30 °C D × 104 (cm2/min)

Number in sample

Reference

2.28–2.64 26.0 90.0 8.46–9.95 1.09–4.61 2.55–4.03

2 1 1 6 21 3

Cho et al. (1980) Steff and Singh (1980) Suzuki et al. (1977) Lee et al. (1983) Kim et al. (1984a) Song et al. (1988)

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The water uptake rate constants of brown and milled rice linearly decrease as storage time increases when rice in laminated film pouch is stored at 4– 30 °C for up to five months (Cho and Kim, 1990, 1993; Han et al., 1996). The change in water uptake rate is more pronounced in milled rice and at elevated temperatures. The EMC remains fairly constant for both brown and milled rice, but the time to reach EMC increases as storage time is prolonged and temperature is elevated (Cho and Kim, 1990, 1993).

19.3.2 Temperature dependence of diffusion coefficient Since diffusion coefficient is influenced by water temperature, diffusion coefficient as a function of temperature can be expressed using Arrhenius relations (Cho et al., 1980): D = D0 exp (–Ea/RT)

[19.3]

where D0 is the diffusion constant, Ea is the activation energy (cal/mol), R is the gas constant (cal/mol·K), and T is the absolute temperature (K). Steff and Singh (1980) reported the temperature dependence of diffusion coefficient (m2/h) of endosperm (D1) and bran (D2) on the assumption that endosperm and bran were homogeneous, isotropic materials and diffusion coefficients were not a function of moisture concentration:

and

D1 = 1.29 × 10–2 exp (–3.43 × 103/T)

[19.4]

D2 = 1.82 exp (–5.40 × 103/T)

[19.5]

where E a for endosperm and bran layer was 6800 cal/mol and 10 700 cal/mol, respectively. Kim et al. (1984b) found that no two rice varieties had the same D among Korean milled rice. The value of D0 ranged from 0.1940 to 2.1173 and Ea from 3600 to 6500 cal/mol with average of 4700 cal/mol. This value agrees well with the findings of Cho et al. (1980) and Lee et al. (1983). Suzuki et al. (1977) reported that Ea of soaking of Japanese rice was approximately equal to 3000 cal/mol. These results imply that Ea of milled rice during soaking at room temperature depends on variety and degree of milling. The temperature dependence of D for Korean brown rice was reported to be (Kim et al., 1984a): D = 0.352 exp (– 4.73 × 103/T)

[19.6]

where D is in cm2/sec and Ea is 9400 cal/mol, which was comparable to 10 700 cal/mol in Eq. (19.5). The D values of brown rice during soaking at 30 °C calculated from Eq. (19.5) and Eq. (19.6) were 5.52 × 10–6 and 3.51 × 10–6 cm2/min, respectively. This result implies that D at the initial stage of soaking of brown rice is controlled by the bran layer, and that D of brown rice may be independent of variety, as discussed earlier.

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19.3.3 Changes in hardness of rice during soaking Kim et al. (1984a) showed that the changes in hardness of brown rice during soaking at 20–80 °C could be expressed: ln H = – k h t H0

[19.7]

where H0 and H are the hardness of brown rice at soaking time of zero and t (min), and kh is the reaction rate constant. The Ea calculated was 12 000 cal/mol at soaking temperature of 40–60 °C and 6500 cal/mol at 70–80°C, which indicates that Ea of hydration of brown rice changed at 60–70°C. In general, heterogeneous catalytic reactions involve the diffusion of reactants; Ea observed in the diffusion-limited reaction is about one-half of Ea observed in the case of reaction only (Suzuki et al., 1976). The gelatinization temperature of starches of rice employed by Kim et al. (1984a) was reported to be 60–65 °C (Chung et al., 1982). The changes of Ea during soaking, therefore, imply first that the decrease in hardness of brown rice at 40–60 °C was due to the gradual absorption of water by the bran layer resulting in the physical changes, and second that at temperature above the gelatinization temperature the diffusion-limited reaction, i.e. partial gelatinization of endosperm, controlled the changes in hardness.

19.4 Factors affecting cooking quality Cooking of rice is closely related to eating quality because the texture of cooked rice depends on the degree of cooking. Since the texture of cooked rice is a major feature of its quality, the prediction of the behavior of rice during cooking is essential to understanding cooking quality of rice.

19.4.1 Cooking mechanism It is reasonable to consider that the cooking process of rice comprises the gradual absorption of water from the surface to the inner portion of the rice grain and physicochemical changes or reactions of rice constituents with water by heating. Suzuki et al. (1976) adopted the rheological method using the parallel plate plastometer for measurement of degree of cooking of rice at 75–150 °C, on the assumption that the ratio of the soft part to the original volume was convertible into the degree of cooking. One gram of rice with 1.4 g of water in a 12 mm (inner diameter) × 28 mm brass vessel, made capable of enduring inner pressure at 150 °C by sealing with a packing and a screw cap, was cooked in an oil bath. The deformation ratio of cooked rice grains showed a clear linear relationship with cooking time at various cooking temperatures (Fig. 19.2). The deformation ratios with each cooking temperature

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Deformation ratio (–)

1.0

0.5 150 °C

130120 110 100 °C

80 °C 75 °C

90 °C 0.1 0.5

1

5 10 Cooking time (min)

50

100

Fig. 19.2 Relation between the deformation ratio of cooked rice grains and the cooking time. (Reprinted, with permission, from J of Food Sci, 41(5), 1180–83, ©Institute of Food Technologists, Chicago, Ill, USA 1976).

reached a constant value after some cooking time, which was called the terminal point of cooking. The degree of cooking (α) can be expressed: α=

X – X0 Xe – X0

[19.8]

where X0, X and Xe are the deformation ratios at cooking time zero, t and the terminal point of cooking, respectively. If it is accepted that the deformation ratio is proportional to the degree of cooking, the rate of the uncooked portion of rice gives a straight line with cooking time: ln (1 – α) = – kt

[19.9]

where k is the reaction rate constant. The Ea for cooking calculated was 19 000 cal/mol at temperatures of 75– 100 °C and 8800 cal/mol at 100–150 °C. As explained earlier, heterogeneous catalytic reactions involve the diffusion of reactants: Ea observed in the diffusion-limited reaction is about one-half of Ea observed in case of reaction only, because Ea of diffusion seems negligibly small compared with the value of the reaction. Based on these results, Suzuki et al. (1976) concluded that the cooking rate was limited by the reaction rate of the rice component with water at temperatures below 100 °C, and it was limited by the diffusion of water through the cooked layer toward the interface of uncooked core where the reaction occurs. Cheigh et al. (1978) measured the hardness of milled rice grains during cooking and found that the reciprocal of hardness followed a similar pattern to that shown in Fig. 19.2. Activation energy they found was 17 200 cal/mol at a temperature below 100 °C and 8900 cal/mol at a temperature above 100 °C. Cho et al. (1980) also reported essentially the same Ea values for milled rice as Cheigh et al. (1978). In the case of brown rice, Ea was 15 000–16 000

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and 8500 cal/mol at cooking temperatures below 100 °C and above 100 °C, respectively (Kim et al., 1984a). Suzuki et al. (1977) studied the cooking rate equations of milled rice by measuring the change in weight of rice accompanying cooking at 70– 98.5 °C, and concluded that the cooking rate was mainly limited by the reaction rate of rice components with water and the equivalent value of Ea of the reaction rate was equal to 20 000 cal/mol, although the cooking rate was relatively influenced by the diffusion rate of water in the cooked rice layer at 98.5 °C. 19.4.2 Effect of degree of milling and soaking on cooking rate Cheigh et al. (1978) reported that the degree of milling did not affect Ea of cooking, but the lower degree of milling resulted in a slower cooking rate, thus increasing the cooking time. Desikachar et al. (1965) reported that removing the outer 1% of the brown rice kernel increased the water absorption during cooking to that of highly milled rice. The wax content of this outer 1% layer was implicated in reducing the rate of water absorption during cooking. Champagne et al. (1990) reported that the removal of the outer 1.3% of rice kernel led to the largest decrease in onset and peak temperature values of differential scanning calorimetry. The pre soaking of rice increases the cooking rate constant of milled rice (Cho et al., 1980; Suzuki et al., 1976) and brown rice (Kim et al., 1984a).

19.4.3 Temperature dependence of terminal point of cooking Kim et al. (1984a) found that the temperature dependence of the terminal point of cooking of brown rice could be expressed as z-value based on cooking temperature and time: z = 4.6 T 2 /Ea = 10/logQ10

[19.10]

Their results are presented in Table 19.3. The z-value at cooking temperature of below 100 °C was about half of that at over 100 °C, which was the opposite to Ea. The data indicate that the cooking process of brown rice could also be explained by z-value.

Table 19.3 Temperature dependence of the terminal point of cooking of brown rice Cooking temperature (°C)

z (°C)

Q10

Ea (cal/mol)

80–100 100–130

43 89

1.71 1.30

13 700 8000

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19.4.4 Diffusion of water during cooking Suzuki et al. (1976) measured the gravimetric change of rice grains during cooking at evaluated temperatures, which was analyzed using a shell and core model, assuming that the gelatinization was much more rapid than the rate of water diffusion in a grain so that the starch gelatinization occurred only at the interface of the uncooked core. They reported that D (cm2/min) of rice was 1.86 × 10–3 at 110 °C, 3.32 × 10–3 at 130 °C, and 5.05 × 10–3 at 150 °C. The gravimetric change of a rice grain which has been immersed in water at 60–80 °C shows that it is the rapid diffusion process which is dominant in the initial stage of the change in moisture content in a grain, but in the middle stage (50–150 min) the rise of moisture content is reduced and approximated to a straight line, of which the gradient may be regarded as an index of the rate of gelatinization (Takeuchi et al., 1997b). Based on this, they proposed the rate of increase in moisture content (M, g water/g rice) referred to as the gelatinization rate as a function of temperature:

dM = 5.208 × 10 –5 ( T – 331) 2 dt

at 331 < T < 355

[19.11]

dM = 0.030 dt

at 355 < T < 373

[19.12]

and

To determine the rate of starch gelatinization which was responsible for the increase in moisture content in the rice grain during cooking, Gomi et al. (1998) observed the water diffusivity of rice starch/water mixture using a pulsed-field-gradient-nuclear magnetic resonance (NMR) method. The water diffusivity decreased as heating time increased. On the assumption that the time course of changes in moisture content followed a first-order process, a rate constant of 10–2 s–1 was obtained at temperatures of 66–80 °C. This value was 100-fold smaller than that reported by Suzuki et al. (1976).

19.4.5 Moisture profile in a rice grain during boiling Takeuchi et al. (1997b) reported the moisture profile in a quenched rice grain after boiling in excess water using an NMR transverse relaxation time (T2 in milliseconds) imaging method at 200 MHz. The NMR image showed that the moisture content (g water/g rice) after 6 min boiling reached 0.6 at the peripheries while the moisture content was less than 0.45 in the middle. The image after 12 min boiling indicated that swelling due to gelatinization was almost completed, although a core of low moisture content of less than 0.55 remained in the middle part. Sometimes a low moisture core was formed not in the middle but somewhat unsymmetrically shifted to the dorsal side. The thicker protein layer on the dorsal surface, which restricted water

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Moisture content (g water/g sample)

percolation in the rice grain during boiling, was attributed to the unsymmetrical behavior of the rice in moisture content. Kim et al. (1996a) reported that most protein bodies existed in the periphery of the milled rice. The moisture distribution map in partly boiled grains of rice which were cellulase treated to destroy the cell wall showed little difference with nontreated grains (Takeuchi et al., 1997b). Therefore, it seems that the effect of the cell walls on moisture transport during boiling was insignificant. The thickness of the endosperm cell wall was reported to be about 0.5 µm in diameter (Kim et al., 1996a). Takeuchi et al. (1997a) developed a method of quick imaging a onedimensional NMR T2 profile to observe moisture profile in a rice grain. They applied it to the real-time measurement of the moisture profile in a rice grain every two minutes during boiling for 40 min. The change of moisture profile in a rice grain during boiling is shown in Fig. 19.3, in which the moisture content along the lateral line from the dorsal surface to the ventral surface is plotted. The moisture content (g water/g rice) increased rapidly in the ventral side to reach about 0.75–0.80 after 14 min boiling, which means that the rate of moisture increase was 0.10 per min. On the other hand, the rise of the moisture profile was moderate in the dorsal side and in the middle part – 0.04 per min between 12 min and 14 min boiling. The rise further declined to about 0.01–0.02 per min between 14 min and 22 min boiling, and reached 0.80 after 22 min boiling, which remained constant for the remaining heating time. The value of moisture content of 0.8 agreed with the maximum moisture content which was attained when a rice grain was boiled in excess water at 100 °C (Takeuchi et al., 1977b).

0.9 0.8 0.7 0.6 0.5

36 min. 22 min. 14 min.

–2

–1

0

1

2 (min)

12 min.

0.4 –1

0 Position (nm)

1

Fig. 19.3 Real-time NMR imaging observation of the change of moisture profile along the parallelepiped virtually dissected in a rice grain during boiling at 100 °C (Reprinted, from Takeuchi et al. (1997a), J of Food Engineering, 33, 181–92 with permission from Elsevier).

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19.5 Testing texture quality The sensory test is the most fundamental test of the eating quality of rice. It provides information on appearance, aroma, taste, hardness, stickiness, and overall quality (Table 19.1). 19.5.1 Sensory test The sensory properties of cooked rice are measured by taste panel. The methods commonly used are the preference test (Kim and Kim, 1986; Kim et al., 1995), ranking test in the Philippines (Juliano, 2001), and descriptive test (Champagne et al., 1998; Meullenet et al., 1998, 2000; Sesmat and Meullenet, 2001). The preference test involves scoring the cooked rice quality on 1–9 or 4– 7 point hedonic scales. A control variety may be used as a reference. The sensory method used in Japan is –3 to + 3 with 0 for the control. The ranking test for preference and acceptability using a 30-member consumer panel is used in the Philippines. The descriptive test is objective and promising but is labor-intensive and time-consuming. The sensory texture attributes in the evaluation of cooked rice include clumpness, stickiness, hardness and moistness in Korea (Kim and Kim, 1986; Kim et al., 1995) and hardness and stickiness in Japan (Ohtsubo et al., 1998) for short-grain japonica rice, and hardness, stickiness, cohesiveness, roughness, toothpack, and loose particles in the USA for short- , medium- and long-grain rice (Champagne et al., 1999; Muellenet et al., 1998, 2000; Sesmat and Muellenet, 2001). The most common sensory texture attributes are therefore hardness and stickiness, which can be applied to all cooked rice regardless of rice types and can also be easily measured with an instrument. Stickiness is a very important sensory property in many Asian countries. For those countries where flaky or non-sticky rice is preferred, stickiness is an undesirable property of rice. For example, medium- and short-grain classes in the USA are generally referred to as the sticky rice and correspond to the japonica varieties. On the other hand, long-grain rices tend to be bland, flaky, and dry when cooked and correspond generally to the indicas. The sensory texture attributes are affected by factors such as variety (Kim et al., 1995; Perez et al., 1993a), amylose content (Perdon et al., 1999; Windham et al., 1997), protein content (Windham et al., 1997), gelatinization temperature (Del Mundo, 1989), post-harvest processing conditions (Champagne et al., 1998; Lyon et al., 1999; Rousset et al., 1995), the rice to water ratio during cooking (Kim et al., 1995), and cooking method (Kim and Kim, 1986). Cooked rice with low amylose is soft and sticky, while rice with high amylose is firm and fluffy (Perdon et al., 1999). The sensory characteristics such as color, shininess, clumpiness, softness, stickiness, and ease of swallowing of rice cooked with a pressure cooker are significantly greater than those with an electric cooker (Kim and Kim, 1986). Lyon et al. (1999) reported that sensory properties relating to stickiness had significant positive correlation

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with amylose content and negative correlation with protein content. Kim et al. (1995) reported that hardness and stickiness of cooked rice were affected by its moisture content. Meullenet et al. (2000) investigated the effect of storage conditions of long-grain rough rice on sensory profiles of cooked rice using sensory descriptive methods. Storage temperature (4, 21, and 38 °C) and duration (up to 36 weeks) significantly affected adhesiveness to lips, an indicator of rice stickiness. Increasing storage temperatures decreased rice stickiness. Rice stickups reached a maximum after 20 weeks of storage and decreased significantly after 36 weeks of storage. Cooked kernel hardness decreased with increasing storage moisture content (10–14%) and reached a maximum between 15 and 22 weeks of storage depending on the rough rice storage moisture content.

19.5.2 Physical properties of cooked rice Among various physical properties, hardness and stickiness are the most frequently tested parameters (Table 19.1). Hardness and stickiness of cooked rice measured with an Instron food tester correlate significantly with amylose content, but because stickiness is easily predictable based on amylose content (r = –0.92, p < 0.01), texture measurement concentrates on hardness (r = 0.77, p < 0.01) (Perez and Juliano, 1979). The instruments used to determine the hardness and texture profile of cooked rice in rice-breeding programs (Table 19.1) are the Instron food tester equipped with an Ottawa texturemeasuring system extrusion cell (Perez and Juliano, 1979; Perez et al., 1993b) and TA-XT2 texture analyzer (Stable Miro System, UK) (Champagne et al., 1998, 1999; Perdon et al., 1999). Perez et al. (1993b) reported that Instron hardness of cooked rice was inversely related with RVA breakdown. The basic principle involved in the determination of hardness is based on the maximum force to press on the cooked rice. Stickiness is the force required to pull a device imbedded in or pressed on the cooked rice (Lee and Peleg, 1988). The force is sometimes treated as adhesiveness according to the texture profile analysis. A typical texture profile analysis curve obtained using a TA-XT2 texture analyzer is shown in Fig. 19.4 (Champagne et al., 1998, 1999). Mossman et al. (1983) developed a method to determine the cooked rice stickiness with an Instron tester using a 2 g sample, which could be distinguished easily between long grain and sticky varieties, and among sticky varieties (Fellers et al., 1983). The stickiness was affected by the water to rice ratio during cooking (Mossman et al., 1983) and by hot-air treatment of rice (Fellers et al., 1983). When a medium rice was treated with a hot air (204 °C) blast, stickiness measured by Instron decreased as toasting time increased. Fellers et al. (1983) found that Instron stickiness of heat-treated rice was positively correlated with organoleptic stickiness scores.

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20 H1

Height of first curve

Force (kg)

15

10

Area first bite Area second bite

5 A1 0 D1 (4.9 mm)

A2 A3

D2 Area of negative curve

–5

Fig. 19.4 Typical texture analysis (TPA) curve of 1-g rice samples. TPA text parameters: force–distance test, compression plate set at 5 mm to travel 4.9 mm at 1 mm/sec. Attributes on curves: H1, hardness (kg), measurement of force at peak of first curve; A1, area under first curve; A2, area under second curve; A4, measured from first data point to probe reversal point; A5, measured from first probe reversal point to point where force returns to zero; A2/A1, cohesiveness, ratio of area under curves A2/A1; A3, adhesiveness, area of negative force curve, representing work to separate plunger from sample on upstroke after first curve; D2/D1, springiness, ratio of D2 to D1, where D1 is total distance (4.9 mm) traveled by plunger on downstroke and D2 is distance traveled on downstroke by plunger from point of sample contact to end of downstroke. (Source: Champagne et al., 1998, 1999).

Lee and Peleg (1988) employed a Roano Surface Tensiometer (Biolar Corp., USA) to determine the attractive forces between individual grains which were mounted one on top of the other in parallel portion, and demonstrated that the attractive forces could be used as a physical criterion to distinguish between sticky and flaky rice cultivars. Increase in the water to rice ratio during cooking resulted in an increase in the attractive forces, which was most probably associated with extractability. Kim et al. (1991) reported that the solubles and soluble amylose content were negatively correlated with Instron hardness and positively correlated with Instron adhesiveness of cooked rice. The Texturometer (Zenken Co., Japan) has been used to measure hardness and stickiness of cooked rice in Japan (Suzuki, 1979) and in Korea (Lee et al., 1989). A new technique for evaluating palatability of cooked rice using a Texturometer was proposed by Okabe (1977), who found that adhesive power/hardness or –H/H of three cooked rice grains agreed well with the sensory evaluation. Lee et al. (1989) reported that Texturometer adhesiveness was negatively correlated with amylose content and Texturometer hardness

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was positively correlated with protein content, which agrees with the results obtained by sensory evaluation (Lyon et al., 1999). Okadome et al. (1999) measured surface hardness and adhesiveness of a single-cooked rice grain at low compression (25% deformation) using a Tensipressure (Myboy System, Taketome Electric Co., Japan). They demonstrated that surface hardness could be an effective tool for differentiating the effect of protein contents on hardness of cooked rice. Surface hardness had a higher positive correlation with protein content (r = 0.80, n = 55) than amylose content (r = 0.44, n = 55). Adhesiveness decreased as the amylose content increased. However, the difference in stickiness among cooked rice samples could be detected better by the surface adhesion distance. The overall hardness at high compression test (90% deformation) was highly correlated with Texturometer hardness. The stickiness of high-amylose cooked rice (26.4–29.7%) could be detected with a Tensipresser, but not with Texturometer. The possibility of predicting cooked rice quality using NIR analysis was reported by Windham et al. (1997) and Barton et al. (1998). The effect of molecular structure of rice starch on Instron hardness and adhesiveness of cooked rice was reported by Kang et al. (1994, 1995). The inherent viscosity, number-average degree of polymerization and molecular weight size of amylose showed a positive correlation with hardness, but a negative correlation with adhesiveness of cooked rice (Kang et al., 1994). The inherent viscosity and average chain length of amylopectin had a positive correlation with hardness and a negative correlation with adhesiveness of cooked rice (Kang et al., 1995).

19.5.3 Effect of storage of cooked rice on texture Cooked rice texture changes with storage (Lima and Singh, 1993; Perdon et al., 1999; Perez et al., 1993). Perez et al. (1993) used an Instron to measure the hardness of staled cooked rice which was cooked at a constant water to rice ratio of 2.0. The results showed that the hardness of freshly cooked, staled, and staled and reheated samples was dependent on variety and linearly correlated with amylose content. Perdon et al. (1999) reported that cooked rice firmness increased, while stickiness decreased, during storage, and that starch retrogradation, measured with a differential scanning calorimeter, had a direct linear relationship with firmness, which was independent of variety (a medium-grain rice low in amylose and a long-grain rice high in amylose), storage temperature (–13.3 and 20 °C), and storage duration (24–96 h). The decrease of rheometer stickiness and ratio of stickiness to hardness as storage time increased at 4 °C was reported by Kim and Kim (1996). Lee et al. (1993) stored the cooked rice in an electric cooker at 60, 70, and 80 °C for up to 12 hr and examined the changes in sensory characteristics. The higher storage temperature resulted in lower glossiness, firmness, moistness, and cohesiveness, and higher adhesiveness and off-flavor. The

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overall desirability decreased as storage time increased at all storage temperatures. Kim et al. (1996b) analyzed the firming rate of cooked rice during storage. The initial hardness of cooked rice decreased as the moisture content of cooked rice increased from 57.5% to 69.5%. The activation energy and Q10 for firming of cooked rice were –4.07 × 103 cal/mol and 1.26, respectively. These factors were independent of variety and moisture content.

19.5.4 Correlation of sensory and instrumental texture attributes Chikubu et al. (1985) demonstrated that overall eating quality, assessed by sensory evaluation, of cooked Japanese rice could be estimated using a multiple regression equation, which was based on protein content, amylograph viscosities, and cooking property. The eating quality was negatively correlated with protein and starch-iodine blue value of residual liquid after cooking, and positively correlated with amylograph viscosities (maximum viscosity, minimum viscosity, and breakdown). The equation based on these five variables had the coefficient of determination of 70.14%. The palatability of cooked rice by sensory evaluation was highly correlated with the estimated value from the equation (r = 0.84, p < 0.01). Based on the concept of Chikubu et al. (1985), Satake (Satake Corp., Japan) developed a Rice Taster which converts various physicochemical parameters of rice into taste scores based on correlations between NIR measurements of key constituents (i.e. amylose, protein, moisture, and fat acidity) and preference sensory scores. Champagne et al. (1996) examined the applicability of a Satake Neuro Fuzzy Rice Taster to quality evaluation of US medium-grain rice. They found that the effect of amylose on Rice Taster score was small, and low-amylose rice of less than 18% fell outside the range of calibration. In general, a low-amylose, low-protein, high-moisture rice scores high because it generally produces a cooked rice that is softer and stickier, characteristics deemed desirable by the Japanese palate. The score of US rice was lower than that of the high-scoring Japanese cultivar Koshihikari. This might be due, in part, to the higher protein content of US rice. Lowering protein values by 0.3–0.4 with deep-milling increased the score values by about five points. Champagne et al. (1999) reported that protein content of milled rice had a poor correlation with sensory attributes. These results clearly indicate that a taste analyzer calibrated using preference sensory scores can only assess whether the rice has the quality characteristics deemed desirable by the target population represented by the sensory panel. Champagne et al. (1999) assessed textural properties of 87 samples representing short- , medium- , and long-grain rice cultivars by descriptive sensory and instrumental texture profile (TPA) analyses and related them to RVA measurements. The results showed that none of the cooked rice textural attributes, whether measured by sensory analysis or TPA, were modeled by RVA with high coefficient of determination. Sensory texture attributes, such

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as cohesiveness of mass, stickiness, and initial starch coating, and the TPA attribute adhesiveness had the strongest correlation with RVA measurements. Set-back explained most of the variance attributed to models describing these attributes. Inclusion of amylose and protein contents in regression analysis did not strengthen models. Meullenet et al. (1998) examined a correlation between sensory texture of one medium-grain and two longgrain cooked rice and instrumental parameters using an extrusion cell, and found that sensory characteristics most effectively predicted were hardness (R2 = 0.62) and toothpack (R2 = 0.70). Sesmat and Meullenet (2001) demonstrated that seven sensory texture attributes (cohesiveness of bolue, adhesion to lips, hardness, cohesiveness of mass, roughness of mass, toothpull, and toothpack) were satisfactorily predicted from a single compression test (90% deformation) using Texture Analyzer by Partial Least Squares Regression optimized by a stepwise method.

19.6 Problems and challenges Juliano (2001) summarized three major problem areas that challenge rice researchers in Asia and the world in general as they try to improve instruments to complement sensory evaluation of raw and cooked milled rice: 1. how to make sensory evaluation more sensitive and reproducible and take into account regional variations in preference; 2. how to make instrument methods which can simulate sensory panels while remaining rapid, accurate, and economical; and 3. how to better understand the relationship among grain properties and sensory quality of the rice grain. The question is ‘is it possible to set up a standard method of sensory evaluation of rice to cover regional variations in preference’? Some countries have a preference for stored rice (e.g. Indica), while others such as Korea, Japan and China favor fresh rice. Champagne et al. (1999) stated that preference sensory scores reflect the quality characteristics of cooked rice deemed desirable by the target population represented by the sensory panel. As far as sensory evaluation of rice is concerned, it seems that Japanese scientists operate a firm standard procedure. The sensory test measures appearance, aroma, taste, hardness, stickiness, and overall quality (Ohtsubo et al., 1998). The sensory texture attributes of hardness and stickiness are rapidly and accurately measured using a Texturometer with three cooked rice grains or a Tensipresser with one grain in Japan. The Texturometer has been successfully applied to Korean rice. Recently scientists in the USA employed a Texture Analyzer using a single compression test (Sesmat and Meullenet, 2001) or an extrusion cell (Meullenet et al., 1998), NIR analysis (Barton II et al., 1998; Delwiche et al., 1996; Windham et al., 1997), RVA (Champagne et al., 1999), and Japanese Taste Analyzer (Champagne et al., 1996) to

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predict sensory texture attributes of cooked rice, but the results are still inadequate. It seems that there is no question that amylose content is a single critical factor in governing rice texture. However, some say that protein content is also important (e.g. Japan), but others do not agree (e.g. USA). In Korea, breeding efforts to produce a high-yield Tongil rice (a hybrid of japonica and indica) with good eating quality did not succeed, despite the fact that amylose content and the textural properties of the cooked rice were almost identical to those of Korean japonica cultivars (Lee et al., 1989). Studies on the contribution of molecular structure of amylose and amylopectin to the texture of cooked rice remain to be elucidated. Since cooking methods affect the texture, standardization of the cooking method in evaluating sensory texture as well as of the instrumental measurements of texture would be worthwhile.

19.7 Sources of further information and advice Readers are recommended to consult an excellent monograph on Rice Chemistry and Technology, published by the American Association of Cereal Chemists (Juliano, 1985a). The third edition of the monograph is expected to be available in 2004. The major rice research bodies include the Philippine Rice Research Institute (Laguna, Philippines), the National Food Research Institute (Tsukuba Science City, Japan), and the US Department of Agriculture. Names and organizations of Asian scientists associated with rice-breeding programs are available in an article (Juliano, 2001). Statistics on rice production and trade in the world are available from the International Rice Commission, FAO. Most research articles on rice have appeared in Cereal Chemistry, Journal of Cereal Science, Journal of Food Science, and Journal of Texture Studies. Readers who are interested in specific topic should consult the references cited in this chapter.

19.8 References American Association of Cereal Chemists (2000) Approved Methods of the AACC, Method 61–02, St Paul, MN, AACC. BARTON II F E, WINDHAM W R, CHAMPAGNE E T and LYON B G (1998) Optical geometries for the development of rice quality spectroscopic chemometric models, Cereal Chem, 75, 315–19. BATCHER O M, STALEY M G and DEARY P A (1963) Palatability characteristics of foreign and domestic rices cooked by different methods, Rice J, 66(9), 19–24. BECKER H A (1960) On the asorption of liquid water by the wheat kernel, Cereal Chem, 37, 309–23. BHATTACHARYA K R and SOWBHAGYA C M (1978) On viscograms and viscography, with special reference to rice, J Texture Studies, 9, 341–51.

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and SOWBHAGYA C M (1979) Pasting behavior of rice: a new method of viscography, J Food Sci, 44, 797–800, 804. BHATTACHARYA K R, SOWBHAGYA C M and INDUDHARA SWAMY Y M (1972) Interrelationship between certain physicochemical properties of rice, J Food Sci, 37, 733–5. BHATTACHARYA K R, SOWBHAGYA C M and INDUDHARA SWAMY Y M (1982) Quality profile of rice: A tentative scheme for classification, J Food Sci, 47, 564–9. BLANKENEY A B, WELSH L A and BANNON D R (1991) Rice quality analysis using a computer controlled RVA. In Cereals Internationals. Eds D J Martin and C W Wrigley, Melbourne, Roy. Aust. Chem. Inst., 180–82. CAGAMPANG G B, PEREZ C M and JULIANO B O (1973) A gel consistency test for eating quality of rice, J Sci Food Agric, 24, 1589–94. CHAMPAGNE E T, MARSHALL W E and GOYNES W R (1990) Effects of degree of milling and lipid removal on starch gelatinization in the brown rice kernel, Cereal Chem, 67, 570–74. CHAMPAGNE E T, RICHARD O A, BETT K L, GRIMM C C, VINYARD B T, WEBB B D, MCCLUNG A M, BARTON II F E, LYON B G, MODENHAUER K, LINSCOMBE S, MOHINDRA R and KOHLWEY D (1996) Quality evaluation of US medium-grain rice using a Japanese taste analyzer, Cereal Chem, 73, 290–94. CHAMPAGNE E T, LYON B G, MIN B K, VINYARD B T, BETT K L, BARTON II F E, WEBB D B, MCCLUNG A M, MOLDENHAUER K A, LINSCOMBE S, MCKENZIE K S and KOHLWEY D E (1998) Effects of postharvest processing on texture profile analysis of cooked rice, Cereal Chem, 75, 181–6. CHAMPAGNE E T, BETT K L, VINYARD B T, MCCLUNG A M, BARTON II F E, MOLDENHAUER K, LINSCOMBE S and MCKENZIE K (1999) Correlation between cooked rice texture and rapid visco analyzer measurements, Cereal Chem, 76, 764–71. CHEIGH H S, KIM S K, PYUN Y R and KWON T W (1978) Kinetic studies on cooking rice of various polishing degrees, Korean J Food Sci Technol, 10, 52–6. CHIANG P Y and YEH A I (2002) Effect of soaking on wet-milling of rice, J Cereal Sci, 35, 85–94. CHIKUBU S, WATANABE S, SUGIMOTO T, MANABE N, SAKAI F and TANIGUCHI Y (1985) Establishment of palatability evaluation formula of rice by multiple regression analysis, J Jpn Soc Starch Sci, 32, 51–60. CHO E J and KIM S K (1990) Changes in physicochemical properties of brown and milled rices during storage, J Korean Agric Chem Soc, 33, 24–33. CHO E J and KIM S K (1993) Effects of storage temperature on the physicochemical properties of milled rice, J Korean Agric Chem Soc, 36, 146–53. CHO E K, PYUN Y R, KIM S K and YU J H (1980) Kinetic studies on hydration and cooking of rice, Korean J Food Sci Technol, 12, 285–91. CHRASTIL J (1994) Effect of storage on the physicochemical properties and quality factors of rice. In Rice Science and Tchnology. Eds W E Marshall and J I Wadsworth, New York, Marcel Dekker Inc., 49–81. CHUNG H M, AHN S Y and KIM S K (1982) Comparison of physicochemical properties of Akibare and Milyang 23 rice starch, J Korean Agric Chem Soc, 25, 67–74. DEL MUNDO A M, KOSCO D A, JULIANO B O, SISCAR J J H and PEREZ C M (1989) Sensory and instrumental evaluation of texture of cooked and raw milled rices with similar starch properties, J Texture Studies, 20, 97–110. DELWICHE S R, MCKENZIE K S and WEBB D B (1996) Quality characteristics in rice by nearinfrared reflectance analysis of whole grain milled samples, Cereal Chem, 73, 257– 63. DESIKACHAR H R S, RAGHAVENDRA RAO S N and NANTHACHAR T K (1965) Effect of degree of milling on water absorption of rice during cooking, J Food Sci Technol, 11, 110–12. FELLERS D A, MOSSMAN A P and SUZUKI H (1983) Rice stickiness. II. Application of an Instron method to make varietal comparisons and to study modification of milled rice by hotair treatment, Cereal Chem, 60, 292–5. BHATTACHARYA K R

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and WATANABE H (1998) The rate of starch gelatinization as observed by PFG-NMR measurement of water diffusivity in rice starch/water mixtures, J Food Eng, 36, 359–69. HAN X Z and HAMAKER B R (2001) Amylopectin fine structure and rice starch paste breakdown, J Cereal Sci, 34, 279–84. HAN J G, KANG K J, KIM K and KIM S K (1996) Water absorption of stored brown rice in laminated film pouch, J Korean Soc Food Sci Nutr, 25, 643–8. International Rice Commission (2000) Food Balance Sheet, Rome, FAO. International Rice Research Institute (1971) Annual report for 1970, Los Baños, Philippines, IRRI, 204. ISHIMA T, TAIRA H, TAIRA H and MIKOSHIBA K (1974) Effect of nitrogenous fertilizer application and protein content in milled rice on organoleptic quality of cooked rice, Shokuhin Sogo Kenkysho Kenkyu Hokoku, 29, 9–15. JULIANO B O (1971) A simplified assay for milled rice amylose, Cereal Sci Today, 16, 334– 40, 360. JULIANO B O (1972) Quality vital in rice marketing, Ricemill News, 9(5), 23–4. JULIANO B O (1985a) Criteria and tests for rice grain qualities. In Rice Chemistry and Technology. Ed. B O Juliano, St Paul, MN, AACC, 443–524. JULIANO B O (1985b) Cooperative tests on cooking properties of milled rice, Cereal Foods World, 30, 651–6. JULIANO B O (1998) Varietal impact on rice quality, Cereal Foods World, 43, 207–22. JULIANO B O (2001) Asian perspective on rice sensory quality, Cereal Foods World, 46, 531–5. JULIANO B O, PEREZ C M, ALYOSHIN E P, ROMANOV V B, BEAN M M, NISHITA K D, BLAKENEY A B, WELSH L A, DELGADO L L, EL BAYÂ A W, FOSSATI G, KONGSEREE N, MENDES F D, BRILHANTE S, SUZUKI H, TADA M and WEBB B D (1985) Cooperative study on amylography on milled rice flour for pasting viscosity and starch gelatinization temperature, Starch/Stärke, 37, 40–50. KANG K J, KIM K, KIM S K and MURATA A (1994) Relationship between molecular structure of amylose and texture of cooked rice of Korean rices, J Applied Glycosci, 41, 35–40. KANG K J, KIM K, and KIM S K (1995) Relationship between molecular structure of amylopectin and texture of cooked rice, Korean J Food Sci Technol, 27, 105–11. KIM H Y and KIM K O (1986) Sensory characteristics of rice cooked with pressure cookers and electric cookers, Korean J Food Sci Technol, 18, 319–24. KIM M H and KIM S K (1996) Influence of cooking condition and storage time after cooking on texture of cooked rice, J Korean Soc Food Nutr, 21, 63–8. KIM S K and SUH C S (1990) Water uptake rate of brown rice at 100 °C, J Korean Agric Chem Soc, 33, 261–3. KIM K J, PYUN Y R, CHO E K, LEE S K and KIM S K (1984a) Kinetic studies on hydration of Akibere and Milyang 23 brown rice, Korean J Food Sci Technol, 16, 297–302. KIM S K, JEONG S J, KIM K, CHAE J C and LEE J H (1984b) Tentative classification of milled rice by sorption kinetics, J Korean Chem Soc, 27, 204–10. KIM S K, HAN K Y, PARK H H, CHAE J C and REE J H (1985) Hydration rate of milled rice, J Korean Agric Chem Soc, 28, 62–7. KIM S M, KIM K O and KIM S K (1986) Effect of defatting on hydration of Akibare (Japonica) and Milyang 30 (J/Indica) rice, Korean J Food Sci Technol, 18, 110–13. KIM K, KANG K J and KIM S K (1991) Relationship between hot water solubles of rice and texture of cooked rice, Korean J Food Sci Technol, 23, 498–502. KIM W J, CHANG N Y, KIM S K and LEE A R (1995) Sensory characteristics of cooked rices differing in moisture contents, Korean J Food Sci Technol, 27, 885–90. KIM S K, CHANG B S and LEE S J (1996a) Ultrastructure of compound starch granules and protein bodies of starchy endosperm cell in rice, Agric Chem Biotechnol, 39, 379–83. KIM S K, LEE A R, LEE S K, KIM K J and CHEON K C (1996b) Firming rates of cooked rice differing in moisture contents, Korean J Food Sci Technol, 28, 877–81.

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and EUN J B (2001) Rice cake production using flake rice and medium-grain brown rice, Food Sci Biotechnol, 10, 315–22. KIM S S, PHYU M R, KIM J M and LEE S H (2003) Authentication of rice using near-infrared reflectance spectroscopy, Cereal Chem, 80 346–49. KOHLWEY D E, KENDALL J H and MOHINDRA R B (1995) Using the physical properties of rice as a guide to formulation, Cereal Foods World, 40, 728–32. LEE S J and KIM S K (1994) Bran structure and water uptake rate of Japonica and Tongiltype brown rices, Agric Chem Biotechnol, 37, 94–9. LEE S J and PELEG M (1988) Direct measurement of the attractive force between individual cooked rice grains of sticky and flaky cultivars, J Food Sci, 53, 1113–15. LEE S O, KIM S K and LEE S K (1983) Kinetic studies on hydration of traditional and highyielding rice varieties, J Korean Agric Chem Soc, 26, 1–7. LEE B Y, YOON I H, TETSUYA I, IKUJI K and TETSUJIRO O (1989) Cooking quality and texture of japonica-indica breeding type and japonica type, Korean rice, Korean J Food Sci Technol, 21, 613–18. LEE Y J, MIN B K, SHIN M G, SUNG N K and KIM K O (1993) Sensory characteristics of cooked rice stored in an electric rice cooker, Korean J Food Sci Technol, 25, 487–93. LII C Y, TSAI M L and TSENG K H (1996) Effect of amylose content on the rheological property of rice starch, Cereal Chem, 73, 415–20. LIMA I and SINGH R P (1993) Objective measurement of retrogradation in cooked rice during storage, J Food Quality, 16, 321–37. LITTLE R P, HILDER G B and DAWSON E H (1958) Differential effect of dilute alkali on 25 varieties of milled white rice, Cereal Chem, 35, 111–26. LYON B G, CHAMPAGNE E T, WINDHAM W R, BARTON F E, WEBB D B, MCCLUNG A M, MOLDENHAUER K A, LINSCOMBE S, MCKENZIE K S and KOHLWEY D E (1999) Effect of degree of milling, drying condition, and final moisture content on sensory texture of cooked rice, Cereal Chem, 76, 56–62. MARTIN M and FITZGERALD M A (2002) Proteins in rice grain influence cooking properties!, J Cereal Sci, 36, 285–94. MATSUKURA U, KANEKO S and MOMMA M (2000) Method for measuring the freshness of individual rice grains by means of a color reaction of catalase activity, J Jpn Soc Food Sci Technol, 47, 523–8. MEULLENET J-F C, GROSS J, MARKS B P and DANIELS M (1998) Sensory descriptive texture analyses of cooked rice and its correlation to instrumental parameters using an extrusion cell, Cereal Chem, 75, 714–20. MEULLENET J-F, MARKS B P, HANKINS J-A, GRIFFIN V K and DANIELS M J (2000) Sensory quality of cooked long-grain rice as affected by rough rice moisture content, storage temperature, and storage duration, Cereal Chem, 77, 259–63. MORITAKA S and YASUMATSU K (1972) Studies on cereals. X. The effect of sulfhydryl groups on storage deterioration of milled rice, Eiyo To Shokuryo, 25, 59–62. MOSSMAN A P, FELLERS D A and SUZUKI H (1983) Rice stickiness. I. Determination of rice stickiness with an Instron tester, Cereal Chem, 60, 286–92. NODA T, NISHIBA Y, SATO T and SUDA I (2003) Properties of starches from several lowamylose rice cultivars, Cereal Chem, 80, 193–7. OHTSUBO K, TOYOSHIMA H, and OKADOME H (1998) Quality assay of rice using traditional and novel tools, Cereal Foods World, 43, 203–6. OKABE M (1977) Studies on eating quality of cooked rice, New Food Ind, 19, 65–71. OKADOME H, TOYOSHIMA H and OHTSUBO K (1999) Multiple measurements of physical properties of individual cooked rice grains with a single apparatus, Cereal Chem, 76, 855–60. OKADOME H, TOYOSHIMA H, SHIMIZU N, AKINAGA T and OHTSUBO K (2002) Chemometric formulas based on physical properties of single-cooked milled rice grain for determination of amylose and protein contents, J Food Sci, 67, 702–7. PERDON A A, SIEBENMORGEN T J, BUESCHER R W and GBUR E E (1999) Starch retrogradation and texture of cooked milled rice during storage, J Food Sci, 84, 828–32.

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(1979) Gel consistency and viscosity of rice, Proceedings of the Workshop on Chemical Aspects of Rice Grain Quality, Los Baños, Laguna, Philippines, IRRI, 293– 302. PEREZ C M and JULIANO B O (1979) Indicators of eating quality for non-waxy rices, Food Chem, 4, 185–95. PEREZ C M, JULIANO B O, BOURNE M C and MORALES A A (1993a) Hardness of cooked milled rice by instrumental and sensory methods, J Texture Studies, 24, 81–94. PEREZ C M, VILLAREAL C P, JULIANO B O and BILIADERIS C G (1993b) Amylopectin-staling of cooked nonwaxy milled rices and starch gels, Cereal Chem, 70, 567–71. ROUSSET S, PONS B and PILANDON C (1995) Sensory texture profile, grain physicochemical characteristics and instrumental measurements of cooked rice, J Texture Studies, 26, 119–35. SASAKI T and MATSUKI J (1998) Effect of wheat starch structure on swelling power, Cereal Chem, 75, 525–9. SESMAT A and MEULLENET J F (2001) Prediction of rice sensory texture attributes from a single compression test, multivariate regression, and a stepwise model optimization method, J Food Sci, 66, 124–31. SONG B H, KIM D Y and KIM S K (1988) Comparison of hydration and cooking rates of brown and milled rices, J Korean Agric Chem Soc, 31, 211–16. SOWBHAGYA C M and BHATTACHARYA K R (2001) Changes in pasting behaviour of rice during ageing, J Cereal Sci, 34, 115–24. STEFF J F and SINGH R P (1980) Diffusivity of starchy endosperm and bran of fresh and rewetted rice, J Food Sci, 45, 356–61. SUZUKI H (1979) Use of the texturometer for measuring the texture of cooked rice, Proceedings of the Workshop on Chemical Aspects of Rice Grain Quality, Los Baños, Laguna, Philippines, IRRI, 327–41. SUZUKI K, KUBOTA K, OMICHI M and HOSAKA H (1976) Kinetic studies on cooking of rice, J Food Sci, 41, 1180–83. SUZUKI K, AKI M, KUBOTA K and HOSAKA H (1977) Studies on the cooking rate equations of rice, J Food Sci, 42, 1545–8. SZCZESNIAK A S (1987) Correlating sensory with instrumental texture measurements – an overview of recent developments, J Texture Studies, 20, 97–110. TAKEUCHI S, FUKOUKA M, GOMI Y, MAEDA M and WATANABE H (1997a) An application of magnetic resonance imaging to the real time measurement of the change of moisture profile in a rice grain during boiling, J Food Eng, 33, 181–92. TAKEUCHI S, MAEDA M, GOMI Y, FUKUOKA and WATANABE H (1997b) The changes of moisture distribution in a rice grain during boiling as observed by NMR imaging, J Food Eng, 33, 281–97. WINDHAM W R, LYON B G, CHAMPAGNE E T, BARTON II F E, WEBB B D, MCCLUNG A M, MODELHAUER K A, LINSCOMBE S and MCKENZIE K S (1997) Prediction of cooked rice texture quality using near-infrared reflectance analysis of whole-grain milled samples, Cereal Chem, 75, 626–32. YOON S H (2002) Physicochemical properties of rice differing in milled degrees, (M S Thesis, Dankook University, Seoul, Korea). ZHANG T Y, BAKSHI A S, GUSTAFSON R J and LUND D B (1984) Finite element analysis of nonlinean water diffusion during rice soaking, J Food Sci, 49, 246–50, 277. ZHOU Z, ROBARDS K, HELLIWELL S and BLANCHARD C (2002) Ageing of stored rice: changes in chemical and physical attributes, J Cereal Sci, 35, 65–78. PEREZ C M

20 Improving the texture of pasta B. A. Marchylo and J. E. Dexter, Canadian Grain Commission and L. J. Malcolmson, Canadian International Grains Institute

20.1

Introduction

Pasta comes in diverse shapes and sizes but, in contrast to Asian noodles, which in some cases may have similar appearance, it is generally prepared by extruding semolina dough through a die. Asian noodles are prepared by passing common wheat dough through sheeting rolls. The texture properties of cooked pasta are the primary factor in overall assessment of pasta quality and play a dominant role in influencing consumer acceptance. The main textural properties which are important in cooked pasta include firmness and elasticity, surface integrity and absence of a sticky surface. These properties can be measured by sensory evaluation or by instrumental methods. Pasta textural properties can be influenced by the raw materials used to prepare the final product. The protein content and protein quality of the main ingredients, i.e. the semolina, farina or flour, are fundamentally associated with cooked pasta textural properties. Consumer preference for pasta textural properties varies around the world, although the Italian tradition of ‘al dente’ eating properties, characterized by high degrees of firmness and elasticity, generally first comes to mind. Although dried pasta continues to be the most prominent product used around the world there is a noticeable trend, especially for consumers in urban centres, towards buying convenience foods containing pasta that are ready to serve and easy to prepare. This trend has implications for pasta texture since the production of many of these foods involves heating and freezing which can affect the firmness and other textural attributes of the cooked product.

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20.1.1 Defining pasta The general category of ‘paste products’ includes Asian noodles and pasta. Some forms of Asian noodles and pasta have similar appearance, but they are distinguished by differences in manufacturing process and raw material preference. Asian noodles are prepared by passing dough through sheeting rolls (Hatcher, 2001). The main raw material is common wheat flour. Asian noodles are diverse, differentiated based on ingredients and processing following sheeting. Asian noodles may or may not contain alkaline salts, and may be marketed fresh, dried, partially cooked, or ‘instant’ (pre-cooked by boiling, steaming or frying) (Miskelly, 1998). The vast majority of pasta products are formed by extruding dough through a die (Marchylo and Dexter, 2001). Pasta comes in diverse shapes and sizes classified into long goods (spaghetti, linguine, vermicelli, etc.) and short goods (elbow macaroni, shells, etc.). Length and shape are determined by die configuration and length of time between extrusion and cutting. Most pasta products are dried following extrusion. Industrially produced fresh pasta is a relatively small market, but is becoming more popular. Forming by sheeting rather than extrusion is more common for fresh pasta. Agnesi (1996) has described the history of pasta and the evolution of pasta manufacturing. She concluded that pasta originated in Sicily in the early Middle Ages and spread north. Initially pasta was sheeted, cut into strips and marketed fresh. Eventually it was discovered that the coastal climate of Italy was ideal for drying, and dried pasta quickly became popular because of its storage stability. Mechanization of pasta manufacturing began during the 18th and 19th centuries with the invention of hydraulic presses and kneaders. Drying cabinets became available in the early 20th century, but pasta manufacturing remained a batch process until the 1930s when continuous extrusion using an extrusion auger was introduced. The modern continuous pasta-manufacturing process has been described in detail by many authors (Antognelli, 1980; Baroni, 1988; Dalbon et al., 1996; Marchylo and Dexter, 2001; Milatovic and Mondelli, 1991), so will be only briefly summarized here. A schematic representation of a continuous long goods pasta line with a conventional press is shown in Fig. 20.1. Water, semolina and optional ingredients are initially mixed in a paddle mixer for lines with conventional presses. Mixing is under vacuum to minimize both oxidation of the yellow pigments that are natural components of semolina, and to prevent formation of air bubbles in the final product. The resulting dough is a loose crumble with lumps of up to 3 cm in diameter. The dough then enters an extrusion worm. A relatively recent innovation is the Polymatik press introduced by Bühler (Marchylo and Dexter, 2001). Rather than using a paddle mixer, the Polymatik mixes and develops dough in 20 seconds by a twin-screw kneader system that feeds into the extrusion chamber. The extrusion worm moves the dough forward, compresses it into a homogeneous mass and forms the desired shape by forcing it through a die. The pasta is then dried under carefully controlled environmental conditions.

1

Press/ spreader

Drying zone no. 2

Stick stacker/Stick magazine

Drying zone no. 1

Water

Stripper/Saw

Drying zone no. 3

Cooling zone

1

Fig. 20.1 A simple schematic representation of a long goods pasta line. Adapted from a diagram kindly provided by Bühler AG, Uzwil Switzerland. Arrows indicate the path taken along the production line. Symbols in dryers represent fans.

Die

Extrusion chamber

Semolina mixer

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20.1.2 Defining the texture of cooked pasta The texture of cooked pasta is the primary criterion for assessing the overall quality of pasta, and plays a dominant role in influencing consumer acceptance. The term ‘texture’ is somewhat misleading since most food products, including pasta, exhibit more than one textural parameter. Thus, it is preferable to use the term ‘textural properties’ since this term implies more than a single parameter. Szczesniak (1963) classified the textural characteristics of food into mechanical and geometric properties and those related to the fat and moisture content of a food. Mechanical characteristics are those parameters related to the reaction of food to stress. They include five primary parameters (hardness, cohesiveness, viscosity, springiness, adhesiveness) and three secondary parameters (fracturability, chewiness, gumminess), which are composites of primary parameters. Geometrical characteristics are related to the geometrical arrangement of the food matrix and are divided into two classes: those related to particle size and shape and those related to particle shape and orientation. This classification system was developed in order to serve as the basis for sensory and instrumental measurements of food texture. It is the foundation of the texture profile analysis (TPA), a comprehensive approach for measuring the texture properties of a food. Based on the classification system proposed by Szczesniak(1963) cooked pasta can be defined by the mechanical parameters listed in Table 20.1. Overall, the main textural properties important in cooked pasta include firmness and elasticity (the ‘al dente’ property), surface integrity and absence of a sticky surface.

20.2 Measuring the texture of cooked pasta The textural properties of cooked pasta can be measured by sensory evaluation or by instrumental methods. Although there are distinct advantages for each technique, both must be carefully standardized in order to provide meaningful and reproducible results. Furthermore, standardization of the cooking procedure Table 20.1

Mechanical properties of cooked pasta. (Adapted from Szczesniak, 1963)

Hardness/firmness Cohesiveness Elasticity/springiness Adhesiveness Chewiness

The force necessary to attain a given deformation The extent to which a material can be deformed before it ruptures The rate at which a deformed material returns to its nondeformed state after the deforming force is removed The work necessary to overcome the attractive forces between the surface of the food and the surface of other materials which come into contact with the food The energy required to reduce the food to a state ready for swallowing; a product of hardness, cohesiveness and elasticity

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is also required since a number of factors have been found to impact on the final texture of pasta. The most important factors to be considered are cooking time, water to pasta ratio, hardness and pH of cooking water, and time elapsed between draining of cooked pasta and testing (Menger, 1979). Time elapsed after draining has been shown to affect pasta firmness and stickiness (Voisey et al., 1978b, Dexter et al., 1983a) whereas water hardness has been found to impact on surface stickiness (Alary et al., 1979; D’Egidio et al., 1981; Dexter et al., 1983b; Malcolmson and Matsuo, 1993; Menger, 1982). Cooking time and water to pasta ratio influence all textural parameters; generally, for experimental purposes, a water to pasta ratio of 10:1 is used and pasta is cooked to optimum, defined as the cooking time corresponding to the disappearance of the centre core.

20.2.1 Sensory method Sensory evaluation is considered to be the most reliable method for measuring the textural properties of cooked pasta since panellists have the ability to measure the overall textural characteristics of cooked pasta. In contrast, instrumental methods can measure only a limited number of characteristics, which may not necessarily relate to sensory judgements. To be meaningful, instrumental measurements must be validated by calibrating against sound sensory measurements. Despite the advantages associated with sensory methods, sensory evaluation procedures are often criticized as being subjective techniques. Part of the problem lies with the failure to acknowledge that two distinct types of sensory tests exist. Product-orientated tests involve the use of selected and trained panellists under controlled testing conditions to evaluate the quality attributes of a product. These tests are objective since they meet the criteria of objectivity: freedom from personal bias and repeatability. Consumer-orientated tests involve the use of consumer panellists to determine product acceptability or degree of liking. These tests by their very nature are subjective since it is the subjective information (personal likes and dislikes) that is of interest. Thus, if sensory tests are done under controlled testing conditions, utilizing trained panellists and appropriate sensory methodologies, the procedures are objective. Only a few studies have been undertaken that involve a comprehensive sensory assessment of cooked pasta texture. Larmond and Voisey (1973) used a trained panel to assess the firmness, chewiness, gumminess, adhesiveness and individuality of spaghetti strands. Panellists were able to distinguish differences among the spaghetti samples for all parameters. When these results were compared with consumer acceptability tests, it was found that consumers preferred spaghetti that was firm, chewy, and maintained individuality and was not gummy or adhesive. Further analysis suggested that firmness and gumminess were sufficient to predict consumer acceptability. In another study, trained panellists were able to distinguish differences among spaghetti samples in firmness, springiness, adhesiveness, and rate of breakdown

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(Voisey et al., 1978a). Attempts to quantify stickiness have involved both oral (Kovacs et al., 1997; Voisey et al., 1978b) and non-oral (Voisey et al., 1978a) procedures including visual and tactile assessments. The International Standards Organization (ISO, 1985) has developed a standard method (TC 34 SC4 7304) using trained assessors for evaluating the firmness and surface condition (stickiness) of cooked spaghetti. A series of reference photographs are used to estimate surface condition. The general appearance, degree of swelling and stickiness are taken into account for assessment of the overall rating. Scores for firmness and surface condition are assigned using nine point rating scales.

20.2.2 Scoring method D’Egidio and Nardi (1996) described a scoring method used in Italy for assessing cooked pasta quality. Experts (at least three persons) evaluate stickiness, bulkiness and firmness using scales ranging from 1 to 100. Evaluations of stickiness and bulkiness are done both visually and manually, while firmness is evaluated orally. An overall quality score is determined by calculating the mean of each of the three scores and summing. An overall score greater than 80 indicates excellent quality while a score below 40 indicates poor-quality pasta. Menger (1985) developed an extensive scoring system for assessing the quality of raw and cooked pasta. Trained panellists assessed 20 factors, six related to the uncooked product, 17 to the cooked product. The scoring system was weighted such that properties of uncooked pasta accounted for 30% of the final score and cooked quality accounted for 70%. Factors such as: retention of shape, surface characteristics, bite/firmness, odour and taste are judged in the assessment of cooked pasta quality. Malcolmson (1991) used a trained texture profile panel to evaluate firmness, elasticity, chewiness, cohesiveness, tooth pack and stickiness of spaghetti using the definitions given in Table 20.2. Unstructured line scales, 15 cm in length, were used by panellists to record their rating of each attribute. Table 20.2 Sensory definitions used in the evaluation of cooked spaghetti. (Adapted from Malcolmson 1991) Parameter

Definition

Firmness Elasticity

The force required to bite through the sample The degree to which the sample returns to its original state after being compressed slightly The degree to which the sample holds together after chewing The amount of energy required to chew the sample to the state ready for swallowing The degree to which the strands adhere to each other and when lightly touched with a finger The degree to which the sample packs around the teeth during and after chewing

Cohesiveness Chewiness Stickiness Tooth pack

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20.2.3 Instrumental methods An extensive effort has been made to establish instrumental methods for measuring the textural properties of cooked pasta since sensory techniques can be time-consuming, expensive and may exhibit poor reproducibility if proper procedures are not followed. In addition, the use of sensory panels to evaluate a large number of samples or to evaluate samples when sample size is limited is not feasible. Nevertheless, to verify that instrumental values have meaning in terms of sensory ratings of texture, an association between the sensory and instrumental measurements must be confirmed. Because the perception of texture involves a complex response to a number of physical and physicochemical properties of food, it is extremely difficult to replicate instrumentally the responses provided by a sensory panel. This suggests that it is unrealistic to assume that one instrumental method can measure all of the textural properties of a food. Indeed, Bourne (1982) has stated that there is no instrument available that can match the sophistication, sensitivity, and range of mechanical motions of the mouth or that can promptly change speed and mode of mastication in response to the sensations received during the previous chew. A spaghetti tenderness testing apparatus was developed by Matsuo and Irvine (1969) to simulate a bite test by applying a continuously increasing force to a cutting edge resting on a sample of spaghetti. A tenderness index was derived from the slope of the linear portion of the curve, which was indicative of the time it took the loaded cutting edge to cut through the sample. Results were found to correlate with sensory measurements of firmness (Matsuo and Irvine, 1974). Furthermore measurement of other spaghetti textural parameters of “doughiness”, “chewiness” or “springiness” was facilitated by adapting the testing apparatus to use a blunt-edged blade to simulate compression and then measure recovery (Matsuo and Irvine, 1971). Resulting compressibility–recovery curves provided an indication of these parameters. Subsequently, cooking quality was specified by using a single number, the Cooking Quality Parameter, derived from the ratio of Recovery/ Tenderness Index × Compressibility (Dexter and Matsuo, 1977). A number of texture testing instruments have been developed commercially for evaluating the textural properties of food. Of these, the Instron Universal Tester (Instron Corp., Canton, MA) and its hybrids such as the TA.XT2 texture analyser (Texture Technologies Corp., Scarsdale, NY) have been the most widely adopted. The basic components of these types of instruments include a drive mechanism for deformation and a recording system of force, time and compression rate. Tests can be performed in tension or compression and a wide variety of test cells can be employed enhancing its versatility. Compression tests Walsh (1971) first described a compression test using the Instron. Firmness was expressed as the amount of work in g-cm required to shear a strand of spaghetti. Results were found to correlate with sensory measurements of

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firmness. Oh et al. (1983) adapted this technique for noodles by shearing three strands with a Plexiglas tooth that was bevelled on both sides of the contact surface. Good correlations were established with sensory measurements of firmness and chewiness. The American Association of Cereal Chemists (2000) adopted a compression method for pasta based on this work that has been widely accepted. Five strands of spaghetti are sheared and firmness is expressed as the energy (work) in g-cm to shear one strand of spaghetti. Since variations exist within and among strands, it is important that more than one strand of spaghetti is measured. For short goods pasta products, a single-blade test is not applicable. A multi-blade shear cell such as the Kramer cell is more appropriate. Spaghetti firmness measured by the Instron Universal Tester (Instron) was compared with the storage modulus and dynamic viscosity obtained by dynamic rheometry (Edwards et al., 1993). A strong correlation was found indicating the sensitivity of dynamic rheometry to changes in pasta firmness. Several workers have developed methods for measuring the compressibility and elasticity of pasta. Dalbon et al. (1985) used the Instron to measure compressibility and recovery (a measure of elasticity) of spaghetti using a flat plunger. The strands were compressed to a fixed load. Compression was defined as the relationship of the diameter of the compressed spaghetti to the original diameter multiplied by 100. Recovery was defined as the relationship of spaghetti diameter after recovery to the diameter of the compressed spaghetti. Oh et al. (1983) described a similar method for measuring the compressibility and recovery of noodles using a plunger with a flat surface. Instron measures were found to correlate with sensory measures of firmness and chewiness. Elasticity and breaking strength of cooked pasta can also be measured using a tension test. However, tension tests present operational difficulties due to the difficulties in gripping the ends of the pasta strands and ensuring that breakage occurs along the extended region of the sample. Maximum stress values obtained by tension tests give an indication of the cooked pasta’s resistance to break and the distance to break values indicate its extensibility (Smewing, 1997). Although Voisey and Larmond (1973) established a correlation between tensile readings and sensory measurements of firmness and chewiness, they found instrumental shear readings obtained from a multi-blade shear compression cell were more closely related to sensory results. This can be attributed to the shearing and compression forces that take place during mastication rather than the application of tensile forces. The viscoelastograph (Chopin/Tripette et Renaud, Villeneuve La Garem, France) has also been used to measure compressibility and recovery of cooked pasta (Ames et al., 1998b, 1999; Autran et al., 1986; Delcour et al., 2000). With this instrument, the sample is compressed between two plates with a constant load applied perpendicularly and then removed. Changes in thickness during and after loading, as well as the deformation and the capacity to

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return to the initial form, are recorded. Compressibility, consistency and relative recovery are obtained from the curve and an index of viscoelasticity can be calculated from compressibility and relative recovery values (D’Egidio and Nardi, 1996). Based on findings by D’Egidio et al. (1993) the viscoelastograph is considered a suitable technique to predict sensory firmness of pasta, particularly when pasta is dried at high temperatures. An instrumental method for measuring surface stickiness has been difficult to establish due to a number of complicating factors including: presence of water on the cooked surface; change in surface properties as time elapses between draining and measuring; difficulty in restraining the sample for measurement; and selection of the correct compression force and probe retraction speed (Smewing, 1997). Difficulty has also been encountered in correlating proposed instrumental methods with sensory judgements of stickiness. Voisey et al. (1978b) reported the use of a multi-strand test fixture mounted on the Instron to assess the stickiness of cooked spaghetti. Ten strands were mounted on a serrated base plate and were compressed to a fixed compression force with a plunger with a flat surface. After allowing the spaghetti to relax, the plates were pulled apart and the maximum tensile force was used as the index of stickiness. This required a load cell with two outputs to record both the compressive forces required to push the plates together and the tensile forces to pull the plates apart. Stickiness readings were found to be related to non-oral sensory assessments of stickiness. Dalbon et al. (1985) and Wood et al. (2001) measured the stickiness of spaghetti by compressing to a fixed force using a flat plunger. Stickiness measurement was derived from the area under the force–distance curve. Guan and Seib (1994) designed a multi-faced probe, a sample restraining device and a sample holder for use in measuring pasta stickiness. Stickiness was defined as the peak tensile force and total tensile work required to separate the probe from the strand surface. Dexter et al. (1983b) reported a method for measuring stickiness using the Grain Research Laboratory (GRL) Compression Tester. A number of strands were compressed with a flat plunger and, upon lifting the plunger, the force of adhesion of the spaghetti to the plunger was measured. Kim et al. (1989) adapted this technique for use with the Instron but encountered some difficulties with the measurement which they attributed to surface water released from the spaghetti during compression. According to the authors, a low stickiness score could mean either a water-logged (over-cooked) spaghetti with water released during compression or a firm spaghetti that was not sticky. It is possible that the use of a lower compression force may have overcome this problem. Although Malcolmson (1991) found instrumental measurements using the Instron and the GRL tenderness testing apparatus highly correlated with sensory measurements of firmness, elasticity and chewiness, poor correlations were found to exist between instrumental tests and sensory measurements of cohesiveness, tooth pack and stickiness.

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Several researchers have found that the amount of residue in the cooking water and the amount of rinsed material collected from the surface of pasta are good indicators of cooked pasta texture. The amount of residue in the cooking water denotes the degree of breakdown of the pasta during cooking and is referred to as cooking loss. The residue can be determined by evaporating the cooking water by either heating or freeze-drying or by measuring the absorption of the iodine-amylose complex (Matsuo et al., 1992). The amount of total organic matter (TOM) that can be isolated from the surface of spaghetti strands by exhaustive rinsing has been reported by D’Egidio et al. (1982) to be a reliable means of predicting overall cooking score and stickiness. Dexter et al. (1985) found good correlations between TOM values and instrumental values of firmness, stickiness and resilience of cooked spaghetti. The TOM method was subsequently accepted as a standard method by the International Association for Cereal Science and Technology (ICC, 1992).

20.3 Influence of raw materials The primary ingredients of traditional pasta comprise durum wheat semolina or flour, common wheat farina or flour, or various combinations of these, plus water (Milatovic and Mondelli, 1991). The main ingredient of premium quality pasta is 100% durum wheat semolina. Good quality pasta also can be made with durum wheat flour or a blend of semolina and durum wheat flour (granular), although inclusion of durum wheat flour results in poorer cooking quality, especially an increase in surface stickiness (Dexter et al., 1981a). Pasta can be produced using common (soft or bread) wheat farina or flour, but it is generally inferior in cooking quality since it is less firm and more sticky compared to pasta made from durum wheat (Dexter et al., 1981a, 1983a; Kim et al., 1989). Pasta made from 100% durum wheat semolina maintains texture better when over-cooked than pasta made from common wheat farina (Fig. 20.2). Farina, however, is used quite extensively when the price of durum wheat is high or when the quality of the end product is less important. Textural characteristics associated with high quality pasta include the absence of a sticky surface such that it does not stick together after cooking and ‘al dente’ (literally “to the tooth”) eating properties that are characterized by high degrees of firmness and elasticity (Antognelli, 1980). The production of acceptable quality pasta begins with durum or common wheat of good physical quality. Besides the impact of the milling process, some physical defects can influence cooked pasta texture. For example, sprouting due to damp harvest conditions results in high levels of the starch degrading enzyme α-amylase. Many manufacturers of premium pasta specify very low levels of sprout damage along with high Falling Number values because they believe that starch degradation due to α-amylase will cause greater loss of solids during cooking, increased surface stickiness and softer cooked pasta texture. However, there is no firm scientific evidence that

Cooking quality parameter (s/m × 10–6)

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25

15

5

12

16

20 24 Cooking time (min)

28

100% HRS 60% durum 100% durum

Fig. 20.2 Change in cooking quality during overcooking of pasta made with hard red spring wheat (HRS), durum wheat and a blend of hard red spring (40%) and durum (60%).

α-amylase has an adverse effect on pasta cooking quality unless sprout damage is very severe (Dexter et al., 1990).

20.3.1 Protein content Protein content and protein quality are fundamentally associated with cooked pasta textural quality and are considered the most important of all the grain components that influence cooking quality (Autran et al., 1986; D’Egidio et al., 1990; Matsuo et al., 1982). Consequently, a minimum hard vitreous kernel (HVK) content is an important physical characteristic because of its relationship to protein content. Generally, as HVK content increases and non-vitreous (commonly referred to as starchy or mealy kernels or yellow berry because of their opaque yellow appearance) kernel content decreases, protein content will increase (Dexter et al., 1988). The primary importance of protein content in determining cooked pasta texture is well documented (Autran et al., 1986; D’Egidio et al., 1990; Dexter and Matsuo, 1977). As protein content increases, cooked pasta becomes firmer and more resilient (Fig. 20.3). High protein pasta is also less sticky, although the relationship to protein content is not as strong (Dexter et al., 1983a). Pasta of high protein content will also remain firm when kept in warm water after cooking before it is served. Because of its fundamental impact on the textural properties of cooked pasta, protein content will continue to be the primary quality factor for pasta production in the future (Marchylo et al., 1998; Marchylo and Dexter, 1996).

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Cooking score (units)

70

50

30

10

12

14

16

Protein content (%)

Fig. 20.3 Impact of durum wheat protein content on cooking score (cooking quality). Data taken from Canadian Grain Commission harvest survey reports.

Gluten Gluten strength, which is related to protein composition, i.e. protein quality, is also universally acknowledged as an important prerequisite for making good-quality pasta (Ames et al., 1999). It should be recognized, however, that the widespread acceptance of high- and ultra-high temperature (HT and UHT, respectively) drying, has made it possible to produce pasta products with reasonable textural characteristics when using weaker gluten strength semolina. As noted by D’Egidio et al. (1990), higher drying temperatures have decreased the importance of gluten strength for cooking quality. A pasta manufacturer who uses HT or UHT drying systems must be concerned primarily with protein content. The processor knows that, as the protein content of semolina increases, there will be a corresponding increase in product quality. A qualification, however, is that semolina possessing strong gluten characteristics will exhibit less sticky dough with better extrusion properties and superior cooked spaghetti textural characteristics compared to weak gluten semolina of comparable protein content. Cooked pasta textural attributes impacted by gluten quality include bite/elasticity, firmness, hardness, bulkiness, smoothness or mouthfeel during mastication and chewiness (Cole, 1991). Scientific evidence obtained so far indicates that the continuity and strength of the protein network is directly related to the textural characteristics of the cooked spaghetti (Zweifel et al., 2003). These characteristics are influenced by total protein content, since as protein content increases so does the extent of the network. Protein quality or composition is believed to affect the properties of the protein network. Some gluten protein components are more effective than others in forming a good network and influencing the plasticity and elasticity of the resultant dough and the extent of the protein network around starch granules. As discussed by Ames et al. (1998a), initial studies identified the presence of the γ-gliadin band 45 and the absence of γ-gliadin 42 as a

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marker for pasta cooking quality (Kosmolak et al., 1980). The positive effect of γ-gliadin 45 on gluten strength and cooking quality has been attributed to its genetic linkage with the low-molecular-weight (LMW) glutenin subunits (GS) identified as LMW-2 controlled at the Glu-B3 locus (Payne et al., 1984; Pogna et al., 1988). Studies have classified durum wheat according to several different LMW subunit models (Payne et al., 1984; Ruiz and Carillo, 1995a). These models have been inadequate, however, because they are a mixture of subunits controlled by different alleles, whereas durum wheat quality depends on specific LMW subunits encoded at the Glu-A3, Glu-B3 and Glu-B2 loci (Ruiz and Carrillo, 1995b; Nieto-Taladriz et al., 1997). It also has been reported that the superior gluten strength of LMW-2 types may be at least partially a quantitative effect, as LMW-2 proteins are expressed in greater amounts than LMW-1 proteins (Autran et al., 1987; D’Ovidio et al., 1999). LMW-2 types exhibit a strength range from moderate to very strong, yet the relationship between cooking quality and gluten strength is not clear for LMW-2 varieties with widely differing strength characteristics (Rao et al., 2001; Schlichting et al., 1998). Furthermore, the improvement in cooking quality associated with increasing drying temperature varies among LMW-2 varieties. In particular, the strongest gluten varieties do not show as large an improvement as some weaker varieties (Schlichting et al., 1998). When blended with weak gluten durum wheat, however, very strong gluten LMW-2 durum wheat types will enhance pasta cooking quality more than moderate strength LMW-2 types (Fig. 20.4) (Schlichting et al., unpublished results). Semolina millstreams exhibit a wide range in protein content which, as discussed earlier, impacts directly on pasta texture (Matsuo and Dexter, 1980). In particular, wheat flour produced as a by-product of semolina milling has a much higher protein content than semolina, imparting superior pasta firmness (Houliaropoulis et al., 1981). However, durum wheat flour and low-grade semolina streams are usually excluded from premium pasta products because of their negative impact on pasta colour due to a higher concentration of oxidative enzymes. Egg Other optional ingredients that can have an influence on the textural characteristics of the cooked product may be added to the primary semolinawater dough. One of the more common or traditional of these ingredients is egg. The egg may be added completely frozen and shelled, fresh, powdered or as egg white (Giese, 1992). Addition of the egg albumin helps to maintain a firm texture and decrease stickiness by strengthening the protein network formed by the gluten proteins (Matsuo et al., 1972; Milatovic and Mondelli, 1991). In countries where common wheat or farina is used to make pasta, the addition of egg can significantly improve texture as well as nutritional characteristics and colour. Sanitation and allergen issues have led to the use

488

Texture in food 1200

40 °C

1000

800

Firmness (g-cm)

1200

70 °C

1000

800

1200

90 °C

1000

800

600 0

20

40 60 80 Blend level (%)

Kyle AC Melita AC pathfinder

100

120

AC navigator Plenty

Fig. 20.4 Regression lines (predicted firmness of cooked pasta vs blend level) showing the effect of blending semolina from a weak-gluten (LMW-1) variety Stewart 63 with semolina from five strong-gluten (LMW-2) varieties on cooked spaghetti firmness for spaghetti dried at 40° C, 70° C and 90° C.

of other ingredients to replace eggs (Kobs, 2000). For example, glycerol monostearate complexes with starch and decreases solubilization and migration of amylose out of the starch granule onto the surface of the pasta during cooking (Matsuo et al., 1986). This process decreases stickiness in products such as retortable pasta. It also helps to maintain the texture and mouthfeel of the pasta during many heating cycles (Giese, 1992; Kobs, 2000). Addition of glycerol monostearate alone can result in a decrease in cutting stress but,

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in combination with the addition of gluten protein and high-temperature drying, it can improve the cooking texture of farina spaghetti (Kim et al., 1989). The addition of L-ascorbic acid has also been reported to have a positive influence on pasta texture by improving the protein network (Milatovic and Mondelli G, 1991). This vitamin is reported to be used widely in many European countries (particularly eastern Europe) where plain white flour is utilized in pasta processing (Anonymous, 2003a). Other additions Other means of improving texture include increasing protein content by the addition of protein from other sources that can form complexes similar to gluten. Research has shown that not all protein improves pasta texture to the same degree. For example, Matsuo et al. (1972) showed that while egg albumin and wheat gluten improved cooked pasta texture, wheat gliadin and high-protein rapeseed meal and soybean flour did not show an improvement. Thus, vital wheat gluten or whey-protein concentrate (Kobs, 2000) can be added to semolina- or farina-based pasta to reduce stickiness and increase firmness. However, addition of soybean flour, which has a role in increasing the body’s antioxidant status (Milo Ohr, 2003), must be made carefully since the soy protein impacts on pasta texture making it less firm and less resilient (Kim et al., 1989; Kobs, 2000). Various gums have been added to canned or frozen pasta products to enhance pasta texture (Teague, 1988) as well as being used to contribute to product fibre content (Andon, 1987). Edwards et al. (1995) showed that the addition of xanthan gums improved pasta firmness. Kobs (2000) reported, however, that while gums improve firmness the bite becomes rubbery. Other sources of dietary fibre obtained by the addition of oat or pea fibre have resulted in a deterioration of the cooked pasta texture (Dougherty et al., 1988; Edwards et al., 1995). Whole-wheat pasta is another source of dietary fibre, but the presence of the bran and germ particles interferes with the continuity of the gluten matrix causing a decrease in firmness (Edwards et al. 1995; Manthey and Schorno, 2002). Traditional coloured pasta made by the addition of tomato or spinach, for example, has been joined by more exotic flavoured pasta that contain herbs and spices such as basil, lemon pepper, garlic, parsley and red pepper (Giese, 1992). Kobs (2000), quoting industry experts, indicates that these ingredients will not influence the texture of cooked pasta as long as 5% or less is present. Finally, it is possible to make unconventional pasta using flours from sources other than wheat including rice, starch, potato, maize, peas, lentils, etc. The protein present in these products is not able to form the gluten network characteristic of pasta made from wheat, and fortification with gluten protein or the use of different processing methods is required to give a reasonable product (Giese, 1992). By and large, the textural properties of these products are poor and they do not exhibit the firm bite characteristic of durum or common wheat pasta.

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20.4 Influence of processing Durum wheat semolina milling conditions and millstream selection have some effect on pasta texture. Extrusion presses are being developed with greater capacity, increased mixing speed and less mixing time. For these presses, to assure dough homogeneity, semolina granulation is made finer for more uniform and rapid absorption of water (Marchylo and Dexter, 2001). Reducing semolina particle size by grinding increases damage to starch because durum wheat is very hard (Resmini et al., 1996). As damaged starch increases, there is greater loss of solids during cooking as the starch is gelatinized (Matsuo and Dexter, 1980) and a greater tendency to surface stickiness (Grant et al., 1993). The negative effects of finer semolina on pasta texture are negated to some extent when pasta is dried at high temperature or ultra-high temperature (Dexter and Marchylo, 2001). 20.4.1 Extrusion The two key stages in the pasta process determining the cooking quality of pasta are extrusion and drying. Extrusion conditions determine the physical properties and internal structure of pasta dough. Over-heated, over-worked dough produces poor quality, sticky, slimy cooked pasta (DeFrancisci, 2003). Correct filling of the extrusion auger is essential to achieve maximum output of extruded product without excessive heating of dough (Dalbon et al., 1996). Mechanical energy is dissipated as heat. Heat, pressure and shear during extrusion make the gluten network within the dough continuous, and the dough becomes plastic and translucent (Matsuo et al., 1978). The continuity of the gluten protein network and how well it is preserved during cooking are primary determinates of pasta texture (Dexter et al., 1978; Donnelly, 1982; Zweifel et al., 2003). Accordingly, extrusion cylinders are water-jacketed with cooling water to control dough temperature and protect gluten protein functionality. If dough temperature in the extruder exceeds 50 ºC, gluten protein denaturation will occur, and the physical properties of pasta dough and texture of cooked pasta will be adversely affected (Abecassis et al., 1994). Extrusion pressure also affects pasta cooking quality. It must be sufficient to create a sufficiently compact structure to stand up to cooking (Ingelbrecht et al., 2001; Pagani et al., 1989). Excessive pressure causes shearing and tearing of the dough inside the extruder which can cause damage to the structural organization of the protein and to pasta cooking quality (Dalbon et al., 1996). Dalbon et al. (1996) recommended an extrusion pressure of 9– 12.5 MPa. Debbouz and Doetkott (1996) reported that pasta firmness is best at water absorption near 31%, barrel temperature between 35 and 45 °C, and screw speed around 25 RPM. The type and condition of the die will affect extrusion pressure, the dimensions of the pasta and the condition of the surface (Maldari and Maldari, 1993). Dies are made from bronze, and usually have a Teflon insert. The advantages of Teflon coating are longer die lifetime, smoother pasta surface,

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better appearance and superior cooking quality (Donnelly, 1982). Presses with Teflon dies are more efficient than those with bronze dies. Teflon coating allows optimal filling of the extrusion worm resulting in higher pressure, higher extrusion speed and higher output per hour (Dalbon et al., 1996). Donnelly (1982) showed by scanning electron microscopy that the superior cooking quality of pasta made from Teflon dies is attributable to a more continuous protein network surrounding starch granules within the pasta, and also covering the pasta surface, compared to pasta extruded through bronze dies.

20.4.2 Drying Drying conditions profoundly influence pasta cooking quality. Temperature, humidity and airflow must be carefully controlled. If pasta dries too quickly the surface will harden and the pasta may fracture due to stress as moisture trapped within attempts to migrate to the surface. This fracturing, known as ‘checking’, results in weak structure and inferior cooking quality (Feillet and Dexter, 1996). There have been enormous advances in drying technology, beginning with the introduction of (HT) drying (> 60 ºC) in the 1970s (Pollini, 1996). The original attraction of HT drying was better control of bacteria and shorter drying time allowing more compact lines for a given capacity. It soon became apparent that HT drying also improved cooked pasta texture (Dexter et al., 1981b, 1983a, 1984; Grant et al., 1993; Malcolmson et al., 1993; Novaro et al., 1993). The benefits of HT drying to pasta texture include lower loss of solids to cooking water, less surface stickiness, better firmness and better tolerance to over-cooking (Table 20.3). It is believed that the benefit of HT drying is due to the combined effects of protein and starch modification. HT drying increases the extent of protein denaturation as evident from reduced solubility in acetic acid (Atkin and Khan, 1992; Dexter et al., 1981b). In contrast to the extrusion stage, protein denaturation is beneficial during drying because it strengthens a network that has already been established. During the initial drying steps, starch granules become less extractable, consistent with increased physical inclusion of starch or interaction between starch and gluten components (Vansteelandt and Delcour, 1998). Thermal stabilization of the protein network allows it to maintain its integrity better during cooking, resulting in greater resistance to structural breakdown during cooking (Zweifel et al., 2003). Perhaps of greater importance, HT drying modifies starch-pasting properties. Dalbon et al. (1985) showed that reconstituted pasta prepared from gluten and from starch that had been extracted from HT dried spaghetti exhibited superior cooking quality compared to reconstituted pasta containing starch extracted from low temperature (LT) dried spaghetti. Dexter et al. (1985) observed that the concentration of amylose on the surface of cooked spaghetti, a cause of surface stickiness, is reduced by HT drying. These observations

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Table 20.3 Effect of pasta drying temperature on texture of cooked pasta. (Data taken from Dexter et al., 1984) Drying temperature

Cooking loss (%)

Stickiness (N/m2)

Cooking score (sec/m × 10–6)

6.9 5.9 7.1 6.6

630 560 510 440

11.5 17.3 20.0 23.6

10.1 8.6 8.7 8.4

855 570 530 455

8.3 11.7 13.8 14.4

Cooked to optimum time (12 min) 39 70 80 85

°C °C °C °C

Over-cooked 10 min (22 min) 39 70 80 85

°C °C °C °C

can be explained by heat moisture treatment (annealing at low moisture content) of starch during HT drying (Cunin et al., 1995; Vansteelandt and Delcour, 1998; Yue et al., 1999; Zweifel et al., 2000). Changes in starch during HT drying result in higher gelatinization temperature, increased paste viscosity, less starch granule swelling and less amylose exudation. Improvement in pasta texture by HT drying is enhanced by increasing drying temperature from 80 ºC to 100 ºC, and by shifting the HT phase to a later stage of drying when pasta moisture is lower (Zweifel et al., 2003).

20.4.3 Regrinds Another processing factor influencing pasta texture is ‘regrinds’. Regrinds are dried pasta that is ground to fine particle size and recycled to the press. Regrinds comprise waste produced by the cutting of long goods, and product that cannot be marketed due to checking or cracking. Donnelly (1980) estimated that about 5–10% of production ends up as regrinds, and reported that levels above 15–20% are not advisable in spaghetti dried by LT because of deleterious effect on cooking loss. Fang and Khan (1996) reported that regrinds produced from spaghetti dried at HT or UHT are more deleterious to pasta firmness and cooking loss than regrinds from spaghetti dried at LT.

20.5 Trends in consumer preference When considering consumer preference for pasta textural characteristics the Italian tradition of ‘al dente’ eating properties, characterized by high degrees of firmness and elasticity, generally first comes to mind. However, it should

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be realized that this consumer preference is not universal and this must be taken into account when evaluating trends. For example, in countries such as Brazil and Colombia, most but not all consumers prefer pasta with softer textural attributes. This preference can be traced to the practice of producing pasta using common wheat farina or patent flour in lieu of durum wheat semolina (Kim et al., 1989). As noted earlier, common wheat pasta typically has softer textural attributes so in these countries consumers have become accustomed to soft pasta. Pasta cooked ‘al dente’ is thought to be under-cooked by many consumers and is not acceptable. In Japan, on the other hand, consumers are more familiar with the textural attributes of oriental style udon noodles, which are also prepared using common wheat. These noodles also would be characterized as having softer or less firm textural attributes. Not surprisingly, many Japanese consumers look for similar textual properties in cooked pasta. In all these countries, however, the Italian cooking tradition is making inroads. In Brazil importation of Italian pasta, where it is served in restaurants, has started to promote the Italian ‘al dente’ tradition. There also is a move by the national pasta industries to promote consumer buying of durum pasta produced nationally. In Japan, demographics also seems to be playing a role in consumer textural preference, with younger Japanese indicating a preference for the firmer textural properties of pasta cooked in the Italian tradition, while older generation consumers are content with softer pasta. Although the dried pasta sector continues to dominate sales (Anonymous, 2000; Harrison, 1999), consumers in urban centres, especially in the industrialized world, are also showing a trend towards buying convenience foods that are ready to serve and easy to prepare. For example, in the USA, it is reported that ready-to-eat and frozen main dishes will surpass homemade as the most often served main dinner item (Sloan, 2003). This trend has implications related to pasta texture since the production of frozen pasta involves heating and freezing which can affect the firmness and other textural attributes of the cooked product. An appropriate choice of shape and thickness of the cut, along with the addition of optional ingredients as noted above, can help in maintaining acceptable textural properties in the frozen pasta (Kobs, 2000). In Japan, production of cooked frozen pasta is of increasing importance because of consumer purchasing of meals in convenience stores. This trend may also influence durum wheat quality requirements, particularly in relation to gluten strength, since final cooked pasta must exhibit appropriate textural characteristics even though it has gone through a freezing and subsequent cooking process. Stronger gluten durum wheat should produce pasta that will better maintain its textural characteristics through the freezing-cooking cycle. Similar issues are encountered with microwavable and retortable pasta, which undergo a variety of heating processes before serving. Additives are used with such products to maintain good textural properties (Giese, 1992). Another trend in the pasta market is the increasing health consciousness of consumers. Manufacturers have been taking advantage of this by promoting

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chilled fresh pasta as representing a healthier and convenient meal source (Anonymous, 2000). Textural quality of these products remains important but can be influenced by the type of treatment used to prevent bacterial growth and maintain shelf life (Anonymous, 2003a,b). The trend to healthy diet has also led many consumers to look for new sources of dietary fibre. Soluble dietary fibre and β-glucan are reported to lower serum cholesterol levels and reduce the risk of cardiovascular diseases (Betschart, 1988; Bhatty 1993; Newman and Newman, 1991; Yokoyama et al., 1997). Research has shown that pasta can be prepared using mixtures of pearled barley fractions (Marconi et al., 2000), barley flour enriched with β-glucan (Knuckles et al., 1997) and naked oat flour (Sgrulletta et al., 2001). Barley has neutral flavour and texture properties (Pszczola, 2003) and consequently pasta with acceptable sensory properties can be produced. Textural properties are negatively influenced by the addition of barley to the semolina but the addition of egg (Knuckles et al., 1997) or the use of HT drying with the addition of vital wheat gluten improves overall cooking quality (Marconi et al., 2000). Addition of naked oat flour at 50% and 30% levels also produces pasta with acceptable organoleptic characteristics (Sgrulletta et al., 2001). Although the impact of oat flour on pasta textural properties was not presented by Sgrulletta et al. (2001) it was noted that cooking time increased, which suggests that the texture of the cooked product is affected. Potential exists for the manipulation of the textural properties of pasta by changing the durum wheat starch characteristics. Durum wheat comprises about 70% starch, which suggests that changes in this major semolina component, particularly its amylose to amylopectin ratio, could impact on cooking quality. Reconstitution studies using starch from non-durum sources (Dexter and Matsuo, 1979) have shown that cooked pasta firmness and resilience are influenced by starch amylose content. Further studies using partially waxy durum lines, which exhibited amylose contents of about 21– 22% as compared to normal lines of 26–27%, however, have not shown changes in textural properties along with the decrease in amylose content (Sharma et al., 2002). It may be possible that a more substantial decrease in amylose content is needed before textural properties of cooked pasta will be affected, which has been shown for Asian salted noodles (Guo et al., 2003).

20.6

References

ABECASSIS J, ABBOU R, CHAURAND M, MOREL M-H

and VERNOUX P (1994) Influence of extrusion conditions on extrusion speed, temperature, and pressure in the extruder on pasta quality, Cereal Chem, 71(3), 247–53. AGNESI E (1996) The history of pasta. In Pasta and Noodle Technology. Eds J E Kruger, R R Matsuo and J W Dick, St Paul, MN, AACC, 1–12. ALARY R, ABECASSIS J, KROBREHEL K, and FEILLET P (1979) Influence de l’eau de cuisson, et notamment de son pH, sur les characteristiques des pâtes alimentarires cuites, Bull ENSMIC, 293, 255–62.

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American Association of Cereal Chemists (2000) Approved Methods of the AACC, 10th Edition, Method No. 66-5, St Paul, MN, AACC. AMES N, CLARKE J, MARCHYLO B, DEXTER J and LUKOW O (1998a) Relationship between BLMW Glutenin Subunit Alleles and Durum Wheat Strength Characteristics. In Wheat Protein – Production and Marketing. Eds D B Fowler, W E Geddes, A M Johnston and K Preston, University of Saskatchewan, Saskatoon, University Extension Press, 224–7. AMES N, CLARKE J, MARCHYLO B, DEXTER J and KOVACS M (1998b) The effect of durum wheat gluten strength on pasta quality, In Wheat Protein – Production and Marketing. Eds D B Fowler, W E Geddes, A M Johnston and K Preston, University of Saskatchewan, Saskatoon University Extension Press, 228–33. AMES N P, CLARKE J M, MARCHYLO B A, DEXTER J E and WOODS S M (1999) Effect of environment and genotype on durum wheat gluten strength and pasta viscoelasticity, Cereal Chem, 76(4), 582–6. ANDON S A (1987) Applications of soluble dietary fibre, Food Technol, 41(1), 74–5. Anonymous (2000) Pasta products, Market-Research-Europe, 32(7), 1–31. Anonymous (2003a) Additives. In Mondelli, G Studio – Italy, Professional Pasta, < http: //www.professionalpasta.it/> (accessed 27 August 2003). Anonymous (2003b) Fresh packaged pasta. In Mondelli, G Studio – Italy, Professional Pasta. < http://www.professionalpasta.it/> (accessed 27 August 2003). ANTOGNELLI C (1980) The manufacture and applications of pasta as a food and as a food ingredient: A review, J Food Tech, 15(2), 125–45. ATKIN B and KHAN K (1992) Influence of high-temperature drying on structural and textural properties of durum wheat pasta, Cereal Chem, 80(3),159–67. AUTRAN J C, ABECASSIS J and FEILLET P (1986) Statistical evaluation of different technological and biochemical tests for quality assessment in durum wheats, Cereal Chem, 63(5), 390–94. AUTRAN J-C, LAIGNELET B, MOREL M-H, BERRIER R and DUSFOUR J (1987) Characterization and quantification of low molecular weight glutenins in durum wheats, Biochimie, 69(16– 17), 699–711. BARONI D (1988) Manufacture of pasta products. In Durum: Chemistry and Technology. Eds G Fabriani and C Lintas, St Paul, MN, AACC, 191–216. BETSCHART A A (1988) Nutritional quality of wheat and wheat foods. In Wheat: Chemistry and Technology. Ed. Y Pomeranz, St Paul, MN, AACC, 91–130. BHATTY R S (1993) Nonmalting uses of barley. In Barley: Chemistry and Technology. Eds A W MacGregor and R S Bhatty, St Paul, MN, AACC, 355–417. BOURNE M C (1982) Food Texture and Viscosity, New York, Academic Press Inc. COLE M E (1991) Review: Prediction and measurement of pasta quality, Int J Fd Sci Tech, 26(2), 133–51. CUNIN C, HANDSCHIN S, WALTHER P and ESCHER F (1995) Structural changes of starch during cooking of durum wheat pasta, Lebens Wiss Technol, 28(3), 323–8. DALBON G, PAGANI M, RESMINI R and LUCISANO M (1985) Einflüsse einer Hitzebehandlung der Weizenstärke während des Trocknungsprozesses, Getreide Mehl Brot, 39(6), 183–9. DALBON C, GRIVON D and PAGANI M (1996) Continuous manufacturing process. In Pasta and Noodle Technology. Eds J E Kruger, R R Matsuo and J W Dick, St Paul, MN, AACC, 13–58. DEBBOUZ, A and DOETKOTT C (1996) Effect of process variables on spaghetti quality, Cereal Chem, 73(6), 672–6. DEFRANCISCI J L (2003) Basics of pasta extrusion systems, New-Food, 5(4), 85–6. D’EGIDIO M G, DE STEFANIS E, FORTINI S, GALTERIO G, NARDI S and SGRULLETTA D (1981) Influenza del tipo di acqua usata nella cottura sulla qualita delle paste, Tec Molitoria, 32(8), 505–11. D’EGIDIO M G and NARDI S (1996) Textural measurements of cooked spaghetti. In Pasta and Noodle Technology. Eds J E Kruger, R R Matsuo and J W Dick, St Paul, MN, AACC, 133–56.

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and BOZZINI A (1982) Standardization of cooking quality analysis in macaroni and pasta products, Cereal Foods World, 27(8), 367–8. D’EGIDIO M G, MARIANI B M, NARDI S, NOVARO P and CUBADDA R (1990) Chemical and technological variables and their relationships: a predictive model for pasta cooking quality, Cereal Chem, 67(3), 275–81. D’EGIDIO M G, MARIANI B M, NARDI S and NOVARO P (1993) Viscoelastograph measures and total organic matter test: suitability in evaluating textural characteristics of cooked pasta, Cereal Chem, 70(1), 67–72. DELCOUR J A, VANSTEELANDT J, HYTHIER M C, ABECASSIS J, SINDIC M and DEROANNE C (2000) Fractionation and reconstitution experiments provide insight into the role of gluten and starch interactions in pasta quality, J Agric Food Chem, 48(9), 3767–73. DEXTER J E and MARCHYLO B A (2001) Recent trends in durum wheat milling and pasta processing: impact on durum wheat quality requirements. In J Abecassis, J-C Autran, P Feillet, Proceedings of the International Workshop of Durum Wheat, Semolina and Pasta Quality: Recent Achievements and New Trends, Montpellier, France, November 27, 2000, 139–64. DEXTER J E and MATSUO R R (1977) Influence of protein content on some durum wheat quality parameters, Can J Plant Sci, 57(3), 717–27. DEXTER J E and MATSUO R R (1979) Effect of starch on pasta dough rheology and spaghetti cooking quality, Cereal Chem, 56(3), 190–95. DEXTER J E, MATSUO R R and DRONZEK B L (1978) Scanning electron microscopy study of cooked spaghetti, Cereal Chem, 55(1), 23–32. DEXTER J E, MATSUO R R, PRESTON K R and KILBORN R H (1981a) Comparison of gluten strength, mixing properties, baking quality and spaghetti quality of some Canadian durum and common wheats, Can Inst Food Sci Tech J, 14(2), 108–11. DEXTER J E, MATSUO R R and MORGAN B C (1981b) High temperature drying: effect on spaghetti properties, J Food Sci, 46(6), 1741–6. DEXTER J E, MATSUO R R and MORGAN B C (1983a) Spaghetti stickiness: some factors influencing stickiness and relationship to other cooking quality characteristics, J Food Sci, 48(5), 1545–51, 1559. DEXTER J E, KILBORN R H, MORGAN B C and MATSUO R R (1983b) Grain Research Laboratory compression tester: Instrumental measurement of cooked spaghetti stickiness, Cereal Chem, 60(2), 139–42. DEXTER J E, TKACHUK, R and MATSUO R R (1984) Amino acid composition of spaghetti: effect of drying conditions on total and available lysine, J Food Sci, 49(1), 225–8. DEXTER J E, MATSUO R R and MACGREGOR A W (1985) Relationship of instrumental assessment of spaghetti cooking quality to the type and the amount of material rinsed from cooked spaghetti, J Cereal Sci, 3(1), 39–53. DEXTER J E, WILLIAMS P C, EDWARDS N M and MARTIN D G (1988) The relationships between durum wheat vitreousness, kernel hardness and processing quality, J Cereal Sci, 7(2),169– 81. DEXTER J E, MATSUO R R and KRUGER J E (1990) The spaghetti-making quality of commercial durum wheat samples with variable alpha-amylase activity, Cereal Chem, 67(5), 405– 12. DONNELLY B J (1980) Pasta regrinds: effect on spaghetti quality, J Agric Food Chem, 28(4), 806–9. DONNELLY B J (1982) Teflon and non-Teflon lined dies: effect on spaghetti quality, J Food Sci, 47(4), 1055–8, 1069. DOUGHERTY M, SOMBKE R and IRVINE J (1988) Oat fibres in low calorie breads, soft-type cookies, and pasta, Cereal Foods World, 33(5), 424–7. D’OVIDIO R, MARCHITELLI C, ERCOLI CARDELLI L and PORCEDDU E (1999) Sequence similarity between allelic Glu-B3 genes related to quality properties of durum wheat, Theor Appl Genet, 98(3/4), 455–61.

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and BILIADERIS C G (1993) Cooked pasta texture: comparison of dynamic viscoelastic properties to instrumental assessment of firmness, Cereal Chem, 72(2), 564–7. EDWARDS N M, BILIADERIS C G and DEXTER J E (1995) Textural characteristics of whole wheat pasta containing non-starch polysaccharides, J Food Sci, 60(6), 1321–4. FANG K and KHAN K (1996) Pasta containing regrinds: effect of high-temperature drying on product quality, Cereal Chem, 73(3), 317–22. FEILLET P and DEXTER J E (1996) Quality requirements of durum wheat for semolina milling and pasta production. In Pasta and Noodle Technology. Eds J E Kruger, R R Matsuo and J W Dick, St Paul, MN, AACC, 95–131. GIESE J (1992) Pasta: new twists on an old product, Food Tech, 46(2), 117–26. GRANT L A, DICK J W and SHELTON D R (1993) Effects of drying temperature, starch damage, sprouting, and additives on spaghetti quality characteristics, Cereal Chem, 70(6), 676–84. GUAN F and SEIB P A (1994) Instrumental probe and method to measure stickiness of cooked spaghetti and noodles, Cereal Chem, 71(4), 330–37. GUO G, JACKSON D S, GRAYBOSCH R A and PARKHURST A M (2003) Asian salted noodle quality: impact of amylose content adjustments using waxy wheat flour, Cereal Chem, 80(4), 437–45. HARRISON D (1999) Manufacturers try to beat the drums for pasta, Frozen Food Age, 47(8), 32–5. HATCHER D W (2001) Asian noodle processing. In G Owens, Cereal Processing, Cambridge, Woodhead, 131–57. HOULIAROPOULOS E, ABECASSIS J and AUTRAN J-C (1981) Produits de mouture du blé dur: coloration et caractéristiques culinaires, Ind Cér, 12(Sept/Oct), 3–19. INGELBRECHT J A, MOERS K, ABECASSIS J, ROUAU X and DELCOUR J A (2001) Influence of arabinoxylans and endoxylanases on pasta processing and quality. Production of highquality pasta with increased levels of soluble fiber, Cereal Chem, 78(6), 721–9. International Association for Cereal Science and Technology (ICC,1992), Standard Methods of the ICC, Method No.153, The Association, Vienna, Austria. International Standards Organization (1995) International Standard TC34 SC4 7304 Durum wheat semolinas and alimentary pasta – Estimation of cooking quality of spaghetti by sensory analysis, Geneva Switzerland, 04–15. KIM H I, SEIB P A, POSNER E, DEYOE C W and YANG H C (1989) Improving the colour and cooking quality of spaghetti from Kansas hard winter wheat, Cereal Foods World, 34(2), 216– 23. KNUCKLES B E, HUDSON C A, CHIU M M and SAYRE R N (1997) β-Glucan enriched fractions in high-fibre bread and pasta, Cereal Foods World, 42(2), 94–9. KOBS L (2000) Frozen pasta and rice dishes, Food-Product-Design, 10(8), 124–6, 129–30, 133–4, 137–43. KOSMOLAK F G, DEXTER J E, MATSUO R R, LEISLE D and MARCHYLO B A (1980) A relationship between durum wheat quality and gliadin electrophoregrams, Can J Plant Sci, 60(2), 427–32. KOVACS M I P, POSTE L M, BUTLER G, WOODS S M, LEISLE D, NOLL J S and DAHLE L K (1997) Durum wheat quality: comparison of chemical and rheological screening tests with sensory analysis, J Cereal Sci, 25(1), 65–75. LARMOND E and VOISEY P W (1973) Evaluation of spaghetti quality by a laboratory panel, Can Inst Food Sci Technol J, 6(4), 209–11. MALCOLMSON L J (1991) Spaghetti optimization using response surface methodology: effect of drying temperature, durum protein level and farina blending (Ph. D. Thesis, University of Manitoba, Winnipeg, Canada). MALCOLMSON L J and MATSUO R R (1993) Effects of cooking water composition on stickiness and cooking loss of spaghetti, Cereal Chem, 70(3), 272–5. MALCOLMSON L J, MATSUO R R and BALSHAW R (1993) Textural optimization of spaghetti using response surface methodology, Cereal Chem, 70(4), 417–23.

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and MALDARI C (1993) Design and performance of pasta dies, Cereal Foods World, 38(11), 807–10. MANTHEY F A and SCHORNO A L (2002) Physical and cooking quality of spaghetti made from whole wheat durum, Cereal Chem, 79(4), 504–10. st MARCHYLO B A and DEXTER J E (1996) Durum wheat now and into 21 century. In C W Wrigley, Conference Proceedings of the 46th Australian Cereal Chemistry Conference– Cereals’96, Sydney, Australia, September, 1996, Royal Australian Chemical Institute, Australia, 345–52. MARCHYLO B A and DEXTER J E (2001) Pasta production. In Cereal Processing. Ed. G Owens, Cambridge, Woodhead, 109–30. MARCHYLO B A, DEXTER J E, CLARKE J M and AMES N (1998) Effects of protein content on CWAD quality. In Wheat Protein – Production and Marketing. Eds D B Fowler, W E Geddes, A M Johnston and K Preston, University Extension Press, University of Saskatchewan, Saskatoon, SK, 53–62. MARCONI E, GRAZIANO M and CUBADDA R (2000) Composition and utilisation of barley pearling by-products for making functional pastas rich in dietary fibre and β-glucans, Cereal Chem, 77(2), 133–9. MATSUO R R and DEXTER J E (1980) Comparison of experimentally milled durum wheat semolina to semolina produced by some Canadian commercial mills, Cereal Chem, 57(2), 117–22. MATSUO R R and IRVINE G N (1969) Spaghetti tenderness testing apparatus, Cereal Chem, 46(1), 1–6. MATSUO R R and IRVINE G N (1971) Note on an improved apparatus for testing spaghetti tenderness, Cereal Chem, 48(5), 554–8. MATSUO R R and IRVINE G N (1974) Relationship between the GRL spaghetti tenderness tester and sensory testing of cooked spaghetti, Can Inst Food Sci Technol J, 7(2), 155– 6. MATSUO R R, BRADLEY J W and IRVINE G N (1972) Effect of protein content on the cooking quality of spaghetti, Cereal Chem, 49(6), 707–11. MATSUO R R, DEXTER J E and DRONZEK B L (1978) Scanning electron microscopy study of spaghetti processing, Cereal Chem, 55(5), 744–53. MATSUO R R, DEXTER J E, KOSMOLAK F G and LEISLE D (1982) Statistical evaluation of tests for assessing spaghetti-making quality of durum wheat, Cereal Chem, 59(3), 222–8. MATSUO R R, DEXTER J E, BOUDREAU A and DAUN J K (1986) The role of lipids in determining spaghetti cooking quality of durum wheat, Cereal Chem, 63(6), 484–9. MATSUO R R, MALCOLMSON L J, EDWARDS N M and DEXTER J E (1992) A colorimetric method for estimating spaghetti cooking loss, Cereal Chem, 69(1), 27–9. MENGER A (1979) Crucial points of view concerning the execution of pasta cooking tests and their evaluation, Comp, Rendus Symposium Inter sur les matieres premieres et pâtes alimentaires (Rome), 53–60. MENGER A (1982) Influenza dell’acqua di cottura sulle paste alimentari di diversa qualità, Tech Molitoria, 33(1), 23–32. MENGER A (1985) Entwicklung eines 5-punkte-prueschemas zur sensorischen Beurteilung von Teigwaren, Getreide Mehl Brot, 39(2), 61. MILATOVIC L and MONDELLI G (1991) Pasta Technology Today, Pinerolo, Italy, Chirotti. MILO OHR L (2003) More for the sport, Food Tech, 57(2), 63–8. MISKELLY D M (1998) Modern noodle-based foods – raw material needs. In Eds A B Blakeney and L O’Brien, Pacific People and Their Food, St Paul, MN, AACC, 123– 42. NEWMAN R K and NEWMAN C W (1991) Barley as a food grain, Cereal Foods World, 36(9), 800–805. NIETO-TALADRIZ M T, RUIZ M, MARTINEZ M C, VAZQUEZ J F and CARRILLO J M (1997) Variation and classification of B-low molecular weight glutenin subunit alleles in durum wheat, Theor Appd Genetics, 95(7), 1155–60. MALDARI D

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and NARDI S (1993) Combined effect of protein content and high-temperature drying systems on pasta quality, Cereal Chem, 70(6), 716–19. OH N H, SEIB P A, DEYOE C W and WARD A B (1983) Noodles I. Measuring the textural characteristics of cooked noodles, Cereal Chem, 60(6), 433–8. PAGANI M A, RESMINI P and DALBON G (1989) Influence of extrusion process on characteristics and structure of pasta, Food Microstructure, 8(7), 173–82. PAYNE P I, JACKSON E A and HOLT L M (1984) The association between gamma gliadin 45 and gluten strength in durum wheat varieties: a direct causal effect or the result of genetic linkage?, J Cereal Sci, 2(2), 73–81. POGNA N, LAFIANDRA D, FEILLET P and AUTRAN J-C (1988) Evidence for a direct causal effect of low molecular weight subunits of glutenins on gluten viscoelasticity in durum wheats, J Cereal Sci, 7(3), 211–14. POLLINI C M (1996) THT technology in the modern industrial pasta drying process. In J E Kruger, R R Matsuo and J W Dick, Pasta and Noodle Technology, St Paul, MN, AACC, 59–74. PSZCZOLA D E (2003) New ingredient developments are going with the grain, Food Technol, 57(2), 46–61. RAO V K, MULVANEY S J, DEXTER J E, EDWARDS N M and PERESSINI D (2001) Stress-relaxation properties of mixograph semolina-water doughs from durum wheat cultivars of varying strength in relation to mixing characteristics and bread-making and pasta-making performance, J Cereal Sci, 34(2), 215–32. RESMINI P, PAGANI M A and PELLIGRINO L (1996) Effect of semolina quality and processing conditions on nonenzymatic browning in dried pasta, Food Australia, 48(8), 362–7. RUIZ M and CARRILLO J M (1995a) Relationship between different prolamin proteins and some quality properties in durum wheat, Plant Breeding, 114(1), 40–44. RUIZ M and CARRILLO J M (1995b) Separate effects on gluten strength of Gli-1 and Glu-3 prolamin genes on chromosomes 1 A and I B in durum wheat, J Cereal Sci, 21(2), 137–44. SCHLICHTING L M, MARCHYLO B A, DEXTER J E and AARTS W (1998) Inter-relationships among gluten strength, drying temperature and spaghetti colour, abstract 122, Cereal Foods World, 43(7), 526–7. SCHLICHTING L M, MARCHYLO B A, DEXTER J E and CLARKE J M (unpublished results) Effect of blending extra strong gluten durum varieties on cooking quality of spaghetti dried at different temperatures, presented at AACC annual meeting Seattle, WA, Nov 3, 1999. SGRULLETTA D, DESTEFANIS E and POLLINI C M (2001) Durum wheat and oat: a good association for innovative alimentary pasta at high nutritional value, Italian Food and Beverage Technology, 23(March), 31–3. SHARMA R, SISSONS M J, RATHJEN A J and JENNER C F (2002) The null-4A allele at the waxy locus in durum wheat affects pasta cooking quality, J Cereal Sci, 35(3), 287–97. SLOAN A E (2003) Top 10 trends to watch and work on: 2003, Food Tech, 57(4), 30–50. SMEWING J (1997) Analyzing the texture of pasta for quality control, Cereal Foods World, 42(1), 8–12. SZCZESNIAK A S (1963) Classification of textural characteristics, J Food Sci, 28(4), 385–9. TEAGUE G D (1988) Xanthan gum and alginates in fresh and dried pasta for texture improvement, abstract 11, Cereal Foods World, 33(8), 662. VANSTEELANDT J and DELCOUR J A (1998) Physical behavior of durum wheat starch (Triticum durum) during industrial pasta processing, J Agric Food Chem, 46(7), 2499–503. VOISEY P W and LARMOND E (1973) Exploratory evaluation of instrumental techniques for measuring some textural characteristics of cooked spaghetti, Cereal Science Today, 18(3), 127–33, 142–3. VOISEY P W, WASIK R J and LOUGHHEED T C (1978a) Measuring the texture of cooked spaghetti, 2. Exploratory work on instrumental assessment of stickiness and its relationship to microstructure, Can Inst Food Sci Technol J, 11(4), 180–88.

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21 Improving the texture of fried food C-J. Shieh and C-Y. Chang, Da-Yeh University and C-S. Chen, Chao-Yang University of Technology, Taiwan

21.1

Introduction

Texture is, in addition to flavor, one of the most important attributes of fried food and is always an issue in the manufacturing of fried food products. In many cases a crunchy fried product indicates freshness, while lack of crunchiness often implies prolonged storage. For fried foods like potato chips, this is generally true. For fried chicken, however, a juicy interior may be a higher priority, and a crispy exterior crust would be a plus. In either case, texture, whether interior or exterior, is key to consumer acceptance upon the first bite. Before a systematic approach to improving the texture of fried food can be implemented, it is important to be able to define, or quantify, texture and screen the factors that determine the texture of the final product. This chapter discusses common methods adopted in the measurement of texture (Section 21.2), factors that affect the texture of the product (Section 21.3). Based on these, a systematic approach, the Response Surface Methodology (RSM), to improving texture will be described (Section 21.4). This is followed by a case study that illustrates the application of RSM (Section 21.5).

21.2 Measuring texture Texture determines the rheological property (the relationship between applied physical stress, forces applied per unit area, and deformation of materials), or the mechanical property of solid foods which, in turn, determines how people feel during mastication. Although mouthfeel is a complex function of

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many variables, for analytical purposes, it is possible to simplify the complex physical interactions which take place during mastication to three basic types of physical forces: compressive, tensile and shear. Compressive force refers to the force acting on the solid food specimen in the direction pointing to the center. Compression testing equipment records the resisting force of the solid specimen to deformation (or strain, commonly expressed as the relative distance traveled by the compression probe, or a plunger, in the direction of the compression), and the deformation during the process of compression. The design of the testing equipment usually involves a mobile probe driven by a motor moving at a preset speed towards the food specimen sitting on a stationary platform. An alternative arrangement uses a stationary sample probe and a mobile platform. Take a piece of battered fried fish fillet as an example. Frequently, it is desirable to have the fish fillet wrapped in a crispy crust, of a certain thickness, formed from batter during frying. Looking at the cross-sectional area of a piece of the crust, one can see that it consists of many small empty chambers separated by thin walls. When this piece of crust is subject to compression force, the resistance to deformation initially increases linearly with distance. On a stress–strain (resistance–deformation) curve, this initial slope is referred to as the ‘elastic modulus’ (Szczesniak, 1983) or the ‘modulus of deformability’ (Mohsenin and Mittal, 1977). This modulus, measured before any degree of structural failure has taken place, is related to the firmness of the sample. As this piece of crust is further compressed, the rate of increase of resistance soon becomes non-linear up to a point at which some structural failure begins to take place (some chambers collapse). This point of initial structural failure is called the ‘bioyield point’. During this stage of compression, the structural failure is local and the resistance may either decline or remain virtually constant. The rupture of these chambers is normally accompanied by the pleasing sound of crunchiness during mastication. Some researchers have studied this acoustical effect of crisp foods and related the sound to the sensation of crunchiness (Christenson and Vickers, 1981; Drake and Halldin, 1974; Mohamed et al., 1982). When local structural failure propagates and reaches a point of massive failure, the resistance force will decline rapidly. The whole stress–strain curve provides quantitative information about the food specimen. Tensile force is the force that pulls the specimen apart, and it is an important tool in evaluating the resistance of a material to elongation. Voisey and deMan (1976) pointed out that during mastication, the wedging action of teeth imposes tensile stress on foods. Tensile measurement is important in areas such as quantifying the strengths of fiber, dough, membrane and muscle. The measurement has been used by a number of researchers to study the mechanical properties of raw and cooked muscle fibers (Bouton and Harris, 1972; Bouton et al., 1975; Stanley, 1976). Tensile measurement provides more rheological information about elastic foods and has limited applications

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in crisp fried foods. However, in cases like pre-fried instant noodles, the tensile strength of the noodle after rehydration is an important factor in its chewy attributes and is an index of quality that should not be overlooked. Shear force refers to the force acting in the direction parallel to the plane of action. Early shear testing equipment was designed to measure the mechanical properties of muscle and, with some improvement in sensitivity, is still widely used in meat research. The shear measurement on a Warner-Bratzler shear apparatus is carried out by applying force across muscle fibers via a blunt edge. Although shear is not the only type of force that could cause the resulting deformation, it is the major contributor. Voisey and Larmond (1974) pointed out that, besides shear, compressive and tensile stresses are also involved in causing the deformation in a shear measurement. Other researchers (Szczesniak, 1983; Peleg, 1987) have reached a similar conclusion, i.e. it is almost impossible to study food texture using shear forces alone. In light of the fact that the shear-deformation pattern varies among different foods, as well as foods that are only slightly different in terms of texture, it is widely accepted as a means of quantitative study of food texture. Szczesniak and coworkers (1970) used a Kramer shear press to study the textures of a variety of foods and published their corresponding characteristic shear-deformation patterns. With the aid of modern microcomputers, rheometers have become more versatile and accurate. Many manufacturers offer inexpensive multi-functional rheometers for various types of stress–strain study. By combining the following four elements – a probe, a driving system, a sensing unit and a readout system – a rheometer can provide valuable information about food texture. Typical types of probe are: flat plunger, plate, piercing rod, penetrating cone, cutting blade, shearing jaws and cutting wires (Szczesniak, 1983). The driving mechanism usually involves an electronically- or computer-controlled motor that can run at a pre-set speed and drive the probe (or the sample platform) to a designated distance with high accuracy. A weight/pulley arrangement or hydraulic system can also be used as the driving mechanism. A sensing unit is able to quantify any resistant force it finds by using a simple spring, strain gauge, loadcell or force transducer. The signal from the sensing unit is usually analog (voltage or current), and it can be read or processed by a recorder that draws the stress–strain curve. Currently, it is common practice to transform an analog signal into a digital signal (AD) through an interface card plugged into a personal computer. Digital data can be stored for later analysis. In more advanced models, for improved efficiency and to minimize human error, operation of the machine can be automated through an AD/DA interface card controlled by a program residing in the computer. Software commands, in the form of digital signals, issued by the program are transformed, by the DA (digital to analog) part of the interface card, into analog signals that control the testing machine.

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21.3 Factors influencing texture More than five factors which determine the texture of the fried product can be easily identified. Before frying, characteristics of the food, such as water content, size, types and amount of protein and starch, composition of coating materials (batter, breadcrumbs, etc.) and additives, are important factors which establish the necessary chemical and physical environment which leads to the desired texture after frying. Processing variables, such as frying time and temperature, cooling condition, moisture control, etc., can be used to fine-tune the texture of products. This section briefly discusses the influence of these variables and picks out a few which might form a suitable basis for a systematic approach to improving texture using techniques such as RSM. 21.3.1 Water Water content is important in almost all kinds of foods, with only a very few exceptions. Frying differs from other cooking methods in that it uses temperatures higher than the normal boiling point of water under atmospheric pressure. During frying, water evaporates at a rate higher than with boiling at 100 °C. As a result, the temperature of the interior portion rises faster than normal boiling. Furthermore, in cases such as battered or breaded frying, high temperature at the food/oil interface leads to the development of a barrier that reduces water loss from the interior portion and keeps the inside tender. The oil temperature can be as high as 160–180 °C during water evaporation, leading to a high heat transfer rate (due to the large temperature difference of 60–80 °C), but some of the heat transferred is carried off as latent heat of vaporization. This physical phenomenon of latent heat removal of energy keeps the temperature at the oil/food interface, during the early stage of frying, virtually constant at normal boiling point (Blumenthal, 1991), thus preventing the food surface from premature charring or burning (in some cases, slight charring or burning is welcomed), and this is the beauty of frying. As the water content decreases, the rate of energy removal in the form of latent heat of vaporization starts to decline and this leads to an increase in the temperature at the interface, and some degree of browning which is desirable during this stage. Water is also one of the factors responsible for forming porous, crunchy networks in many fried foods. Battered frying is a good example to explain the mechanism involved in forming such a texture. Starting from regions close to the interface, high-rate water evaporation begins upon contact with high-temperature frying oil. A barrier or crust-like layer soon develops and gradually becomes tough. As long as the interior water that becomes vapor and rushes to the interface keeps supplying this barrier layer, the temperature of the frying object at the interface will not exceed normal boiling point. The viscosity of the pasty batter, together with the barrier layer, leads to resistance to bubbles formed from the rapid increase in the molecular volume of water.

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When the water vapor does not have a clear passage to the interface, pressure builds up and the interior temperature may be higher than normal boiling point which results in faster cooking. Another effect of the resistance to vapor release is the volume expansion of the fried object, leaving small chambers produced by rapid boiling of interior water. The size of the chambers is determined by the rate of dehydration, and the relative ease of migration of water through the surface matrix, which is a function of the composition of the battering material. The former is a function of frying temperature and the latter depends on the strength of the walls (or membranes) separating the chambers which, in turn, is set by the physical properties (density, viscosity, surface tension, etc.) of the battering paste as decided by its chemical composition. While the quest for a crispy exterior crust is of great concern in the manufacture of fried foods, a juicy interior, in the case of foods like fried fish or chicken, is just as important. The initial water content of the raw material, the water-holding capacity, and the remaining water after frying are all of great importance in controlling both the interior and the exterior texture. Too little interior water results in loss of tenderness and too much exterior water results in loss of crispness. Water, as well as the rate of water evaporation, is the key to fine-tuning texture.

21.3.2 Size The size of the frying object determines the time required for the temperature at the center to reach the desired level. If the size is too large, the surface will be charred or burned while the interior portion will still be undercooked. For instance, for French fries, proper time and temperature are required for the interior starch gelatinization to proceed to the right degree and texture. If the potato pieces are too large, there may not be enough time for the temperature at the center to reach the right level for starch gelatinization to proceed before charring and burning at the surface takes place. On the other hand, if the size is too small, the time for developing an acceptable crisp skin will be slightly shorter than normal, due to less water supply, the interior temperature will rise much faster, water may start to be lost and this will result in unacceptable interior texture. The balance between size and energy supply is therefore important.

21.3.3 Protein and starch Protein and starch are the major constituents in foods that are responsible for forming the various characteristic types of texture. At the elevated temperature reached during frying, reactions of both a physical (phase change, volume expansion, solute concentration, etc.) and a chemical (destruction and formation of chemical bonds) nature are taking place. Protein denaturation and starch gelatinization are typical examples of the combined effect, physical as well

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as multiple-order chemical, reactions. These reactions may involve breaking of hydrogen bonds, formation (or breaking) of covalent bonds between amino acids, breaking of glycosidic bonds, rearrangements of three-dimensional structure, hydration and dehydration, and fragmentation of large molecules. The resulting protein-starch network is important in determining the rheological properties either of the exterior coating or the interior food body. Protein and starch content can frequently be manipulated to affect water-holding capacity, and consequently influence the texture. Kadan and coworkers (1997) reported that the protein/starch ratio is an important factor which can be adjusted in order to optimize the texture of both the interior food body and the crust. A variety of proteins have been used in formulating modern batter recipes: cheese powder, egg albumen, whey protein, gluten, soy protein. Some of them are prehydrolysed in order to serve different purposes. Protein is a versatile compound that can function as an emulsifying agent, a film forming agent, a structural material, and many others (Cheftel et al., 1985). Soy protein is sometimes added to meat products to improve water-holding capacity, flavour, and cohesiveness (Brewer et al., 1992; Kotula, 1976). Fibrous muscle protein tends to lose water and form aggregates that are tough and dry after prolonged cooking or frying. Mechanical fine cutting of meat to manufacture meatball (fish, pork or beef) is a good example of protein manipulation to improve water-holding capacity. After fine cutting, fibrous muscle protein molecules are smaller and less organized and thus less likely to form aggregates during cooking or frying and more capable of retaining water. Starch is the major component in many commercial premixed battering powders and is responsible for the body of the crust of battered fried products. Starch gelatinization is crucial in frying, since it enables water-retention and provides volume expansion. Carbohydrates, serving various functions and in different forms, are also used in many new formulations of batters: gums, pregelatinized starch, modified starch, high amylose starch (starch with less branched structure) and dietary fibers. Kadan and co-workers (1997) studied the effect of amylose and protein on the texture of rice-based fries. They reported that in high protein content rice-based fries, protein molecules tended to form a barrier around starch granules and this retarded water uptake during starch gelatinization. Consequently, the water-holding capacity was reduced, which led to loss of more water during extrusion at 90 °C, and the resulting texture was hard and tough. Their experience demonstrated that the types, states and interactions and the ratio of the two macromolecules (protein and starch, originally present or added) play important roles in setting the textures of fried foods. In addition to structural contributions, chemical reactions between proteins and carbohydrates, through browning reactions, are also known to develop special flavour and pigments in fried foods. 21.3.4 Additives Additives like salts often induce subtle changes in functionality of the proteins The addition of phosphates has been shown to improve a protein’s water-

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holding capacity in meat (Moore et al., 1976; Neer and Mandigo, 1977; Whiting 1984). Lin and Kuo (1994) studied low-temperature storage (0 °C and –20 °C) of battered and breaded chicken breast, and compared the effect of injected phosphate solution, soy protein and oil emulsions on its texture and palatability when fried. They concluded, by panel studies, that for 0 °C storage, injection of phosphate significantly improved tenderness and juiciness of the meat. Storage at –20 °C, together with a slow freeze–thaw process to separate water from the fibrous muscle protein, and injection of olive oil emulsion, was found to improve the fried product. Other additives, like sodium chloride, chemical leavening agent and stabilizers, although minor in quantity, are also significant. Leavening agents, added to batters, provide volume expansion during frying, and affect the texture of the crust. The presence of salt such as sodium chloride increases the water boiling point and consequently influences heating rate. Fat is particularly important in the mouthfeel of a fried product, for instance, in improving the tenderness of turkey breast (Larmond and Moran, 1983; Moran 1992; Moran and Larmond, 1981). Some compounds are not added for a specific purpose, but are generated during frying. One example is oil degradation at high temperature which releases long chain free fatty acids. An important theory which is related to this phenomenon is the ‘Surfactant Theory of Frying’ (Blumenthal and Stockler, 1986; Blumenthal, 1991;). The theory states that as the frying oil degrades, more surfactants (i.e. metal salts of fatty acids) are formed, which increase contact between the frying oil and water-based foods. Consequently, the heat transfer rate to the food surface is increased, leading to enhancement of local darkening and drying. The quality of the frying oil, especially in terms of free fatty acid released, is therefore a crucial factor in the texture of fried foods.

21.3.5 Processing variables Processing variables like frying time and temperature are factors that can be easily adjusted without too much trouble in order to improve texture. As mentioned above, high oil temperature means high temperature gradient and consequently high heat penetration rate and faster increase of temperature near the center. However, before the operator boosts up the oil temperature, it should be noted that, at high temperature, the effect of increased water evaporation rate is more profound near the surface than in the interior portion. The exterior is likely to become dry and on the point of becoming burned or charred while the interior still remains undercooked. Often, the cut size could be adjusted to counterbalance the increased heat transfer rate. However, this is commonly limited by customer preferences or economic criteria; moreover, adjusting size may involve other effects on, for example, oil absorption, flavor development, or even production cost.

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21.4 The use of response surface methodology (RSM) An awareness of the influences of various factors on the texture of the product will give a clear picture of the possible variables which can be manipulated (the independent variables) in order to improve or optimize the texture (the dependent variable). Among dozens of possible candidates, use of some is limited by consumer habit or unavailability of the required tool, while others may involve long-term research and development. The experienced manager may advise giving a higher priority to those variables that are convenient to adjust, and leaving those that demand more time and effort for later study. After screening, the remaining few may thus form the basis for a focused study. However, optimization is not easy even with only two or three variables. RSM has been developed particularly for the optimization of sophisticated multivariable systems where the quantitative relationship between key variables is not always clear, as is the case with a complex operation such as frying. RSM allows simultaneous consideration of more than one variable at many different levels, and of the corresponding interactions between these variables, on the basis of a relatively small number of experiments.

21.4.1 Principles of RSM The word ‘response’ refers to how a function (to be optimized) reacts to change(s) in its independent variable(s). Take a quadratic function of the form: f (x) = 10 – x2, for illustration. Changing the independent variable ‘x’ from 1 to 2, the ‘response’ of ‘f ’ would be ‘changing from 9 to 6’. The curve of f (x) with ‘x’ (the independent variable) being the abscissa is a concave down parabola. The highest point of the curve can easily be located graphically at x = 0, where f (x) = 10. Another way of looking at the question of locating the highest point (or optimum point) is to use calculus: equating the first derivative of ‘f ’ (with respect to x) to zero, and finding the solution. In this example the equation would be f ′ = 2x = 0, and the solution is x = 0 which is the same answer as was obtained by the graphical method. Graphical and analytical methods are two powerful tools, if not the only two, in optimization. It is much more complex when the function ‘f ’ involves two independent variables: z = f (x, y). The graphical representation of the function will be a surface in three-dimensional space, on which z, the dependent variable, changes as x and y vary according to the relationship defined by f. Standing on the highest point of the curved surface, as is the case with a one variable function, calculus tells us that fx and fy (the first partial derivatives of f with respect to x and y) are zero. However, it necessary to be aware that the reverse is not necessarily true. An analytical solution can be obtained by equating both ( fx and fy) to zero and solving the simultaneous equations. Since an optimum point is merely one form of critical point – the solution gained may represent a saddle point – its solution should be verified by further mathematical testing. Numerical tools may be required for the solution of the above

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simultaneous equations when they are non-linear, and it will be even more difficult if non-linear differential equations are involved. However, these are beyond the scope of this chapter. Should this be the case, readers are encouraged to turn to references about numerical methods, or computer software packages for numerical solutions. In spite of all the trouble and efforts which may be needed in order to obtain an analytical or numerical solution, the graphical method may be considered to be the friendlier option. In practical applications, it is quite likely that quantitative description of the relationship between the dependent variable (e.g. texture indices, quality, production cost) and the independent variable(s) (e.g. water content, frying temperature, surface to volume ratio) is impossible, and the optimum point cannot be found analytically. On such occasions, the graphical method, although less precise, is a practical and straightforward option. It provides the graphical version of the description of the function without knowing how the function is mathematically defined. Should the graphical method be chosen to study the behavior (the response) of the unknown function, the next question would be: whether the independent variables are to be changed one at a time, or simultaneously? There are times when one can find the optimum point by adjusting one variable at a time while holding other variable(s) constant: the one-variable-at-a-time technique. Consider an example where the amount of a new type of starch in a batter mix (x %) and the oil temperature (y °C) are the two variables under investigation. Assume that at present, an oil temperature (y) of 175 °C is being used. Therefore, while holding y constant at 175 °C, a series of experiments involving changing x from 0 to 50% is carried out and volume expansion (%) is chosen as the dependent variable (z). From the graph of z vs x the optimum xopt, can be decided. In the second run, x is held constant at xopt, and y is changed. A z vs y graph is thus obtained, and yopt can be decided. This procedure is, given the mathematical definition of f, equivalent to solving fx = 0 for x while holding y constant, and solving fy = 0 for y while holding x constant, instead of solving simultaneous equations. Although the method is strategically simple, the potential interaction between independent variables is not accounted for. In cases of significant interaction, the optimum point located by the one-variable-at-a-time technique could be significantly different from the true optimum point. All experimentation is prone to human error and, in addition, sampling reproducibility in non-homogeneous systems such as fried foods is poor; hence fluctuations of data are not surprising. To counter these inherent errors, scattered data points on a graph need to be smoothed by finding a curve (or surface) that best represents the pattern of obtained experimental data. Regression is a mathematical tool that helps us to decide which line, or curve, or surface best fits (represents) given experimental data by minimizing an object function called ‘sum of squares’, which is the sum of the square of the distances between the data points and the line, curve or surface. Most graphical software packages are capable of performing versatile regressions.

510

Texture in food

Statistically, a larger sampling size (more experiments) will bring us closer to the true solution. However, ‘how many is enough?, is the question we tend to ask under pressure of increased time and effort. In addition to regression, sampling size is another question which needs to be addressed. As stated above, regression involves minimizing the ‘sum of squares’, which is calculated based on the distances (or deviations) between data points and a ‘curve’ (or surface) which best represents scattered data. Therefore, in order to have a target for measuring distances, we need to assume a function (e.g. a polynomial) whose characteristic shape is similar to the pattern of the data presented. Mathematical theories can then be applied to find the minimum of ‘sum of squares’ by adjusting parameters (e.g. coefficients of a polynomial). Typical mathematical models used in such cases are polynomial, sigmoidal, Gaussian functions, etc. The chosen mathematical functions (or models) for regression do not necessarily by themselves imply any physical or chemical significance. These functions could be adopted solely for the purpose of finding a curve or surface that best represents the data. A mathematical model that has a sound theoretical background would provide far more insight to the problem under investigation; however, this is rarely the case. In RSM, it is assumed that in the neighborhood of an optimum point, concavity (whether concave up or down, or even twisted) of an arbitrary surface makes it reasonable to use a quadratic function as an approximation of the surface. The assumption may still hold even if the region of concern is not near an optimum point, but is small enough for the approximation to be valid. Most of the time, this simplification is good enough for practical purposes. Box and Wilson first proposed the concept of RSM in 1951 (Box and Wilson, 1951). RSM uses a quadratic function of the form: Y = a0 + a1 X1 + a2X2 + a3X1X2 + a4 X 12 + a5 X 22 to approximate the surface in the neighborhood of an optimum point. Where Y is the dependent variable (one of the texture indices in this case) a0 through a5 are the coefficients (to be determined by regression) and X1, X2 are the factors (or operation variables which can be manipulated in the production process) that influence texture, the dependent variable. The shape of the surface representing the quadratic model is determined by the coefficients. The six adjustable coefficients (a0 through a5) together with its quadratic nature make the model sufficient for most practical applications. Theoretically, manipulating six parameters should enable the shape of the surface to be adjusted to fit any set of data that are smooth and quadratic in nature, given that the data fall either in the vicinity of an optimum point, or in a small enough region. However, in practice, it is quite likely that one or more of the following situations might occur. • Experimental data tend to fluctuate, and sometimes the true object function is not quadratic even in the neighborhood of the optimum point (poor degree of fitness for quadratic model).

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• Independent variables do not fall in the vicinity of the optimum point. • One or more of the chosen factors might not affect the dependent variable to a sufficiently significant level to be included in the model. While understanding and differentiating amongst the above listed possibilities is crucial, it is just as important to answer the following: is the experimental error within tolerable limits or is the response of the dependent variable quadratic in nature; is the region covered small enough to be represented by the model; and, are the chosen factors sufficently significant to be included in the model? Statistical analysis is therefore necessary in order to verify the data, and to reach a conclusion which has a scientific basis. In order to minimize the time and effort involved in carrying out numerous experiments and, at the same time, have a large enough sample size for statistical validity, careful design of the experiment is important. Experiment design is a technique developed to establish an optimal number of experiments (Mason et al., 1989; Montgomery, 1984; Thompson, 1982) which can simultaneously satisfy the criteria of minimum number of experimental runs and large enough sample size to claim statistical significance. The next section discusses the basics of experiment design. 21.4.2 Experiment design As mentioned before, people tend intuitively to turn to the one-variable-ata-time technique for its conceptual simplicity and ignore the possible interaction between independent variables. A good example of the interaction between factors is that between water content (W ) and frying temperature (T ). Assuming W and T are the chosen factors for optimization, one possible interaction will be that T tends to influence the way W affects the texture and vice versa. Since water molecules are removed at a higher rate at high temperature than at low temperature, the rate of water evaporation is affected by temperature, and this will inevitably affect texture of the product. Should the interaction be minor or negligible, a one-factor-at-a-time search will give a satisfactory result. If it is thought that interaction may be taking place then a more complex experiment design should be considered. The price paid will be a minor one in comparison with the benefits in terms of more convincing results and more statistical information. An experiment design for one-variable-at-a-time optimization is shown in Table 21.1. The experiment involves five levels for each factor. The levels are expressed in coded form, which can be linearly transformed back to their corresponding true values, so that the arrangement could be applied to other systems. In the coded form, one unit could represent 10 °C difference in oil temperature, or 5% water content. From run numbers 1 to 5, X2 was held constant at 0 (the center), and it was assumed that X1 = –1 was found to produce the highest response (Y2). Then, from run numbers 6 to 9 (the combination of X1 = –1 and X2 = 0 was carried out in run number 2), X1 was fixed at –1, while X2 varied from –2 to +2, and it was assumed that X2 = +1

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Table 21.1

Experiment design for one-factor a time optimization (two-factor five-level)

Run number

Independent variables

Dependent variable

X1

X2

Y

1 2 3 4 5 6 7 8 9

–2 –1 0 +1 +2 –1 –1 –1 –1

0 0 0 0 0 –2 –1 +1 +2

Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9

Optimum point

–1

+1

Yoptimum = Y8

was found to be optimum (Y8). X1 = –1 and X2 = +1 which produced the highest response (Y8) was therefore identified as the optimum point. It cannot be over-emphasized here that the true optimum point could be elsewhere, should interaction effects be significant. Central Composite Design (Mason et al., 1989; Montgomery, 1984; Thompson, 1982) is commonly employed for systems with potential interactions effect(s) between factors. For a n-factor-five-level design, five coded levels (–d, –1, 0, +1, +d ) are assigned to each factor, where d is called the extended level and d = (2)n/4. An example of two-factor-five-level design is given in Table 21.2. For two-factor design d = (2)2/4 = 1.414, for three-factor design d = (2)3/4 = 1.682, and so on. In Table 21.2, run numbers 1 to 4 correspond to a two-level factorial design, and run numbers 9 and 10 are duplicate experiments so that statistical diagnosis can estimate experimental error. For more detailed explanation of the theories involved in the experiment design, Table 21.2 Run number

1 2 3 4 5 6 7 8 9 10

Central composite design (two-factor five-level) Independent variables

Dependent variable

X1

X2

Y

1 1 –1 –1 0 0 1.414 –1.414 0 0

1 –1 1 –1 –1.414 1.414 0 0 0 0

Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10

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readers are encouraged to go to the cited references. Comparing Tables 21.1 and 21.2, it is clear that Central Composite Design can provide more information, and the price is as little as a 10% increase in time and effort. Considering the benefit from verification of the potential interactions between factors, this small price should be worth paying.

21.4.3 Statistical analysis After doing experiments according to Table 21.2, a group of ten observations (Y1 through Y10) can be used to carry out regression using the quadratic model as stated above. The obtained model, with the six coefficients determined by regression, represents a surface in the Y – X1 – X2 space. Using a computer graphic tool, one can visually pinpoint the critical point, be it a stationary point (where varying factors will not significantly change the dependent variable) or extrema (maxima or minima). While getting the response surface, the questions about error, interactions, or even whether or not the factor(s) should be included in the model in the first place, remain to be answered. Statistical analysis could provide answers to these questions. Statistical analysis can be done by ‘analysis of variance’ (ANOVA, Box et al., 1978). If it is thought that interaction may be taking place then a more complex experiment design should be considered. The price paid will be a minor one in comparison with the benefits in terms of more convincing results and more statistical information. One of the most important ANOVA terms in the application of RSM is R2 (R-square), which shows the goodness of fit of the mathematical model. R2 is determined by calculating the ratio of regression sum of square (SSR) to the total sum of square (SST): R2 = SSR/SST. Since SST is the sum of SSR (which comes from lack of fit) and SSE (the error sum of squares, which comes from pure experimental error), the closer R2 is to 1 (or 100 on a percentage basis) the smaller SSE is and, therefore, the more intimate the relationship between the model prediction and the true response. Another quantity, the P-value, indicates how significant is each term in the model (X1, X2, X1X2, X 12 and X 22 ). The P-value for i-th term in the model reveals that for the term Xi in the model, the probability that Xi is not significant to the response is Pi. For example, P2 = 0.05 means that the probability that X2 is not significant to the response is 0.05. In common language, there is 95% of the chance that X2 is significant. It can also be said that X2 is significant at 5% level. In practice, depending on the desired level of accuracy, a P-value lower than 0.1 (10% level) is commonly considered acceptable. Recently, advances in engineering and the sciences have provoked the development of all-in-one computer software packages that merge computer graphics, experiment design, regression, statistical analysis, worksheet and documentation. SAS (SAS 1989), the Statistical Analysis System, is widely used. Design-Expert (1996) by Stat-Ease is very user-friendly. Similar software packages like STATISTICA (2000) and SPSS (2000) are also popular in the scientific software market. These software packages normally have a

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powerful tutorial system that will guide new users through the steps of RSM optimization. RSM optimization has wide applications in various fields such as food technology, chemical technology, material engineering and the list goes on. Many successful examples can be found in the literature. Junqua et al. (1997) maximized the production of microbial transglutaminase, an important enzyme in protein-related foods, using RSM. They narrowed down the search domain by the one-factor-at-a-time technique and used the obtained optimum point as the center of the ‘central composite design’ in subsequent RSM optimization. A three-fold increase of the enzyme was achieved using RSM. Mahoney and co-workers (1974) optimized lactase production by the conventional onefactor-at-a-time procedure. Building on their work, Chen et al. (1992) applied RSM to further increase the enzyme production by 60%.

21.5 A case study: fried gluten balls Fried gluten ball is a popular fried food in the Chinese community. Added to soups or stir-fried dishes, it absorbs mixed flavors and becomes rehydrated, tasty and very chewy. Tonnes of fried gluten ball is sold each year on the food market in Taiwan. The manufacturing process is as follows: 1 2 3 4

wheat flour is washed with water to separate gluten from the starch; the wet gluten is immersed in water for 30 minutes; it is then collected, cut and shaped into wet gluten balls; wet gluten balls are then deep fried using three or four deep frying pans controlled at different temperatures; 5 in the first and second frying pans, water evaporation expands the gluten balls, establishing their basic volume, shape, texture and colour; 6 frying in the final pan(s) completes the ageing of the balls. During the stage of gluten extraction, as the flour is washed with water, the networked structure of gluten is gradually forming (Bietz and Wall, 1980; Huebner, 1977). Upon hydration, glutenin becomes swollen and, at the same time, absorbs gliadin together with some of the albumin and globulin. The network structure of gluten is co-stabilized by disulfide bonds and hydrogen bonds, as well as hydrophobic interactions (Huebner, 1977). This kind of network is the key factor in the development of the texture of fried gluten ball during frying. Initially, the water content of the wet gluten balls is high, and fast evaporation of water leads to major volume expansion (as much as 15–20 times the initial size) during the first stage of frying (the first and the second frying pan). This produces highly porous, dried and crispy gluten balls. Fryings in the first and the second pans are the most important steps in setting the final quality of the gluten balls. Oil temperatures of the first and the second frying

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pans are therefore chosen to be the two factors for RSM optimization of the texture (Chen et al., 1998). The flour used was untreated, milled commercially from mixed grist of hard red wheat imported from America. The protein content, on 13.5% moisture basis, was 13.96%, and ash content was 0.53%. Wet gluten balls weighing 100 g each were fried continuously in three consecutive frying pans. Each frying pan, with identical dimensions, contained 10 l of soybean oil. The frying time in each pan was 120, 90 and 70 seconds respectively. For the reasons stated above and from previous experiences, the texture and color of the fried gluten balls were nearly fixed after the first and second frying pans, and the oil temperature of the third pan was controlled at 195 ± 3 °C. 21.5.1 Experiment design The rotatable central composite design (Mason et al., 1989) consisting of a two-factor-five-level arrangement with 10 observations (eight combinations with two replications at the center point) was adopted. The two factors (oil temperatures of the first and second frying pans, T1 and T2), and the coded values of the five levels of each factor are listed in Table 21.3. In order to cover various ways of looking at the quality of the product, it is better to encompass as many quality indices as possible; four instrumental texture indices, three subjective panel test scores and spectrophotometer measured color were selected to be the dependent variables which were studied individually and cross-referenced. These are discussed in the remaining part of this section. Sensory evaluations, although subjective, directly reflect consumer preferences. However, mouthfeel is a complex, abstract and entangled overall sensation that is hard, if not impossible, to trace back to individual physical properties. Instrumental measurements can provide a quantified basis that is best used as a complement to sensory analysis; and instrumental results can be seen as reliable only if they are validated against sensory measurements. 21.5.2 Measurements A group of 30 experienced panelists was organized for sensory evaluation. Panelists scored the samples in three ways: appearance score (AS), texture Table 21.3 Coded values and corresponding real values of independent variables. (Chen et al., reproduced with permission from John Wiley & Sons) Independent variables

Coded levels –1.414

–1

0

1

1.414

X1(T1 °C) X2(T2 °C)

126 151

130 155

140 165

150 175

154 179

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score (TS) and total acceptance score (TAS). AS and TAS were analyzed by a hedonistic test, and TS was analyzed by a comparison test using three samples. Volume expansion is the characteristic in fried products which is responsible for the porous network structure. For the same mass of gluten ball, larger size indicates larger void volume and thinner membranes dividing these void cells. Consequently, it results in a texture which is not too tough and yet crispy, and which in the end is to be judged by consumer preferences. The expansion volume (EV) and expansion ratio (ER) were therefore considered relevant to the overall quality as dependent variables for this study. It is expected that higher EV/ER would correlate to higher AS and TAS. Compression testing was applied, using a rheometer, to obtain a force– distance curve, and form the curve, peak force (PF) and brittleness breakdown (BB) that are important texture indices in this case. PF is a measure of the hardness of the specimen and is defined as the maximum force at 75% compression during the first bite. BB is a measure of crispiness and is defined as the first major peak (or force) at failure before the maximum force is obtained during the first bite. A Sun rheometer (Sun CR200D, Sun Scientific Co. Ltd, Japan), mounted with a plunger (adapter No.14) was used to measure PF and BB. The specimen was put on a sample platform, which traveled upward against the plunger at a constant speed of 60 mm/min, and the compression distance was set to be 12 mm. The measured values of 30 grains of fried gluten balls, produced using the same T1 and T2 were averaged. Typical force–distance curves are shown in Fig. 21.1. A colorimeter (Color Analyzer, Color Mate OEM, Milton Roy Co., USA) was used to measure 30 grains of the fried gluten balls and averaged. Of the three parameters in the Hunter color system, the Hunter b value (HB) was found to correlate best with AS, TS and TAS. Panelists suggested that a light yellow color implies fresh frying oil and a product which has not been over fried. As will be seen next, HB is negatively correlated to AS, TS and TAS.

21.5.3 Analysis of the results The quadratic regression for the eight selected quality indices, with T1 and T2 being the independent variables, was carried out using SAS’s RSREG (response surface regression) procedure (SAS, 1989). Obtained coefficients and R2 are listed in Table 21.4. From the satisfactory values of R2 (the ones for EV and ER are slightly lower) it can be said that the quadratic model relating quality indices and T1, T2 fits the experimental data fairly well. Obtained models (from regression) were used to generate response surfaces for each dependent variable, as shown in Fig. 21.2. First, look at the EV and ER surfaces. As T2 is low (~150 °C), optimum T1 is around 135 °C. But at low T1, increasing T2 would significantly improve EV as well as ER. Meanwhile, as we shift to higher T2 (say, 180 °C), optimum T1 shifts to a lower temperature (126 °C). Curvature of the EV and ER surfaces and the shift of optimum T1

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517

(a)

1000 0 (b) 2000 1000 0 (c)

Force (g)

2000 1000 0 (d) 2000 1000 0 (e) 2000 1000 0

0

3

6 9 Distance (mm)

12

15

Fig. 21.1 An example of force–distance curves used to determine peak force (PF) and brittleness breakdown (BB) of fried gluten balls.

upon changing T2, imply that the assumption of quadratic behavior should stand, and that the interaction between the two temperatures should be significant. However, this conclusion would be too hasty without examining the ANOVA result (Table 21.5). The P-values of the five terms in the model for EV and ER listed in Table 21.5 tell us that only T1 is significant at 5% (P < 0.05) level while the rest of the five terms are not significant (P > 0.05). What ANOVA is suggesting here is that the proposed quadratic model (for EV or ER) is not suitable for representing experimental data, and that reconsideration is in order. Nevertheless, an important piece of information that the surface indicated might be considered to be true is that at low T1 we need to increase T2, while at high T1, the major portion of water evaporation would be carried out in the first frying pan and increasing T2 will not further improve EV and ER. The reason for inadequate fitness between model and

–222.32 3.39 0.058 –0.0079 0.0034 –0.0079 82.57

a0 a1 a2 a3 a4 a5 R2 –17666 271.22 4.38 –0.64 0.28 –0.63 82.55

ER 1543.74 –236.28 183.79 0.83 –0.62 0.13 92.89

PF 1737.92 –243.53 187.35 0.85 –0.64 0.14 93.29

BB 157.88 –0.36 –1.57 –0.0018 0.0025 0.0056 91.02

HB –189.95 2.98 –0.082 –0.009 0.0018 –0.0035 89.74

AS

–73.94 1.78 –0.46 –0.0065 0.0017 –0.0006 93.72

TS

–145.57 2.92 –0.57 –0.0092 0.0029 –0.0028 90.22

TAS

EV, expansion volume; ER, expansion ratio; PF, peak force; BB, brittleness breakdown; HB, Hunter-b value; AS, appearance score; TS, texture score; TAS, total acceptance score b Y = a0 + a1X1 + a2X2 + a3 X 12 + a4 X 22 + a5X1X2, where Y represents the quality indices, X1 is T1; X2 is T2

a

EV

Coeffb

Table 21.4 Quadratic model coefficients and R2 values for the response surfaces of different quality indices. (Chen et al., 1998, reproduced with permission from John Wiley and Sons)

518 Texture in food

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2000

17 13 126 140 T1(°C)

180 165 154

ER(%)

EV(cm3)

21

150 T2(°C)

1310 910 126 140 T1(°C)

180 165

154 150 T2(°C)

1120 720 126 140 T1(°C)

154 150 T2(°C)

7.9

180 165 154 150 T2(°C)

TS

6

4.9 1.9 126

140

T1(°C)

180 165 154 150 T2(°C)

4.5 2.0 –0.5 126

180 165 140

T1(°C)

3 0 126

154 150 T2(°C)

180 165 140

T1(°C)

Hunter b value

AS

180 165

1520

BB(g)

PF(g)

1600 1200 126 140 T1(°C)

1710

TAS

519

154 150 T2(°C)

13.2 11.2 9.2 126

180 165 140

T1(°C)

154 150 T2(°C)

Fig. 21.2 The response surfaces of frying temperatures and quality indices of fried gluten balls. T1, the temperature of the first deep frying pan; T2, the temperature of the second deep frying pan; EV, expansion volume; ER, exapansion ratio; PF peak force; BB, brittleness breakdown; AS, appearance score; TS, texture score; TAS, total acceptance score. (Chen et al., 1998, reproduced with permission from John Wiley & Sons).

data could be the inherent experimental error, and increasing sample number might help. PF and BB surfaces revealed that, knowing that a significant increase in PF and BB may result in a texture that is tough, it is advisable to operate in the region of low T1 and moderate T2. Although variation of T2 did not dramatically affect PF and BB (the curvature due to variation of T2 is not substantial), the effect of slight change in PF and BB might be magnified

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Table 21.5 ANOVA for the frying temperatures vs the quality indices of the fried gluten balls. (Chen et al., reproduced with permission from John Wiley & Sons) Source

Responsea (P-values) EV

ER

PF

BB

HB

AS

TS

TAS

Model T1 T2 T 12

0.110 0.032 0.122 0.159

0.111 0.032 0.123 0.159

0.020 0.004 0.298 0.032

0.018 0.003 0.300 0.030

0.073 0.016 0.110 0.242

0.042 0.010 0.457 0.030

0.017 0.002 0.574 0.030

0.038 0.009 0.417 0.026

T 22 T1T2

0.601 0.291

0.600 0.292

0.176 0.772

0.167 0.741

0.462 0.228

0.618 0.380

0.515 0.820

0.427 0.478

a

EV, expansion volume; ER, expansion ratio; PF, peak force; BB, brittleness breakdown; HB, Hunter b value; AS, appearance score; TS, txture score; TAS, total acceptance score.

when measuring TS and TAS. Therefore, the implication from PF and BB surfaces should be cross-checked with TS and TAS surfaces. However, in this case, TS and TAS are not very sensitive to variation in T2 either. The Pvalues of the model and the five terms for PF and BB (Table 21.5) are telling the same story: model, T1 and T12 are significant (P < 0.05), while the terms involving T2 (T2, T22 and T1T2) are not significant. Similar conclusions could be reached for TS and TAS. The lack of significance of T2 to these dependent variables discussed indicated that serious precision control of T2 is not required. Although texture is the main concern in this chapter, the response surfaces of HB and AS would be a good example of elaborating a negative correlation between the two dependent variables, and the choice of an optimum operating condition. The P-values for HB (Table 21.5) indicate significance of the model at 10% level, and T1 at 5% level, and T2 at 11% level. This is in line with what was shown on the response surface of HB, since all the curves of constant T1 on the surfaces are relatively flat with respect to T2 (Fig. 21.2). Consumers prefer a light brown color (low HB) of fried gluten ball to a dark one (high HB), therefore HB should be negatively correlated to AS, as is shown in Fig. 21.2. The critical values of the frying temperatures and the characteristics of the stationary points (where the dependent variable is not very sensitive to variations of the independent variables) of the response surfaces for the quality indices studied are shown in Table 21.6. The optimum temperatures were chosen on the basis of merging RSM search and engineering principles not included in the RSM procedure. Frying at lower temperature implies less energy consumption and a lower rate of oxidative oil degradation. The figure shows that below T1~130 °C, for constant T2, PF and BB remained virtually constant. AS and TAS were at their maximum when T1~135 °C. Although AS, TS and TAS showed further increases beyond T2~155 °C, the increases were minor. Besides, HB would increase (darkening) with T2 > 155 °C (in the vicinity of T1~140 °C). T1 of 130–143 °C and T2 of 155–161 °C were

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Table 21.6 The critical values of the frying temperature and the characteristics of stationary points for the quality indices of fried gluten balls obtained using RSM. (Chen et al., reproduced with permission from John Wiley & Sons) Process variables

PF

BB

HB

AS

TS

TAS

T1(°C) T2(°C) Stationary point

130 161 Saddle

130 161 Saddle

143 156 Saddle

136 155 Saddle

131 160 Saddle

134 159 Saddle

a

PF, peak force; BB, brittleness breakdown; HB, Hunter b value; AS, appearance score; TS, texture score; TAS, total acceptance score.

therefore chosen as the optimum frying temperatures for the first and second frying pans. For verification, while fixing temperature of the third frying pan at 195 ± 3 °C, the optimum T1 and T2 (130 ± 3 °C and 155 ± 3 °C) were adopted for a trial production of gluten balls. The result was compared to products made before RSM optimization. The improved texture was soft and elastic rather than brittle and rigid. Sensory evaluation scores (AS, TS and TAS) were all higher than the commercial standard scores of 4–5. The result of RSM optimization is therefore justified.

21.6

Conclusions

A systematic search for optimum conditions requires a quantified basis (by instrumentation or panels) and efficient experiment design. RSM optimization is a powerful tool which is particularly useful in areas such as improving the texture of fried food where the function correlating texture indices and manufacturing factors is frequently not clear. Among dozens of potential factors, careful screening, based on professional judgment, must be performed, in order to reduce the number of independent variables, and focus on a few key factors. The window of search should be kept within realistic limits if the quadratic model is to be applicable. Finally, statistical analysis can help to justify a regression result and provide a basis for decision-making. In mathematical optimization for many applications, it is commonly expected that the response surface will have a bell shape for which the point located at the top of the surface can be picked as the optimum point without any debate. However, in the case study of this chapter, such a type of response surface was not obtained. Other engineering criteria were applied to compromise between pros and cons, according to implications revealed by the response surfaces. Cross-checking of the optimization result based on instrumental measured quantities with that from panel studies is important for validation. Although it is a powerful optimization tool with versatile applications, without professional knowledge and experience, the contribution of RSM is limited.

522

Texture in food

21.7

References

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Index

acoustic emissions see sound input tests acoustic firmness sensors 266 acoustic myography (AMG) 59 acoustic signatures 151, 155 air channels and texture 213 air drying 394–5 air-conducted sound 147–8 air-coupled transducers 103 airflow planimeter 69 alcohol insoluble solids 274–5 Alternative Forced Choice (3-AFC) tests 10 AMG (acoustic myography) 59 amplitude-time plots 22, 152, 154 amylose content of rice 452–3, 455, 470 analytical tests 8 antisense technology 307, 324, 329 appearance definition 4 visual flavour 4 apples air channels and texture 213 calcium treatments 374–6 force/deformation tests 118, 133 freezing 374, 381–2 harvest maturity 226–7 mastication 75–6 mealiness 197–8 near infrared (NIR) tests 176 sound input tests 160, 161 transport and storage 196, 222–7 vacuum infusion 372, 374–6, 378–9 and freezing 381–2 apricots 377

aqueous freezant (AF) 398 aqueous preservative solutions 374–5 Arabidopsis genes 322 artificial neural networks (ANN) 103 Asco Firmness Meter 131 Asian noodles 475, 476 Asian pears see Japanese pears asparagus 356–7 attribute ratings 39, 42–3 attribute-property relationship 206–7 avocados 175 ß-galactosidase 330–1 bacteria 323 baguette-type bread products 435–6, 443 baked products see bread bananas 334 batters 84–5, 504–5, 506 beef see meat beetroot 250, 357 bending tests 125–6 BerryBounce 137 beverages 42 biopolymers 216–18, 234 bioyield point 115, 129 biscuits 76 bite forces 61 bite tenderometer 15 bite test 481 black olives in brine 422–4 black oxidised olives 419–22 blanching 391, 393–4 Blanching Response Amplification Model (BRAM) 227–31

526

Index

bolus formation 7, 55, 71 bone-conducted sounds 147–8, 150–1 Brabender viscoamylograph 453–4 bread 432–9 baguette-type products 435–6, 443 cellular structure 441 Chorleywood Bread Process (CBP) 442–3, 448 cohesiveness 437 Compressimeter 438–9 compression tests 438–40 Cone Indenter 438–9 crispness 215 crumb and crust 434, 447 deformation tests 438 density 441, 446 dough mixing 442–3, 448 firmness 436, 439 flour properties 441–2 freezing 445 gas retention 441–2 gluten formation 432–4, 441 hardness 436, 440 improvers 442 moistness 436, 440–1, 444 mould growth 444 no-time dough mixing 442–3 nuclear magnetic resonance tests 191–2 part-baking 447 Pressure-Vacuum mixer 442–3, 448 processing 442–3 puncture test 440 raw material quality 441–2 refrigerating 445 retrogradation process 445 sandwich-type products 435–6 sensory assessment 437–8 softness 437, 438, 439 springiness 437 squeeze test 438, 439 staleness 437, 444–5, 446–7 storage 443–5 texture improvement 445–7 texture measurements 437–42 Texture Profile Analysis (TPA) 439– 40 bread coatings 84–5, 93–4, 96–7 Bread Quality Imaging System 25–6 brittleness 86–7, 213

bruise volume/threshold measurement 285–7 C-Cell 441 calcium treatments 374–7 of apples 374–6 of apricots 377 of carrots 376 and cell wall strengthening 374 of eggplants 376 of lemons 376 of mushrooms 376 of olives 425–6 of strawberries 376, 377 of tomatoes 351, 352 calibration equations 281–2 canned apricots 377 canned tomatoes 352 canonical variate analysis (CVA) 75 capsaicin 5 carrots 75, 358, 376 cassava 347 category scales 36 cell adhesion 251–4 cell turgor 213, 244–7, 266, 275–6 cell walls breakage 246–7 and cell adhesion 252–4 degradation and enzymes 321–2 formation 297–8 of olives 412–14 polysaccharides in 271, 349–54 ripening changes in 272–4 strengthening with calcium 374 structural components 244, 271 cellular basis of crispness 244–7 cellular solids 215 cellular stability 249–51 cellular structure 244–55 of bread 441 cellulose degradation 222 cereals 124, 170–3, 484, 494 chalkiness 44–5 cheese 75, 192, 198 chemical irritants 5 chemical structure see plant structure Chen models 263 chewing 55, 66–7, 206 see also mastication; oral processing chicken nuggets 94, 96–7

Index chimeric gene constructs 325–6 Chinese water chestnut 250, 357 chocolate 16, 17, 178–9 chocolate beverages 42 Chorleywood Bread Process (CBP) 442– 3, 448 chufa 250, 357 citrus fruits 280, 376 coatings batters 84–5, 504–5, 506 breadings 84–5, 93–4, 96–7 freezing and glazing compounds 393 and osmotic dehydration 396 cohesiveness 92, 437 colorimeter 516 colour formation 4, 297 Compressimeter 14, 438–9 Compression Tester 483 compression tests 14, 119, 122–3, 127 of bread 438–40 of fried food 502, 503, 516 multiple compression tests 439–40 of olives 421–2 of pasta 481–4 of pears 260, 261 shear tests 87, 119, 123–4, 503 Cone Indenter 438–9 confectionery 16, 17, 178–9 consumer perceptions data analysis 73–5 description problems 33–4, 44 of food enjoyment 5–6 importance of texture 34, 39 linear relationships 40 multivariate analysis techniques 41–4 non-linear relationships 40–1 of pasta 492–4 of quality 206–9, 451, 452–5 single attribute analysis 41 testing 36–9 understanding 3, 39–48 see also in vivo measurement; instrumental measurement; sensory evaluation corn tortilla chips 47–8 correlation coefficient 19 crackliness 159–60 crispness and crunchiness 82–103 and batters 84–5, 504–5, 506 of bread 215

527

and breadings 84–5, 93–4, 96–7 cellular basis 244–7 data evaluation and analysis 91–4 definition 83, 84, 92 geometrical properties 85–6 measuring 21–3 mechanical tests 86–7, 88, 96, 99–100 panellist training 91–3 and plant structure 244–7 sound tests 83–4, 87–91, 158–60, 162, 243 storage conditions 91 structural properties 85–6 ultrasonic property measurements 95–6, 98, 100–2 cross-linking of proteins 304–6 cultivar effects 345, 349–50 cultivation conditions 275–8, 358 of apples 226–7 of olives 410 soil treatments 345, 359 of tomatoes 350–1 of wheat 484–5 curing 346 cutting devices 14 data analysis 73–5, 91–4 force deformation tests 126–7 instrumental measurements 18–20 magnetic resonance imaging (MRI) 188–9 nuclear magnetic resonance (NMR) 188–9 sound input tests 149–50, 152–5, 157–8, 163 deformation of the food matrix 367 deformation tests see force deformation tests Delwiche model 263 density of bread 441, 446 destructive tests 109–10, 118–27, 146, 148–55 difference from control tests 10 difference tests 9–10 dipping 392–3 discrimination tests 9 dough mixing 442–3 drop tests 136–7, 138 drupes 410 dry-salted olives 424–5

528

Index

drying conditions 491–2 duo-trio tests 9 durum wheat 484, 494 dynamic tests 117–18, 133–4 ears 147–8 edograms 62 eggplants 376 eggs 487–9 elastic properties 24–5, 74, 112–15 of pasta 482 theory of linear elasticity 113–15 time-dependent 113 time-independent 112–13 viscoelasticity 113, 116 electromyography (EMG) 20–1, 57–9, 60, 75–6 electropalatography (EPG) 21 empirical tests 14, 73, 109, 118, 243 endo-glucanase 331–3 engineering techniques 335 enjoyment of food 5–6 environmental conditions see cultivation conditions enzymes 218–21, 228–9, 234 ß-galactosidase 330–1 and cell wall degradation 321–2 endo-glucanase 331–3 and genetic modification of tomatoes 327–33 pectate lyase (PEL) 334 pectinesterase 329–30, 392 polygalacturonase 327–9 and ripening-related softening 248 and vacuum infusion 377–80 see also pectin; peroxidases (PODs); polyphenoloxidases (PPOs) EPG (electropalatography) 21 ethylene production 224 European pears 259–61 exhaled-breath sampling 72 expansin 333, 352 extensins 358–9 external preference mapping 45–8 extruder sounds 162 extrusion 476, 490–1 Falling Number Values 484 farina 484 Fast Fourier Transform 19

ferulic acid 355, 357 fibre 216, 411–12, 494 finite element modelling 268–9 firmness 128–34 acoustic sensors 266 Asco Firmness Meter 131 and biopolymers 216–18 of bread 436, 439 and calcium infusion 374–7 chemical compounds affecting 270–8 impact force responses 135–8 laser air-puff tester 133 Magness-Taylor (MT) test 119–20, 128–31, 266, 268 of pears 262–6 and polyamines infusion 374–7 ratio of force to velocity 133 stiffness coefficient 161–2 fish 190, 196, 198, 502 Fito model 365–71 flavour 206 definition 4 exhaled-breath sampling 72 release of 6, 7, 71–2 visual flavour 4 flour 171, 441–2, 484 flow devices 14 food bolus 7, 55, 71 food breakdown 68–70, 112 see also mastication; oral processing food enjoyment 5–6 force deformation tests 19–20, 24–5, 28, 109–39, 438 bending tests 125–6 bioyield point 115, 129 data analysis 126–7 destructive methods 109–10, 118–27 dynamic tests 117–18, 133–4 elastic properties 112–15 empirical methods 109, 118 fundamental methods 109, 118 impact force responses 135–8, 262–3 impact probes 138 loading rates 110 meat 115 mechanical properties 110–18 non-destructive methods 110, 128– 38 puncture tests 14, 119–20, 127, 267, 284, 375, 440

Index quasi-static tests 117–18, 129–33 and strain 111, 260–1 and stress 110–11, 260–1 structural properties 110–18 tension tests 125, 482, 502–3 torsion/twisting tests 124–5 vibration tests 134–5 viscoelastic properties 113, 116 viscoplastic properties 113 see also compression tests force measurement 61 force to velocity ratio 133 Fourier transformation 19, 74, 103 fractal analysis 103, 154–5 fracture mechanics 18, 92, 244 freezing 381, 388–405 apples 374, 381–2 and blanching 391, 393–4 bread 445 and coatings 393, 396 dipping 392–3 glazing compounds 393 high-pressure treatments 388, 399– 400, 404, 405 immersion chilling and freezing 398– 9 jams 400–5 juice loss 390 osmotic dehydration 395–8 partial air drying 394–5 pre-freezing treatments 390–400 for jams 401–4 problems with 390–1 rate of freezing 388 strawberries 381, 388, 389 tomatoes 350–1 frequencies 147, 148, 155 fried food 82, 501–21 additives 506–7 battered frying 504–5, 506 compression tests 502, 503, 516 fish fillet 502 gluten balls 514–21 measuring texture 501–3 oil degradation 507 oil temperature 504, 507 processing variables 507 and protein 505–6 response surface methodology (RSM) 508–14, 521

529

shrimp 84–5 size of food 505, 507 and starch 505–6 storage 507 Surfactant Theory of Frying 507 tensile tests 502–3 volume expansion 516 water content 504–5 fructose 270 fruit pastilles 75 fruit and vegetables crosslink forming reactions 357–8 drop tests 136–7, 138 flavour release 71 and gelatinisation 342–3 grading costs 176 heat processing 249–51, 359 impact force responses 135–8 impact probe tests 138 lignin formation 355–7 magnetic resonance imaging (MRI) 196, 198–9 near infrared (NIR) diffuse reflectance 173–6, 179, 279–83 organically grown 359 pasteurised fruit 382 phenolic reactions 354–8 post-harvest shelf life 374–5 processing effects 213–14, 249–51, 359 resonance frequencies 157 sorting devices 134–5, 136–7, 138 sound input tests 160–2 sources of texture 211–13 starch and texture of 275–6, 342–8 storage 259–60, 275–8 torsion/twisting tests 124–5 turgor 213, 244–7, 266, 275–6 see also cultivation conditions; plant structure fundamental tests 15–16, 73, 109, 118, 209, 210–11, 243 gas absorption isotherm (BET) 69 gas retention of bread 441–2 gelatin 26, 69, 342–3 gelling agents 380–2 genetic transformation 307, 321–36, 358–9 ß-galactosidase 330–1

530

Index

chimeric gene constructs 325–6 endo-glucanase 331–3 expansin 333 extensins 358–9 pectate lyase (PEL) 334 pectinesterase activity 329–30 polygalacturonase activity 327–9 quantitative trait loci (QTL) 326–7 Ti plasmid 323–6 of tomato puree 358 of tomatoes 327–33 geometrical properties and crispness 85–6 of pasta 478 glazing compounds 393 glucose 270 gluten balls 514–21 gluten formation 432–4, 441 gluten strength 486–7 glycerol monostearate addition 488–9 gnathosonics 64 grading costs 176 Grain Research Laboratory (GRL) 483 green beans 353–4 green pickled olives 418–19 gum addition 489 hardcore 357 hardness 34, 86–7, 92 of bread 436, 440 of rice 459, 460, 465–7 of wheat 124, 171, 173 harvest conditions see cultivation conditions heat treatments 218–19, 249–51, 359 high temperature drying 491–2 mechanical behaviour of tissues 373 oven-drying 426–7 pre-freezing 392 thermal softening 250–1 vacuum infusion 376–7 hedonic scales 36–7 hedonic tests 8 high temperature drying 491–2 high-pressure treatments 388, 399–400, 404, 405 hot water blanching 391 human senses see sensory evaluation hydrodynamic mass transfers 364, 365– 71, 395

hydrophobic compounds 309 image analysis 25–6, 441 see also magnetic resonance imaging (MRI) image histograms 189 imitative tests 15, 73, 243 immersion chilling and freezing 398–9 impact force responses 135–8, 262–3 impact probe tests 138 importance of texture 34, 39 in vitro measurement 304–6 in vivo measurement 20–3, 53 acoustic myography (AMG) 59 electromyography (EMG) 20–1, 57– 9, 60, 75–6 electropalatography (EPG) 21 vibromyography (VMG) 59–60 see also sound input tests instrumental measurement 13–20, 54–5 analysis and validation 18–20 compared to sensory evaluation 243– 4 development of methods 16–18 empirical tests 14, 73, 109, 118, 243 force deformation tests 19–20, 24–5, 28, 109–39 fracture mechanics 18, 244 fundamental tests 15–16, 73, 109, 118, 209, 210–11, 243 imitative tests 15, 73, 243 magnetic resonance imaging (MRI) 187, 188, 195–9 near infrared (NIR) diffuse reflectance 167–80 nuclear magnetic resonance (NMR) 184–95 of pasta 481–4 of rice 468–9 see also sound input tests ionic strength 307–8 jams 400–5 Japanese pears 259, 261–2, 270–8 Jasmine rice 45–6 jaw movement 56, 61–4 juiciness juice loss when freezing 390 and plant structure 247 just-about-right (JAR) scales 37–8, 49

Index kinesiology 58, 61–4 kinesthesis 5, 7 kinetic modelling 210 kiwifruits 134 Kramer Shear Process 123 L-ascorbic acid addition 489 labelled affective magnitude (LAM) scales 48 laser air-puff firmness tester 133 leavening agents 507 legume beans 355–6 legume seeds 251 lemons 376 lignin formation 298–9, 355–7 line scales 36 linear regression 19, 48–9 linear relationships 40 linear voltage displacement transducer (LVDT) 63–4 loading rates 110 low-field systems 187, 189–90, 192–5 lye treatment 418–19, 421–2 Magness-Taylor (MT) test 119–20, 128– 31, 266, 268 magnetic resonance imaging (MRI) 187, 188, 195–9 and cheese 198 data analysis 188–9 and fish 196, 198 and fruit and vegetables 196, 198–9 image histograms 189 proton-density 187 qualitative evaluation 195–6 quantitative evaluation 196–9 small-bore scanners 188 wide-bore scanners 188 malocclusion 64 MARS (multivariate adaptive regression splines) 49–50 Martin Tenderometer 175 mass transfer 364, 365–71, 395 mastication 7, 20–1, 53–66, 75–6 chewing 55, 66–7, 206 and food breakdown 68–70, 112 jaw movement 56, 61–4 measuring 56–66 muscle activity 56, 58–9 oral sensitivity 56

531

teeth movement 56, 72, 246–7 bite forces 61 see also oral processing mealiness 197–8, 247, 344, 350 measurement of bread texture 437–42 of crispness 21–3 of fried food texture 501–3 of mastication 56–66 of pasta texture 478–84 see also instrumental measurement; sensory evaluation meat chicken nuggets 94, 96–7 force deformation behaviour 115 near infrared (NIR) diffuse reflectance 176–8, 179 nuclear magnetic resonance (NMR) 190, 194 poultry breast meat 40 storage conditions 178 tenderness 33, 75 Mechanical models 116 mechanical properties and crispness 86–7, 88, 96, 99–100 and force deformation tests 110–18 measuring mechanical texture 242–3 of olives 411, 414–15, 422, 424–5 of pasta 478 and sound input tests 151–2 of strawberries 390 and thermal treatments 373 microphones 149, 150, 162–3 microwave blanching 391, 393–4 milk-based products 178 milling see cereals mixing devices 14 modelling food texture 205–35 apple transport and storage 222–7 attribute-property relationship 206–7 biopolymers 216–18, 234 Blanching Response Amplification Model (BRAM) 227–31 cellular solids 215 consistency 234 dedicated models 206 disciplines 210–11 and enzymes 218–21, 228–9, 234 finite element modelling 268–9 fundamental models 209, 210–11

532

Index

goals of 209, 233 kinetic modelling 210 man-made products 214–16 notation 234–5 pears 268–70, 287–8 process-oriented modelling 211 quality assignment 206–9 reversed engineering 231–3 sources of texture 211–13 moistness of bread 436, 440–1, 444 monosodium glutamate 5 motility effect 55 mould growth 444 mouthfeel 7, 501–2 MRI see magnetic resonance imaging (MRI) multi-tasking 60 multiple compression tests 439–40 multiple linear regression (MLR) 19 multivariate adaptive regression splines (MARS) 49–50 multivariate modelling 41–4, 74 muscle activity 56, 58–9 mushrooms 354, 376, 380–1 near infrared (NIR) diffuse reflectance 167–80 calibration equations 281–2 and cereals 170–3, 179 and chocolate 178–9 and fruit and vegetables 173–6, 179, 279–83 and meat 176–8, 179 and milk-based products 178 and pulses 178 and rice 467 spectra types 280–1 V-method 173 nectarines 176 NMR see nuclear magnetic resonance (NMR) no-time dough mixing 442–3 non-destructive tests 110, 128–38, 146, 147, 155–8 non-linear relationships 40–1 noodles 475, 476 nuclear magnetic resonance (NMR) 184– 95 bread products 191–2 and cheese 192 data analysis 188–9

and fish 190 low-field systems 187, 189–90, 192–5 and meat 190, 194 and potatoes 192 starch-based products 191–2 and water distribution 184–5, 189– 90 and water protons 185 and water-holding capacity 192–5 odour responses 5 oil degradation 507 oil temperature 504, 507 olive oil content 417 olives 410–30 black olives in brine 422–4 black oxidised olives 419–22 calcium treatment 425–6 cell walls 412–14 chemical composition 410 compression test 421–2 cultivation 410 dry-salted olives 424–5 fibre content 411–12 green pickled olives 418–19 lye treatment 418–19, 421–2 mechanical properties 411, 414–15, 422, 424–5 olive oil content 417 oven-drying processing 426–7 processing 411, 418–25, 426–7 ripening 412–16 structural properties 410–11 oral processing 6–7, 55, 214 bolus formation 7, 55, 71 food breakdown 68–70, 112 motility effect 55 salivation 7, 55, 68 swallowing 7, 55, 60–1, 62, 67–8 see also mastication oral sensitivity 56 ordinary least squares (OLS) regression 48–9 organically grown fruit and vegetables 359 osmotic dehydration (OD) 372, 383, 395–8 improving mass transfer rate 395 pulsed vacuum osmotic dehydration 397–8

Index salt concentrations 395–6 vacuum osmotic dehydration 396–7 Ottawa Texture Measuring System 123 oven-drying processing 426–7 oxalate soluble pectin 275, 283–5 pain responses 5 paired comparison tests 9 panellist selection 24 panellist training 91–3 part-baking bread 447 partial air drying 394–5 partial least squares analysis (PLS) 19, 43–4 pasta 475–94 additions to 487–9 bite test 481 breaking strength 482 coloured pasta 489 compression tests 481–4 consumer preference trends 492–4 cooked frozen pasta 493 cooking times 479 drying conditions 491–2 egg addition 487–9 elasticity 482 extrusion 476, 490–1 geometric properties 478 gluten strength 486–7 glycerol monostearate addition 488–9 gum addition 489 instrumental measurement 481–4 L-ascorbic acid addition 489 manufacturing process 476–7 measurement of texture 478–84 mechanical properties 478 origins 476 processing effects 490–2 protein content 485–9 raw materials quality 475, 484–9 regrinds 492 residue in cooking water 484 scoring systems 480 sensory evaluation 479–80 spaghetti 479–80, 481–2 stickiness 483 tenderness index 481 tension tests 482 water to pasta ratio 479 pasteurised fruit 382

533

peaches 136–7, 197 pears 259–90 bruise volume/threshold measurement 285–7 compression tests 260, 261 European pears 259–61 firmness testers 262–6 Japanese pears 259, 261–2, 270–8 model of texture 268–70, 287–8 sensitivity of measurement methods 266–7 storage 259–60, 275–8 turgidity 276 Pearson correlation coefficient 19 peas 175, 343–4 pectate lyase (PEL) 334 pectin methyl-esterase (PME) 377–80, 392 pectinesterase 329–30, 392 pectins 222, 224–5, 227–8, 271–2, 274– 5, 276–8, 321–2, 328 from citrus fruits 280 and green beans 354 oxalate soluble pectin 275, 283–5 and potato texture 353 peroxidases (PODs) 295–312 biochemical and physiological role 296–9 and cell wall formation 297–8 controlling activity of 307–11 cross-linking of proteins 304–6 and the lignification process 298–9 location, biosynthesis and transport 296 modification through processing 310–11 molecular structure 300–1 occurrence of 295–6 reactions catalysed by 301–4 regeneration of activity 310–11 role in food texture 311 and stress/pathogen defences 299 thermal inactivation 310 phenolic reactions 354–8 crosslink forming 357–8 lignin forming 355–7 phosphates 506–7 piezoelectric sensors 67 pineapples 283 plant structure 244–55

534

Index

alcohol insoluble solids 274–5 cell adhesion 251–4 cell turgor 213, 244–7, 266, 275–6 cellular stability 249–51 and crispness 244–7 and juiciness 247 and mealiness 197–8, 247 of olives 410 pre-cooking effects 249–50 and processing 249–51 of rice 452–3 ripening-related softening 247–9, 252, 272–3 of strawberries 389–90 thermal softening 250–1 and thermal treatments 249–51 water content 270 see also cell walls PODs see peroxidases (PODs) Poisson’s ratio 113–14 polyamines infusion 374–7 polygalacturonase activity 327–9 polyphenoloxidases (PPOs) 295–312 biochemical and physiological role 296–9 and colour formation 297 controlling activity of 307–11 cross-linking of proteins 304, 306 and hydrophobic compounds 309 and ionic strength 307–8 modification through processing 307–10 molecular structure 300–1 occurrence of 295 and pressure 308 proteolytic activation 309–10 reactions catalysed by 301–4 role in food texture 311 and temperature 308 and viscosity 308 and water activity 308 polysaccharides 271, 304–6, 349–54, 412–16 porosity values 367 post-harvest shelf life 374–5 potato chips 159, 501 potatoes 344–7 cultivar effects 345 curing 346 environmental influences 345

magnetic resonance imaging (MRI) 198–9 mealy behaviour 344 near infrared (NIR) analysis 174–5 nuclear magnetic resonance (NMR) 192 pectins and texture of 353 peroxidases activity 299 soggy texture 344–5 starch breakdown 346–7 storage 346–7 poultry breast meat 40 PPOs see polyphenoloxidases (PPOs) pre-cooking effects 249–50 pre-freezing treatments 390–400 for jams 401–4 preference mapping 41, 48–9 external preference mapping 45–8 pressure waves sound input tests 147 pressure-shift freezing see high-pressure treatments Pressure-Vacuum mixer 442–3, 448 principal component analysis (PCA) 19 process-oriented modelling 211 processing effects on bread 442–3 on fried food 507 on fruit and vegetables 213–14, 249– 51, 359 on olives 411, 418–25, 426–7 on pasta 490–2 and plant structure 249–51 see also heat treatments product design 6 proportional odds models (POM) 49 proprioception 5, 7 proteins cross-linking 304–6 engineering techniques 335 expansin 333 flour protein 441–2 in fried food 505–6 and fruit texture 283 in pasta 485–9 proteolytic activation 309–10 proton-density 187 pulsed vacuum osmotic dehydration 397–8 pulses 178

Index puncture tests 14, 119–20, 127, 267, 284, 375, 440 quality evaluation 206–9 of rice 451, 452–5 Quantitative Descriptive Analysis (QDA) 11–13, 75 quantitative trait loci (QTL) 326–7 quasi-static tests 117–18, 129–33 quinones 301 R-index tests 10 Rapid Visco Analyser (RVA) 453–5 raspberries 378 raw materials quality 441–2, 475, 484–9 recording of sounds 150–2 refrigerating bread 445 regression analysis 19, 48–9 regrinds 492 release of flavour 6, 7, 71–2 Repertory Grid methods 22 resonance frequencies 25, 147, 155, 157 response surface methodology (RSM) 508–14, 521 retrogradation process 445 reversed engineering 231–3 rheometers 503, 516 rice 35, 191, 451–70 ageing 451–2 amylose content 452–3, 455, 470 chemical composition 452–3 cooked rice 451, 452, 455, 459–63 moisture profile during boiling 462–3 cooking tests 453–5 hardness 459, 460, 465–7 hydration 455–9, 461 water diffusion 458, 462 water uptake rate 456–8 instrumental measurement 468–9 Jasmine rice 45–6 milling effects 461 near infrared (NIR) analysis 467 physiochemical properties 452–3 quality evaluation 451, 452–5 sensory evaluation 464–5, 468–9 stickiness 464, 465–7 storage 451–2, 465, 467–8 viscosity 453–5 Rice Taster 468

535

ripening 247–9, 252, 272–4 of olives 412–16 of tomatoes 330, 351–2 Roano Surface Tensiometer 466 RSM (response surface methodology) 508–14, 521 salivation 7, 55, 68 salt concentrations 395–6 sandwich-type bread products 435–6 Satake Neuro Fuzzy Rice Taster 468 scales category scales 36 hedonic scales 36–7 just-about-right (JAR) scales 37–8, 49 labelled affective magnitude (LAM) scales 48 line scales 36 scoring systems 480 selection of panellists 24 semolina 484, 487 sensitivity of measurement methods 266–7 sensory evaluation 3–13, 54–5, 242–4 analytical tests 8 of bread 437–8 classification of procedures 8–9 colour 4 compared to instrumental measurement 243–4 difference tests 9–10 discrimination tests 9 hedonic tests 8 multi-tasking 60 odour 5 pain responses 5 of pasta 479–80 quantitative descriptive tests 11–13 of rice 464–5, 468–9 of strawberries 389 taste sensations 4–5 time-intensity (T-I) tests 60–1 touch stimulii 5 see also mastication; oral processing shear compression tests 14, 87, 119, 123–4, 503 shrimp 84–5 single attribute analysis 41 Sirognathograph 63

536

Index

size of fried food 505, 507 small-bore scanners 188 smoothness 44 snack foods 82 sodium chloride 507 softness of bread 437, 438, 439 SoftSense 137 SoftSort 137 soggy texture in potatoes 344–5 soil treatments see cultivation conditions sonic resonance tests 25 sonic transmission tests 157 sonotubometry 68 sorbitol 270 sorting devices 134–5, 136–7, 138 sound input tests 21–3, 25, 64, 146–63 acoustic signatures 151, 155 air-conducted sound 147–8 amplification of signal 149 amplitude-time plots 152, 154 bone-conducted sounds 147–8, 150–1 and crackliness 159–60 and crispness and crunchiness 83–4, 87–91, 158–60, 162, 243 data analysis and storage 149–50, 152–5, 157–8, 163 destructive tests 146, 148–55 detection of sound 147–8 extruder sounds 162 fractal analysis 154–5 frequencies 147, 148, 155 and fruit quality 160–2 mechanical testing 151–2 microphones 149, 150, 162–3 non-destructive tests 146, 147, 155–8 pressure waves 147 recording of sounds 150–2 resonance 147, 155 sonic transmission tests 157 sound pressure levels 152 sound wave analysis 152–5, 163 sources of texture 211–13 spaghetti see pasta spectroscopy 167–8 see also near infrared (NIR) diffuse reflectance Spectrum Method 12–13 springiness of bread 437 squeeze test 438 Squeezometers 439 staleness in bread 437, 444–5, 446–7

starch degradation in fruit 275–6 in fried food 505–6 and near infrared analysis 171–2 nuclear magnetic resonance (NMR) 191–2 in potatoes 346–7 retrogradation process 445 and soil treatments 345 synthesis 336 and vegetable texture 342–8 statistical analysis and validation 18–19, 513–14, 516–21 see also regression analysis steam blanching 391 stickiness 17–18, 464, 465–7, 483 stiffness 161–2, 213 storage of apples 196, 222–7 of bread 443–5 and crispness 91 of fried food 507 of fruit 259–60, 275–8 of meat 178 of mushrooms 354 of pears 259–60, 275–8 post-harvest shelf life 374–5 of potatoes 346–7 of rice 451–2, 465, 467–8 of sweet potato 348 temperatures 225–6 of tomatoes 350–1 strain 111, 260–1 strawberries calcium treatments 376, 377 firmness 277 freezing 381, 388, 389 genetic transformation 334 and jams 400–5 mechanical properties 390 microstructure 389–90 pre-freezing treatments 393 sensory quality 389 thawing 400–1 water binding properties 390 stress 110–11, 260–1 stress/pathogen defences 299 structural properties and crispness 85–6 and force deformation tests 110–18 of olives 410–11

Index and vacuum infusion 371 sugarbeet 250, 357 Surfactant Theory of Frying 507 swallowing 7, 55, 60–1, 62, 67–8 sweet potato 347–8, 348, 357 sweet rolls 196 symmetrised dot-pattern (SDP) displays 20 taste buds 5 taste sensations 4–5 teeth movement 56, 72, 246–7 bite forces 61 malocclusion 64 Teflon dies 491 tenderness 33, 75, 481 tenderometers 344 tension tests 125, 482, 502–3 Tensipressure 467 texture, definition 4, 33, 241–2 Texture Profile Analysis (TPA) 11, 56, 126–7, 439–40 Texturometer 15, 466–7, 469 thawing strawberries 400–1 theory of linear elasticity 113–15 thermal processing see heat treatments Ti plasmid 323–6 time-dependent elasticity 113 time-independent elasticity 112–13 time-intensity (T-I) tests 13, 60–1 tissue tension 213 tomato puree 358 tomatoes calcium treatments 351, 352 canned tomatoes 352 cell wall polysaccharides 349–52 cultivar and maturity 349–50 environmental conditions 350–1 force/deformation tests 131, 135 freezing 350–1 genetic modification 327–33 imaging analysis 196 mealiness 350 ripening related softening 330, 351–2 and storage 350–1 tongue 5, 7, 21, 55 torsion/twisting tests 124–5 tortilla chips 47–8 touch stimuli 5 training panellists 91–3 triangle tests 9–10

537

turgor 213, 244–7, 266, 275–6 ultrasonic tests 25, 95–6, 98, 100–2 ultrasonic velocity 96, 98 uniaxial compression test 122 V-method 173 vacuum infusion 364–84 and apples 372, 376, 378–9, 381–2 calcium infusion 374–7 deformation of the food matrix 367 enzyme addition 377–80 and freezing 381–2 gelling agents 380–2 mass transfer phenomena 364, 365– 71 polyamines infusion 374–7 porosity values 367 structural modifications 371 thermal treatments 376–7 viscosity effects 370–1 vacuum osmotic dehydration 396–7 vegetables see fruit and vegetables vibration tests 134–5 vibromyography (VMG) 59–60 viruses 323 viscoelastic properties 113, 116 viscoelastograph 482–3 viscoplastic properties 113 viscosity 308, 370–1 of rice 453–5 visual flavour 4 vitamin C 297, 299 Volodkevich bite tenderometer 15 volume expansion 516 Warner-Bratzler (WB) test 123–4 water content and distribution 184–5, 189–90, 222, 270, 308 binding properties 390 fried food 504–5 holding capacity 192–5 water protons 185 water to pasta ratio 479 wheat 124, 170–3, 484, 494 white corn tortilla chips 47–8 wide-bore scanners 188 xanthan impregnation 380–1 yams 348, 355 Young’s modulus 113, 115