Rapid and on-line instrumentation for food quality assurance

Rapid and on-line instrumentation for food quality assurance

Related titles from Woodhead’s food science, technology and nutrition list: Food authenticity and traceability: ISBN 1 85573 526 1 With recent problems such as the use of genetically-modified ingredients and BSE, the need to trace and authenticate the contents of food products has never been more urgent. The first part of this authoritative collection reviews the range of established and new techniques for food authentication. Part II explores how such techniques are applied in particular sectors, whilst Part III reviews the latest developments in traceability systems for differing food products. 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. Taints and off-flavours in foods: ISBN 1 85573 449 4 Taints and off-flavours are a major problem for the food industry. Part 1 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 (email: [email protected]; fax: +44 (0) 1223 893694; tel.: +44 (0) 1223 891358 ext. 30; address: Woodhead Publishing Limited, 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, telephone and fax as above; e-mail: [email protected]). Please confirm which subject areas you are interested in.

Rapid and on-line instrumentation for food quality assurance Edited by Ibtisam E. Tothill

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 2003, Woodhead Publishing Limited and CRC Press LLC ß 2003, Woodhead Publishing Limited 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 Limited and CRC Press LLC 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 Limited or CRC Press LLC 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 Limited ISBN 1 85573 674 8 (book); 1 85573 710 8 (e-book) CRC Press ISBN 0-8493-1759-2 CRC Press order number: WP1759 Cover design by The ColourStudio Project managed by Macfarlane Production Services, Markyate, Hertfordshire (e-mail: [email protected]) Typeset by MHL Typesetting Limited, Coventry, Warwickshire Printed by TJ International Limited, Padstow, Cornwall, England

Contents

Contributor contact details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xi

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xv

Part I Product safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

1

3

2

On-line detection of contaminants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Righelato, Ashbourne Biosciences, UK 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Process issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Detection of chemical contaminants . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Detection of foreign bodies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Sources of further information and advice . . . . . . . . . . . . . . . . . . . 1.7 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . On-line immunochemical assays for contaminant analysis . . . . . . . I.E. Tothill, Cranfield University, UK 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Principles and applications of immunochemical assays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Immunoassays for food contaminant analysis . . . . . . . . . . . . . . . . 2.4 Immunochemical sensors (immunosensors) . . . . . . . . . . . . . . . . . . 2.5 On-line immunosensors in food processing . . . . . . . . . . . . . . . . . . 2.6 Future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 5 6 7 10 12 12 14 14 15 20 21 25 30

vi

Contents 2.7 2.8 2.9

3

4

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sources of further information and advice . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Using bioassays in contaminant analysis . . . . . . . . . . . . . . . . . . . . . . . . . . L.A.P. Hoogenboom, State Institute for Quality Control of Agricultural Products (RIKILT), The Netherlands 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 The use of bioassays: the case of dioxins . . . . . . . . . . . . . . . . . . . . 3.3 The use of bioassays for other contaminants . . . . . . . . . . . . . . . . . 3.4 Future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The rapid detection of pesticides in food . . . . . . . . . . . . . . . . . . . . . . . . . R. Luxton and J. Hart, University of the West of England, UK 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Detecting pesticides: physicochemical methods . . . . . . . . . . . . . . 4.3 Detecting pesticides: biological methods . . . . . . . . . . . . . . . . . . . . . 4.4 The principles of biosensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Developing low-cost biosensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Using biosensors: pesticide residues in grain, fruit and vegetables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8 Sources of further information and advice . . . . . . . . . . . . . . . . . . . 4.9 Further reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

34 34 35 40

40 41 49 51 51 51 55 55 58 59 62 69 70 72 73 73

5 Detecting antimicrobial drug residues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A˚. Sternesjo¨, Swedish University of Agricultural Sciences 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Current screening methods for residue detection . . . . . . . . . . . . . 5.3 Developing biosensors: the use of surface plasmon resonance 5.4 Using biosensors to detect veterinary drug residues . . . . . . . . . . 5.5 Biosensor applications in the food industry . . . . . . . . . . . . . . . . . . 5.6 Future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Sources of further information and advice . . . . . . . . . . . . . . . . . . . 5.8 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

75 76 79 81 83 86 88 88

6

91

Detecting veterinary drug residues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. van Hoof, K. de Wasch, H. Noppe, S. Poelmans and H.F. de Brabender, University of Ghent, Belgium 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Veterinary medicinal products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Methods for detecting residues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Validating detection methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

75

91 92 93 96

Contents 6.5 6.6 6.7 6.8 7

8

9

Rapid on-line confirmation of different veterinary residues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The rapid detection of toxins in food: a case study . . . . . . . . . . . . . . G. Palleschi, D. Moscone and L. Micheli, University of Rome `Tor Vergata’, Italy 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Immunosensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Detecting toxins: domoic acid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Detecting toxins: okadaic acid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Detecting toxins: saxitoxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Developing on-line applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.8 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.9 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rapid detection methods for microbial contamination . . . . . . . . . . . I. E. Tothill and N. Magan, Cranfield University, UK 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Conventional methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Specialised techniques: epifluorescence (DEFT), bioluminescence and particle counting . . . . . . . . . . . . . . . . . . . . . . . 8.4 Specialised techniques: flow cytometry, electron microscopy and immunoassay techniques . . . . . . . . . . . . . . . . . . . . 8.5 Cellular components detection: API, metabolising enzymes and nucleic acids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6 Electrochemical methods: impedimetry, conductivity and other methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.7 Immunosensors: amperometric, potentiometric, acoustic wave-based and optical sensors . . . . . . . . . . . . . . . . . . . . . 8.8 Detection of moulds using biochemical methods . . . . . . . . . . . . . 8.9 Electronic noses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.10 Conclusions: commercial products . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.11 Sources of further information and advice . . . . . . . . . . . . . . . . . . . 8.12 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rapid analysis of microbial contamination of water . . . . . . . . . . . . . L. Bonadonna, Istituto Superiore di Sanita` – Rome, Italy 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Current techniques and their limitations . . . . . . . . . . . . . . . . . . . . . 9.3 Identifying indicator organisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 The development of more rapid detection methods . . . . . . . . . .

vii

98 112 113 113 116

116 117 118 122 125 129 132 132 132 136 136 136 139 141 143 145 147 150 153 154 155 155 161 161 162 163 167

viii

Contents

9.5 9.6 9.7 9.8

Developing online monitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sources of further information and advice . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

173 176 178 179

Part II Product quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

183

10

11

12

13

Rapid techniques for analysing food additives and micronutrients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.J. Blake, Nestle´ Research Centre, Switzerland 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 The range of rapid methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Chromatographic techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 X-ray fluorescence and other indirect methods . . . . . . . . . . . . . . . 10.5 PCR, immunoassays and biosensors . . . . . . . . . . . . . . . . . . . . . . . . . 10.6 Other rapid methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.7 Future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.8 Sources of further information and advice . . . . . . . . . . . . . . . . . . . 10.9 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Detecting genetically-modified ingredients . . . . . . . . . . . . . . . . . . . . . . M. Pla, T. Esteve and P. Puigdome`nech, Insitut de Biologia Molecular de Barcelona – CSIC, Spain 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Principles of analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Polymerase chain reaction (PCR) techniques . . . . . . . . . . . . . . . . . 11.4 Identifying genetically-modified ingredients in practice . . . . . . 11.5 Future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . In-line sensors for food process monitoring and control . . . . . . . . P.D. Patel and C. Beveridge, Leatherhead Food International Ltd, UK 12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Principles of in-line sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3 Current commercial sensor systems . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4 Dealing with complex food matrices . . . . . . . . . . . . . . . . . . . . . . . . . 12.5 Future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.6 Sources of further information and advice . . . . . . . . . . . . . . . . . . . 12.7 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measurement of added water in foodstuffs . . . . . . . . . . . . . . . . . . . . . M. Kent, Consultant, UK 13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2 Problems in measuring added water . . . . . . . . . . . . . . . . . . . . . . . . .

185 185 186 186 189 191 193 196 197 198 205

205 206 208 211 212 213 215 215 216 219 227 236 237 238 240 240 241

Contents 13.3 13.4 13.5 13.6 13.7 13.8 14

15

16

ix

Measuring the dielectric properties of water . . . . . . . . . . . . . . . . . Instrumentation for measuring dielectric properties . . . . . . . . . . Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sources of further information and advice . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

245 251 258 264 267 267

Spectroscopic techniques for analysing raw material quality . . R. Cubeddu, A. Pifferi, P. Taroni and A. Torricelli, INFM – Dipartimento di Fisica and Politecnico di Milano, Italy 14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2 Advantages of time-resolved optical methods . . . . . . . . . . . . . . . . 14.3 Principles of time-resolved reflectance . . . . . . . . . . . . . . . . . . . . . . . 14.4 Instrumentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.5 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.6 Effect of skin and penetration depth . . . . . . . . . . . . . . . . . . . . . . . . . 14.7 Optical properties of fruits and vegetables . . . . . . . . . . . . . . . . . . . 14.8 Applications: analysing fruit maturity and quality defects . . . 14.9 Future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.10 Sources of further information and advice . . . . . . . . . . . . . . . . . . . 14.11 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

270

Using spectroscopic techniques to monitor food composition . . P. Grenier and V. Bellon-Maurel, Cemagref, France, R. Wilson, Institute of Food Research, UK and P. Niemela¨, VTT Biotechnology, Finland 15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2 Spectroscopic techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3 Instrument design for on-line applications . . . . . . . . . . . . . . . . . . . 15.4 Design or adaptation of MIR, optothermal and Raman spectrometers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.5 Applications: analysing the composition of cereal and dairy products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.6 Future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.7 Sources of further information and advice . . . . . . . . . . . . . . . . . . . 15.8 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.9 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Confocal scanning laser microscopy (CSLM) for monitoring food composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R.H. Tromp, Y. Nicolas, F. van de Velde and M. Paques, Wageningen Centre for Food Sciences, The Netherlands 16.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.2 The principles of CSLM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.3 Sample preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

270 271 272 274 277 278 280 285 287 288 289 291

291 292 296 298 300 302 303 304 305

306

306 308 310

x

Contents 16.4 16.5 16.6

17

18

19

Applications: food composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

313 319 321

Using electronic noses to assess food quality . . . . . . . . . . . . . . . . . . . . H. Zhang, University of Florida, USA 17.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2 The theory of electronic noses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.3 Comparing sensor types of electronic nose . . . . . . . . . . . . . . . . . . . 17.4 Current commercial instruments and selection criteria . . . . . . . 17.5 Data analysis metods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.6 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.7 Future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.8 Sources of further information and advice . . . . . . . . . . . . . . . . . . . 17.9 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

324

Rapid olfaction arrays for determining fish quality . . . . . . . . . . . . ´ lafsdo´ttir, Icelandic Fisheries Laboratories . . . . . . . . . . . . . . . . . . . . . G, O 18.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2 Spoilage odours and product quality: the case of fish . . . . . . . . 18.3 Electronic noses: principles and applications . . . . . . . . . . . . . . . . . 18.4 Validation of the performance of the electronic nose . . . . . . . . 18.5 Developing rapid and on-line applications . . . . . . . . . . . . . . . . . . . 18.6 Future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.7 Sources of further information and advice . . . . . . . . . . . . . . . . . . . 18.8 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . On-line analysis and control of product quality . . . . . . . . . . . . . . . . G. Montague, E. Martin and J. Morris, University of Newcastle, UK 19.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2 Process models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.3 Case study 1: quality assessment in breakfast cereal production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.4 Building models of breakfast cereal production . . . . . . . . . . . . . . 19.5 On-line implementation and performance . . . . . . . . . . . . . . . . . . . . 19.6 Case Study 2: improving process control in french-fry manufacture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.7 On-line application and performance . . . . . . . . . . . . . . . . . . . . . . . . . 19.8 Future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.9 Sources of further information and advice . . . . . . . . . . . . . . . . . . . 19.10 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.11 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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

324 325 325 327 329 331 334 335 335 339 339 340 341 348 352 354 355 356 361 361 362 363 367 373 376 384 391 392 392 393 395

Contributor contact details

Chapter 1

Chapter 3

Professor Renton Righelato Ashbourne Biosciences 63 Hamilton Road Reading RG1 5RA UK

Dr Ron Hoogenboom Department of Food Safety and Health RIKILT – Institute of Food Safety PO Box 230 6700AE Wageningen The Netherlands

E-mail: [email protected]

Tel: +31 317 475623 Fax: +31 317 417717 E-mail: [email protected]

Chapter 2 Dr Ibtisam E. Tothill Institute of Bioscience and Technology Cranfield University Silsoe Bedfordshire MK45 4DT UK Tel: +44 (0) 1525 863531 Fax: +44 (0) 1525 863533 E-mail: [email protected]

Chapter 4 Dr R. Luxton and J. Hart Faculty of Applied Sciences University of the West of England Frenchay Campus Coldharbour Lane Bristol BS16 1QY UK E-mail: [email protected] [email protected]

xii

Contributor contact details

Chapter 5

Chapter 8

˚ se Sternesjo¨ Dr A Department of Food Science Swedish University of Agricultural Sciences PO Box 7051 S-750 07 Uppsala Sweden

Dr Ibtisam E. Tothill and Professor N. Magan Institute of Bioscience and Technology Cranfield University Silsoe Bedfordshire MK45 4DT UK

Tel: +46 18 67 20 37 Fax: +46 18 67 29 95 E-mail: [email protected]

E-mail: [email protected]

Chapter 9 Chapter 6 Dr Katia de Wasch Faculty of Veterinary Medicine University of Ghent Veterinary Food Inspection – Lab Chemical Analysis Salisburylaan 133 B-9820 Merelbeke Belgium E-mail: [email protected]

Chapter 7 Professor Giuseppe Palleschi Dipartimento di Scienze e Tecnologie Chimiche Universita` di Roma ‘Tor Vergata’ Via della Ricerca Scientifica 00133 Roma Italy E-mail: [email protected]

Dr Lucia Bonadonna Laboratorio di Igiene Ambientale Istituto Superiore di Sanita` Viale Regina Elena, 299 00161 Roma Italy E-mail: [email protected]

Chapter 10 Mr. Christopher J. Blake Manager, Micronutrients and Additives Group Quality and Safety Assurance Department Nestle´ Research Center Lausanne 1000 Switzerland E-mail: Christopher-john.blake@ rdls.nestle.com

Contributor contact details

Chapter 11

Chapter 14

Dr Pere Puigdome`nech, Teresa Esleve and Maria Pla Institut de Biologia Molecular de Barcelona CID-CSIC Jordi Girona 18 08034 Barcelona Spain

Professor Rinaldo Cubeddu Physics Department Politecnico di Milano Piazza L. da Vinci 32 20133 Milan Italy

Tel: +34 934006100 Fax: +34 932045904 E-mail: [email protected] E-mail: [email protected] E-mail: [email protected]

Chapter 12 P.D. Patel and C. Beveridge Leatherhead Food International Ltd Randalls Road Leatherhead Surrey KT22 7RY UK Tel: 01372 822200 Fax: 01372 386228 E-mail: [email protected]

Chapter 13 Dr M. Kent Kent & Partner The White House Greystone Carmyllie Angus DD11 2RJ UK Tel: +44 1241 860323 Fax: +44 1241 860323 E-mail: [email protected]

xiii

Tel: +39 0023 996110 Fax: +39 0223 996126 E-mail: [email protected]

Chapter 15 Dr Pierre Grenier Cemagref, BP 5095 34033 Montpellier Cedex 1 France Tel: +33 (0) 4670463 (21 or 15 or 86) Fax: +33 (0) 4 670463 06 E-mail: [email protected]

Chapter 16 Dr R. Hans Tromp NIZO Food Research PO Box 20 6710 BA Ede The Netherlands E-mail: [email protected]

xiv

Contributor contact details

Chapter 17 Dr Haoxian Zhang Agricultural and Biological Engineering Department University of Florida 1 Frazier Rogers Hall PO Box 110570 Gainesville FL 32611-0570 USA E-mail: [email protected]

Chapter 18 ´ lafsdo´ttir Ms Guoru´n O Icelandic Fisheries Laboratories Sku´lagata 4 PO Box 1405 121 Reykjavik Iceland

Tel: (354) 562 0240 Fax: (354) 562 0740 E-mail: [email protected]

Chapter 19 Professor Julian Morris Head of School of Chemical Engineering and Advanced Materials Director Centre for Process Analytics and Control Technology (CPACT) Merz Court University of Newcastle Newcastle upon Tyne NE1 7RU UK Tel: +44 191 222 7342 (Direct) Fax: +44 191 222 5748 (CPACT Office) E-mail: [email protected]

Introduction

The terms ‘in-line’, ‘on-line’, ‘at-line’ and ‘off-line’ are used variously in discussions of rapid instrumentation. These terms may be distinguished as follows: • in-line measurements are performed directly on the process line • on-line measurements may be performed in a bypass loop from the main process line which may then return the material or product to the main process line after measurement • at-line measurements involve removing product from the production line and measuring it with suitable instrumentation in the production area • off-line measurements entail removing product and taking it to a quality control laboratory for analysis Off-line measurement typically involves delays measured in hours or longer. As an example, conventional microbiological assays, which still provide a standard of accuracy against which newer microbiological methods are judged, can take days to perform. At-line measurements may, in some cases, be made in a matter of minutes. In-line and on-line measurement may take a matter of seconds. The food industry has long relied on off-line measurement to ensure product safety and quality. However, a number of trends have accelerated the need both to make off-line measurement more rapid and to move to the ideal of continuous, real-time in-line or on-line measurement: • consumers demand higher and more consistent quality, leading to the need for more frequent measurements of process variables and product attributes • volumes and rates of production have increased, making it more difficult for traditional off-line measurement systems to cope with rising workloads

xvi

Introduction

• competitive pressures and consumer demands for longer shelf-life products have made it less acceptable to hold product in quarantine, whether during or after production, whilst waiting for the results of safety or quality checks • the trend towards continuous automated production in place of batch processing necessitates tight feedback loops based on rapid in-line and online monitoring techniques • quality assurance and safety management systems such as HACCP have shifted the emphasis from a reactive approach, based on final product testing, to a proactive and preventative approach based on effective real-time process control As well as increased speed, modern instrumentation must also take account of other criteria such as: • appropriate accuracy and sensitivity for the task • hygienic design and construction • non-destructive operation: the measurement should not disrupt the process or damage the quality of the product • sufficient robustness to withstand often hostile operating conditions during food processing • automatic operation or capacity for use by non-skilled operators • low maintenance requirements • total costs (capital, operating and maintenance costs) proportionate to the benefits gained The chapters in this book summarise some of the most important developments in the shift from slower and off-line traditional measurement to more rapid and on-line process and product control. Part 1 reviews the key area of product safety. Chapter 1 looks at the development of new on-line techniques in such areas as the detection of foreign bodies. This is followed by a group of chapters looking specifically at immunochemical assays which exploit the immune system’s ability to produce antibodies in response to invasion by a foreign organic molecule. Chapter 2 reviews general principles and the development of robust, low-cost and portable immunosensors capable of at-line or on-line use by non-specialist personnel. The following chapters then consider the application of these new immunosensors in the detection of environmental contaminants such as dioxins (Chapter 3), pesticides (chapter 4), veterinary residues (Chapters 5 and 6) and toxins (Chapter 7). The final two chapters in Part I then review the development of rapid detection methods in the critical area of pathogenic and spoilage microorganisms, and the specific application of these new methods to detect microbial contamination of water. Part II considers developments in the analysis of product quality. A number of chapters discuss rapid and on-line analysis of ingredients from additives and micronutrients (Chapter 10) to genetically-modified organisms (Chapter 11) and added water (Chapter 13). Chapter 12 addresses in-line applications of these techniques and, in particular, ways of analysing complex food matrices during

Introduction

xvii

processing. The following group of chapters look at the use of spectroscopic techniques (Chapters 14 and 15) and confocal laser microscopy (Chapter 16) to monitor food composition. Chapters 17 and 18 then discuss the important area of electronic noses to detect volatiles and their use to monitor qualities such as flavour and freshness. The final chapter in the book reviews how a wide range of measurements such as these can be used to monitor and improve process control.

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Part I Product safety

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1 On-line detection of contaminants R. Righelato, Ashbourne Biosciences, UK

1.1

Introduction

One of the most common causes of customer complaints for many food producers is ‘foreign bodies’ in the product: nasty surprises like the slug in the lettuce or an insect, or, more typically, a piece of bone or plastic that should not be there. At a recent workshop, manufacturers quoted foreign bodies as constituting around a quarter to a third of customer complaints (Righelato, 2002). Plastic tops the list, with bone, extraneous vegetable matter and insects and mites as significant problems (Table 1.1). A large proportion (16–35%) of the unwanted bodies in food products were not foreign, but intrinsic – elements of the raw material that should have been removed in processing, such as bone,

Table 1.1

Typical causes of customer complaints

Foreign body type

% all foreign body complaints

Plastic Psocid mites Other infestations Fruit and vegetable matter Stones Glass Bone Metal Wood

15.5 9.2 9.2 7.8 7.8 7.2 6.0 6.0 2.1

Data refer to customer complaints received by RHM in 2001 and were kindly provided by Dr Bob Marsh, RHM Technology.

4

Rapid and on-line instrumentation for food quality assurance

cartilage, shells and stalks, over-ripe/under-ripe fruit and vegetables. These items are discrete, generally occur infrequently and are often very similar to the food in composition. They therefore usually require monitoring approaches that can differentiate the unwanted material from the rest of the food on the basis of small physical differences, size and shape. Other contaminants such as residues, toxins and taints present different problems: they are usually present at very low concentrations (ppm or lower) and, in some cases, such as protein allergens or hormones, are structurally very similar to normal components of the food (Table 1.2). Most arise from the raw material and for the food manufacturer control generally relies on supplier assurances and periodic off-line testing. If not eliminated at the raw material stage, they become diluted and distributed throughout the food batch. For those contaminants that might arise from the process, however, such as coolants or cleaning agents, off-line testing is not a practical option. The speed of most manufacturing is such that contaminated material would be packed and distributed before laboratory results were available. Reliance is therefore on plant design and good manufacturing practice (GMP). Despite this, on rare occasions, incidents do occur, and if suitable on-line monitoring technology were available for some of the contaminants that might prejudice safety or quality, it would probably be adopted. Malicious contamination of foods in manufacture and distribution is a rare occurrence that may involve introduction of foreign bodies or dissolved contaminants. Its control relies on minimising opportunities for adulteration in manufacturing, security measures such as tamper-evident packaging, forensic investigation and prosecution of offenders. Because it is impractical to monitor on-line effectively for the huge range of potential malicious adulterants, their detection is not considered here. The primary assurance against product contamination is the preventative measures taken in manufacturing. The case for installing technology to detect foreign bodies and other contaminants that may have escaped these measures depends on the risk of them failing, the potential for causing real harm to Table 1.2

Some examples of food contaminants

Source

Contaminant

Sensitivity range (w/w)

Water

Benzene Chlorophenol taints Mycotoxins Pesticide residues Nut allergens Growth promoters Antibiotic residues Coolants e.g., ethylene glycol; cleaning fluids Plasticisers

10ÿ8–10ÿ9 10ÿ7–10ÿ9 10ÿ6–10ÿ8 10ÿ6–10ÿ8 10ÿ8–10ÿ10 10ÿ6 10ÿ8

Grains, nuts, fruit Meat Process plant Package

10ÿ5 10ÿ7 10ÿ7–10ÿ9

On-line detection of contaminants

5

consumers, the risk to brand reputation and the effectiveness of the detection/ segregation system. The financial costs of failure can be huge. A contamination incident perceived to affect the safety or quality of a product can result in product recalls costing many millions of dollars and, at worst, irrecoverable loss of customer confidence and collapse of the brand. For a global brand this can mean losses of billions of US$. The values of most food processing operations are lower and, in practice, equipment for detection and exclusion in excess of US$100,000 was considered unlikely to find significant application by a selection of food manufacturers in 2002.

1.2

Process issues

Supplier assurance and auditing and the application of GMP and HACCP through the chain are the main ways in which the product is protected. But these can fail, so technology to detect and remove the unwanted materials is needed. For materials such as stone or metal, where large density or electro-magnetic differences exist between the food and the contaminant body, adequate technology can usually be applied, such as magnetic metal detectors or X-ray inspection. Plastics and materials of biological origin are much more difficult to detect and require for new approaches. For dissolved trace contaminants there is little on-line monitoring technology available and reliance is usually placed on good practice and retrospective laboratory analysis. Monitoring technology must be non-invasive and match the speeds of raw material flows, up to millions of units per minute for grains, several m3 per minute of liquid flows and lines packaging 103–104 items per minute passing at several metres per second. These speed requirements, together with the need for robust, non-invasive hardware that can readily interface with computer control, favour electromagnetic measurement systems (e.g. light, X-ray, impedance, magnetic resonance). For on-line monitoring technology to be useful, it should: • provide for 100% inspection of the process material • be appropriately sensitive to the analyte and insensitive to other variations in the food • be stable in operation • be mechanically robust and engineered to meet the necessary hygiene standards; ideally the equipment should be non-invasive • be linked to ways in which the contaminated item or flow can be segregated from the sound product and preferably be cleaned and recovered. It is often the segregation and handling of the contaminated material that is the most complex and costly part of the equipment involved • have acceptable ‘false reject rates’ so that good product is not unnecessarily rejected.

6

1.3

Rapid and on-line instrumentation for food quality assurance

Detection of chemical contaminants

Although impedance monitoring can be used for some contaminants in dilute aqueous media (see Section 1.4.3), for the majority of dispersed contaminants, such as those exemplified in Table 1.2, robust on-line measuring systems are not available. The complexity of most food matrices, the need to examine the whole of a heterogeneous food and the high sensitivity requirement generally preclude conventional instrumental approaches such as spectrophotometry, NMR and MS. Approaches which overcome some of these problems include biosensors and electronic nose techniques. Both techniques have the potential to provide the requisite sensitivity, and volatiles detection techniques avoid problems of interference from the food components by sampling the gas phase only. As yet, however, neither has been widely adopted for on-line operation in a manufacturing environment.

1.3.1 Biosensors Biosensors and biomimetic devices have the advantage of high specificity and sensitivity. A wide range of antibody and enzyme-based sensors have been developed for detection and quantification of important food contaminants – pesticide residues, growth promoters, mycotoxins. Most are suitable only for laboratory or field use off-line (Schmidt and Bilitewski, 2001). Although biosensors and biological test kits have revolutionised analytical biological chemistry, there are serious drawbacks to their use on-line in manufacturing in clean environments: • they are invasive – contact with the analyte is required, which can present problems of hygienic operation and fouling • in heterogeneous food systems it is difficult to ensure effective contact between the sensor and all elements of the food • the biological component of the sensor, usually a protein, is not stable to repeated high temperatures and cleaning agents. Some of these problems can be overcome by constructing the binding sites of the sensor from more stable polymers (Piletsky et al., 2001). Biomimetic antibodies have been created with specificities and sensitivities close to those of their protein homologue.

1.3.2 Volatiles detection techniques Detection of volatile indicators of contamination has been developed extensively over the last decade. Where the contaminant itself is volatile or there is a volatile surrogate compound, this approach can overcome the problems presented by the opacity and heterogeneity of most foods. In essence air (or a carrier gas) is used to sample the whole food item or stream. The gas stream can then be presented to a detection system such as gas chromatography, ion mobility spectroscopy, or the so-called ‘electronic noses’ – sensing arrays of conducting polymers, metal

On-line detection of contaminants

7

oxide semiconductor field effect transistors or piezo-electric quartz crystals. The sensitivity of these methods for some important food volatiles is often very high, of the same order as the human nose. Electronic noses have been developed largely for laboratory use in flavour recognition (Gardner and Bartlett, 1994), though more recently they have also been researched for the detection of food contaminants off-line and on-line. Detection of fungal contamination of potatoes (de Lacy Costello et al., 2000), staling of wheat (Brown et al., 2001) and the presence of mite infestation has been demonstrated successfully. In each of these cases complex sets of changes occur in the pattern of volatiles present. Early spoilage detection is potentially valuable for raw materials in storage and transit and does not require continuous on-line measurement for which current e-noses are unsuitable. Identification of individual volatiles by electronic noses based on sensor arrays depends on recognition of the pattern of differential absorption of the compound between the individual sensors in the array (Bartlett et al., 1997). Thus they inherently lack molecular specificity and are influenced by other volatiles present. They are therefore likely to be superseded by other detectors systems with better specificity, for example: • Ion mobility spectrometers have been developed by Graseby Limited as hand-held units for military and forensic uses, such as detection of nerve gases. They can have ppm sensitivity and have been used experimentally to monitor volatiles produced by low levels of bacterial cell growth through the volatiles emitted (Ogden and Strachan, 1998). • Tunable diode laser absorption spectroscopy (TDLAS) is used to identify and monitor trace (ppm) levels of volatile solvents and other gases (Fried, 2002) and may be applicable to some food contaminants. More speculatively, work by Pickett and colleagues on the exquisite specificity and sensitivity of the insect antenna (Coglan, 1999) may form the basis for novel biosensors for trace volatile compounds. Although attractive as a way of sampling complex food structures non-invasively, a major drawback to volatiles detection for on-line monitoring of manufacturing processes is speed of response. The timescale for diffusion of a volatile into the headspace, sampling and analysis of the gas is in the order of seconds rather than the milliseconds needed in most operations. Thus, whilst it has found limited uses on-line in manufacturing operations, the use of volatile markers is probably more applicable to field, storage and off-line use.

1.4

Detection of foreign bodies

Detection of foreign bodies presents quite different challenges to chemical contaminants – they are discrete, occur at low frequency and are often similar in composition to the food itself. Metal detection is standard in most food manufacturing and current X-ray inspection technology can be used for stone,

8

Rapid and on-line instrumentation for food quality assurance

bone and glass in many situations. However, plastics and most of the foreign bodies of biological origin are more difficult to detect. A wide range of imaging technologies, many of them developed for clinical use, has been proposed for application to food manufacturing. Some of these non-invasive techniques are outlined below. In addition, for some types of unwanted body, other approaches may be possible, for instance detection of contaminating insects or spoilt raw materials through the volatiles they emit.

1.4.1 Light-based systems Light-based measurement systems can usually be designed to meet the speed, hygiene and robustness requirements of food processes. Whilst visible light transmission can be used for many drinks, most foods are opaque to visible wavelengths and reflectance methods are used, providing surface shape and colour information from which the presence of unwanted items must be inferred. In some cases, compositional information can be obtained and subsurface damage detected though penetration of the longer wavelengths permits the use of NIR for some applications. Image analysis allows rapid signal processing and machine vision systems have been developed to scan and sort millions of items per minute (Low et al., 2001). The main applications of vision systems are in the detection of foreign bodies and damaged food items. They may in certain circumstances be applied to detection of trace contaminants where the target compound is present at the surface of the food and has a strong characteristic signal, such as the fluorescence of aflatoxin (Pearson 1996; Pearson et al., 2001).

1.4.2 X-ray based systems X-ray imaging is well-established for the detection of bone and other X-ray opaque bodies in foods. Machine vision systems using single and dual energy imaging are used (Graves et al., 1994), though current equipment will not resolve many less dense items such as most vegetable matter and many plastic items. Conventional X-ray radiography relies on differences in absorption that result from differences in density, thickness and elemental composition. Whilst good contrast can be obtained for metal, glass and bone, the low absorption coefficient of most biological tissue limits the ability of X-ray absorption to discriminate many types of foreign body. Recent development of phase contrast X-ray imaging, which enhances edges of structures, may offer an approach that will provide the level of discrimination needed to detect foreign bodies such as insects, hair and extraneous vegetable matter (Wilkins et al., 1996; Kagoshima et al., 1999).

1.4.3 Impedance The dielectric properties of foods are a function of the composition of the aqueous and non-aqueous phases present in it and information on contaminants

On-line detection of contaminants

9

can sometimes be obtained from their interrogation. Impedance is a function of the resistive and reactive elements of a circuit that vary with the applied current frequency. Thus by measuring conductance of a food over a range of frequencies, an impedance spectrum can be obtained, whose characteristics give limited information on the types and quantities of solutes and the presence of particulate matter with different dielectric properties from the continuous phase. Dowdeswell of Kaiku Limited has described using impedance monitoring on-line on aqueous liquid streams to detect the presence of glycol leakages across heat exchangers, cleaning fluids and plastic and metal particles (summarised in Righelato, 2002). Impedance tomography is being developed for imaging in some medical applications and may have potential for detecting some types of foreign body in foods (Bolton, 2002). Engineering impedance detectors into tanks or pipelines is relatively straightforward; for other applications, for example to discrete solid food items and packages, engineering the electrical coupling is more problematic. Whilst impedance sensors can be designed to meet the criteria for hygiene, robustness and speed of detection, the necessary sensitivity and specificity are unlikely to be achieved for most applications in contaminant and foreign body detection.

1.4.4 Microwave radar Surface penetrating, microwave radar is used in military applications such as mine detection, in remote sensing of environmental parameters such as soil moisture and is being developed experimentally for medical diagnostic imaging. Microwaves travel at different speeds and are subject to different levels of damping in media of different dielectric properties. Microwave radiation can penetrate solids but the long wavelengths limit size resolution. The halfwavelength limit can to some degree be overcome for smaller bodies where edges create diffraction patterns that can be detected by measurements at different frequencies and at different positions around the object of scrutiny (Barr et al., 2001). The potential for application of microwave radar to foreign body detection in foods has been studied by Barr et al., (2001). Whilst it offers the advantage of being a remote sensing method with good penetration of foods, it has limited size resolution and poor discrimination in aqueous media. The screening effect of metal precludes its use for foods in metallic packs or cans and for many process plant applications.

1.4.5 Ultrasonic imaging High frequency, low power ultrasonic interrogation of food has been widely explored and, with frequency-scanning, can provide a range of information on composition and structure (Coupland and Clements, 2000; Hackley and Texter, 1998). Depending on the materials characteristics, it can provide size resolution

10

Rapid and on-line instrumentation for food quality assurance

in the order of one millimetre or less. It has been used for inspection of package integrity and is capable of detecting leakage paths down to c. 10 m in width (Ozguler et al., 1998). It is a well-established imaging technique, widely used in medicine, but, although less costly than most of the other imaging techniques described, it has not yet been developed commercially for foreign bodies in foods. Direct contact or acoustic coupling between the signal generator and microphone and the sample is required; this can be simplified by using a pulsed echo rather than transmission method. However, although coupling may be straightforward in pipeline applications it presents difficulties in many processing operations dealing with fast moving discrete food items or packages.

1.4.6 Magnetic resonance imaging (MRI) MRI has become a major tool for non-invasive medical diagnosis: it is capable of resolving soft tissues and many biochemical changes occurring in them with spatial resolution of a millimetre or less. It has been used experimentally to observe many aspects of food quality, processing conditions and food structure (e.g. Duce et al., 1995; Metzler et al., 1995; Miquel and Hall, 1998), and is a candidate for the imaging of foreign bodies. For on-line process monitoring, however, it suffers from some major drawbacks. The process line must be surrounded by large, expensive magnets that are supported by complex data acquisition and processing facilities. To obtain two- and three-dimensional images with adequate spatial resolution, data acquisition can take seconds, too slow for many processing and packaging operations. Over the next decade, MRI may become a practical tool in food processing, if these drawbacks are overcome with novel magnet technology and new approaches to image analysis.

1.5

Conclusions

The complexity of most foods, the need for 100% sampling and for non-invasive techniques, and the speeds of operation of most processing severely limit the technologies that might be applied to on-line detection of contaminants. Several of the non-invasive and remote sensing technologies developed for clinical and environmental monitoring are potential candidates for foreign body detection (Table 1.3). Development of image analysis software is crucial to enable machine vision systems to discriminate most of the commonest foreign bodies in heterogeneous food systems. Although the costs of many of these systems are currently prohibitive, most are at an early stage of technological development and costs will become more attractive with time. Electronic nose technology has had much academic attention over the last decade, but little commercial application. Although attractive as a way of sampling complex food structures non-invasively, a major drawback to volatiles detection for on-line monitoring of manufacturing processes is speed of

Table 1.3

Comparison of some potential on-line detection techniques for contaminants and foreign bodies

Interrogation technique

Application1

Targets; size resolution

Cost order3 £/unit

Comments

Visible light

Processing

Plastic, EVM,2 insects; <1mm

104–105

Suitable for discrete bodies and surface features only, or for transparent products

X-ray

Processing Packaging

Bone, plastic, EVM, insects etc.; <1mm

105

Enhanced discrimination likely by phase contrast X-ray

Microwave radar

Processing Packaging

Bone, plastic, EVM >1mm

104

Performance heavily influenced by food composition

Impedance

Processing Liquids only

Coolants, cleaning fluids

104

MRI

Processing Packaging

EVM, bone, insects; c. 1mm

104–105

Researched for quality and spoilage; potentially suitable for foreign body detection

Ultrasound

Processing

>1mm

104

Poor discrimination likely in complex foods

E-nose 1 2 3

Storage and distribution

Moulds, pest infestations, taints

Match to speeds, hygiene and other requirements. EVM = extraneous vegetable matter. Approximate order of cost of a detection unit.

4

10

Limited to contaminants with a volatile component or surrogate.

12

Rapid and on-line instrumentation for food quality assurance

response. This is likely to limit its use to storage and distribution of raw materials and certain finished products, rather than the processing and packaging operations. Because they lack specificity, sensor array e-noses are likely to be superseded by other detection systems with better specificity, such as mass spectroscopy and, in some applications, tunable diode lasers. The principles of HACCP and GMP are the first, and most important, line of defence against contaminants and foreign bodies entering foods and reaching consumers. Systems, however, do occasionally fail and were cost-effective technology available to detect and reject unwanted material, it would no doubt be used to augment current practice.

1.6

Some sources of further information and advice

Sensors for food manufacturing applications, including biosensors: E Kress-Rogers and C J B Brimelow, Instrumentation and Sensors for the Food Industry, 2nd edition, Cambridge, Woodhead. X-ray imaging: Spectral fusion technologies: www.spectralft.com X-ray technologies: www.xrt.com.au CSIRO: www.cmst.csiro.au/photonic/xrayimaging.htm MRI: Herschel Smith Laboratory of Medical Chemistry: www.hslmc.cam.ac.uk

1.7

References

and MERKEL H (2001), ‘Detection of foreign objects in foods – from need to prototype’, Proceedings Food factory of the future, 27–29 Gothenburg, SIK (document 144, ISSN-280–9737). BARTLETT P N, ELLIOTT J M and GARDNER J W (1997), ‘Electronic noses and their application within the food industry’, Food Technology, 51, 44–8. BOLTON G (2002), ‘Finding out what goes on inside a process plant – prospects for the application of electrical impedance tomography to the food industry’, Food Science and Technology, 16, 52–4. BROWN H, GUNSON H E, PATTON D, RATCLIFFE N M and SPENCER-PHILLIPS P T N (2001), ‘Causes and detection of malodours in wheat grain’, Abstracts: Bioactive Fungal Metabolites – Impact and Exploitation. Swansea, British Mycological Society. COGLAN A (1999), ‘Something rotten’, New Scientist ,161, 15. COUPLAND J N and CLEMENTS D J (2000), ‘Ultrasonic evaluation of food properties’ in Gunasekaran S, Non-destructive food evaluation: techniques to analyze properties and quality, Marcel Dekker, New York. DUCE S L, ABLETT S, DARKE A H, PICKLES J, HART C and HALL L D (1995), ‘Nuclear magnetic resonance imaging and spectroscopic studies of wheat flake biscuits during baking’, Cereal Chemistry, 72, 105–8. FRIED A (2002), ‘Diode laser applications in atmospheric sensing’. Proceedings of SPIE BARR U-K, REIMERS M

On-line detection of contaminants

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Conference 4817 July 11 2002, Seattle. and BARTLETT P N (1994), A brief history of electronic noses, Sensors and Actuators, B18–19, 211–20. GRAVES M, BATCHELOR B G and PALMER S (1994) ‘3D X-ray inspection of food products’, Proc. SPIE Conf. on Applications of Digital Image Processing 17, 2298, 248–59. HACKLEY V A and TEXTER J (1998), ‘Conference Report: International workshop on ultrasonic and dielectric techniques for suspended particulates, Gaithersburg, MD’, Journal of Research of the National Institute of Standards and Technology, 103, 217–223. KAGOSHIMA Y, TSUSAKA Y, YOKOYAME K, TAKAI K, TAKEDA S and MATSUI J (1999) ‘Phase contrast X-ray imaging using both vertically and horizontally expanded synchotron radiation X-rays with asymmetric Bragg reflection’, Japanese Journal of Applied Physics, 38, 470–2. DE LACY COSTELLO B J P, EWEN, R J, GUNSON, H E, RATCLIFFE N M and SPENCER-PHILLIPS P T N (2000), ‘The development of a sensor system for the early detection of soft rot in stored potato tubers’, Measurement Science and Technology, 11, 1685–91. LOW J M, MAUGHAN W S, BEE S C and HONEYWOOD M J (2001), ‘Sorting by colour in the food industry’ in E Kress-Rogers and C J B Brimelow, Instrumentation and Sensors for the Food Industry, 2nd edition, Cambridge, Woodhead. GARDNER J W

METZLER A, IZQUIERDO M, ZIEGLER A, KOCKENBERGER W, KOMR E, VON KIENLIN M, HAASE A

and DECORPS M (1995) ‘Plant histochemistry by correlation peak imaging’, Proceedings of the National Academy of Sciences of the USA, 92, 11912–15. MIQUEL M E and HALL L D (1998), ‘A general survey of chocolate confectionery by magnetic resonance imaging’, Lebensmittel Wissenschaft und Technologie, 31, 93–9. OGDEN I and STRACHAN N J C (1998), ‘Applications of ion mobility spectroscopy for food analysis’, in Biosensors for Food Analysis, Royal Society of Chemistry, Cambridge, 162–9. OZGULER A, MORRIS S A and O’BRIEN JR. W (1998), ‘Ultrasonic imaging of micro-leaks and seal contamination in flexible food packages by the pulse-echo technique’, Journal of Food Science, 63, 673–8. PEARSON T (1996), ‘Machine vision system for automated detection of stained pistachio nuts’, Lebensmittel Wissenschaft und Technologie 29, 203–9. PEARSON TC, WICKLOW D T, MAGHIRANG E B, XIE F and DOWELL F E (2001), ‘Detecting aflatoxin in single corn kernels by transmittance and reflectance spectroscopy’, Transactions of the American Society of Agricultural Engineers, 44, 1247–54. PILETSKY S A, ALCOCK S and TURNER A P F (2001), ‘Molecular imprinting: at the edge of the third millennium’, Trends in Biotechnology, 19, 9–12. RIGHELATO R C (2002), ‘Sticks, stones, bones and moans’, in Food Link News, 39, 10–11, London, DEFRA. SCHMIDT A and BILITEWSKI U (2001), ‘Biosensors for process monitoring and quality assurance in the food industry’ in E Kress-Rogers and C J B Brimelow, Instrumentation and Sensors for the Food Industry, 2nd edition, Cambridge, Woodhead. WILKINS S W, GUREYEV T E, GAO D, POGANY A and STEVENSON A W (1996), ‘Phase contrast imaging using polychromatic hard X-rays’ Nature, 384, 335–8.

2 On-line immunochemical assays for contaminant analysis I. E. Tothill, Cranfield University, UK

2.1

Introduction

Food analysis to date has usually been carried out using off-site testing where the samples are transported to a laboratory for the analysis to take place. This method allows accurate quantification, high recovery rates and low detection limits due to the availability of a dedicated laboratory equipped with the required instrumentation and highly-skilled personnel. However, these analyses are often time consuming and costly and an on-site analysis is always more favourable. Using robust, low cost, portable and rapid technologies that can make a determination of target analyte at the sampling site will overcome the problems of off-site analysis. Such screening methods can save valuable time and resources especially in food processing. Food analysis methods have to be sensitive and accurate to comply with legislation. Increasing customer confidence in the food that they consume is also important by eliminating any contaminated products entering the food chain. Recent cases, such as BSE, the foot and mouth outbreak and GMO foods (geneticallymodified organism), have aroused public concern over the potential health hazards of chemical and biological contaminants. This has prompted more stringent legislation related to the accepted concentration of these compounds and maximum residue limits (MRL) measured in ppb for most food contaminants have been set. Contaminant analysis is vital in food quality assurance and consumers demand high-quality foods free from polluting chemical compounds and pathogenic microorganisms. To ensure optimum quality is delivered to the consumer rapid assessment using cost-effective on-line measurements is required in the food industry. Immunochemical assays are dominating the market today in contaminant diagnostics and their use has increased in food quality testing. The impact of

On-line immunochemical assays for contaminant analysis 15 immunoassays on contaminant analysis for environmental and food applications is evident in the extensive diversity of kits which are available. Assay kits for the sensing of trace contaminants, including pesticides and herbicides, industrial residues and their degradation products, PCBs, microbial toxins and pathogens are available. A range of enzyme-linked immunosorbent assay (ELISA) kits for contaminant analysis has been developed and these kits are used as screening tools where the results can demonstrate the presence or absence of a particular analyte. The use of these tests will reduce the number of samples to be sent for further analysis using off-site methods. Although these tests can be carried out on-site they still require experienced personnel and also entail a multi-stage procedure with results taking 2–3 hours to be available. The time taken to carry out the test is too long for food manufacturers and more rapid monitoring is needed to speed up the analysis. Therefore, the demand for high sensitivity, speed and accuracy of all the analytes requiring testing has stimulated the interest in on-line diagnostics tools based on immunotechniques. Bio- and affinity sensors have the potential to provide rapid and specific sensing for food quality assurance. Sensors can be divided into two categories: catalytic and affinity. Details on the catalytic approach are covered in Tothill and Turner (1997) and Tothill (2001). Affinity sensors use mainly antibody–antigen binding reactions, but other biological components such as cell receptors, singlestranded DNA, lectins and artificial receptors have also been applied. A range of transducers has been employed and include electrochemical, optical, calorimetric, piezoelectric and magnetic ones (Tothill and Turner, 2003). This chapter, however, will concentrate on the use of immunochemical assays and immunosensors for contaminant analysis in food. Emerging on-line methods based on immunochemical assays will also be covered. On-line monitoring is very important in enabling effective process control and also helps in preventing low-quality products from reaching the consumer. Any sample preparation that may be required can also be accommodated using on-line methods such as filtration and dilution.

2.2

Principles and applications of immunochemical assays

Immunochemical assays (immunoassays) are analytical tests based on the selective and sensitive antibody (Ab)–antigen (Ag) interaction. These tests exploit the immune system’s ability to produce antibodies in response to any invasion by foreign organic molecules. The immune system’s function is to protect animals from infectious organisms and their toxic products. Therefore, it has evolved a powerful range of mechanisms to locate foreign macromolecules (antigen) and neutralise them by producing proteins (antibodies) to locate and interact with them and eliminate their harmful effect. The produced antibodies specifically recognise and attach to the antigen to form a complex. The high specific nature of the antibody–antigen reaction (Fig. 2.1) makes them fundamental reagent components in immunochemical techniques and this

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Rapid and on-line instrumentation for food quality assurance

Fig. 2.1

A schematic of the antibody structure and its interaction with the antigen.

interaction forms the basis of all immunochemical tests. The interaction is highly specific and follows the basic thermodynamic principles of any reversible biomolecular interaction: Ab ‡ Ag „ Ab ÿ Ag The affinity constant KA = [Ab–Ag]/([Ab] + [Ag]), where [Ab] is the molar concentration of unoccupied Ab binding sites, [Ag] is the molar concentration of unoccupied Ag binding sites and [Ab–Ag] is the molar concentration of the AbAg complex. The region of an antigen that interacts with an antibody is defined as the ‘epitope’. Because antibodies can recognise relatively small regions of antigens, occasionally they can find similar epitopes on other molecules. This forms the molecular basis for cross-reaction. The binding of the Ab–Ag is entirely dependent on reversible non-covalent interactions such as Van der Waals forces, electrostatic bonds, hydrogen bonds and hydrophobic interactions. The resulting complex is in equilibrium with the free compounds. The immune complex is stabilised by the combination of the above weak interactions that depend on the precise alignment of the antigen and antibody. The stringent binding requirements between the Ab–Ag makes immunoassays very selective. Small changes in antigen structure can affect profoundly the strength of the interaction. Also changes in the epitope structure can prevent antigen recognition. Therefore, antibodies have been isolated that will differentiate between conformations of protein antigens, detect single amino acids substitution, or act as weak enzymes by stabilising transition forms (Tothill and Stephens, 2001). Most organic contaminants of current interest are small molecules and have a molecular weight (MW) of less than 1000 Daltons. These small chemicals

On-line immunochemical assays for contaminant analysis 17 can be used to raise antibodies, if they are coupled to larger protein molecules. The small compounds are known as ‘haptens’, while the proteins to which they are coupled are called ‘carriers’. Bovine serum albumin (BSA) and keyhole limpet hemacyanin (KLH) are both popular carriers. Great care must be taken during the conjugation process since the hapten-carrier design and purity has crucial influence on the sensitivity and cross-reactivity of the antibody produced. Other food contaminants such as microorganisms and other macromolecules which have MW > 5000 Daltons are generally good immunogens and can elicit antibody production without the need for protein conjugation (Despande, 1996). An antibody of the desired affinity and specificity is vital to the process of developing immunochemical techniques. Most of the antibodies used in immunoassays are polyclonal antibodies raised or induced by injection of a solution or suspension of the appropriate antigen into an animal. Following a series of inoculations, blood is taken from the animal and the serum is separated from it. The resulting liquid is termed antisera and is a complex mix of proteins and specific antibodies directed against the inoculated immunogen. Because of the multiplicity of antigenic sites on any single immunogen or of impurities in the immunogen inoculated, a heterogeneous mixture of different antibodies of varying specificity and affinity are produced, i.e. polyclonal antibodies (PAbs). Purification of immunogen helps to narrow the range of affinities and specificities of the induced PAbs. When antibodies of monospecificity are required, antibodies can be derived from single cell lines using hybridoma technology to produce monoclonal antibodies. Since monoclonal antibodies are produced from a single chosen clone they can have high affinity and specificity to a single molecular compound. This will reduce the test cross-reactivity with other similar compounds and increase the specificity of the test. However, hybridoma technology can be expensive and assay specificity using monoclonal antibodies can be too narrow for some screening tasks. Another route for antibody production is to use antibody engineering, where recombinant antibodies can be produced at a fraction of the cost of the production of poly- or monoclonal systems. Due to the difficulty in cloning, assembling and expression of antibody molecules, this task is complex. There are two main types of recombinant antibodies: those produced by a cloning system (using an existing monoclonal cell line) and those produced in vitro (by passing the animal entirely). Research in this area is expanding to design and produce antibodies with the required test properties cheaply and without the use of animals. A range of labels are used today in immunochemical techniques to monitor the antibody–antigen binding reaction, including: latex particles (blue latex); radioisotopes (I125, H3); metal and dye soles (colloidal gold, fluorescent chromophore); enzymes (horseradish peroxidase, alkaline phosphatase and -Dgalactosidase); substrates and cofactors. Although fluorescent and chemiluminescent labels have been gaining popularity in the last few years, enzyme labels are still the most popular in immunoassay kits used for

18

Rapid and on-line instrumentation for food quality assurance

contaminant detection. Depending on the assay format, the label is incorporated into either the Abs (primary or secondary) or the analyte (antigen or hapten). Immunoassays can be classified into three major formats: competitive, noncompetitive (sandwich) and displacement assays. In these formats either the Abs or Ags are immobilised on a solid phase support. Competitive assays are usually used for small molecular weight compounds such as food contaminates and environmental pollution. Analytes of environmental importance are too small to allow binding of two Abs simultaneously, therefore competitive assays are usually used in the diagnosis of compounds such as pesticides, herbicides, toxins and drugs, etc. Sandwich immunoassay utilises two antibodies, which bind the antigen and so form the sandwich. This type of format is used to detect analytes with a molecular weight that can allow the binding of two Abs simultaneously. Such tests are used for microbial and macromolecule detection. Figure 2.2 illustrates the competitive and noncompetitive assay formats. The displacement assay format is similar to the competitive assay. At the start of the assay all of the available binding sites on the immobilised antibodies are occupied by labelled antigen. On the addition of unlabelled antigen there is a displacement of labelled material and under appropriate conditions the extent of this displacement will be dependent on the amount of analyte in the sample. For more detailed information on immunochemical methods the reader is referred to the literature (Hammock and Gee, 1995; Despande, 1996). Detection techniques in immunoassays can be divided into two groups: 1. 2.

Direct detection, the antigen-specific antibody is labelled and used to bind to the antigen. Indirect detection, the antigen-specific antibody is unlabelled and its binding to the antigen is detected by a secondary reagent, such as labelled anti-immunoglobulin antibodies.

The choice of the direct or indirect method depends on the required test. The use of directly-labelled antibodies in an immunoassay involves fewer steps, is less prone to background problems, but is less sensitive than indirect methods and requires a new labelling step for every analyte to be tested. In contrast, indirect methods offer the advantages of widely available labelled reagents, which can be used to detect a large range of antigens, and are available commercially. Since the primary antibody is not modified by the label the loss of activity is also avoided. A further major distinction between immunoassays is the way in which the test is carried out: • Homogeneous immunoassay: A homogeneous system does not require separation of free and bound antigen: the assay relies on the alteration of the properties or function of the label on formation of the antibody–antigen complex. For example the Ab–Ag interaction will either inhibit or enhance the enzyme label used in the assay or change the signal in the case of radioactive isotopes and fluorescent labels. The assays are simple and easy to automate, therefore are commonly used in the diagnostic industry.

On-line immunochemical assays for contaminant analysis 19

Fig. 2.2

Most commonly used configurations for immunoassays for food analysis.

• Heterogeneous immunoassay: In a heterogeneous system there is a separation step to remove the unbound reagents before the label (tracer) is determined. This assay format is a more sensitive approach, less prone to interference and is most commonly employed in test kits. Other types of immunoassay use magnetic separation as enzyme immunoassays with paramagnetic particles as the solid phase. In this case the antibody is immobilised on the magnetic particle surface. This allows the separation of the desired measurable immune complex from excess reagents and sample (Perez et al., 1998). A range of assay kits based on this principle are available for food and agriculture analysis, some of which are marketed by Strategic Diagnostics Inc. (Newark, USA).

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Rapid and on-line instrumentation for food quality assurance

Immunoassay test strips for use outside the laboratory based on flow devices such as the pregnancy strips developed by Unipath (ClearblueTM one step) have also been constructed for environmental contaminant detection. The tests are based on immunochromatography strips using coloured latex as the label. Recently equipment has also been developed to measure the intensity of the coloured line making the test more quantitative. However, immunoassay test kits are the most commonly available kits on the market for contaminant testing. Most immunoassays are used to measure one analyte per kit (e.g. the urea herbicide Isoproturon) or group specific assays that utilise heterogenecity of the polyclonal antibodies to analyse for a group of compounds that are similar in their chemical structure such as the group of urea herbicides (urons). Assays that can measure two or more analytes simultaneously have many advantages in enabling the detection of different analytes using the same sample. Development in this type of assay is thriving. An example of this is the development of immunosensor arrays and microspot arrays which use multispot arrays (50 M2) on the wells that are coated with different Abs. The microspot arrays require a detection system that can resolve the different signals associated with the different spots such as the use of dual channel confocal microscope.

2.3

Immunoassays for food contaminant analysis

A large number of ELISA tests have been developed for the determination of environmental contaminants (for example, for pesticides, such as atrazine, urea herbicides and phenoxyacids, etc). Immunoassays are also used to detect veterinary drugs, microtoxins and other chemical contaminants in agrofood matrices. These tests have also been adapted and used for food analysis. However, suitable extraction methods may be required to remove a sample for testing. Many of the methods developed for soil extraction are now adapted for food sample extraction. Methods based on solvent extraction, microwave-assisted solvent extraction, solid phase extraction and supercritical fluid extraction have been used for liquid and solid sample preparation. Immunoassay techniques are ideal for contaminant analysis due to their selectivity, sensitivity and rapid turnover time (2–3 h). The robustness of the methods can generate on-the-spot data that can be used to make quick decisions. Many of these tests have been correlated to conventional methods such as HPLC and GC-MS and have been accepted by regulatory bodies. But before these tests can be widely used in routine monitoring, issues such as stability and reproducibility need to be improved in order to compete with established conventional methods. Immunoassays today have gained greater acceptance and this is illustrated by the US Environmental Protection Agency’s (EPA) implementation of the EPA ‘400 Series’ of approved commercial ELISA test kits for pollution analysis. The field of immunoassay has shown enormous growth in the last decades. To date there are a range of automated immunoassay analysers available on the market for use in medical analysis (Wild, 2001), which can be modified for use in food testing. Companies

On-line immunochemical assays for contaminant analysis 21 such as SDI Europe Ltd (Hampshire, UK) and Guildhay Ltd. (Surrey, UK) market a range of immunoassay kits for environmental and food diagnostics. Immunoassay kits can be classified as on-site or off-site methods of analysis. In order to lower costs and speed up the testing programme, on-site and on-line analysis is becoming increasingly important. Therefore, biosensors based on the use of antibody as the receptor (immunosensors) exhibit the potential to complement laboratory-based analytical methods and can be used for on-line testing.

2.4

Immunochemical sensors (immunosensors)

Immunosensors are analytical devices incorporating an antibody-based biorecognition molecule utilised in conjunction with or integrated within a physicochemical transducer or transducing microsystems and yielding a digital electronic signal, which is proportional to the concentration of a specific analyte or group of analytes. Immunosensors are very attractive due to their high specificity and the signal achieved is related to a single analyte or small number of related compounds. This is usually dependent on the cross-reactivity of the antibody applied. The direct transfer of existing immunoassay techniques to sensor format can facilitate the development of these devices. Interest in the development and application of these sensing tools is growing rapidly due to: • advances in hybridoma technology • developments in transduction methodologies • increasing demands for simple sensitive and rapid analytical tools for decentralised analysis • stringent environmental legislation. The most attractive advantages of immunosensors over immunoassays are simplification of the analysis procedure (fewer stages), decrease of analysis time, miniaturisation of equipment and automation. The markets for these types of devices are variable and require very different products. The controlling influences are; instrumentation cost, accuracy required, sensitivity and speed and portability. The devices can fall into several categories depending on the analysis place and time, and can include large multi-analysers, portable bench-top instruments and one-shot disposable sensors. Miniaturisation and improved processing power of modern microelectronics have increased the analytical capability of bio- and immunosensors. Research on Lab-on-a chip is exploding and interest from multinational companies in this area is also increasing. The use of antibodies as receptors in sensor configuration combined with a suitable transducer can result in a sensitive and specific affinity sensor. Immunosensors can be divided into direct sensing devices detecting the recognition event and the complex produced between the antibody and the analyte and indirect devices relying on the use of label compounds (enzyme, florescence marker, etc.) to create the signal. Indirect devices rely on the same principle and format used in heterogeneous immunoassays. The majority of immunosensor research and

22

Rapid and on-line instrumentation for food quality assurance

development to date for disposable one-shot sensors is concentrating on indirect detection using enzyme and fluorescent labels. This is due to the low cost of this type of device when compared to direct methods. A range of immunosensors for pesticides, herbicides, toxins, antibiotics, hormones, additives, endocrine disrupting chemicals and microorganisms has been developed for environmental and food analysis. Immunosensors have already established their ease of use and costeffectiveness, thus allowing non-trained operators to employ a relatively cheap assay. The devices have been adapted and used with flow injection analysis for online monitoring of pesticides in water samples. Immunochemical techniques and immunosensors for contaminant monitoring have been reviewed in the literature (Marco et al., 1995; Gizeli and Lowe, 1996; Wittmann and Schmid, 1997).

2.4.1 Optical immunosensors To date affinity sensors based on the use of optical transducers have received considerable attention and have been applied to food sensing. Advances in optical fibres and laser technology have contributed to the wide use of this type of transducer. However, the high cost of some of the equipment based on optical sensing has hindered the widespread use of these devices outside the laboratory. The ability to monitor binding events between the antigen and antibody directly is the main attractiveness of this technique. Optical sensors based on surface plasmon resonance (SPR) such as the BIAcoreTM range of equipment developed by Pharmacia (Uppsala, Sweden) and the IAsys using resonant mirror from Affinity Sensors Ltd (Cambridge, UK) symbolise a significant breakthrough in optical sensor technology (Tothill, 2001; Mello and Kubota, 2002). The instruments are mostly used for kinetic interaction analysis in biochemical research and not as a monitoring tool. But the systems are semiautomated and analysis can be carried out at a rapid rate. The BIAcore has been used to develop immunosensors for a range of analytes in water and soil extracted samples. Skla´dal et al. (1999) used the IAsys optical sensor system to develop an immunosensor to detect atrazine in soil extracts. A detection of 1g lÿ1 was achieved repeatedly with sensor regeneration for up to 120 assays. A total internal reflectance fibre-optic immunosensor against 2,4-D with Mabs was developed by Mosiello et al. (1997). This immunosensor achieved a detection limit of about 60 nM for 2,4-D. The approach of using fluorescent labels detected via evanescent wave interaction with an optical fibre has been applied successfully in immunosensor development. Examples of these devices are the Raptor developed by the US Navy Research Laboratory and the fluorescence-based EW immunosensor that incorporates the capillary fill device developed by Unilever (Bedfordshire, UK).

2.4.2 Electrochemical Immunosensors Electrochemical affinity sensors generally rely on the use of electroactive label, usually employing enzyme labelling and amplification techniques. Detection

On-line immunochemical assays for contaminant analysis 23 using electrochemical immunosensors can be inexpensive and may achieve low detection limits. Different types of electrochemical affinity sensors have been developed including potentiometric, capacitive, conductometric and amperometric. A range of amperometric electrochemical immunosensors has been developed for pesticide detection. Most are based on screen-printing electrodes for one-shot analysis. However these sensors can also be adapted for on-line monitoring. A screen-printing immunosensor for 2,4-D analysis in soil extract was developed by Kro¨ger et al. (1999). The sensor was based on an indirect competitive assay with the antigen conjugate adsorbed directly onto the working electrode surface. The disposable sensor enabled ppm concentrations of the herbicide to be analysed in soil extracts. Another sensor for 2,4-D was develop by Wilmer et al. (1997) using a competitive assay with sequential injection analysis techniques sensing through an immunosensor with the analyte immobilised on a glass capillary or to Eupergit packed in the capillary. This was conducted for automatic measurement of 2,4-D in drinking water and groundwater and can be applied for on-line monitoring. An immunosensor based on a competitive direct enzyme immunoassay (EIA) system coupled to amperometric transducer for the detection of microcystin-LR in water and seafood samples has been developed by Lotierzo et al. (2001). The sensor implements a membrane-bound configuration of the EIA system. Signal detection is by amperometric transduction of the HRP between the carbon working and Ag/AgCl reference electrode via a hydroquinone mediator with hydrogen peroxide as substrate. The detection limit for the electrochemical immunosensor is in the range of 10 g lÿ1 (Fig. 2.3). The sensor was also used in a flow injection analysis (FIA) system for on-line application for water testing. Amperometric detection is the most applied system in these devices. However, more research on the efficiency of coupling of the biological electron-generating steps to the electrode is needed to improve the sensitivity of these devices. Also the effect of interfering compounds can be a problem with samples containing ppb analyte concentrations. Potentiometric devices rely on the measurement of changes in potential that arise from reaction of an analyte with a specific receptor. A range of devices has been developed using this type of transducer where the antibody has been immobilised on ion-selective electrodes. Devices such as the ion-sensitive fieldeffective transducers (ISFETs), chemically sensitive field-effective transistor (CHEMFET) and the light-addressable potentiometric sensor (LAPS) have all been reported in the literature (Colapicchioni et al., 1991; Poghossian et al., 2001; Selvanayagam et al., 2002). Another device, which uses ion-channel switches (ICSs) that mimic biological sensory functions have also been reported (Cornell et al., 1997; Cornell, 2002). The ICS sensor uses a gold electrode to which is tethered a lipid membrane that incorporates gramicidin ion-channels linked to antibodies. These ion channels are mobile within the membrane, which encloses an ionic reservoir between the gold electrode and the membrane. In the presence of an applied potential, ions flow between the reservoir and the

24

Rapid and on-line instrumentation for food quality assurance

Fig. 2.3

Electrochemical disposable immunosensor format (Lotierzo et al., 2001).

external solution. Ion flux stops when dimerisation of the mobile channels in the outer half of the membrane with those on the inner half is prevented by binding of the antibody to its analyte (Malan, 2001). The presence of the analytes of interest can be measured by the change in membrane conductance. The sensor instrument AMBRI SENSIDX System is marketed by the Australian Membrane and Biotechnology Research Institute (AMBRI, Sydney) and can be used for veterinary, food and environmental diagnostics. Food testing applications include detection of dangerous organisms such as salmonella, enterecoccusin and faecal coliform bacteria in the food processing industry.

2.4.3 Piezoelectric/acoustic immunosensors Piezoelectric/acoustic sensors such as the quartz crystal microbalance (QCM) and surface acoustic devices (SAW) can also be classified as direct immunosensors when immunoreagents are used as the receptor. Piezoelectric transducers have gained attention recently in affinity sensor development. A quartz crystal is used for sensor fabrication and is sandwiched between two electrodes, which are generally gold or silver, prepared by thermal evaporation. The region between the electrodes is piezoelectric active and the change in the resonant frequency will depend on the change in the mass of the crystal (Bunde et al., 1998). These devices are also called quartz crystal microbalance (QCM), due to the frequency with which the crystal oscillates and thus the resulting acoustic wave depends on the mass of the molecules attached to the crystsl. A review of piezoelectric immunosensors and their application for food analysis have been reported by Minunni et al. (1995). Devices such as the surface acoustic wave (SAW) sensor and the acoustic wave guide (AWG) have been developed.

On-line immunochemical assays for contaminant analysis 25 Piezoelectric sensors have been used for the detection of contaminants such as pesticides in the gas and also liquid phase. By immobilising the antibody for the target analytes on the crystal, immunosensors for parathion, atrazine and 2,4D have been developed. However, these type of sensors have been found to have low sensitivity for small compounds such as environmental contaminants, and detection limits in the range of ppm are usually reported in environmental and food extracted samples. Coupling the sensor with a flow system to concentrate the samples using solid phase extraction columns has increased the sensitivity of these devices. The use of a piezoelectric sensor combined with a flow system using a flow cell has been developed and used to provide real-time data on the binding events between the analyte and its receptor (Chianella et al., 2003).

2.5

On-line immuno sensors in food processing

Analytical systems for on-line monitoring in food processing should not introduce contamination and compromise the sterility of the products, and also allow accurate and rapid results. The high complexity of food samples makes this very challenging for on-line monitoring systems. However, opportunities exist for on-line monitoring during manufacture and in shelf-life monitoring during distribution and storage. A range of immunochemical methods can be adapted and applied for on-line monitoring. Flow injection analysis (FIA) is an analytical technique, which has broad acceptance for on-line monitoring and has also been proved to be highly versatile and allows high measurement frequency. The technique has been coupled to a range of detection principles including spectrophotometry, chemiluminescence, fluorimetry, mass spectrometry, atomic absorption, flame emission, refractive index measurement and various electrochemical techniques such as amprometry and potentiometry. However, applying immunotechniques for on-line monitoring in the food industry requires extracting and interfacing the sample with the detection system. Also, the sensor mechanism must be reversible so that it can be reused without loss of sensitivity. Regeneration of the sensor surface so that it can be re-used in situ for long periods of time can be very challenging when using food samples. Several methods have been used in the literature to generate the sensors for on-line monitoring (GonzalezMartinez et al., 1997; Zeravik and Skla´dal, 1999; Paek and Schramm, 1997; Blonder et al., 1997). Many have reported successful reuse of the sensors with 100 assay cycles being reported. However, most studies used water samples or spiked buffer samples. One of the on-line immunosensor systems developed in recent years and attracting much attention is the prototype FIA River ANALyser (RIANA system) which was developed under European Commission funding. The RIANA system incorporates a multiple analytes immunoanalysis based on total internal reflection fluorescence with 15 minutes for each analysis (Mallat et al., 1999, 2001a,b). The transducer consists of a quartz slide with spatially resolved surface modification for antigen immobilisation, along which a coupled laser

26

Rapid and on-line instrumentation for food quality assurance

beam propagates by total internal reflection. The antibodies are labelled with Cy5.5 fluorescent dye and compete with the free analyte. The system has been applied for the detection of chlorotriazines, atrazine, simazine and isoproturon. Detection limit for isoproturon in river water was 0.14 g lÿ1. The system is currently being developed to be a fully automated on-line system capable of detecting a range of contaminants in river water. Instruments such as the RIANA system can be further developed for wine and milk testing. Immunosensors have also been applied to quasi-continuous flow through monitoring in a system similar to immunoaffinity chromatography where the capture antibodies immobilised are in a column (Vianello et al., 1998). The activity of the label was monitored continuously from the micro column using amperometric detection. Flow injection capillary chemiluminescent ELISA using an imprinted polymer instead of the antibody has also been developed (Surugiu et al., 2001).

2.5.1 Application of on-line monitoring in the food industry Liquid and gas chromatography and mass spectrometry usually form the basis for routine contaminant analysis in the food industry. These techniques are still being used regularly for food quality assurance. Contaminants such as ethyl carbamate, anthocyanin, phenolic antioxidants and 2,4,6-trichloroanisole in wine are analysed using the above methods. There are many companies advertising their services on the Internet for wine and fruit juices and dairies analysis using conventional methods. However, the use of immunochemical techniques and sensors for this application is gaining attention, although most are still research tools. Immunosensors for contaminant monitoring are largely dominated by research into piezoelectric and optical devices (SPR, fibre optics and evanescent wave). This is mainly due to the simplicity of the tests since these devices can enable multi-analyte analysis and assays carried out in a direct format. Also the tests can be easily automated and run on-line and on-site. Amperometric devices are more attractive for field analysis using one-shot disposable devices. A range of immunosensors has been developed in the literature which adapt immunoassay technology and these can be applied for food testing. Immunodiagnostics tests are used today for pollution detection in food and for on-farm screening of livestock diseases and also monitoring of livestock reproduction (milk progesterone) and quality control of foodstuffs such as authenticity and adulteration (Van Der Lende, 1994). Many of these tests can be further developed to immunosensors and immunoprobes and used for real-time and on-line analysis. Antibiotic and chemotherapeutic use in animal husbandry has also led to the occurrence of veterinary drug residues in food of animal origin. To be able to analyse and detect all of these contaminating compounds, more rapid tests such as immunosensor assays need to be developed and implemented to speed up the screening process. Monitoring in dairies and bakeries, fruit juices and wine mainly concentrate on pathogenic microbial contamination screening and their contaminating by-

On-line immunochemical assays for contaminant analysis 27 products. However, detection of a range of toxins produced by microorganisms, metal contaminants, drugs residues, pesticide and herbicide contents are also becoming of importance. This is in order to comply with legislation and also to reduce the harmful effects of these contaminants on human health, which has become more apparent in recent years, especially the chronic exposure to low levels of these contaminants. Examples of some of the developments in this area will be covered in this section. Optical biosensors based on SPR technology have been used to develop rapid diagnostics tests for food analysis. SPR-based immunosensor for sulfamethazine detection in milk has been reported by Sternesjo et al. (1995) with detection limit of 1ppb. BIAcore AB have also launched the BIAcoreQuantTM, a version of their SPR technology for the automated analysis of vitamins in food. The sensor chip used in these instruments is shown in Fig. 2.4. Similar principles have also been applied for the determination of drug residues in meat and milk products and also microorganisms such as E. coli O157 in food samples using BIAcore 2000. Homola et al. (2002) reported the development of SPR biosensor for the detection of Staphylococcal enterotoxin B in milk. The sensor was able to detect the toxins in buffer as low as 5 ng lÿ1. By applying a sandwich detection format the sensor sensitivity was enhanced to 0.5 ng lÿ1 in both buffer and milk samples. Optical sensors have been demonstrated for the detection of various foodborne pathogens and their toxins such as Salmonella typhimurium and Listeria monocytogenes. Progesterone immunosensors have been developed and applied for milk analysis using the same principle as the immunodiagnostics test kit used to analyse it. A rapid and automated immunosensor using BIAcoreTM surface plasmon resonance biosensor was developed for the detection of progesterone in milk with a limit of detection found to be 3.56 ng mlÿ1 (Gillis et al., 2002). BIAcoreTM has also been use to detect levamisole residues in milk with a detection limit of 0.5 ng mlÿ1 (Crooks et al., 2002). Polido-Tofin˜o et al. (2000, 2001) developed an FIA fluoroimmunosensor for food analysis. The system was based on direct immunoassay with fluoroscein

Fig. 2.4

The sensor chip used in the BIAcoreTM range of instrumnts.

28

Rapid and on-line instrumentation for food quality assurance

isothiocyanate labelled antigen. The antibody was immobilised on a controlled pore glass transducer. Foodstuffs such as wheat, barley, potatoes and peas were spiked with isoproturon and used for the analysis. Detection limit of 3.0 g lÿ1 was achieved which exceeded the EU Directive maximum levels of 0.05 mg kgÿ1 in agricultural foodstuffs. However, matrix effects were apparent in some of the samples especially potato samples. The developed sensor system can be regenerated with citric acid and be used for 1000 measurements. Winemaking and fruit juice production poses many qualities (appearance, taste) as well as safety (chemicals, metals and pathogens) hazards. Christaki and Tzia (2002) have reviewed winemaking covering quality and safety analysis of the process as well as the different hazards that can contaminate the wine. Most of wine and fruit juice analysis, however, is still carried out using conventional methods. Recently immunochemical techniques have been investigated and also on-line analysis has been reported. Most of these concern pesticide, mycotoxin and microorganism concentration and detection. Ochratoxin A occurs in a variety of food commodities of which cereals and cereal products, beer and wine are the most important sources. The occurrence of Ochratoxin A in wine has been reported in various studies dealing with European wine (Leitner, et al., 2002). Most of the current analytical methods for the determination of this toxin and for pesticides (Wu et al., 2002) use immunoaffinity columns as sample clean-up and concentration methods. However, analysis is usually carried out using HPLC-MS or LC-MS. Methods based on immunosensors are still under development, although are increasingly being reported in the literature (De Saeger et al., 2002). A flow injection analysis manifold with three channels, using a dialysis unit to eliminate sample matrix interference and to accomplish on-line dilution has been developed for the spectrophotometrical determination of tartaric acid in wine (Silva and Alvares-Ribeiro, 2002). This method of on-line analysis was found to be fast, accurate, simple, economic and does not require any sample pre-treatment. Recently, FIA has been used frequently in the literature for the development of new methods for wine analysis (Azevedo et al., 1999; De Campos Costa and Arau´jo, 2002). The availability of antibodies for the analysis of contaminants is increasing rapidly. Also the use of specifically-tailored and mass-produced alternatives to conventional antibodies is likely to alleviate the production of immunosensors and affinity sensor devices. An example is plantibodies produced in plants, recombinant antibodies, catalytic antibodies or abzymes (Breitling and Du¨bel, 1999; Motherwell et al., 2001), artificial receptors such as molecularly imprinted polymers, synthetic peptides and aptamers, which are artificial nucleic acid ligands (Chianella et al., 2002; O’Sullivan, 2002). Many companies and research organisations are developing one-shot disposable immunosensors or on-line immunosensors for food diagnostics. These devices will reach the market in the near future. However, more investment is needed to accomplish this goal. Table 2.1 gives examples of some immunosensors that can be applied or have been used for food analysis or water analysis.

Table 2.1

Examples of immunosensors developed for contaminant detection

Analyte

Type of sensor

2,4-D

Disposable amperometric multi-channel sensor based on screen-printing electrode with acetylcholinesterase as the label Nonseparation electrochemical enzyme binding/immunoassay using microporous gold electrodes Flow through column with glass as the solid support to immobilise the antibody Indirect competitive immunosensor using fibreoptic transduction with flow injection analysis Label-free immunosensor using reflectometric interference spectroscopy FIA fluoroimmunosensor for on-line monitoring system SPR detection coupled with identification using mass spectrometry SPR-based immunosensor Automated SPR-based immunosensor

Dioxin Carbaryl insecticide Okadaic acid toxin Isoproturon Diuron Staphylococcal enterotoxin B Sulfamethazine residues Insulin-like growth factor-1 (1GF-1)

Detection limit

Detection time Matrix

Reference

0.01 g lÿ1

30 minute

Kalab and Skla´dal (1997)

0.01 g lÿ1

–

water and sheep serum

Ducey et al. (1997)

0.029 g lÿ1

20 minute

water

0.1 g lÿ1

20 minute

0.7 g lÿ1

–

mussel homogenates water

0.02 g lÿ1

–

water

1 ng lÿ1

–

1 ppb

20 minute

milk and mushroom milk

1 g lÿ1

–

milk

GonzalezMartinez et al. (1997) Marquette et al. (1999) Haake et al. (2000) Kra¨mer et al. (1997) Nedelkov et al. (2000) Sternesjo et al. (1995) Guidi et al. (2002)

water

30

2.6

Rapid and on-line instrumentation for food quality assurance

Future trends

Future development in diagnostics is already progressing and turning fiction into reality. Advances in microfluidics, genomics and proteomics are transforming biochemical analysis and creating breakthroughs in microarray systems capable of multianalyte analysis for high-throughput screening tests. The emergence of microarray technology (Lab-on-a-chip), and the development of new receptor systems will have a major impact on food analysis.

2.6.1 Microarrays The advances in nanotechnology and microfluidics have created new products and materials which have enabled countless applications based on new capabilities. Microspot assays or multianalyte microarray are developing rapidly in the diagnostics industry and represent a powerful new set of tools. All microarray assays contain five experimental steps including biological query, sample preparation, biochemical reaction, detection and data visualisation and modelling (Schena et al., 1998). Protein microarrays have emerged after the development of DNA microarrays. In this section the application of antibodies in this type of assay is covered. In protein microarray tests the capture antibody is located on the solid support and exposed to an analyte-containing sample. Occupancy of the antibody within the spot may be determined by its exposure to a second (labelled) developing antibody or antigen reactive with either occupied or unoccupied sites following the noncompetitive and competitive assay format used in conventional immunoassays (Self and Winger, 2001). Since the assays rely on measurement of the fractional occupancy of the sensor antibody, the immobilised antibody on the microarray is also labelled. The signal is then observed by the ratio of signal emitted by the immobilised antibody with either the signal emitted by the second labelled antibody (noncompetitive assay) or the labelled antigen (competitive assay) (Fig. 2.5). Fluorescent labels are used in this system since they possess very high specific activity and permit good microspot distribution on the surface of the chip to be optically scanned using a confocal microscope or CCD camera. Other labels are also used such as chemiluminescent labels, but these have lower assay sensitivities. Microspot assays can yield higher sensitivities than other immunoassay formats. The amount of antibody immobilised on the microspot approximates 0.01/K. The tests are reviewed by Schena et al. (1998) and Self and Winger (2001). Microarrays have the great advantages of sensitivity, shorter assay time and multianalyte assay when compared with conventional immunoassays. However, they require good instrumentation and greater attention to non-specific binding of the labelled reagents, the background signal of the microspots and reagent stability. The instrumentation needed to support and implement this technology can be very expensive, such as microarray chip manufacturing, scanning the chips using fully automated analysers with microfluidic systems and sample preparation process. However, the multianalyte sensing capability of these devices will revolutionise the diagnostic industry.

On-line immunochemical assays for contaminant analysis 31

Fig. 2.5

Protein microarrays.

2.6.2 New emerging receptors Several state-of-the-art approaches are used today for the production of affinity receptor molecules for analytes and contaminant separation and detection. Biological molecules are usually very specific and sensitive for the target analytes and when applied to sensor format they can produce good sensor specification. Antibodies are widely applied today in a range of immunotechnique devices ranging from immunoassay kits to dipsticks and biosensors. Antibody fragments and molecularly-engineered antibodies are being developed for immunosensor application. By using direct and combinatorial mutagenesis the affinity and selectivity of recombinant antibodies are being enhanced and adapted to make them better suited for specific devices, although, for some applications, biomolecules with the required affinity are either not available or lack the properties necessary for a successful sensor, such as stability, which can hinder wider application and lack of commercialisation. Replacing natural biomolecules with artificial receptors or biomimics has therefore become an attractive area of research in recent years. Development of artificial receptors for contaminant detection is being favoured and the increase of research in the area of biomimetics is growing. This may also be due to the fact that approaches used in receptor synthesis obviate the need for animals, which is essential for antibody production. Also the stability problems facing antibodies in extreme environmental conditions (such as pH and organic solvents and high temperature) need to be overcome. Studies of mimicking natural receptors have been a challenge to many researchers (Andersson et al., 1996). A range of suitable sensing layers are being developed such as combinatorial synthesis of molecular receptors, combinatorial library of nucleic acids (aptamers) and imprinted polymers. These receptors can be specific for a chosen analyte or a whole class of target analytes.

32

Rapid and on-line instrumentation for food quality assurance

The use of organic polymers to specifically imprint the target molecule has been carried out and the production of a molecularly-imprinted polymer (MIP) as a synthetic receptor has been realised (Mosbach, 1994; Mosbach and Ramstrom, 1996). Artificial antibodies and receptors prepared by using molecular imprinting are conceptually attractive due to their ease of preparation, high thermal and chemical stability, and long shelf-life in ambient temperature and humidity (Andersson, 2000a,b). Molecular modelling is a powerful tool to implement conformational study of molecules such as drugs, proteins, macromolecules, etc., and it allows computational chemists to generate and refine molecular geometry (Chianella et al., 2002). Molecularly-imprinted polymer for the pesticide bentazone has been synthesised and used to sense the pollutant (Baggiani et al., 1999). Herbicide assay using an imprinted polymer-based system analogous to competitive fluoroimmunoassays has also been reported (Haupt et al., 1998). These new types of receptors were used not only in sensor conformations but also in column chromatography and solid phase extraction columns to concentrate contaminants before analysis. The use of molecularly-imprinted solid-phase extraction for the selective concentration of clenbuterol from calf urine and bovine liver has been reported by Berggren et al. (2000) and Crescenzi et al. (2001). On-line solid-phase extraction of the triazine herbicides using a molecularly-imprinted polymer for selective sample enrichment has been conducted (Bjarnason et al., 1999). Reports on on-line solid-phase extraction using MIP technology is increasingly being reported in the literature (Masque et al., 2000; Koeber et al., 2001). Recent work at Cranfield University (Chianella et al., 2002) used a computational approach for the design of a molecularly-imprinted polymer specific for Cyanobacterial toxin, microcystin-LR. This toxin is the most widespread hepatotoxin which has a tumour-promoting activity that can threaten human health by low-level chronic exposure to these toxins in contaminated drinking water and also from consuming contaminated seafood. By using molecular modelling software, a virtual library of functional monomers was designed and screened against the target toxin, which was employed as a template for MIP synthesis. The monomers giving the highest binding energy were selected and used in a molecular dynamics process to investigate their interaction with the toxin (Fig. 2.6). The stoichiometric ratio observed from the study was used in the artificial receptor synthesis for microcystin-LR. The synthesised polymer was used both as a material for solid-phase extraction (SPE) and as a sensing element in a piezoelectric sensor (Chianella et al., 2003). Using the combination of SPE concentration followed by piezoelectric sensor detection the minimum detection limit achieved was 0.35 nM for microcystin-LR. The combinatorial library technique (combinatorial chemistry) has been an expanding area of development for receptor discovery and lead compound optimisation and is widely used by the drug industry. Combinatorial libraries consist of a large array of diverse molecular entities, generated by the systematic and repetitive covalent connection of a set of different ‘building blocks’ of

On-line immunochemical assays for contaminant analysis 33

Fig. 2.6 Microcystin-LR, in balls and sticks in the centre and its interaction with six molecules of urocanis acid ethyl ester (UAEE) and 1 molecule of 2-acrylamido-2-methyl-1 propanesulfonic acid (AMPSA) (Chianella et al., 2002).

varying structures. Effort has been devoted in the development of new strategies for peptide and non-peptide libraries. A large number of compounds can be generated using combinatorial library techniques. Molecular modelling is usually used as a combined approach to facilitate a more efficient receptor discovery process. Combinatorial chemistry has been used to generate affinity peptide ligands which can be applied as receptors in diagnostic devices such as biosensors (Chen et al., 1998). At Cranfield University, we are working on the development of peptide receptors for anabolically active illegal androgens used to boost animal performance, such as boldenone and stanozolol. The receptors will be designed based on a combined approach of molecular modelling and combinatorial chemistry. The developed receptors will then be implemented in a sensor format for the detection of these compounds in meat and foodstuff. This research is part of the (RADAR) project funded by the European Commission. Aptamers (derived from the latin aptus, meaning ‘to fit’) are artificial nucleic acid ligands selected from combinatorial libraries of synthetic nucleic acids by an iterative process of adsorption, recovery and reamplification (O’Sullivan, 2002). These can be generated against amino acids, drugs, proteins and other molecules and used as receptors for sensor and kit developments. Aptamers are attracting interest in the area of diagnostics and are being developed to be implemented in future devices. Research activity in the area of artificial receptor discovery and synthesis is an exciting and rapidly growing area in ligand discovery. It should be possible to overcome the stability problems inherent in natural receptors using these techniques.

34

2.7

Rapid and on-line instrumentation for food quality assurance

Conclusions

Detection of contaminants in food and food processing plants is of increasing importance to consumers, the food industry and regulatory authorities. Significant effects on quality improvement and cost reduction in practical agriculture, horticulture and food processing are expected by the establishment of appropriate technologies to apply rapid sensing methods such as on-line immunotechniques and immunosensors. Current instrumentation developments centre around miniaturisation, improved signal processing and sensor array technology where multiple analytes can be detected simultaneously on the same sensor chip. The chemistry, biochemistry and molecular biology communities are working together to improve currently available receptors such as antibodies, and also to design and synthesise new and more stable receptors. Techniques such as reagent deposition assay format and sensor design and fabrication are being developed to enhance immunosensor capabilities. The emergence and widespread use of miniaturised multianalyte chip-based microarray methods for DNA analysis and immunodiagnostics have a significant impact on the diagnostics food market. Advances in pattern-recognition methods and the availability of the instruments is also revolutionising the diagnostic industry. However, all developed assays must be compatible with regulatory requirements across the globe, and must ensure that design control practices, such as the ISO 9000 and other regulatory guidance, are followed. The time and expense involved with the detection of contaminants such as sample acquisition, sample preparation, and laboratory analysis have placed limitations on the number of tests that can be carried out on- and off-site. However, with recent advances in biotechnology, microelectronics and fibre optics the classical definition of biosensor has emerged to include a variety of analytical devices based on bioaffinity sensing. For immunosensors to have a significant impact on this monitoring task, several challenges must be addressed especially in food processing. These include compound diversity and complexity; matrix diversity and complexity; screening and monitoring requirements and method approval. For liquid matrix it is easier to apply these types of methods such as for milk and wine testing, but for more solid and high viscosity matrix a more complex sample preparation may have to be implemented to introduce the sample to the sensing devices. The potential success of immunosensors in agriculture, food processing and veterinary diagnosis is being established since new tests and instrumentation are appearing on the market for these applications.

2.8

Source of further information and advice

(2001). ‘The ELISA Guidebook’, Methods in Molecular Biology, Volume 149 (John M. Walker, series ed.), Humana Press Inc. DESPANDE, S.S. (1996). Enzyme immunoassay: From concept to product development. 1st edn. Chapman and Hall. CROWTHER, J.R.

On-line immunochemical assays for contaminant analysis 35 and GEE, S.J. (1995). ‘Impact of emerging technologies on immunochemical method for environmental analysis’ ACS Symposium Series 586, Immunoanalysis of Agrochemicals. ACS Press. HARLOW, E. and LANE, D. (1999). Using Antibodies: A Laboratory Manual. Cold Spring Harbour Laboratory Press, Cold Spring Harbour, NY. KRESS-ROGERS, E. (ed.) (1998). Handbook of Biosensors and Electronic Noses. Boca Raton, FL: CRC Press. SCOTT, A.O. (ed.) (1998). Biosensors for Food Analysis, Cambridge, The Royal Society of Chemistry. HAMMOCK, B.D.

2.9

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microbalance as biosensor. A status report on its future, Anal. Letters, 28, 749–764. (1994). Molecular imprinting, Trends in Biochemical Science, 19, 9–14. MOSBACH, K. and RAMSTROM, O. (1996). The emerging technique of molecular imprinting and its future impact on biotechnology, Biotechnology, 14, 163–170. MOSIELLO, L., NENCINI, L., SEGRE, L. and SPANO, M. (1997). A fibre-optic immunosensor for 2,4–dichlorophenoxyacetic acid detection. Sensors and Actuators B – Chemical, 39 (1–3), 353–359. MOTHERWELL, W.B., BINGHAM, M. J. and SIX, Y. (2001). Recent progress in the design and synthesis of artificial enzymes, Tetrahedron, 57, 4663–4686. NEDELKOV, D., RASOOLY, A. and NELSON, R.W. (2000). Multitoxin biosensor-mass spectrometry analysis: a new approach for rapid, real-time, sensitive analysis of staphylococcal toxins in food, International J. of Food Microbiology, 60, 1–13. O’SULLIVAN, C.K. (2002). Aptasensors – the future of biosensors? Anal. Bioanal. Chem., 372, 44–48. PAEK, S.H. and SCHRAMM, W. (1997). Performance characteristics of a reversible immunosensor with a heterobifunctional enzyme conjugate as signal generator, Biotechnology and Bioengineering, 56, (2), 221–231. PEREZ, F.G., MASCINI, M., TOTHILL, I.E. and TURNER, A.P.F. (1998). Immunomagnetic separation with mediated FIA amperometric detection of viable E. coli O157. Anal. Chem., 70, 2380–2386. ¨ NING, M.J. (2001). ¨ TH, H. and SCHO POGHOSSIAN, A., YOSHINOBU, T., SIMONIS, A., ECKEN, H., LU Penicillin detection by means of field-effect based sensors: EnFET, capacitive EIS sensor or LAPS?, Sensors and Actuators B: Chemical, 78 (1–3), 237–242. ˜ O, P., BARRERO-MORENO, J.M. and PE´REZ-CONDE, M.C. (2000). Flow-through POLIDO-TOFIN fluoroimmunosensor for isoproturon determination in agricultural foodstuff: Evaluation of antibody immobilisation on solid support, Analytica Chimica Acta, 417, 85–94. ˜ O, P., BARRERO-MORENO, J.M. and PE´REZ-CONDE, M.C. (2001). Sol-gel glass POLIDO-TOFIN doped with isoproturon antibody as selective support for the development of a flow-through fluoroimmunosensor, Analytica Chimica Acta, 429, 337–345. SCHENA, M., HELLER, R.A., THERIAULT, T.P., KONRAD, K., LACHENMEIER, E. and DAVIS R. (1998). Micoarrays; biotechnology’s discovery platform for functional genomics, Trends Biotechnology, 16, 301–306. SELF, C.H. and WINGER, L.A. (2001). 2 Anti-complex and selective antibody immunometric assays for small molecules. In: The Immunoassay Handbook, 2nd edn (David Wild, ed.), Nature Publishing Group, pp. 229–239. SELVANAYAGAM, Z.E., NEUZIL, P., GOPALAKRISHNAKONE, P., SRIDHAR, U., SINGH, M. and HO, L.C. (2002). An ISFET-based immunosensor for the detection of -Bungarotoxin. Biosensors and Bioelectronics, 17, 821–826. SILVA, H., A.D.F.O. and ALVARES-RIBEIRO, L.M.B.C. (2002). Optimisation of a flow injection analysis system for tartaric acid determination in wines, Talanta, 58, 1311–1318. ´ DAL, P., DENG, A. and KOLA ´ Rˇ, V. (1999). Resonant mirror-based optical immunosensor: SKLA application for the measurement of atrazine in soil, Analytica Chimica Acta, 399, 29–36. ˚ ., MELLGREN, C. and BJORCK, L. (1995). Determination of sulfamethazine ¨, A STERNESJO residues in milk by a surface plasmon resonance-based biosensoassay, Anal. Biochem. 226, 175–181. SURUGIU, I., SVITEL, J., YE, L., HAUPT, K. and DANIELSSON, B. (2001). Flow injection capillary chemiluminescent ELISA using an imprinted polymer instead of the antibody, MOSBACH, K.

On-line immunochemical assays for contaminant analysis 39 Anal. Chem., 73, 4388–4392. (2001). Biosensors Developments and potential applications in the agricultural diagnosis sector, Computers and Electronics in Agriculture, 30, 205–218. TOTHILL, I.E. and TURNER, A.P.F. (1998). Biosensors: New developments and opportunities in the diagnosis of livestock diseases. Towards livestock disease diagnosis and control in the 21st century. International Atomic Energy Agency, 79–94. TOTHILL, I.E. and TURNER, A.P.F. (2003). Biosensors. In: Encyclopaedia of Food Sciences and Nutrition (2nd edn), Benjamin Caballero (Editor in Chief), Luiz Trugo and Paul Finglas (editors), Academic Press, ISBN: 0-12-227055-X. TOTHILL, I.E. and STEPHENS, S. (2001). Methods for environmental monitoring: biological methods. In: Analytical Methods for Environmental Monitoring, (Ahmad, R., Cartwright, C. and Taylor, F. eds), Prentice Hall, pp 224–258. VAN DER LENDE, T. (1994). Generation and applications of monoclonal antibodies for livestock production, Biotechnology Advance, 12, 71–87. TOTHILL, I.E.

VIANELLO, F., SIGNOR, L., PIZZARIELLO, A., DIPAOLO, M.L., SCARPA, M., HOCK, B., GIERSCH, T.

and RIGO, A. (1998). Continuous flow immunosensor for atrazine detection, Biosensors & Bioelectronics, 13 (1), 45–53. WILD, D. (2001). The Immunoassay Handbook, 2nd edn, Nature Publishing Group. WILMER, M., TRAU, D., RENNEBERG, R. and SPENER, F. (1997). Amperometric immunosensor for the detection of 2,4-dichlorophenoxyacetic acid in water. Anal. Lett., 30 (3), 515–525. WITTMANN, C. and SCHMID, R.D. (1997). Bioaffinity sensors for environmental monitoring. In: Handbook of Biosensors and Electronic Noses – Medicine, Food, and the Environment (E. Kress-Rogers, ed.) Boca Raton: CRC Press, pp. 333–349. WU, J., TRAGAS, C. LORD, H. and PAWLISZYN, J. (2002). Analysis of polar pesticides in water and wine samples by automated in-tube solid-phase microextraction coupled with high-performance liquid chromatography-mass spectrometry, Journal of Chromatography A, 975, 357–367. ´ DAL, P. (1999). Screen-printed amperometric immunosensor for ZERAVIK, J. and SKLA repeated use in the flow-through mode, Electroanalysis, 11 (12), 851–856.

3 Using bioassays in contaminant analysis L. A. P. Hoogenboom, State Institute for Quality Control of Agricultural Products (RIKILT), The Netherlands

3.1

Introduction

Since ancient history, humankind has relied on bioassays to determine the safety of food and environment. In medieval times, food tasters were employed to ensure that food was free of poisons. Miners used small birds to detect the possible presence of toxic gases in mining tunnels. With increasing knowledge about the responsible toxicants, improvements in analytical chemistry, combined with the need to reduce animal experiments, we now rely on chemical methods aimed at the detection of compounds by their physicochemical properties. The use of animal bioassays is more or less restricted to the testing of the safety of specific substances, thereby supported by in vitro models with mammalian and prokaryotic cells. However, even today, bioassays with mice and rats are still the only reliable way to detect paralytic and diuretic poisons in shellfish,1,2 and the neurotoxins produced by Clostridium botulinum.3 Fish assays are widely used for testing the quality of drinking water. However, despite the rapid improvements in analytical chemistry, at the same time we start to realize that these methods may no longer be sufficient to deal with the often very complex mixtures of chemicals or ever changing chemical structures of toxicants present as residues in our food chain. Furthermore, there is a strong need for rapid screening assays that can be used for extensive monitoring programmes. Bioassays with pro- or eukaryotic cells capable of detecting compounds based on their effects, offer a possible solution. For the detection of antibiotics in milk and meat, a number of different tests are used for the screening4,5 and in many cases, chemical identification of the responsible substances is no longer required. Recent advances in cell biology and in particular biotechnology have allowed the

Using bioassays in contaminant analysis 41 development of a new generation of bioassays, based on the possibilities to introduce specific properties and reporter genes into stable cellular systems. This chapter will describe this new generation of bioassays and demonstrate their advantages, especially when used in combination with sensitive analytical methods. This will be demonstrated by the experiences obtained with the socalled DR-CALUX assay, a bioassay used for the detection of dioxins. The inclusion of bioassays in modern test strategies will allow rapid screening and detection of new, possibly unknown, agonists and help to evaluate the possible health hazards involved with the presence of such compounds in the food chain.

3.2

The use of bioassays: the case of dioxins

3.2.1 Development of the DR-CALUX assay After the discovery of dioxins in the food chain, it became clear that it would be impossible to set up large monitoring programmes for this group of compounds. The major reason for this was the very expensive and laborious analytical procedure required to detect 17 different 2,3,7,8-chlorinated dibenzo-p-dioxins (PCDDs) or furans (PCDFs) at the pg/g level. This can only be achieved after extensive clean-up and by using a high resolution mass spectrometer. A set of socalled toxic equivalency factors (TEFs), ranging from 1 to 0.0001, has been developed in order to express the concentrations of each of the congeners into one figure, which represents the group as if it was only the most toxic congener 2,3,7,8-tetrachloro-dibenzo-p-dioxin (TCDD).6 Previously this was expressed as i-TEQ but during the last reevaluation of the TEFs in 1998 this was changed to WHO-TEQ or simply TEQ. Typical limits for food are the maximum residue limits of 0.75 to 3 pg TEQ/g fat set by the EU in 2002. Limits like these are required to ensure that consumers do not exceed the tolerable weekly intake (TWI) of 12 pg TEQ/kg body weight as set by the EU. It has become clear that many other substances like the planar non-ortho and mono-ortho PCBs and also some brominated polyaromatic hydrocarbons behave similar to dioxins and should be included in these limits. Actually dioxin-like PCBs are already included in the exposure limit (TWI), and will be included in the EU food limits in the near future. In response to the limited analytical capacity, bioassays with mammalian cells have been developed, initially based on the known effects of these compounds. Receptor assays have been developed based on the binding of dioxins to a specific receptor (Ah-receptor) in the cells. The so-called ERODassay measures the deethylation of ethoxy-resorufin by certain cytochrome P450 enzymes, following the binding of dioxins to the cytosolic Ah-receptor, the binding of the complex to specific sites (DREs) in the DNA and the increased transcription of the gene encoding for the enzyme. Assays based on this principle are used for determining the levels of dioxin-like compounds in environmental samples like sewage sludge7 and sediments.8 A major drawback of this system is the possible inhibition of the enzyme by many different compounds, including natural occurring substances. The specificity of the test

42

Rapid and on-line instrumentation for food quality assurance

Fig. 3.1 Principle behind the CALUX bioassay for Ah-receptor agonists. Following binding of the agonist to the Ah-receptor, the complex will be transported to the nucleus and bind to a so-called dioxin responsive element, resulting in the increased transcription of the luciferase gene and production of luciferase. Following incubation this enzyme can subsequently be measured in cell lysates by a light producing reaction.

was therefore tremendously increased by the development of a cell-line which contains the reporter gene luciferase under control of a murine DRE.9, 10, 11 In response to dioxins, this H4IIE rat hepatoma cell-line will synthesize luciferase in a dose-dependent way, which can subsequently be quantified by an enzymatic light producing reaction (Fig. 3.1). Figure 3.2 presents a typical dose-response curve, showing an increased luciferase production at concentrations as low as 0.5 pM. Since the test can be performed in 96 well-plates, a response is obtained with less than 50 fg TCDD. In principle, the amount of dioxins can be quantified by comparison of the response in the test with the calibration curve for TCDD. Several other dioxin and PCB congeners have been tested and were shown to give a response that reflects the differences in the TEF values (Fig. 3.3). However, congeners with a low TEF value showed a relatively low response in the test. This is similary true for 1,2,3,7,8-PeCDD which TEF value was recently adjusted from 0.5 to 1, and which is often a relatively important contributor to the total dioxin content. As a result the test may underestimate the total TEQ content, if calculations were based on the calibration curve for TCDD. However, in general it is evident that the bioassay obeys the TEQ principle and that the result will reflect the total TEQ content of the sample.

3.2.2 Validation for milk fat Following the succesful development of the cells, a rapid clean-up procedure for fat samples was developed, based on the use of an acid silica column. Using

Using bioassays in contaminant analysis 43

Fig. 3.2 Dose-response curve for 2,3,7,8-tetrachloro-p-dibenzodioxin (TCDD) in the CALUX bioassay. The concentration (expressed as TCDD) can subsequently be determined by comparing the response obtained with a sample extract with the calibration curve.

Fig. 3.3 Comparison of the relative response of a number of dioxins and non-ortho (#126, 169) and mono-ortho (#105, 118 and 156) PCBs in the CALUX bioassay and the TEF values established by the WHO.6 Compounds were selected based on their relative importance (contribution to total TEQ levels) in food samples. A major difference between the WHO-TEF values and the i-TEF values used in previous studies is the increase of the TEF for 1,2,3,7,8-penta-PCDD from 0.5 to 1, which is not supported by the CALUX assay.

44

Rapid and on-line instrumentation for food quality assurance

Table 3.1 Reproducibility of the CALUX assay with milk fat samples. Spiked samples were tested singly in three independent test series (adapted from 12) Sample number

1 2 3 4 5 6

GC/MS CALUX determined dioxin content* determined (pg/g) level series 1 series 2 series 3 Mean (pg i±SD TEQ/g)

CV (%)

(%)

1 3 6 9 12 15

97 4 54 10 27 11

80 110 75 83 78 87

0.0 3.5 6.9 7.1 12.3 14.6

1.0 3.2 4.6 7.2 8.1 11.8

1.3 3.2 2.1 8.3 7.8 12.8

0.8±0.7 3.3±0.2 4.5±2.4 7.5±0.7 9.4±2.5 13.1±1.4

Recovery

* CALUX determined levels were corrected for the blank sample being respectively 6.3, 2.0 and 3.5 pg/g fat for series 1, 2 and 3 respectively. In addition values were corrected for the difference between relative responses in the CALUX assay and i-TEF values.

dimethylsulphoxide as intermediate, the extracted dioxins are transfered to the tissue culture medium and subsequently added to the cells. After exposure for 24 hours the luciferase concentration in the cells is determined. The test was validated for milk fat using a number of samples spiked at 1 to 15 pg i-TEQ/g fat (1.2–17.5 pg WHO-TEQ/g) with a mix containing the 17 congeners at equal amounts.12 Table 3.1 shows the reproducibility obtained in three independent tests with these samples. Concentrations were calculated based on the TCDD calibration curve, and subsequently corrected for the 15% difference between CALUX and i-TEF values. The calculated limit of detection was around 1 pg iTEQ/g fat, explaining the high variation obtained with the lowest sample. When calculated in i-TEFs, the recovery varied between 70% and 103%. These results demonstrate the suitability of the test to screen milk fat samples. Another important conclusion from these studies was the need to include reference samples with levels around 0 and the residue limit, in order to control for possible impurities introduced with the chemicals, recovery losses and differences in TEF values. Based on this approach, dioxin-like compounds were measured in oil obtained from a large number of different fish and shellfish products. Levels up to 100 pg i-TEQ were detected but based on general agreements these should be corrected for the sometimes very low oil levels in, for example, shellfish. As shown in Fig. 3.4, a good correlation was obtained with the combined dioxin and non-ortho PCB contents in these oils, although in a few cases relatively large differences were observed. Although this might be caused by high levels of mono-ortho PCBs (not included in GC/MS measurements), it cannot be excluded that other compounds are responsible for this effect.

Using bioassays in contaminant analysis 45

Fig. 3.4 Comparison of CALUX determined dioxin levels and combined GC/MS determined levels of dioxins and non-ortho PCBs (77, 126 and 169) in fish and shellfish oil. Since oil levels vary widely in these samples, dioxin levels are normally expressed on a pg/g product base.

3.2.3 Citrus pulp incident Following the succesful validation of the test for milk fat, the bioassay was first used in the food and feed area during the Brazilian citrus pulp incident. Increasing milk levels in German cows were traced back to the use of citrus pulp that had been mixed with contaminated lime. Pulp samples of 5 g were extracted and cleaned by the same procedure as used for the milk fat. A rapid comparison between CALUX and GC/MS data showed that the assay was capable of selecting the highly contaminated samples, using a cut-off value of 5000 pg iTEQ/kg. Most samples contained levels higher than this limit and required GC/ MS confirmation. At the end of the crisis the limit was officially set at 500 pg iTEQ/kg, based on the detection limit of the GC/MS method. The test procedure was subsequently optimized and validated. Based on the consideration that an increased response is not necessarily caused by dioxins or dioxin-like PCBs, and that samples with an increased response would still have to be confirmed by GC/ MS, it was decided to switch to a screening approach. This approach is based on the comparison of the response obtained with test sample with that of a reference sample, containing 400 pg i-TEQ/kg. Table 3.2 shows the results obtained with 71 citrus pulp samples containing GC/MS determined levels between <250 and 6800 pg i-TEQ/kg. From 41 samples with a level below 500 pg i-TEQ/kg, 38 were negative and 3 (7%) showed an elevated response. From 30 samples with a level above 500 pg i-TEQ/kg, 27 (90%) showed an elevated response and no false-negatives were obtained. Three samples caused a cytotoxic effect, but

46

Rapid and on-line instrumentation for food quality assurance

Table 3.2 Evaluation of field samples citrus pulp and citrus pulp containing animal feed samples* measured by GC/MS and CALUX Content (pg i-TEQ/kg)

Total

Negative

Suspected

500 or lower 500–6800**

41 30***

38 0

3 27

* This series included ten pulp-containing animal feeds, three low and seven high samples. ** Ten samples between 500 and 2000 pg i-TEQ/kg and 20 between 2000 and 6800 pg i-TEQ/kg. *** Three samples showed a cytotoxic response and could not be evaluated.

following further fivefold dilution they showed a positive effect. This shows that the test performs extremely well even at these low residue limits.

3.2.4 Belgian dioxin incident Following the analysis of 100–1000-fold increased dioxin levels in three chicken feed, fat and egg samples in spring 1999, it soon became clear that a major food incident had happened. Due to poor traceability of the contaminated feed, many food samples became suspect and required testing. During the first month of the crisis, hundreds of samples, particularly milk fat, were screened with the bioassay. Later in the year, this was followed by feed samples due to contaminated kaolinic clay, fish meal, dried grass, and breadmeal. By the end of September, four months after the start of the crisis, almost 1400 samples had been screened (Table 3.3). Fat samples were screened by comparison with a milk fat sample containing 5 pg i-TEQ (2.7 pg i-TEQ dioxins and 2.3 pg WHOTEQ non-ortho PCBs), feed samples with a citrus pulp sample containing 400 pg i-TEQ dioxins. About 10% of these samples showed an elevated response. For various reasons only 38% of these samples was investigated by GC/MS and 55% of the samples was confirmed to contain elevated dioxin levels. Another 20% of the elevated samples could be explained by the presence of elevated levels of PCBs, which are not included in the residue limits. The remaining 25% of suspected samples could not be explained. The majority of the samples (88%) showed a negative response and were reported as such. A routine control programme selected 82 samples for GC/MS analysis. One of these samples, a

Table 3.3 Numbers of samples analysed with the CALUX bioassay in June–September 1999, including the number and fraction of GC/MS analysed negative, toxic and suspected samples. Method

Analysed

Toxic

Negative

Suspected

CALUX GC/MS % GC/MS

1380 157 11

28 28 100

1213 82 7

139 53 38

Using bioassays in contaminant analysis 47 feed sample containing kaolinic clay, was slightly above the limit, 767 versus 500 pg i-TEQ/kg. This could be explained by the lowered extractability of dioxins from the clay. Starting in 2000 the CALUX bioassay was introduced into monitoring programmes for dioxins in feed and feed ingredients in the Netherlands and in 2001 this was further extended to meat, eggs, fish, milk and other food samples.

3.2.5 Use of the CALUX assay for other types of samples Other food samples like egg, animal fat and fish oil appear to behave very similarly to milk fat and extensive validation has not been carried out thus far. The suitability was, however, demonstrated by inclusion of positive or spiked samples. The assay was validated for blood samples from wildlife species for high concentrations of dioxins and dioxin-like PCBs.13 A special clean-up procedure was developed and validated for sediment, pore water and other environmental samples, allowing the use of the assay for official testing of these type of samples.14

3.2.6 Specificity In addition to a number of dioxin and PCB congeners, several polyaromatic hydrocarbons15 as well as - and -naphtoflavone, known agonists of the Ahreceptor, were shown to give a response in the test. A similar result was observed with a number of benzimidazole drugs,16 used as fungicides and anthelmintic drugs. The latter compounds showed negative results in the EROD assay with H4IIE cells,17 confirming the insensitivity of the CALUX assay for false-negative results. A typical effect was the slightly elevated response obtained with corticosteroids, in particular dexamethasone.16 These compounds also stimulated the response obtained with TCDD and are suspected to act indirectly on the steps involved in the pathway leading to the response of the cells. This observation has consequences for the direct analysis of plasma samples, which contain varying concentrations of corticosteroids. Very typical was the strong response observed with a hexane extract of blank citrus pulp, which was not further cleaned with acid silica. Translated to TEQ levels, these probably natural compounds would amount to more than 500 ng TEQ/kg, being more than 1000-fold over the limit for dioxins. Several compounds have been reported as potent antagonists of the Ahreceptor pathway and such compounds might in theory cause false-negative effects. On the other hand such compounds could be used to investigate whether an increased signal was caused by a real Ah-receptor agonist. Resveratrol, naphtoflavone and 4-amino-3-methoxyflavone were tested but failed to show the expected effect. The latter two compounds actually caused a positive effect themselves. At present the only clear cause of false-negative effects may be compounds causing cytotoxic effects and thus prevent the cells from responding to the agonists. However, in general cytotoxic effects are clearly recognized by

48

Rapid and on-line instrumentation for food quality assurance

visual control of the cells after the exposure and a decreased response in comparison to the control. Although in principle any compound causing a positive response may be regarded as a possibly dioxin-like and therefore health-threatening compound, it may be very difficult to regulate bioactive compounds in food on this basis. Limits for food are normally the result of risk management, taking into account the use of a number of possibly conservative uncertainty factors and sometimes the ALARA (as low as reasonably achievable) principle. As a result, a level above the limit does not necessarily comprise a direct health risk. This is especially clear for certain naturally occurring substances, which would prevent the consumption of many food commodities if treated as food contaminants. Furthermore, the bioassays do not take into account factors like absorption, distribution, metabolism and excretion of compounds, which to a large extent are responsible for the toxicity of the accumulating and persistent dioxins. For this reason it is questionable whether a response in the test should be expressed in dioxin levels, or whether a sample should be regarded as suspected rather than positive when showing an elevated response in the test. However, the specificity of the test can be largely improved by the use of a selective clean-up process like the acid-silica (33% H2SO4) procedure, which is likely to destroy many compounds. Furthermore, in the case of the CALUX assay, the metabolic capacity of the cells and the use of long exposure periods may help to further improve the specificity of the test. In particular some of the polyaromatic hydrocarbons, like benzo(a)pyrene were shown to give a response in the test but only when incubated for a short period. At longer exposure times, the cells were able to metabolize both the active compound and the luciferase produced during the first hours of incubation. In practice, however, samples spiked with benzo(a)pyrene failed to show any effect even after short incubation, when purified on acid silica columns. Future investigations will have to reveal which compounds may actually interfere with the test result and whether these compounds should be included in the TEQ principle and legislation. Ideally, these bioassays should be supported by databanks with compounds that may act as agonists or antagonists.

3.2.7 Future developments Regarding the strong need for rapid and high-throughput screening tests for dioxins, it is essential that the bioassay will be evaluated in an international validation project. In addition a data bank should be established with known agonists in the test but also possible antagonists. This should include possible information on the behaviour of the compounds in the clean-up procedure. In addition, methods should be developed for the identification of unknown agonists which may be of toxicological concern.

Using bioassays in contaminant analysis 49

3.3

The use of bioassays for other contaminants

3.3.1 Estrogen assays For a number of years there has been an increased awareness of possible adverse effects due to hormonal activities (endocrine disruption) of compounds that were previously considered harmless. Many different compounds, including synthetic hormones, natural plant ingredients, pesticides and plasticizers, were shown to possess estrogenic and antiestrogenic activity. In order to test compounds for their estrogenic potency and to detect such compounds in the environment, a number of bioassays have been developed,18 initially based on the proliferating effects of estrogens on breast cell-lines.19 The use of the E-screen, a test based on the increased proliferation of MCF-7 breast tumor cells, by accident resulted in the recognition of the estrogenic activity of p-nonyl-phenol due to its introduction into plastic used for tissue culture tubes.20 The estrogenic potency was later confirmed in the more classical rodent uterotrophic assay,21 based on an increased weight gain of the uterus in immature rats or mice. The discovery of the estrogenic potency of this widely used class of plastic additives and surfactants by the E-screen demonstrates one of the advantages of the inclusion of bioassays as screening tools into monitoring programmes. Using molecular biological techniques, several reporter gene assays were developed, most of them using yeast cells.22, 23 This was achieved by introducing both the gene encoding for the human estrogen receptor, as well as the reporter gene under control of an estrogen responsive element. Yeast-based assays have been used to measure estrogen activity in environmental samples, and on bovine24 and human plasma samples.25 The latter study revealed that serum estrogen levels and as a result the endogenous production in young children may thus far have been overestimated by the use of too insensitive immunoassays. This much lower endogenous production is an important issue in the safety evaluation of estrogens in food. In analogy with the DR-CALUX assay, the ER-CALUX assay, a new cellline has been developed which produces luciferase in response to estrogens.26 Again the luc-gene under control of estrogen responsive elements (ERE) was introduced into the T47D human breast tumor cell-line, which responds to 17ßestradiol at concentrations below 1 pM (50 fg/well). This sensitivity should in theory allow the detection of estradiol in meat and blood at the pg/g levels. Thus far the test has only been used on environmental samples and requires further investigation in the food area. Similar assays have been developed using other cell-lines, like human HeLa cells.27

3.3.2 Acetyl-choline esterase inhibitors An extremely interesting area for bioassays is the group of acetylcholineesterase inhibitors, the organophosphate and carbamate pesticides. First of all this is based on the need for rapid screening tests, allowing on-site testing of fruits and vegetables. Secondly there is the awareness that the cumulative effects

50

Rapid and on-line instrumentation for food quality assurance

of these groups of compounds should be taken into account when evaluating the safe use of these pesticides.28, 29 Several enzyme inhibition assays have been developed based on the use of crude enzyme preparations and the enzymatic conversion of a colourless compound into a coloured substance. Tejada et al.30 developed an assay presented by Chiu et al.31 into an on-site test, based on the use of a fly head or pig liver extract. Filter papers were partly dipped into an acetone extract of the fruit or vegetable, subsequently sprayed with the enzyme preparation and then treated with a solution of indoxyl acetate, which upon enzymatic conversion to indoxyl turns into the blue dye indigoid. In the case of the presence of an acetylcholinesterase inhibitor, the filter half dipped into the extract will remain white. A similar assay based on purified acetylcholinesterase from electric eel or a crude homogenate of honeybee heads was developed by Hamers et al.32 and used to measure the presence of these type of compounds in water. The sensitivity for different pesticides varies with the type of enzyme extract, which may partly be explained by the need for an enzymatic activation of the compound into its active metabolite. A possible solution may be a chemical or biochemical conversion of the pesticides prior to the testing. Procedures using bromine or rat liver microsomes were developed by Barber et al.33 who used a bioassay for organophosphates based on human neuroblastoma cells. Further optimization and validation should be focused on these facts and ideally the response of different pesticides in the test should be in line with future possibly species-specific TEF values, based on dose-response curves in insects or mammalian organisms.

3.3.3 Shellfish poisons Numerous attempts have been made to replace the bioassays with rats and mice by chemical analytical or immunological assays. In general these methods are unable to detect all the different toxic compounds.34 At present over 21 saxitoxin congeners (paralytic shellfish poisoning), nine different brevitoxins (neurotoxic shellfish poisoning), eight congeners of okadaic acid (diarethic shellfish poisoning) and seven congeners of domoic acid (amnesic shellfish poisoning) have been identified.35 The first two types of compounds are capable of binding to specific sites on the voltage-dependent sodium channel, in the first case resulting in a blockade of neuronal activity, in the second case in a persistent activation. The latter effect is also observed with the Ciguatera toxins, which are structurally related to the brevitoxins. DSPs are inhibitors of ser/thr protein phosphatases and exposure is thought to result in hyperphosphorylation of ion channel proteins in the GI tract, thereby causing impaired water balance and loss of fluids. ASPs are capable of binding to glutamate receptors, resulting in activation of voltage-dependent calcium channels, elevated intracellular calcium levels and eventually neuronal cell death. Regarding the large number of different active compounds, bioassays based on, for example, the use of mouse neuroblastoma cells might be more promising than the existing mouse bioassays and chemical analytical methods. One of these assays, initially developed for

Using bioassays in contaminant analysis 51 sodium channel blocking agents (PSP) is based on the reduced cytotoxicity of ouabain and veratridine, which in the presence of, for example, saxitoxins are no longer able to cause the toxic sodium influx in the mouse neuroblastoma cells used in the test. 36, 37, 38 A similar test, using cytotoxicity in mouse neuroblastoma cells, was developed for tetrodotoxins.39 A shipable kit for PSP called MIST (Maritime In Vitro Shellfish Test) has been developed and is also based on the same principles. Fairey et al.40 transformed the neuroblastoma cell test into a reporter gene assay by introducing a c-fos-luciferase construct into the cells. In addition to brevitoxin the test was sensitive to ciguatoxins but also to saxitoxins. The latter was achieved by treating the cells with brevitoxin, following treatment with saxitoxins. By blocking the ion channels, the latter compounds reduced the increase in luciferase production caused by brevitoxin. Domoic acid did not show any effect in the test, demonstrating that these cells do not possess the receptors for this group of compounds. Since the mechanism underlying DSP is quite different, assays based on competition for the active site of protein phosphatase have been developed.41, 42

3.4

Future trends

The increasing knowledge on the effects of compounds on signal transduction pathways in cells, including the transcription of specific genes, should lead to the development of many new bioassays. New high throughput techniques may eventually allow rapid screening of many samples for many different types of activities in a relatively short period of time. This will not be possible without the development of rapid but specific clean-up procedures which at present are the rate-limiting step. Areas that are of great interest include hormonal effects like ßagonist and corticosteroid activity, but also neurotoxic effects. Another major area is the shellfish poisons where at present bioassays with animals are still required.

3.5

Acknowledgements

Transfected H4IIE cells were developed by the Department of Toxicology at the Agricultural University in Wageningen, The Netherlands, in cooperation with the University of California in Davis, USA. At present these cells are commercially available from Biodetection Systems (BDS) in Amsterdam, The Netherlands.

3.6 1.

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PCDDs, PCDFs for humans and wildlife’, Environm Health Persp 1998 106 775– 92. ¨ M B, NA ¨ F C and HJELM K, ‘Levels of dioxin-like ENGWALL M, BRUNSTRO compounds in sewage sludge determined with a bioassay based on EROD induction in chicken embryo liver cultures’, Chemosphere 1999 38 2327–43. GALE R W, LONG E R, SCHWARTZ T R and TILLETT D E, ‘Evaluation of planar halogenated and polycyclic aromatic hydrocarbons in estuarine sediments using ethoxyresorufin-O-deethylase induction of H4IIE cells’, Environm Toxic Chem 2000 19 1348–59. AARTS J M M J G, DENISON M S, DE HAAN L H J, SCHALK J A C, COX M A and BROUWER A, ‘Ah receptor-mediated luciferase expression: a tool for monitoring dioxin-like toxicity’, Organohal Comp 1993 13 361–4. AARTS J M M J G, DENISON M S, COX M A, SCHALK A C, GARRISON P A, TULLIS K, DE HAAN L H J and BROUWER A, ‘Species-specific antagonism of Ah receptor

action by 2,2’,5,5’-tetrachloro- and 2,2’,3,3’,4,4’-hexachlorobiphenyl’, Eur J Pharm Environ Tox 1995 293 463–74. SANDERSON J T, AARTS J M M J G , BROUWER A, FROESE K L , DENISON M S and GIESY J P, ‘Comparison of Ah-receptor-mediated luciferase and ethoxyresorufin O-deethylase induction in H4IIE cells: implications for their use as bioanalytical tools for the detection of polyhalogenated aromatic compounds’, Toxicol Appl Pharmacol 1996 137 316–25.

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BOVEE T F H, HOOGENBOOM L A P, HAMERS A R M, AARTS J M M J G, BROUWER A and KUIPER H A, ‘Validation and use of the CALUX-bioassay for the detection

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MURK A J, LEONARDS P E G, BULDER A S, JONAS A S, ROZEMEIER M J C, DENISON M S, KOEMAN J H and BROUWER A, ‘The CALUX (Chemical-Activated

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Luciferase gene Expression) assay adapted and validated for measuring TCDD equivalents in blood plasma’, Environ Toxic Chem 1997 16 1583–7. MURK A J, LEGLER J, DENISON M S, GIESY J P, VAN DE GUCHTE C and BROUWER A, ‘Chemical-Activated Luciferase gene Expression (CALUX): a novel in vitro bioassay for Ah-receptor active compounds in sediments and pore water’, Fund Appl Toxic 1996 33 149–60.

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‘Biological screening of Ah receptor agonist activity in butter fat and coconut oil by means of chemical-activated luciferase expression in a genetically engineered cell line (CALUX)’, Organohal Comp 1996 27 303–7. HOOGENBOOM L A P, HAMERS A R M and BOVEE T F H, ‘Bioassays for the detection of growth-promoting agents, veterinary drugs and environmental contaminants in food’, The Analyst 1999 124 79–85. HOOGENBOOM L A P and HAMERS A R M, ‘Effects of oxfendazole on the Ah receptor-mediated induction of ethoxyresorufin-O-deethylase and luciferase activity by 2,3,7,8–tetrachlorodibenzo-p-dioxin in Hepa-1c1c7 and H4IIE celllines’, Organohal Comp 1995 25 53–6. ANDERSEN H R, ANDERSSON A-M, ARNOLD S F, AUTRUP H, BARFOED M, BERESFORD N A, BJERREGAARD P, CHRISTIANSEN L B, GISSEL B, HUMMEL R, BONEFELD JØRGENSEN E, KORSGAARD B, LE GUEVEL R, LEFFERS H, MCLACLAN J, MØLLER A, NIELSEN J B, OLEA N, OLES-KARASKO A, PAKDEL F, PEDERSEN K L, PEREZ P, SKAKKEBÆK N E, SONNENSCHEIN C, SOTO A M, SUMPTER J P, THORPE S M and GRANDJEAN P, ‘Comparison of short-term

estrogenicity tests for identification of hormone-disrupting chemicals’, Environ Health Persp 1999 107 (suppl. 1) 89–108. SOTO A M, SONNENSCEIN C, CHUNG K L, FERNANDEZ M F, OLEA N and OLEASERRANO F, ‘The E-screen assay as a tool to identify estrogens: an update on estrogenic environmental pollutants’, Environm Health Persp 1995 103 (suppl. 7) 113–22. SOTO A M, JUSTICIA H, WRAY J W and SONNENSCEIN C, ‘p-Nonyl-phenol: an estrogenic xenobiotic released from ‘‘modified’’ polysterene’, Environm Health Perp 1991 92 167–73. ODUM J, LEFEVRE P A, TITTENSOR S, PATON D, ROUTLEDGE E J, BERESFORD N A, SUMPTER J P and ASHBY J, ‘The rodent uterotrophic assay: critical protocol

features, studies with nonyl phenols, and comparison with a yeast estrogenic assay’, Regul Toxic Pharmacol 1997 25 176–88. PHAM T A, HWUNG Y P, SANTISO M D, MCDONNELL D P and O’MALLEY B W, ‘Ligand-dependent and independent function of the transactivation regions of the human estrogen receptor in yeast’, Mol Endocr 1992 6 1043–50. ROUTHLEDGE E J and SUMPTER J P, ‘Structural features of alkylphenolic chemicals associated with estrogenic activity’, J Biol Chem 1997 272 3280–8. BURDGE G C, COLDHAM N G, DAVE H, SAUER M J and BLEACH E C L, ‘Determination of oestrogen concentrations in bovine plasma by a recombinant oestrogen receptor-reporter gene yeast bioassay’, Analyst, 1998 123 2585–8. KLEIN K O, BARON J, COLLI M J, MCDONNELL D P and CUTLER G B, ‘Estrogen levels in childhood determined by an ultrasensitive recombinant cell bioassay’, J Clin Invest 1994 94 2475–80. LEGLER J, VAN DEN BRINK C E, BROUWER A, MURK A J, VAN DER SAAG P T, VETHAAK A D and VAN DER BURG B, ‘Development of a stably transfected

estrogen receptor-mediated luciferase reporter gene assay in the human T47D breast cancer cell line’, Toxicol Sci 1999 48 55–66. BALAGUER P, JOYEAX A, DENISON M S, VINCENT R, GILLESBY B E and ZACHEREWSKI T, ‘Assessing the estrogenic and dioxin-like activities of chemicals and complex mixtures using in vitro recombinant receptor-reporter gene assays’,

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4 The rapid detection of pesticides in food R. Luxton and J. Hart, University of the West of England, UK

4.1

Introduction

In today’s modern world many different chemicals are used to protect food and our environment from spoilage by a range of pests such as rodents, weeds, insects and fungi. This has a great positive economic value by increasing the yield in the food supply chain. Despite having great benefit to society, the very nature of their use means that pesticides are highly toxic to humans and measures must be taken to prevent accidental exposure, whether from occupational exposure or more covertly via the food supply chain itself. A wide range of compounds are used as pesticides such as the chlorinated hydrocarbons, which have been shown to be highly toxic and may have longlasting effects on the environment. Research has shown that dichlorodiphenyltrichloroethane (DDT) has had a devastating effect on parts of the food chain. Another important group of pesticides is the organophosphate compounds, which are safer than chlorinated hydrocarbons but are still highly toxic. It is thought that the safest pesticides are those derived from plants, such as pyrethrum, but a disadvantage in using these compounds is that they require more frequent application. For the purpose of this chapter, further discussion will be focused on the detection and measurement of organophosphate compounds, although much of the discussion could be applied to other types of pesticides such as those mentioned above. Organophosphates (OPs) are small molecules derived from phosphoric acid with the oxygen atoms being either replaced by other atoms, for example sulphur, and/or linked to aliphatic, aromatic, anhydrides or heterocyclic groups. Table 4.1 lists the more important categories of OP compounds with their particular side chains, and Fig. 4.1 shows the structure of three common OPs.

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Table 4.1

Main side groups on different classes of organophosphate compounds

Class of organophosphorus

X

Y

Z

Phosphate Phosphorothionate Phosphorothiolate Phosphorodithionothiolate Phosphorodithiolate Phosphoramidate Phosphordiamidate Phosphoramidothionate Phosphoramidothiolate Phosphonate Phosphonothionate Phosphonothionothiolate

ÿO ÿO ÿS ÿS ÿS ˆN ˆN ˆN ˆN C C C

ÿO ÿO ÿO ÿO ÿS ÿO ˆN ÿO ÿS ÿO ÿO ÿS

O S O S O O O S O O S S

Fig. 4.1

Three examples of organophosphates.

The rapid detection of pesticides in food

57

For pesticide applications the sulphur containing compounds are more widely used than the other derivatives. However, all types of OPs are highly toxic to mammals, to differing extents; some are considered ‘relatively’ safe, such as malathion and dimethoate. OP compounds exert their toxic effects by their propensity to inhibit a number of important enzymes in particular the enzyme acetylcholine esterase. This enzyme is important in the inactivation of the fastacting neurotransmitter acetylcholine found in the nerve synapses of the neuromuscular junction and brain nicotinic junctions. The inherent toxicity of OP compounds has been exploited by various nations in the production of chemical warfare nerve gas agents, such as sarin and tabun. Accidental occupational exposure to agricultural OP results in similar symptoms as being exposed to OP nerve gas agents. Symptoms include: nausea, vomiting, cramps, headache, dizziness, blurred vision, muscle twitches, difficulty in breathing, convulsions, respiratory paralysis and death The widest application for OPs has been their use as an insecticide, although they are also used as nematocides, helminthicides and have fungicidal and herbicidal properties. Due to the inherent toxicity of organophosphates, there is strict control over their use, particularly in their application to foodstuffs, which is supported by legislation. In many countries, the use of the most harmful compounds is banned but illegal application can still be a problem. From the results of a number of studies it has been estimated that, worldwide, pesticides are responsible for 10,000 deaths a year. The problems are associated with overapplication to crops and spray drift with subsequent contamination of surrounding areas. To prevent harmful effects to the population in general, the use of agricultural pesticides is strictly regulated, and tables have been produced detailing the maximum permissible level of OP residue, known as the maximum residue limit (MRL) measured in ppm. MRL levels are set for different pesticides and different crops and additional variation is also seen between the different regulating authorities. In addition, only certain OPs are licensed, with many OPs being banned. Table 4.2 gives examples of MRLs for three different OP compounds and three different foods. Tenfold differences, or more, in the MRLs for particular crops are not uncommon. For health and litigation considerations it is necessary to monitor the use of pesticides applied to crops, as the pesticide residues may find their way into the food chain. The technology available for measuring pesticide residues is becoming much more sophisticated and sensitive, and consequently MRL levels are now being set at much lower levels. The sophisticated analytical techniques used within laboratories tend to be expensive, relatively time consuming and require a sample of the foodstuff that is then transported to the laboratory where skilled personnel perform the analysis. Increasingly, there is a need for inexpensive, rapid tests to detect and measure levels of pesticides at, and below, ever reducing MRLs on raw food, which can be used on site by unskilled operatives. These new rapid tests may act as a preliminary screen giving assurance that there is no pesticide residue present on the food being tested, with a positive test being verified by traditional analytical techniques. In the near

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Table 4.2 Maximum residue limit for three different crops and three different OP compounds Organophosphate compound

Crop

Maximum residual limit (ppm)

Chlorpyrifos Chlorpyrifos Chlorpyrifos Chlorpyrifos-methyl Chlorpyrifos-methyl Chlorpyrifos-methyl Dichlorvos Dichlorvos Dichlorvos

Apples Bananas Cucumbers Apples Bananas Cucumbers Apples Bananas Cucumbers

0.50 3.00 0.05 0.50 0.05 0.05 0.10 0.10 0.50

future, as new rapid tests become more reliable, and are validated against ‘gold standard’ methods, the rapid test could replace expensive analytical technology.

4.2

Detecting pesticides: physicochemical methods

Traditionally, OPs have been measured by exploiting their chemical and physicochemical properties using a separation technique such as chromatography or electrophoresis. The spectral characteristics of pesticide residues have also been used in NMR techniques and mass spectroscopy to aid identification and measurement. In recent years, other approaches using the biochemical and immunological properties of pesticide residues have been developed and are now widely used; these are the immunoassay and enzyme inhibition techniques. In order to detect and measure pesticide residues at and below current MRL levels any method of analysis should demonstrate appropriate selectivity and sensitivity. For example, many chemical reactions are only specific for groups of compounds, and do not show selectivity, but when combined with separation techniques, individual compounds can be identified; an example of this approach is thin layer chromatography. Conventional analysis of pesticide compounds is dominated by techniques employing a separation stage. This group of methods achieves selectivity by separating a mixture into individual components that are then identified by comparing the separation to pure standards. These approaches have the advantage that they can measure more than one compound simultaneously. Thin layer chromatography (TLC) and high performance thin layer chromatography (HPTLC) separate compounds according to their polarity and differential adsorption to silica gel. Visualisation of the separated OP compounds can be with direct ultraviolet irradiation or a chemical reaction to produce a coloured spot. These methods, although allowing a number of samples to be analysed simultaneously, are simple and relatively quick; however, they are only qualitative and involve the use of solvents. Recent studies have shown

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59

that quantitation can be achieved by measuring the density of the spots with detection limits being recorded from 0.05 g to 1.0 g of pesticide residue applied. These constraints limit the use of these methods to an analytical laboratory where skilled personnel perform them. Quantification is more traditionally achieved using gas chromatography (GC) or high performance liquid chromatography (HPLC). In GC, the sample is heated to volatilise the OPs which are carried through a column by a flowing inert gas and separated by differential adsorption to a solid phase in the column. Newer instruments use a capillary column where adsorption takes place on the capillary wall rather than packing in the column, which leads to a faster separation and greater sensitivity. In some instances, called gas liquid chromatography (GLC), the solid phase may be covered with ‘waxy’ liquid to promote greater separation. OPs are measured as they come off the column by a thermionic emission or alkali-flame detector. Some OPs decompose at elevated temperatures resulting in misleading results. The technique is sensitive and relatively quick but uses expensive equipment that must have a gas supply, so is limited to laboratory use. HPLC does not have the disadvantage of thermal degradation of the sample and is perhaps a preferred method for OP analysis. In this technique the sample is injected into a flowing solvent and is carried through a column containing a solid phase. Again, separation is due to differential adsorption to the solid phase and is determined to some extent by differing polarities of the OP compound in the sample. Detection is by UV absorption or refractive index change. As with the previous techniques described there are a number of limitations on its application; the equipment is expensive, uses solvents and requires trained operators and as such is limited to laboratory use. Where identification is required, this can be achieved using spectral methods such as NMR and mass spectroscopy. NMR allows identification of a single pesticide residue whereas mass spectroscopy, when interfaced to either GC or HPLC, can identify a number of different pesticide residues. GC-MS is considered to be the gold standard for pesticide measurement and identification. Extremely low detection limits can be reached using tandem-mass spectroscopy with examples of 1300 ppt for dichlorvos and 0.1 ppt for trifluralin being quoted. These techniques are highly specialised, expensive and limited to laboratories where trained personnel perform the analysis. As indicated in the above discussion, separation techniques do not lend themselves to rapid analysis times that are required for use in the field. Generally they rely on the use of expensive instrumentation, skilled personnel and are not easily transported.

4.3

Detecting pesticides: biological methods

These methods differ from the techniques described in the previous section as they depend on the interaction between a biological molecule and the pesticide

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residue. This interaction may be specific for a particular pesticide as in the interaction with an antibody, or non-specific as in the way a number of different pesticides interfere with an enzyme reaction.

4.3.1 Antibody methods Antibodies are biological molecules formed as a part of a host response to foreign substances or microorganisms, for example toxins, viruses or bacteria. The substance to which an antibody is formed is called an antigen. Antibodies bind very specifically to the antigen they are directed against. Thus, methods that use antibodies are generally very specific for a particular pesticide but similar molecules may show some cross-reactivity. Antibodies are produced from animal cells either in a live animal or a cell culture. In both cases an immune response has to be initiated to start cells of the immune system synthesising specific antibody. In order to trigger the immune system to produce an antibody the antigen involved must be a large, complex molecule. Small molecules such as OP compounds do not generally trigger antibody production on their own. In order for small molecules to be recognised by the immune system and start antibody production, they have to be conjugated to a larger molecule such as a protein. Antibodies are used in a group of techniques collectively called immunoassays. Here the antibody binds to a specific pesticide which it has been designed to recognise and forms an immune complex consisting of the antibody molecule binding with the pesticide residue. The higher the concentration of pesticide in the sample the more immune-complex formed. The immunoassay measures the amount of immune-complex formed and relates this to pesticide concentration. As pesticides are small molecules, the immunoassay is designed to be a competitive technique where the pesticide in the sample is mixed with a fixed amount of labelled pesticide and then competes with it for a limited number of antibody binding sites. After an incubation period, the antibody has reacted to both the sample pesticide and the labelled pesticide. In order to make the measurement the unreacted label has to be removed leaving only the label associated with the antibody. In this competitive system, as the sample concentration increases the greater numbers of pesticide molecules from the sample will occupy more and more binding sites on the antibody. As a result there is less labelled pesticide in the antibody-binding sites. This gives rise to an inverse dose response curve with a high signal being seen with a low concentration of pesticide. Commonly the labels used in an immunoassay are enzyme labels, a fluorescent molecule, or sometimes a radioactive label. Enzyme labels can be used to generate a coloured product; a fluorescent product or an electroactive compound. The range of different end points of the immunoassay gives rise to a number of different measurement technologies that can be employed to detect the immune reaction. Immunoassays are often performed in test tubes, 96 well plates and more recently by using lateral flow devices such as those used in pregnancy tests.

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These lateral flow devices give a rapid answer, are convenient and can be easily used by non-skilled personnel in the field, but are only semi-quantitative and limited in sensitivity, thus only useful as a screening test. A positive result is seen as the absence or presence of a coloured line, depending on how the test has been devised. Recently equipment has been developed to measure the intensity of the coloured line making the test more quantifiable.

4.3.2 Enzyme methods These methods rely on the fact that OP compounds inhibit the biological activity of particular enzymes preventing them forming their products from given substrates, in other words the enzyme is poisoned. As different OPs will inhibit the enzymes, these methods are not specific for a particular OP as are the antibody methods, but give an indication of total OP concentration. The most commonly used enzyme used in these methods is acetylcholine esterase (AChE) although butyrylcholine-esterase, organophosphorus hydrolase and ascorbate oxidase have been used. The principle behind enzyme methods is that the organophosphate enters the active site of the enzyme and binds to the protein structure through a serinehydroxyl group. This organophosphate binds strongly and is not released from the active site for many hours, in effect inactivating the enzyme. The natural substrate, acetylcholine, binds through the same serine-hydroxyl group. The natural substrate is cleaved by the enzyme, releasing choline and at the same time acetylating the serine-hydroxyl group. After only a few milliseconds the acetyl group is released returning the enzyme to its native state. The amount of organophosphate that is required to inhibit enzyme activity by 50% is called the IC50 (inhibitory concentration-50%). It should be noted that different organophosphates have different IC50 values depending on both the particular organophosphate and the source of AChE. This is due to the particular side groups on the organophosphate causing steric hindrance and preventing the molecule entering the active site fully or at all. Secondly, AChE from different sources has an active site of differing sizes. Those enzymes possessing a small active site are not inhibited by larger organophosphates but only by smaller organophosphates. Conversely enzymes with large active sites are also inhibited by larger organophosphates. For example frogs, which tend to be resistant to acute organophosphate poisoning, have an AChE that has a smaller active site, and shows greater enzyme activity with acetylcholine compared with propionylcholine, a larger molecule. Conversely, in chickens, which are sensitive to acute organophosphate poisoning, AChE has a larger active site, and shows greater activity for propionylcholine compared to acetylcholine. In addition to the size of the active site, susceptibility of a particular AChE to poisoning by organophosphate also depends on the hydrophobicity and electrophilicity of that organophosphate and the nucleophilic strength of the serine residue within the active site. For example, trout AChE shows greater inhibition of enzyme activity as the acidity of the phosphorus atom increases. In

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other types of AChE, such as from monkeys or rats, it is the nucleophilic strength of the active site that is more important in determining susceptibility of that enzyme to the organophosphate. The enzyme assay depends on measuring the activity of the enzyme in the absence and presence of the sample. If organophosphate residues are present then there will be a decrease in enzyme activity noted. Enzyme activity is measured by monitoring the disappearance of substrate or the accumulation of product. This can be linked to a chemical reaction that produces a colour and the change in colour monitored. In developing new and rapid detection methods for the detection and measurement of pesticides it is the biological technologies that have been exploited. In particular it has been the development of biosensor technology where the greatest advances have been made. Very sensitive instruments can be constructed to be light, portable, easy-to-use, inexpensive and can be operated by untrained personnel.

4.4

The principles of biosensors

Biosensors are analytical devices that use a biological molecule to interact with the analyte in question to produce a measurable output. Figure 4.2 shows a schematic of a biosensor device. The discussion below examines the parts of the biosensor that form the sensing element and briefly reviews the approaches that have been used in developing biosensors for pesticide analysis. The unique feature of a biosensor is the biological layer, which is integral to the device and interacts with the analyte. The biological molecule is important for giving the device specificity and selectivity. Many different types of biological molecules exhibit selective or specific binding as part of their biological function. These include antibody molecules, enzymes, receptor molecules and lectins. In addition to these protein molecules, specific binding is also seen between complementary strands of nucleic acids. Nucleic acids are used to detect DNA from microbiological samples to detect bacteria or viruses. The great majority of biosensors for other analytes, including pesticides, use a protein molecule in the sensing element. The biological molecules employed in the sensing element are immobilised on the surface of the transducer to form the sensing surface of the biosensor. Many different approaches have been employed to capture and hold biological molecules depending on the nature of the transducer surface and the biological molecule. These methods fall into three categories: • adsorption type methods • chemical coupling • biological coupling. The simplest of these is adsorption of the biological molecule to the transducer surface through the formation of non-covalent chemical bonds, such as

The rapid detection of pesticides in food

Fig. 4.2

63

Schematic diagram of a biosensor.

electrostatic and hydrophobic bonds. Electrostatic bonds can be formed between charges on the transducer surface and charged groups on the protein whereas hydrophobic bonds are formed between hydrophobic surfaces and hydrophobic domains of proteins. This type of immobilisation is simple and does not require any chemical reactions but has the disadvantage that the biological molecules are randomly orientated on the transducer surface. A proportion of the molecules will have parts of the molecule containing the reactive site for the analyte forming non-covalent bonds with the transducer surface and the reactive site will not be available to the analyte, leading to a loss of sensitivity; this is particularly true of antibodies. The other potentially major drawback of this type of immobilisation is that biological molecules can be lost from the surface during incubation and wash stages of the assay, again leading to loss of sensitivity. Covalent coupling is achieved through chemical reactions between reactive groups on the surface of the transducer and the protein molecule. The principal groups used to cross-link proteins to a surface are amine, carboxyl and sulphydryl groups. A wide range of coupling chemistries using cross-linking agents is available for use with different reactive groups. The coupling or crosslinking agents can be broadly divided into those with homofunctional or heterofunctional activity. Homofunctional agents have the same reactive group at either end of the molecule and react with the same type of group on the

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transducer surface and protein molecule, such as an amino group. Glutaraldehyde is a good example of a homofunctional cross-linking agent. Heterofunctional agents have a different reactive site on either end of the cross-linking molecule and therefore can react with different reactive groups on the transducer surface and the protein, for example an amine group being coupled with a hydroxyl group. The advantage of chemically coupled biological molecules is the fact that they are not lost from the transducer surface during the assay and are necessary if a reusable biosensor is being developed. A potential disadvantage of chemical coupling is that if the chemistry could inactivate a percentage of the biological molecules thus reducing sensitivity. This depends somewhat on the harshness of the chemical reaction used. The advantage of covalent cross-linking is that biological molecules can be orientated on the transducer surface to present the reactive part of the molecule to the analyte, allowing greater sensitivity. This is particularly important in the orientation of antibodies on the transducer surface, where to gain maximum sensitivity the antigen-binding site should be orientated towards the sample. As with covalent cross-linking biological coupling also ensures the correct orientation of the biological molecule interacting with the analyte. These methods are usually employed with antibody coated biosensors and use another protein that binds to an antibody by the non-specific Fc portion of the antibody. This leaves the antigen specific, antigen binding sites in the correct orientation to interact with the antigen. The interaction between the biosensor and the analyte can be broadly grouped into three modes of action. Figure 4.3 shows these different modes of action. In the first mode of action, the direct mode (Fig. 4.3(a)), the analyte interacts directly with the biological layer on the surface of the transducer to produce a signal. Here, it is the analyte itself interacting with the biological layer that generates the change in signal measured by the transducer. The second mode (Fig. 4.3(b)) of action involves competition between analyte and a labelled species for binding sites on the transducer surface. It is the label that is detected by the transducer. This competitive mode is a form of indirect detection, and commonly involves a fluorescent label or an enzyme label that produces the fluorescent or electroactive product. A third type of interaction is where the analyte binds to the biological layer on the transducer surface and causes a change in the biological activity or function (Fig. 4.3(c)). A good example of this is seen where enzyme is immobilised on the transducer surface, the reaction of pesticide to the biosensor inactivates the enzyme changing its biological activity. The role of the transducer in a biosensor is to generate a measurable signal when the analyte interacts with the biological molecule associated with the transducer surface. The two common forms of transducers used for pesticide analysis are optical transducers and electrochemical transducers. Optical transducers generate a signal measured as a light intensity proportional to the concentration of pesticide in the sample; this may be an inverse relationship. Electrochemical transducers generate a current or voltage in proportion to the pesticide being measured; again this may be an inverse relationship.

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Fig. 4.3

65

Three modes of action for a biosensor determining pesticides: (a) direct mode, (b) competitive mode, (c) functional mode.

4.4.1 Optical biosensors Optical transducers used in biosensors utilise the evanescent wave effect. The evanescent wave may interact directly with molecules on the surface of the transducer bringing about a change in signal, this is the principle behind surface

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plasmon resonance technologies. These devices measure a very small change in the refractive index at the surface of the transducer. As antigen binds to antibody immobilised to the transducer surface there is a mass change which in turn brings about a change in the refractive index measured. The greater the mass of the component binding to the immobilised biological layer the greater the signal generated. This has important implications when trying to detect small molecules using an antibody immobilised on the surface of the transducer. When small molecules bind there is only a small change in the mass on the surface, generating only a small signal. So for the detection and measurement of small molecules, such as pesticides, surface plasmon resonance type technologies can suffer from a lack of sensitivity. Typical detection limits quoted in the literature range from 0.05 to 5.0 l/l, two specific examples for pesticide residues are atrazine and simazine, which have detection limits of 1.0 l/l and 0.1 l/l respectively. Instrumentation used for surface plasmon measurement is often large and not particularly portable and can be very expensive. New developments in this technology have seen surface plasmon resonance devices that utilise a capillary or fibre-optic rod that can be dipped manually into the sample. Using this type of technology a rapid handheld device is easily constructed, but is still expensive. Optical sensors suffer from the problem of non-specific binding, any interaction on the surface results in a change of the measured signal, so there is an issue of specificity. With high affinity antibodies immobilised on the transducer surface and for use of good blocking chemistry, non-specific interaction should be minimised. In another type of optical transducer, the evanescent wave interacts with a fluorescent marker or label mixed with the sample as seen in Fig. 4.3(b). A fluorescently labelled antigen competes with antigen from the sample for antibody binding sites at the surface of the transducer. The evanescent wave penetrates into the sample interacting with the fluorescent label which absorbs light and emits its fluorescent signal which enters the wave guide and is measured. When high concentrations of antigen in the sample are found, only small amounts of labelled antigen can bind to the antibody, generating a small signal. Conversely, with a low concentration of antigen in the sample, greater numbers of antibody binding sides are occupied with fluorescently labelled antigen, giving rise to a larger signal.

4.4.2 Electrochemical biosensors In recent years, there has been increasing interest in the construction and operation of organophosphate pesticide biosensors based on electrochemical transducers. One of the most common approaches has involved the use of acetylcholinesterase (AChE) as the biological recognition element, which has been integrated with a variety of carbon electrodes as transducers. Hart and coworkers have been investigating OP biosensors based on screen-printed carbon electrodes (SPCEs) which contain cobalt phthalocyanine (CoPC) as an electrocatalyst. In one approach, AChE (from electric eel) was immobilised

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Fig. 4.4 Diagram to show the reactions taking place on the surface of a screen-printed carbon electrode (SPCE) biosensor using acetylcholinesterase as the biological layer. In the absence of an OP, acetyliocholine is enzymatically converted to thiocholine as it passes through the AChE layer; this species then chemically reduces the central cobalt ion, which is in the +2 state, to the +1 state. This latter ion is re-oxidised at the SPCE back to the +2 state by loss of an electron, and this current constitutes the analytical response. In the presence of an OP, the enzymatic conversion of acetylthiocholine to thiocholine is inhibited which results in less thiocholine being produced; consequently, the current is attenuated and this decrease is proportional to the OP concentration.

onto the CoPC-SPCE by simply drop coating a solution of this enzyme onto its surface, followed by a solution containing the cross-linking agent glutaraldehyde. Figure 4.4 shows a schematic diagram of the biosensor and the various reactions taking place during its operation. Studies by Hart and co-workers have focused on optimisation of the OP biosensor for operation in several different modes. The first mode involved amperometry in stirred solution and two approaches were investigated. Initial studies were performed by transferring an aliquot of phosphate buffer pH 7.4 into a voltammetric cell at 37ºC; the biosensor was then immersed in the solution and the potential applied. After nine minutes, 50 l of acetylcholine was added to initiate the enzymatic reaction, giving a final concentration of 0.5 mM. When a steady state signal was achieved, the reaction was allowed to proceed for a further nine minutes before the addition of pesticides. Initial rates of decrease in current were measured and this data was used in the construction of calibration plots. It was found that plots of initial rates of current decrease vs log concentration of paraoxon were linear between 3.24  10ÿ7 M and 3.24  10ÿ6 M, the former representing the detection limit. Similarly for dichlorvos, the linear range was from 1.7  10ÿ6 M to 1.4  10ÿ5 M, the former representing the detection limit. The second method utilising amperometry in stirred solution was performed by placing an OP biosensor into a solution containing buffer only, switching the cell on, then adding pesticide after three minutes; after a further ten minutes, acetylthiocholine was added and the resulting currents allowed to reach steady state (iss). The inhibition was calculated by measuring the difference between the

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iss values in the absence, and presence, of pesticide and representing this difference as a percentage of the former value. Calibration plots were constructed by plotting percentage inhibition vs log pesticide concentration; in the case of dichlorvos the plot was linear from 7.1  10ÿ7 to 5.6  10ÿ6 M, the former representing the limit of detection. It should be mentioned that the sensitivity of this second approach could be improved by simply increasing the incubation time with pesticide before addition of the substrate. The second mode of operation of the OP biosensor, investigated by Hart and co-workers, involved flow-injection analysis with amperometric detection. The biosensor was incorporated into a thin-layer flow cell and mobile phase allowed to flow over the surface at a rate of 1 ml minÿ1. Pesticide determinations were carried out in three stages. First the amperometric response was recorded when a 20 l aliquot of 1 mM acetylthiocholine was injected into the system. Next, the sample stream containing an OP was directed through the flow cell; thirdly, the flow was switched back to buffer only and the current measured after making an injection of 20 l of substrate. The concentration of pesticide was determined from any decrease in the biosensor response. The detection limits obtained with an enzyme loading of 1.0 U per sensor were 6  10ÿ9 M and 7  10ÿ11 M for dichlorvos and paraoxon, respectively; with an enzyme loading of 0.05 U per sensor, a detection limit of 4.0  10ÿ11 M was achieved for paraoxon. The use of a flow cell, in conjunction with amperometry, does seem to offer certain advantages, perhaps the most important being the possibility to produce a fully automated system. Further research is under way to develop an array of biosensors based on this technology for the identification and quantification of multiple OPs in a single food sample. In this case, mutations of AChE from drosophila are being investigated as the biorecognition elements of the proposed array; these are immobilised onto the SPCE array and interrogated using chronoamperometry. The goal of this research is to develop a fully automated system to determine OPs in a variety of raw food produce. An alternative pesticide biorecognition system, for use with electrochemical transducers, has been developed by Wang and co-workers. The enzyme organophosphorus hydrolase (OPH) is reported to have broad substrate specificity and is able to hydrolyse a number of pesticides including parathion, methyl parathion, fenitrothion and paraoxon. In these cases, the enzyme catalyses hydrolysis of the OP compounds to generate p-nitrophenol, which is electroactive. Consequently biosensors could be constructed which were based on the direct oxidation of p-nitrophenol, and the magnitude of the response is directly proportional to the concentration of the pesticide. These workers constructed a remote OP biosensor by incorporating the device into a PVC housing tube attached to a 16 m long shielded cable via three-pin environmentally sealed rubber connections; Ag/AgCl reference electrode and platinum counter electrode completed the cell system. The biosensor was operated in the chronoamperometric mode by stepping from open circuit to +0.85 V vs Ag/AgCl. The response was found to be linear in the range 4.6– 46 M for paraoxon and up to 5 M for methyl parathion; the limits of detection

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69

for these two pesticides were 0.9 M and 0.4 M respectively. It was reported that an advantage of this system is that the biosensors are reusable. However, they do not yet appear to possess the sensitivity achieved with the AChE based systems.

4.5

Developing low-cost biosensors

Biosensors are the ideal technology for developing rapid low-cost devices for measuring pesticides on the site of food production or food intake, alleviating the need to send a sample to a specialist laboratory. The sensor elements on which the biological layer is incorporated are small and can be built into a robust housing incorporated into the portable device. Ideally the measurement time will only be a few minutes, the incorporated electronics take the signal from the transducer, process the signal and present a result to the operator. The device should be easy to use enabling unskilled operators to make measurements. But the biggest factor in determining whether any such device will be a commercial success is the cost of the analysis. Factors that influence the cost of analysis include choice of sensor, the nature of the biological material immobilised to the sensor surface and the number of units to be manufactured. For example, optical and surface plasmon resonance sensors are more expensive than screen-printed electrodes. The biological layer on the biosensor surface has to be from a reliable source ensuring consistency of purity and reactivity. The source of the biological material may have a significant impact on the price of the sensor particularly if genetically modified biological molecules are used. This in turn will influence the way the biosensor is used in practice. There are two fundamentally different ways in which low-cost biosensors have been employed for pesticide analysis. The first approach is to use a reusable sensor where a number of different samples are applied to the same instrument, here the same sensor surface is regenerated between samples and can give a number of sequential readings. Obviously, this will reduce the cost of each analysis, recycling expensive biological molecules. The exact number of measurements that can be made from a single biosensor depends on the immobilisation chemistries, the nature of the biological molecule being used in the biosensor, the nature of the analyte being detected and other physical parameters such as the temperature. Typically, between 5 and 100 measurements have been described, but as the biosensor ages the sensitivity decreases. The second approach is to develop a sensor using a disposable chip, in this case a new sensor is used for each sample, with the loss of the biological material. The manufacturing process controls the reproducibility of these systems and the operator does not have to worry about the biosensor’s performance slowly becoming degraded. Whether using a reusable biosensor or a single-shot biosensor, sample presentation is a critical factor in the design of an instrument for the measurement of pesticides. Again there are two fundamentally different approaches to this.

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Firstly, a system of fluidics or microfluidics can be used to present the sample to the sensor surface thereby necessitating the use of a sample entry port. The fluidics system delivers sample, wash buffer and regeneration solutions in turn. Ideally, as the sensor surface is regenerated there is no change in the activity and density of the biological layer so each subsequent reaction will occur under identical conditions. In practice, some of the biological layer is lost as a result of inactivation or being washed from the sensor surface during regeneration. The advantage of this type of system is that it can be self-contained with minimal user interaction. The second method of sample presentation is to design the biosensor in such a way as to enable it to be dipped into the sample. This has the advantage of not requiring any fluidics and keeping the device simple to operate and minimising costs. The big disadvantage of a dipping system is the potential problem of damage of the sensor surface. Cost notwithstanding, the reliability and reproducibility of any biosensor device is vitally important for a commercial biosensor designed for unskilled use, whether it is has a reusable sensor or a disposable sensor. Although still in its infancy, biosensor systems designed for pesticide analysis more commonly employ a fluidics or microfluidics system that allows the reaction and the biosensor surface to be carefully controlled ensuring greater reproducibility for use by semi- or unskilled personnel at the point of sampling.

4.6 Using biosensors: pesticide residues in grain, fruit and vegetables The detection and measurement of pesticide residues in water presents little problem in terms of sample presentation to the biosensor. On the other hand the analysis of foodstuffs such as grain, fruit and vegetables presents other problems. Pesticide residues have to be extracted from the food sample and then presented to the biosensor for the analytical measurement. In terms of developing a commercialised system for the detection and measurement of pesticide residues, the extraction and interfacing with the analytical module is a serious concern. Traditional extraction techniques are not applicable to portable devices; the use of solvents is incompatible with the technology and the environment in which the measurements are being made. Pesticide residues are extracted from food samples and have been ground up, in the case of grain or mechanically homogenised in the case of fruit and vegetables. Solvent is added to extract the pesticide, the solid material has to be removed and the extract presented to the biosensor. Organic solvents are incompatible with the biological layer and thus have to be removed and the extracted pesticide re-dissolved in a solvent compatible with the biological layer of the biosensor. Newer techniques such as supercritical fluid extraction (SFE) have been used to extract pesticide residue from food samples. Gas such as carbon dioxide is in a supercritical state when the pressure and temperature equals or exceeds the critical

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point (31ºC and 73 atm for carbon dioxide). Supercritical fluids have been known for about 100 years and have both gas-like and liquid-like properties, with high solvation power making them ideal for rapid extractions with high recoveries. This also gives supercritical fluids lower viscosity and higher diffusivity than other liquid solvents, allowing them to penetrate into the sample more efficiently. By controlling the pressure or temperature the density and solvation power can be controlled thus simulating traditional organic solvents, for example for chloroform or hexane. By adjusting the solvation power targeted compounds can be preferentially extracted. Carbon dioxide has been greatly used in supercritical fluid extraction systems, as it is non-toxic, inexpensive, and can be obtained at high purity. As the extraction process is usually carried out at a low temperature this reduces decomposition of organic compounds and prevents other reactions. Supercritical carbon dioxide is very good for extracting hydrocarbons and nonpolar compounds, but in order to extract polar compounds a modifier can be added to the supercritical carbon dioxide. A range of different modifiers has been used but the most common is methanol although this is rather toxic for food applications. To alleviate this problem ethanol has been used as an alternative in a number of applications. The disadvantage of using supercritical carbon dioxide for extraction is that it involves expensive equipment operating at high pressures and puts additional costs onto the analytical procedure. Other gases have been used in supercritical fluid extraction methods including freons and nitrous oxide, which are particularly useful for the extraction of polar compounds. Due to environmental considerations these are rarely used. Another new extraction technique is that involving the solvents containing phytosol, which is based on the compound 1,1,1,2-tetrafluoroethane. These solvents are non-flammable, non-toxic, have a neutral pH and are liquid at low temperatures and pressures such as those found in aerosol cans. The processed sample is placed into a heavy extraction vessel with a valve inlet that can take an aerosol can containing phytosol solvent. A measured quantity of solvent is added to the extraction vessel and allowed to mix with the food sample. This process is rather similar to using a supercritical fluid but does not involve the high pressures or temperatures. By releasing the valve on the extraction vessel the phytosol solvent is pushed into a second collection vessel under pressure. When the pressure is released the phytosol solvent evaporates leaving the pesticide residue in the collection vessel. In order to present the pesticide residue to the biosensor the residue must be dissolved in a small amount of solvent that is compatible with the biological layer of the biosensor. This may entail dissolving the residue in a small amount of solvent such as methanol or ethanol and then making the volume up with an aqueous buffer solution suitable for presenting the sample to the biosensor. This particular extraction procedure is simple, inexpensive and does not require complicated equipment and is easily adapted to interface with portable analytical biosensor modules. For a rapid, low-cost, portable detection system for pesticide residues in food there has to be the amalgamation of an extraction process and an analytical device based on a biosensor. It is expected that the complete analytical process,

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from sample introduction to presentation of the result, should take less than 30 minutes with minimal intervention from the operator.

4.7

Future trends

As with all areas of technology, the field of biosensors is moving forward at a terrific pace. There are a range of new technologies being developed at the moment to enhance the performance of rapid detection and measurement of pesticides. We have seen in this chapter how technology ranges from expensive sophisticated instrumentation requiring highly skilled personnel and dedicated laboratory space, to small portable units that can be operated on-site by unskilled personnel. Using one biosensor, information about a single pesticide can be obtained if the biological layer has the specificity to that particular pesticide. By introducing more than one biosensor in the device, then multi-analyte detection and measurement is achievable. Using pattern recognition technologies, such as neural networks, the integration of many biosensors will lead to the simultaneous detection of a number of different pesticides. These technologies are being used for both antibody-based biosensors and enzyme-based biosensors. The logical extension to having multiple biosensors in a device is to incorporate the active surfaces onto a single chip thereby reducing the amount of fluidics in the instrument. The challenge here is to develop isolated transducer elements on the chip to which the different biological layers are immobilised. With new techniques in nanotechnology and micro-engineered machines (MEM technology) this will soon be possible. While array technology develops, new transducer technology is also being developed for use with biosensors. Magnetic technology is being developed in competition with optical, electrochemical and piezoelectric transducers. Magnetic biosensors will have the advantage that no chemistry or enzyme reaction is required nor is there any need for optical systems. The magnetic transducer will respond directly to magnetic or paramagnetic material associated with the biosensor surface. This has the advantage of potentially reducing the size and enhancing the portability of the device. Looking further ahead it is possible to foresee the integration of other technologies such as radio telemetry being incorporated into biosensors that can be left on-site. For continuous and on-line monitoring of food in the manufacturing process, biosensors could also be incorporated with robotic technology. It can be seen that the development of biosensors has been an important technological advance in monitoring pesticide residues in food. This is a core technology that goes beyond the detection and measurement of pesticide residues but can be employed for the detection and measurement of any other compound where a biological interaction can be integrated into the biosensor.

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Sources of further information and advice

There are many websites giving information and further details of pesticide related topics. The following websites are a small selection giving information regarding the measurement and the impact of pesticides in food and the environment. http://www.pesticides.gov.uk/ http://www.defra.gov.uk/ http://www.environment-agency.gov.uk/ http://www.hgca.co.uk/ http://www.fsis.usda.gov/ http://www.epa.gov/pesticides http://www.pesticideinfo.org/

4.9

Further reading

A selection of review articles and scientific papers relating to areas discussed in the text is given below for further information.

4.9.1

Selection of review articles

Pesticides: a review article, Journal of Environmental Pathology, Toxicology and Oncology: Official Organ of the International Society for Environmental Toxicology and Cancer, Volume 13, Issue 3, 1994, pp. 151–161. APREA, C, COLOSIO, C, MAMMONE, T, MINOIA, C and MARONI, M, Biological monitoring of pesticide exposure: a review of analytical methods, Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, Volume 769, Issue 2, 5 April 2002, pp. 191–219. ´ N, J A, MAQUIEIRA, A, PUCHADES, R, Current trends in immunoassay-based kits for GABALDO pesticide analysis, Critical Reviews in Food Science and Nutrition, Volume 39, Issue 6, November 1999, pp. 519–538. HENNION, M-C and BARCELO D, Strengths and limitations of immunoassays for effective and efficient use for pesticide analysis in water samples: A review, Analytica Chimica Acta, Volume 362, Issue 1, 24 April 1998, pp. 3–34. LANG, Q and WAI C M, Supercritical fluid extraction in herbal and natural product studies – a practical review, Talanta, Volume 53, Issue 4, 5 January 2001, pp. 771–782. MULCHANDANI, A, CHEN, W, MULCHANDANI, P, WANG, J and ROGERS, K R, Biosensors for direct determination of organophosphate pesticides, Biosensors and Bioelectronics, Volume 16, Issues 4–5, June 2001, pp. 225–230. REKHA, K, THAKUR, M S, KARANTH, N G, Biosensors for the detection of organophosphorous pesticides, Critical Reviews in Biotechnology, Volume 20, Issue 3, 2000, pp. 213– 235. SHERMA, J, Pesticide residue analysis (1999–2000): a review, Journal of AOAC International, Volume 84, Issue 5, September–October 2001, pp. 1303–1312. AL-SALEH, I A,

74 4.9.2

Rapid and on-line instrumentation for food quality assurance Selected scientific papers

´ NDEZ-ALBA, A R, ¨ ERA, A, CONTRERAS, M, CRESPO, J, FERNA AGU

Multiresidue method for the analysis of multiclass pesticides in agricultural products by gas chromatographytandem mass spectrometry, The Analyst, Volume 127, Issue 3, March 2002, pp. 347–354. ANDREOU, V G, CLONIS, Y D, A portable fiber-optic pesticide biosensor based on immobilized cholinesterase and sol – gel entrapped bromcresol purple for in-field use, Biosensors and Bioelectronics, Volume 17, Issues 1–2, January 2002, pp. 61–69. GULLA, K C, GOUDA, M D, THAKUR, M S, KARANTH, N G, Reactivation of immobilized acetyl cholinesterase in an amperometric biosensor for organophosphorus pesticide, Biochimica et Biophysica Acta, Volume 1597, Issue 1, May 20, 2002, pp. 133–139. HARTLEY, I C, HART, J P, Amperometric Measurement of Organophosphate Pesticides using a Screen-printed Disposable Sensor and Biosensor Based on Cobalt Phthalocyanine, Anal. Proceed., 1994, Volume 31, pp. 333–337. HYE-SUNG LEE, YOUNG AH KIM, YOUNG AE CHO, YONG TAE LEE , Oxidation of organophosphorus pesticides for the sensitive detection by a cholinesterase-based biosensor, Chemosphere, Volume 46, Issue 4, January 2002, pp. 571–576. KARCHER, A, EL RASSI, Z, Capillary electrophoresis and electrochromatography of pesticides and metabolites, Electrophoresis, Volume 20, Issue 15–16, October 1999, pp. 3280–3296. LEHOTAY, S J, LIGHTFIELD, A R, HARMAN-FETCHO, J A, DONOGHUE, D J, Analysis of pesticide residues in eggs by direct sample introduction/gas chromatography/tandem mass spectrometry, Journal of Agricultural and Food Chemistry, Volume 49, Issue 10, October 2001, pp. 4589–4596. ´ NCHEZ, M, Application of gas MARTI´NEZ VIDAL, J L, ARREBOLA, F J and MATEU-SA chromatography – tandem mass spectrometry to the analysis of pesticides in fruits and vegetables, Journal of Chromatography A, Volume 959, Issues 1–2, 14 June 2002, pp. 203–213. MULCHANDANI, P, CHEN, W, MULCHANDANI, A, WANG J, CHEN L, Amperometric microbial biosensor for direct determination of organophosphate pesticides using recombinant microorganism with surface expressed organophosphorus hydrolase, Biosensors and Bioelectronics, Volume 16, Issues 7–8, September 2001, pp. 433–437. RIPPETH, J J, GIBSON, T D, HART, J P, HARTLEY, I C and NELSON, G, Flow-injection Detector Incorporating a Screen-printed Disposable Amperometric Biosensor for Monitoring Organophosphate Pesticides, Analyst, 1997, Volume 122, pp. 1425– 1429. TEGELER, T, EL RASSI, Z, Capillary electrophoresis and electrochromatography of pesticides and metabolites, Electrophoresis, Volume 22, Issue 19, November 2001, pp. 4281– 4293. THURMAN, E M, AGA, D S, Detection of pesticides and pesticide metabolites using the cross reactivity of enzyme immunoassays, Journal of AOAC International, Volume 84, Issue 1, January–February 2001, pp. 162–167. WANG, J, CHATRATHI, M P, MULCHANDANI, A, CHEN, W, Capillary electrophoresis microchips for separation and detection of organophosphate nerve agents, Analytical Chemistry, Volume 73, Issue 8, April, 2001, pp. 1804–1808.

5 Detecting antimicrobial drug residues ˚ . Sternesjo¨, Swedish University of Agricultural Sciences A

5.1

Introduction

As a consequence of the intensive rearing of animals in modern agriculture, veterinary drugs are used on a large scale, for therapeutic purposes, to prevent the outbreak of disease and to improve the growth of animals. The risk for contamination of milk and meat is evident and the public concern about potential residues in foods is strong. With a growing need for food safety assurance it is obvious that government regulators are not the only ones responsible, but that producers and the food industry also play an important role. The development of various control mechanisms for veterinary drugs, e.g. control of their distribution, use, determination of safe residue levels and residue detection technologies to be employed, involves both national and international organisations. In all EC countries, national control programmes are compulsory and executed by authorities to ensure that animal derived foods are free from veterinary drug residues (EC, 1996). Parallel to this official control, food producers and industries perform extensive self-monitoring programmes to meet regulatory standards for export and consumer concerns regarding the safety of the food supply (EEC, 1992). Established residue limits, e.g. tolerances and MRLs (maximum residue limits), for drug residues in foods are important in considering the design of control programmes and also for the choice of appropriate analytical tools. The availability of suitable screening tests is a key element in any residue detection programme. Although the range of analytical methods applied in the control of veterinary drug residues is extensive, few techniques allow rapid, reliable, sensitive and automated analysis. This chapter will consider methods for detection of antimicrobial drug residues in milk and meat with a focus on the application of optical biosensor technology and its potential in residue control.

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Rapid and on-line instrumentation for food quality assurance

Current screening methods for residue detection

It was not consumers that expressed initial concerns regarding the presence of antimicrobial drug residues in foods but dairy processors who found that the milk they were using occasionally inhibited the starter cultures in the manufacture of fermented dairy products. Shortly after the introduction of antibiotics for dairy cows the microbial inhibitor tests began to be used by the dairy industry. Most of the research related to the development of these tests was performed with milk and in the 1950s and 1960s. These tests are usually developed as screening tools for qualitative analysis of inhibitory substances, detecting a wide pattern of compounds with an inhibitory effect on the test organism (Table 5.1). In the dairy industry this is usually considered an advantage, since the result will not only detect antibiotic residues but also indicate the acidification properties of the raw milk. In many countries screening for antimicrobial residues for quality control and payment purposes by the dairy and meat industries is still based on the use of microbial inhibitor tests (Suhren, 1995; Korsrud et al., 1998; Mitchell et al., 1998) although the demands on these tests have changed. From being a tool to evaluate the acidification properties of Table 5.1 Features of common screening tests for detection of antimicrobial drug residues in milk Type of assay

General features

Examples of test kits

Microbial inhibitor assay

Easy to perform and low demands with respect to instrumentation Inexpensive High capacity Broad spectrum assays for detection of ‘inhibitory substances’, unspecific results Insufficient sensitivity to certain substances Usually visual interpretation Extended incubation time (2.5–18 h) Narrow-spectrum detection, specific results High sensitivity to target analyte Often several incubation and wash steps, long time of analysis (30 min– several hours) Instrumental interpretation High capacity Difficult to automate Generic detection of a class of antimicrobials Rapid (5–15 minutes) Usually easy to perform Low capacity

Delvotest SP, BR-test (DSMgroup) T101 test (Valio) AIM-96 test (Charm Sciences, Inc.)

Immunoassays

Receptor protein assays

Ridascreen (r-biopharm) Betascreen (Advanced Instruments, Inc.)

Penzym, -star test (UCB Bioproducts) Charm II, ROSA (Charm Sciences, Inc.) SNAP (IDEXX Lab., Inc.)

Detecting antimicrobial drug residues 77 the raw milk, attention has shifted to human health hazards. During the last decades the methods have been adjusted to reach detection limits that correspond to the MRLs for the most important drugs. Methods with high specificity to individual antimicrobials have also been described (Suhren and Heeschen, 1993; Nouws et al., 1999). The immunoassays were originally used in the clinical laboratory for bacterial typing and toxin identification. Since that time, various antibody-based techniques have been developed and applied to determine a wide range of analytes in foods. The development of procedures to label the antigen or antibody greatly increased the potential of immunoassays as an analytical tool. With the development of the radioimmunoassay (RIA) in the 1950s and the enzyme-linked immunosorbent assay (ELISA or EIA) in the 1970s the screening of a large number of samples became possible (Korsrud et al., 1998). During recent years, the perfection of this technology has resulted in immunoassays moving out of the laboratory and into the field. EIA methods used in residue control generally have low demand for sample clean-up, they are sensitive and several samples can be run simultaneously. The analysis time is, however, often long, ranging from a few to several hours and the traditional immunoassays are difficult to automate. The extended time of analysis for inhibitor tests and immunoassays limits their use in certain applications. The time of analysis is often too long to allow withdrawal of a contaminated food item and most existing methods only allow for a fraction of the total production to be analysed. Control systems aiming at separation and discarding of contaminated items are becoming more frequent in Europe, especially in the dairy sector. An increasing number of rapid techniques, the majority receptor-based, have reached the market during the last five to ten years and made control for separation of contaminated milk possible. Receptor binding assays are in general more generic than an enzyme immunoassay. Many of these rapid methods were developed for detection of beta-lactam antibiotics in milk utilising penicillin-binding proteins. These tests are very rapid (approximately five minutes), to be applied on farm before the milk is collected by the tanker or at the dairy plant before the tanker unloads into a processing silo. The low capacity of existing test systems, allowing only a few samples to be analysed at a time, and the limited number of analytes for which tests are available, limit the application of these tests at present. Clearly, there is not one single, ideal test that can detect all antimicrobials at their respective MRL (see Table 5.2 for established MRLs in bovine target tissues) and integrated systems combining different techniques to cover all substances in a control programme are widely in use. With the increasing rate of centralisation of food control and a shift towards automated analysis, there is, however, a growing demand for new technologies, allowing rapid, highcapacity, automated multi-residue analysis.

Table 5.2 Antimicrobial substances for which EU maximum residue limits in bovine target tissues have been fixed. (Modified from a consolidated version of the Annexes I to IV of Council Regulation No. 2377/90 updated on 25.11.2002, http://pharmacos.eudra.org) Group and substance Milk

EU MRL (g/kg) Target tissue Muscle Kidney Liver

Fat

-lactams amoxicillin ampicillin benzylpenicillin cloxacillin dicloxacillin nafcillin oxacillin penethamat

30 4 4 30 30 30 30 4

300 50 50 300 300 300 300 50

300 50 50 300 300 300 300 50

300 50 50 300 300 300 300 50

300 50 50 300 300 300 300 50

cefacetril cefalexin cefapirin cefazolin cefoperazon cefquinome ceftiofur

125 100 60 50 50 20 100

200 50

1000 100

200

200 50

50 1000

200 6000

100 2000

100 100

100 100 100 100

600 600 600 600

300 300 300 300

Sulphonamides1

100

100

100

100

Macrolides Erythromycin Spiramycin Tilmicosin Tylosin

40 200 50 50

200 200 50 100

200 300 1000 100

200 300 1000 100

Tetracyclines Chlortetracycline Doxycycline Oxytetracycline Tetracycline

1

100

Sum of all substances belonging to the group of sulphonamides

Group and substance

50 2000

200 300 50 100

Milk

EU MRL (g/kg) Target tissue Muscle Kidney Liver

Fat

Lincosamides Linkomycin Pirlimycin

150 100

100 100

1500 400

500 1000

50 100

Aminoglycosides Apramycin Gentamicin Neomycin incl. framycetin Spectinomycin Strepto- /dihydrostreptomycin

100 1500 200 200

1000 50 500 300 500

20000 750 5000 5000 1000

10000 200 500 1000 500

1000 50 500 500 500

200 400 100 200 150

400 800 200 1500 150

400 1400 300 500 150

100 100 100 300 50

100 150 200

150 400 200 300

300 200 150 3000

10 100 150

50 50

50 50

50 50

50 50

Quinolones Danofloxacin Difloxacin Enrofloxacin Flumequin Marbofloxacin Various Bactitracin Baquiloprim Clavulanic acid Colistin Florfenicol Novobiocin Rifaximin Thiamphenicol Trimethoprim

30 100 50 75 100 30 200 50 60 50 50 50

Detecting antimicrobial drug residues 79

5.3 Developing biosensors: the use of surface plasmon resonance 5.3.1 Key issues During the last 20 years biosensors have received substantial interest, medicine providing the main driving force behind their development (Turner and Newman, 1998). Despite its obvious benefits, biosensor technology has been slow to penetrate the food industry and the number of commercial technologies available is still limited. Biosensor technology based on optical transducers has been most successful in developing instruments for the market, benefiting from rapid progress in adjacent fields. Much of the recent work on optical biosensors has focused on the use of evanescent wave technology, e.g. surface plasmon resonance (SPR). SPR is an optical phenomenon which occurs between incoming photons and the electrons in a thin metal film coated onto a glass support. At a specific wavelength and angle of incident light, i.e. the SPR angle, energy is transferred to the electrons in the metal film causing a reduction in the intensity of the reflected light. The SPR angle is dependent on the refractive index in the close vicinity of the gold surface and the refractive index is in turn a linear function of the mass concentration at the surface. When a macromolecule binds to the surface, the mass, and thereby the refractive index on the sensor surface, changes, causing a shift in the SPR angle that can be used for biosensing purposes (Liedberg et al., 1983). By continuously monitoring the SPR angle and plotting the value against time a sensorgram is obtained. In the following, applications of a commercial SPR biosensor, the Biacore instrument (Biacore AB, Uppsala, Sweden), for control of antimicrobial drug residues in milk will be described. The basic components of the analytical system are the sensor chip, the microfluidic system and the SPR detection system. The sensor chip consists of a glass substrate onto which a thin gold film has been deposited (Lo¨fa˚s and Johnsson, 1990) and attached to the gold is a layer of carboxymethylated dextran. The dextran provides a hydrophilic environment suitable for studies of biospecific interactions and it enhances the immobilisation capacity of the surface. On existing instruments there are four flow cells on each sensor chip, enabling analysis of different substances if desired.

5.3.2 Assay development The development of a Biacore immunosensor assay follows some general steps, from the preparation of the analyte-specific sensor surface to the regeneration step to dissociate the bonds between the two interacting biomolecules. For detection of low-molecular substances, e.g. antimicrobials, it is common to use the inhibition assay format, i.e. to immobilise the analyte on the sensor chip and add a fix concentration of analyte-specific antiserum or Mab to the sample. Any analyte available in the sample will then form a complex with the antibody and subsequently inhibit the antibody from binding to the sensor surface.

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Rapid and on-line instrumentation for food quality assurance

There are a number of coupling chemistries that can be applied to covalently bind the ligand to the dextran layer of the sensor chip. One common reaction is amine coupling by derivatisation of the carboxymethylated dextran with Nhydroxysuccinimide (NHS) mediated by N-ethyl-N 0 -(3–dimethylaminopropyl) carbodiimide hydrochloride (EDC). The NHS-esters formed react readily with uncharged primary amine groups and remaining esters are deactivated with ethanolamine. Depending on the molecular structure of the ligand, modifications of the immobilisation procedure may be required (O’Shannessy et al., 1992; Lo¨fa˚s et al., 1995). During the preparation of the sensor surface it is important to provide immobilisation conditions promoting a high ligand density on the sensor surface. This can be achieved by optimisation of various parameters, e.g. pH, ligand concentration, ionic strength and reaction times (Johnsson et al., 1991). These parameters are of major importance for coupling of proteins, but should not be neglected when coupling small molecules such as antimicrobials. In the instrument, the microfluidic system in combination with an autosampler controls the delivery of sample into a transport buffer passing continuously over the sensor surface. Despite the complex matrix that food makes up, usually only minor sample preparations are required. In the case of fresh raw milk, dilution of the sample with buffer is sufficient. For analysis of previously frozen milk samples a centrifugation step and removal of the fat layer is recommended. Before the sample is injected in the instrument, antibody is added. Each analysis cycle is recorded as a sensorgram, with report points taken before and after sample injection to measure the amount of Ab binding to the surface (Fig. 5.1). If the first report point is assumed as zero (baseline), the second report point presents the sample response value (relative response). The

Fig. 5.1 Schematic illustration of a biosensor assay for detection of sulphamethazine. The events during the analytical cycle are as follows: 1. Continuous buffer flowing across the sensor surface with sulphamethazine (•) immobilised to the dextran, establishment of the baseline response. 2. Injection of sample and antibody (Y) mixture, free antibody not inhibited by sulphamethazine residues in the sample will bind to the surface. 3. Injection of the regeneration solution causes dissociation between antibody and sulphamethazine on the surface. 4. Bound antibody has been removed from ligand and the biosensor response has returned to baseline level.

Detecting antimicrobial drug residues 81 surface is then regenerated and ready for injection of the next sample. To quantify the residue concentration in a sample, the sample response is compared with a standard curve, constructed by analysis of calibration samples with known concentrations of the analyte. To optimise the performance of the assay, there are a number of parameters to vary, e.g. buffer flow rate, composition of running buffer, sample injection volume and active antibody concentration. When optimising the regeneration procedure it is important to ensure that the binding activity of the ligand is maintained and that the surface is efficiently regenerated, so as not to lose binding capacity (Andersson et al., 1999). For the type of applications described in this chapter it is common to use an acidic (HCl, cysteine) or alkaline solution (NaOH), occasionally with the addition of organic solvent (acetonitrile). Effects resulting from unspecific binding of matrix components sometimes need to be considered. Depending on the nature of the ligand and the interactions between ligand and interacting biomolecule other types of applications may require different solutions. In this critical part of assay optimisation it is important to observe effects on the non-corrected baseline, i.e. the absolute response.

5.4

Using biosensors to detect veterinary drug residues

5.4.1 Immunosensor assays for detection of antimicrobial residues in milk and meat One of the earliest reported Biacore applications for veterinary drug residue analysis was an assay for detection of sulphamethazine residues in milk (Sternesjo¨ et al., 1995). A sensor surface was prepared by amine coupling of sulphamethazine to the dextran. Before injection of the milk sample a polyclonal sulphamethazine antibody, precipitated from rabbit serum by ammonium sulphate saturation, was added to the milk sample. The detection limit of the assay was 0.2 g/kg and the standard deviation (CV response) between milk samples from individual cows, farms and tankers was low, ranging between 2.2 and 4.4 per cent. Since then a number of assays have been developed for detection of antimicrobial agents in milk, e.g. enrofloxacin and ciprofloxacin (Mellgren and Sternesjo¨, 1998), chloramphenicol (Gaudin and Maris, 2001), streptomycin (Baxter et al., 2001) and penicillin (Gaudin et al., 2001). All these methods are in principle based on the same inhibition assay format, i.e. analyte is coupled to the sensor surface and antibodies are used as detection molecules. In contrast with this procedure, Haasnoot and Verheijen (2001) described a direct, noncompetitive assay for gentamicin (molecular weight 466 Da) using the Biacore 3000 instrument. Their work verified claims of the biosensor manufacturer regarding the possibility of direct detection of small molecules. The gentamicin concentration in milk resulting in 50 per cent of the maximum binding capacity of the surface was 35 g/kg, which is well below the MRL of 100 g/kg. Sulphonamides are widely used in pig production to control outbreaks of disease and occasionally residues in excess of permitted safety levels can be

82

Rapid and on-line instrumentation for food quality assurance

detected in edible tissues. Since the methods used to determine levels in meat require lengthy sample extraction procedures, body fluids, e.g. bile, serum and urine, are often used and the levels in these give accurate indications of levels in muscle samples. Assays for detection of sulphonamides in bile were developed by Elliott et al. (1999) and the methods were used in a routine laboratory to screen for sulphamethazine and sulphadiazine residues in pig production. Conventional EIAs were used for comparison and HPLC was used to analyse the corresponding kidney sample from a bile sample testing positive (Crooks et al., 1998). The Biacore assays delivered more reliable results than the EIAs, both with respect to false positive and false negative rates. To further challenge the Biacore technology, the instrument was transferred out of the laboratory into a pig abattoir and installed on-line to study the robustness and reliability of the technology (Baxter et al., 1999). Testing was performed immediately after sample collection and the goal was to make results available before the carcasses left the chill rooms for processing. The biosensor proved itself to be very robust; however, for withdrawal of positive-screening carcasses it was obvious that the speed of analysis needed to be increased. In addition to assays for analysis of bile samples, an assay for detection of sulphonamides in pork muscle has been described (Bjurling et al., 2000). A simple extraction procedure with aqueous buffer and a centrifugation step was used. The validation proved the assay to be very accurate; however, crossreacting metabolites gave rise to false positive results in the biosensor assay.

5.4.2 Development of penicillin-binding protein sensor assays for detection of -lactams -lactam antibiotics, e.g. the penicillins, are considered the most important family of antibiotics used in dairy cows. Consequently, they are also the most commonly occurring type of residue detected in milk. Since established safe levels and MRLs only include the active forms of -lactams, the development of an assay that can distinguish the active compound from the inactive is essential. The assay described by Gaudin et al. (2001) thus suffers from the fact that it is based on measurements of the inactivated form of -lactams after a sample pretreatment step to hydrolyse the -lactam structure. A drawback with many of the biosensor assays described so far is that they are all based on antibodies, resulting in methods with rather narrow detection spectra. Considering the number of compounds belonging to the -lactam family and their wide use in mastitis treatment, much effort has been invested to develop broad specificity lactam antibodies, unfortunaltey with little success. To produce a generic biosensor assay for detection of active -lactams, we decided to investigate the possibility of using a penicillin-binding protein. The well-characterised carboxypeptidase from Streptomyces R39 is known to form stable complexes with -lactams whereby its enzymatic activity is lost. Inhibition of this enzyme (R39) serves as the basis for the Penzym test (UCB Bioproducts, Braine l’Alleud, Belgium), a milk screening test for -lactams that

Detecting antimicrobial drug residues 83 has been commercially available and used by dairies since the 1980s. Due to the covalent binding between -lactam antibiotics and R39, new concepts for preparation of a -lactam specific sensor surface were necessary. Likewise, due to the instability of the -lactam structure a -lactam surface would not stand repeated regeneration with acidic or alkaline solutions. One way to overcome these problems is by use of a general capturing surface, similar to the approach described by Bergstro¨m et al. (1999). A conjugate between a -lactam antibiotic and a monoclonal antibody to the small organic molecule (H1) previously used (Bergstro¨m et al., 1999) was synthesised. To provide a -lactam surface the conjugate was injected over a sensor chip prepared by immobilisation of H1 (Gustavsson et al., 2002a). The injection of the conjugate was immediately followed by injection of a mixture of sample and R39. If the sample contains lactams, the binding between R39 and conjugate on the surface will be inhibited. The limit of detection of the assay using spiked milk samples was below or close to the MRL for a number of commonly used -lactams. However, effects due to non-specific binding resulted in a high variation between different -lactam-free milk samples, reducing the usefulness of the assay in field. A more successful approach is based on inhibition of the enzymatic activity of R39 by -lactams, using a 3-peptide as substrate (Gustavsson et al., 2002b). R39 catalyses the following reaction: Ac-L-Lys-D-Ala-D-Ala + H2O ! D-Ala + Ac-L-Lys-D-Ala (Fre`re et al., 1980). Based on the hydrolysis of the 3-peptide, two different assays have been developed, one measuring the production of 2peptide, the other one measuring the disappearance of 3-peptide. The milk sample is mixed with 3-peptide and R39 and incubated at 47ºC for five minutes. During incubation R39 will hydrolyse the 3-peptide into 2-peptide. In the presence of -lactams R39 will be inhibited and less 2-peptide will be formed. After incubation 2-peptide antibody is added to the sample and the mixture is injected over a 2-peptide sensor surface. With a -lactam-free sample the antibody will be inhibited by 2-peptide formed in the sample. If the sample contains -lactams less 2-peptide will be produced and the antibody will bind to the immobilised 2-peptide on the sensor chip surface. Alternatively, 3-peptide antibody is added to the sample and the mixture is injected over a 3-peptide sensor surface. With a -lactam-free sample there will be no 3-peptide left after the incubation and the antibody will bind to the 3-peptide surface. In the presence of -lactams, the antibody will instead be inhibited by unreacted 3peptide and less antibody will bind to the surface.

5.5

Biosensor applications in the food industry

SPR biosensors have demonstrated several features that are beneficial in food control analysis (Table 5.3). The technology is time saving since the need for sample pre-treatment is low, analysis involves only few assay steps and the analysis time is short (minutes instead of hours). The technique is fully automated and the multi-channel system provides the possibility to screen for

84

Rapid and on-line instrumentation for food quality assurance

Table 5.3

Features of the optical biosensor used in residue analysis

Feature

Comment

No or low sample preparation requirements Fast response time

Reducing total time for analysis

Highly sensitive Multi-flow channel systems

Fully automated sample handling and analysis system Limited availability of test kits Cost

Results are available in short time (2–5 min) in case of single checks, high throughput in automatic analysers Detection limits in the same range as those obtained in enzyme immunoassays Allows simultaneous analysis of different substances in a sample. Recently developed prototypes are even more versatile, allowing simultaneous analysis of more than one substance in one or more samples Reduced ‘hands-on’ time and costs for laboratory personnel The number of test kits commercially available is limited, user needs to develop own method/provide own reagents for most compounds Available instruments not adapted to analysis in food routine control laboratories, the price is still too high

several residues simultaneously. The real time signal itself gives a built-in quality control providing high reliability of the result. Despite the benefits offered by SPR biosensors, few applications have been described for routine analysis in the food industry. One important reason for this is that existing instruments have been designed for research purposes and find their applications in research centres, not in routine laboratories. During recent years, immunosensors have been used for determination of vitamins, e.g. biotin and folate, by some large food manufacturers (Indyk et al., 2000). However, the cost for the sophisticated instruments cannot usually be justified in the food industry. To make the technology applicable on a practical scale more rugged instruments need to be developed and sample capacity has to be increased even more. With this in mind, an EU project (Foodsense, FAIR-CT98-3630) with the objective to demonstrate the applicability of optical biosensors for screening of veterinary drug residues in foods was performed during the years 1998–2000 (www.slv.se/ foodsense). The project involved the construction of a new biosensor prototype based on existing SPR technology, however, modified to better meet the demands of the food industry regarding speed, sample throughput and multi-residue screening. The new biosensor featured several new and innovative construction details that made the analysis more efficient, e.g. a more compact optical detection unit, a smaller sensor chip and eight parallel flow channels with separate injection needles. With the prototype up to eight samples could be injected from a standard microwell plate and analysed simultaneously at high speed, i.e. 30–60 minutes per plate depending on test kit and target analyte. Furthermore, the instrument was integrated with a commercial robot for automated liquid handling and sample preparation. Recent method developments were exploited and biosensor assay kits

Detecting antimicrobial drug residues 85 were constructed and validated to meet target specifications. At the end of the project, both instrument and assay kit prototypes were used on a pilot scale basis by the food industry and European regulatory laboratories to evaluate their performance. One successful application in Foodsense that illustrates the potential of the technology with respect to high-throughput analysis was the screening for sulphonamides in porcine bile. The assay was used both in the official control and in a producer control programme in Northern Ireland. After some modifications of the prototype the rate of false positive predictions in the official pig testing scheme was 0 per cent and 0.7 per cent (n ˆ 615) for sulphamethazine and sulphadiazine, respectively. Of all predicted negative bile samples 2 per cent of the corresponding kidney samples were analysed by HPLC, the results indicating that the assay did not deliver any false negative predictions. The results from the producer control programme on-site at the pig abattoir included 6069 bile samples and the results with respect to false positive and negative predictions were in agreement with those obtained in the official control (Table 5.4). As in the official control, analysis of corresponding kidney samples by HPLC from bile samples testing negative in the biosensor prototype did not detect any false negative predictions. The analysed bile samples represented 21 per cent of the total number of slaughtered animals during the testing period and 84 per cent of all producers submitting pigs for slaughter had at least one pig tested for sulphonamide residues. The reason for not achieving 100 per cent was related to logistics rather than the capacity of the biosensor, and, in conclusion, this application was considered as very successful. Another application from the project illustrating the possibility to analyse several substances simultaneously was the milk test kit for simultaneous screening of sulphamethazine, sulphadiazine and enro-/ciprofloxacin in tanker milk. In total 1400 milk samples were analysed with the biosensor at a dairy plant in Germany. All samples tested negative by the biosensor and the routine methods used in the producer control programme (Delvotest SP, E. coli test, ELISA). In Sweden, the same biosensor test kit was used at the national control Table 5.4 Biosensor analysis of porcine bile for the presence of sulphonamide residues on-site at a pig abattoir (Foodsense, FAIR-CT98-3630, from Dep. Veterinary Science, Queens University of Belfast, Northern Ireland)

Number of bile samples analysed Bile samples with a positive result in the biosensor assay Corresponding levels in kidney samples confirmed to be above MRL Corresponding levels in kidney samples confirmed to be close to MRL Frequency (%) of false positive results

Sulphamethazine

Sulphadiazine

6069 8

6069 47

0

2

2

1

0.10%

0.72%

86

Rapid and on-line instrumentation for food quality assurance

laboratory and 426 tanker milk samples were analysed for sulphonamides and fluoroquinolones. There were no sulphonamide positive results whereas one milk sample was found suspect of containing fluoroquinolones. This was found to be a false positive result with the conventional HPLC procedure used in the laboratory. This application was also considered a success, although the high capacity of the instrument was not utilised in the same way as in the screening of pigs in the slaughterhouse. In conclusion, the instrument prototype performed above expectations during the project. It was pointed out, however, that test kits for detection of additional analytes needed to be developed. As a spin-off of the Foodsense project, XenoSense Ltd was formed in October 2000. The company, based in Belfast, has commercialised certain products and areas of research that have been developed in the field of food analysis. At present the company has some ten test kits offering analysis of biotin, folic acid, vitamin B12, clenbuterol, sulphadiazine, sulphamethazine, streptomycin and ractopamine in various food matrixes to Biacore users.

5.6

Future trends

The steadily increasing number of biosensor applications for food control and the obvious benefits associated with the technology in no way matches its use in the food industry. There are several reasons for this mis-match, some related to technology manufacturers, others to the food industry itself. Whether the technology will be more successful in penetrating the food industry in the future depends on a number of factors (Table 5.5). The features associated with SPR affinity biosensors have already been described and obvious benefits have been pointed out. The Foodsense project indicated that by adapting existing instruments in accordance with the critical Table 5.5 Factors influencing the future use of biosensors in residue control by food industry and regulatory laborities Factors related to technology producer

Factors related to technology user

1. Features of new technology providing benefits as compared to existing, traditional technology 2. Adaptation of research instruments to the needs of food control laboratories 3. Availability of test kits 4. Competitive instrument manufacturers 5. Cost per analysis

6. Acceptance by food industry and regulatory bodies 7. Changes in future control programmes • increasing number of compounds that food has to be tested for • more testing for separation and withdrawal of contaminated food items 8. Centralisation of analytical work to a few, large laboratories requiring automated techniques

Detecting antimicrobial drug residues 87 user issues, e.g. sample throughput, robustness, convenience of analysis, reduced need for sample-pretreatment, its performance in routine laboratories even exceeded conventional methods, e.g. ELISA. Today, the lack of appropriate instruments and limited availability of validated test kits on the market in addition to the high cost (based on the use of existing instruments) to invest in the technology would constitute major problems for most potential users. The prototype biosensor developed in Foodsense is a very robust instrument, partly due to the fact that a number of options associated with the more advanced instruments have been omitted. Normally, it could be assumed that the cost for such an instrument would be lower. Since there also seems to be a demand for similar instruments in the pharmaceutical industry, there is an evident risk that the price of future instruments will be too high for the food industry. The establishment of a commercial test kit manufacturer for food analysis is a step forward. However, with a limited number of users in the food industry, the number of products is likely to be limited. The food industry itself must, on the other hand, show a greater interest in new technologies that have been developed and also proved to be suitable for routine analysis. There is a general resistance to the introduction of new technologies and the shift from microbiological inhibitor tests to biosensors may seem enormous. Regulatory laboratories have more experience of advanced technologies and the introduction of biosensors in food laboratories would probably benefit from a situation where national reference laboratories and other accredited laboratories decide to use the technology. The need for high throughput and high capacity is, however, much higher in producer control than in national control programmes and the need for new instruments is thus more obvious in producer control. Future changes in official control programmes may, however, have a large impact on the acceptance of new technologies. With an increasing number of substances that food needs to be tested for, technologies allowing multi-residue analyses have obvious advantages. In the food producer control such changes could also be brought about as a consequence of new consumers’ demands. With the knowledge that appropriate technology is available consumers may ask for producers testing their milk or meat before processing and that contaminated food items are withdrawn and discarded. The choice of methods and technologies is to a high degree also affected by the organisation of future food control. In the dairy sector there has been a dramatic centralisation of the milk testing for milk payment purposes and in many countries only one or a few large laboratories remain. Producer milk samples may be tested from once per day to once per month and multiplied by the number of producers and substances that the milk is tested for, the resulting number of analyses is considerable. In such a control system, the high throughput of the prototype and the option to perform multi-residue analysis on an automated instrument could be really valuable. It is obvious that the success of biosensors in food analysis will depend on not only one, but on a combination of factors. The market would certainly benefit from competitive technology producers since the price of the instruments is a

88

Rapid and on-line instrumentation for food quality assurance

major concern for its users. Up to today, the manufacturer has made little distinction between pharmaceutical and food industry when it comes to development of new instruments and pricing of the technology. This will probably be one reason why the food industry will continue to rely on traditional analytical technologies for some time to come.

5.7

Sources of further information and advice

Companies: Biacore AB, Uppsala (www.biacore.com), XenoSense Ltd., UK (www.xenosense.co.uk) Universities: Department of Food Science, Swedish University of Agricultural Sciences, Uppsala, Sweden; Department of Veterinary Science, the Queens University of Belfast, Northern Ireland; Dublin City University, School of Biotechnology, National Centre for Sensors; Department of Public Health and Food Safety, University of Utrecht, The Netherlands. Governmental organisations: Community Reference Laboratory for Veterinary Drug Residues, Fougeres, France; National Food Administration, Uppsala, Sweden. Non-governmental organisations: Rikilt, Wageningen, The Netherlands; TNO Voeding, The Netherlands. FoodSENSE: project coordinator Karl-Erik Hellena¨s, [email protected]; FoodSense newsletters, ed. [email protected]; www.slv.se/foodsense. Conferences and meetings: Euroresidue, organised by Working Party on Food Chemistry, div. of the Federation of European Chemical Societies (FECS) and Netherlands Society for Nutrition and Food Technology (NVVL); International symposium on hormone and veterinary drug residue analysis, organised by Ghent Univetsity, Belgium; Food and Agricultural Antibodies, organised by the Society for Food and Agricultural Immunology; BIAsymposia and workshops, organised by Biacore AB, Uppsala, Sweden.

5.8

References

¨ MA ¨ LA ¨ INEN M ANDERSSON K, HA

and MALMQVIST M (1999), ‘Identification and optimisation of regeneration conditions for affinity-based biosensor assays. A multivariate cocktail approach’, Anal Chem, 71, 2475–2481. BAXTER G A, O’CONNOR M, HAUGHEY S A, CROOKS S R H and ELLIOTT C T (1999), ‘Evaluation of an immunobiosensor for the on-site testing of veterinary drug residues at an abattoir. Screening for sulfamethazine in pigs’, Analyst, 124, 1315–1318. BAXTER G A, FERGUSON J P, O’CONNOR M C and ELLIOTT C T (2001), ‘Detection of streptomycin residues in whole milk using an optimal immunobiosensor, J Agric Food Chem, 49, 3204–3207. ˚ , BJURLING P and Lo¨Fa˚S S (1999), ‘Design and use of a general BERGSTRo¨M C, STERNESJo¨ A capturing surface in optical biosensor analysis of sulphamethazine in milk’, Food Agric Immunol, 11, 329–338. BJURLING P, BAXTER G A, CASELUNGHE M, JONSON C, O’CONNOR M C, PERSSON B and ELLIOTT C T (2000), ‘Biosensor assay of sulfadiazine and sulfamethazine residues in pork’, Analyst, 125, 1771–1774.

Detecting antimicrobial drug residues 89 and ELLIOTT C T (1998), ‘Immunobiosensor – an alternative to enzyme immunoassay screening for residues of two sulfonamides in pigs’, Analyst, 123, 2755–2757. EC (1996), ‘Council Directive 96/23/EC of 29 April 1996 on measures to monitor certain substances and residues thereof in live animals and animal products and repealing Directives 85/358/EEC and 86/469/EEC and Decisions 89/187/EEC and 91/664/ EEC’, Off J Eur Commun, L125, 10–32. EEC (1992), ‘Council Directive 92/46/EEC of 16 June 1992 laying down the health rules for the production and placing on the market of raw milk, heat-treated milk and milk-based products’, Off J Eur Commun, L268, 1–32. ELLIOTT C T, BAXTER G A, CROOKS S R H and MCCAUGHEY W J (1999), ‘The development of a rapid immunobiosensor screening method for the detection of residues of sulphadiazine’, Food Agric Immunol, 11, 19–27. FRE`RE J-M, KLEIN D and GHUYSEN J-M (1980), ‘Enzymatic method for rapid and sensitive determination of -lactam antibiotics’, Antimicrob Agents Ch, 18 (4), 506–510. GAUDIN V and MARIS P (2001), ‘Development of a biosensor-based immunoassay for screening of chloramphenicol residues in milk’, Food Agric Immunol, 13, 77–86. GAUDIN V, FONTAINE J and MARIS P (2001), ‘Screening of penicillin residues in milk by a surface plasmon resonance based biosensor assay: comparison of chemical and enzymatic sample pre-treatment’, Anal Chim Acta, 436, 191–198. ˚ (2002a), ‘Analysis of -lactam GUSTAVSSON E, BJURLING P, DEGELAEN J and STERNESJo¨ A antibiotics using a microbial receptor protein-based biosensor assay’, Food Agric Immunol, 14, 121–131. ˚ (2002b), ‘Biosensor analysis of penicillin G ¨ A GUSTAVSSON E, BJURLING P and STERNESJO in milk based on the inhibition of carboxypeptidase activity’. Anal. Chim. Acta, 468, 153–159. HAASNOOT W and VERHEIJEN R (2001), ‘A direct (non-competitive) immunoassay for gentamicin residues with an optical biosensor’, Food Agric Immunol, 13, 131–134. CROOKS S R H, BAXTER G A, O’CONNOR M C

¨ M-CASELUNGHE M C, PERSSON B S, FINGLAS P M, WOOLLARD D C INDYK H E, EVANS E A, BOSTRO

and FILONZI E L (2000), ‘Determination of biotin and folate in infant formula and milk by optical biosensor-based immunoassay’, J AOAC Int, 83, 1141–1148. ˚ S S and LINDQUIST G (1991), ‘Immobilization of proteins to a ¨ FA JOHNSSON B, LO carboxymethyldextran modified gold surface for biospecific interaction analysis in surface plasmon resonance sensors, Anal Biochem, 198, 268–277. KORSRUD G, BOISON J, NOUWS J and MACNEIL J (1998), ‘Bacterial inhibition tests used to screen for antimicrobial veterinary drug residues in slaughtered animals’, J AOAC Int, 81, 21–24. ¨ M I (1983), ‘Surface plasmon resonce for gas LIEDBERG B, NYLANDER C and LUNDSTRO detection and biosensing’, Sens. Actuators, 4, 299–304. ˚ S S and JOHNSSON B (1990), ‘A novel hydrogel matrix on gold surfaces in surface ¨ FA LO plasmon resonance sensors for fast and efficient covalent immobilization of ligands’, J Chem Soc, Chem Commun, 21, 1526–1528. ˚ S S, JOHNSSON B, EDSTRO ˚ , HANSSON A, LINDQUIST G, MULLER-HILLGREN R-M and ¨ FA ¨M A LO STIGH L (1995), ‘Methods for site controlled coupling to carboxymethyldextran surfaces in surface plasmon resonance sensors’, Biosens Bioelectron, 10, 813–822. ˚ (1998), ‘Optical immunobiosensor assay for determining MELLGREN C AND STERNESJo¨ A enrofloxacin and ciprofloxacin in bovine milk’, J AOAC Int, 81, 394–397. MITCHELL J M, GRIFFITHS M W, MCEWEN S A, MCNAB W B and YEE J (1998), ‘Antimicrobial drug residues in milk and meat: causes, concerns, prevalence, regulations, tests and

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and STEGEMAN H (1999), ‘A microbiological assay system for assessment of raw milk exceeding EU maximum residue levels, Int Dairy J, 9, 85–90. O’SHANNESSY D J, BRIGHAM-BURKE M and PECK K (1992), ‘Immobilisation chemistries suitable for use in the Biacore surface plasmon resonance detector’, Anal Biochem, 205, 132–135. ˚ , MELLGREN C and BJO ¨ A ¨ RCK L (1995), ‘Determination of sulphamethazine STERNESJO residues in milk by a surface plasmon resonance-based biosensor assay’, Anal Biochem, 226, 175–181. SUHREN G (1995), ‘Possibilities and limitations of microbiological inhibitor tests’, in International Dairy Federation, Residues of Antimicrobial Drugs and Other Inhibitors in Milk, Brussels, International Dairy Federation, 159–171. SUHREN G and HEESCHEN W (1993), ‘Detection of tetracyclines in milk by a Bacillus cereus microtitre test with indicator, Milchwissenschaft, 48, 259–263. TURNER A P F and NEWMAN J D (1998), ‘An introduction to biosensors’, in Scott A O, Biosensors for Food Analysis, Cambridge, The Royal Society of Chemistry, 13–27. NOUWS J, VAN EGMOND H, SMULDERS I, LOEFFEN G, SCHOUTEN J

6 Detecting veterinary drug residues N. van Hoof, K. de Wasch, H. Noppe, S. Poelmans and H.F. de Brabander, University of Ghent, Belgium

6.1

Introduction

An increase in education and consumer awareness has led to an evolution or revolution in the demand for safe and healthy food. One of the challenges in the food industry is to meet the requirements and demands of the consumer. Consumers want to know what they eat, it has to be healthy and not harmful. Objective information has to be passed on to the consumer. The confidence of the consumer has been tested several times over the last few years. After the dioxin crisis, BSE, foot and mouth disease, the nitrophen crisis, MPA crisis, to name just a few. Consumers have become very critical when it comes to their food. Also the attitude of the consumer towards the intense use of antibiotics is not positive. A wide range of veterinary medicinal products (VMP) such as antibiotics is administered legitimately to farm animals to treat outbreaks of disease or prevent disease from spreading. Necessary medication should be applied in the prescribed dose and carefully recorded. These records must be traceable by government inspection services. In order to reduce the likelihood of harmful levels of these substances reaching the human food chain, the European Union and many other countries have set maximum residue limits (MRLs). Regulatory bodies are required to enforce and verify these requirements. Laboratory testing of food products has to ensure that regulations are met. Official samples taken at the slaughterhouse or the farm are analysed for forbidden substances but also for registered veterinary medicinal products, legally or illegally applied. The results of these analyses are used as part of a holistic approach in food quality assurance. Both violative and compliant results are reported. The criteria that are used for generating the results, i.e. for identification and quantification of the analytes, meet the strict internationally

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required standards. In residue analysis, uncompromising quality is not an option – it is an obligation. It is not a vague goal – it is standard operating procedure. Quality begins in the farms where every aspect of good working practice should ensure the quality of the end product. Assurance of the different aspects of the quality is also given by laboratory results. Therefore residue laboratories working for the government have to be accredited (ISO 17025). Every step, from the incoming sample to the outgoing result, is traceable. Quality control in every step of the procedure assures trustworthy results. By maintaining this intense level of quality control, by developing new methods and using high technological equipment operated by well trained personnel, laboratories will maintain their important position as part of the chain of food quality assurance. A wide range of different groups of veterinary medicinal products is used in practice. Since every group requires a specific extraction and detection procedure, it has become too expensive to check every sample for a whole batch of different groups. Screening methods are developed to eliminate a large number of false positive samples and give an indication of the specific group of veterinary medicinal products. Only the truly ‘suspect’ samples are transferred for confirmation. This confirmation procedure is more expensive than the screening but at this stage a positive identification of an analyte can be combined with a quantification. A quantification compares the concentration of the analyte with the MRL value prescribed by law.

6.2

Veterinary medical products

6.2.1 Definition A ‘medicinal product’ is defined in Article 1 of Directive 65/65/EEC as any substance or combination of substances presented for treating or preventing diseases in human beings or animals. Any substance or combination of substances which may be administered to human beings or animals with a view to making a medicinal diagnosis or to restoring, correcting or modifying physiological functions in human beings or animals is likewise considered a medicinal product. A veterinary medicinal product is used to mean any product within the above definition that is subject to Article 1 of Directive 81/851/EEC. The major goal of the animal health industry is to make veterinary medicinal products available which improve the health, welfare and productivity of animals whilst ensuring food and environmental safety (Council Directive 65/ 65/EEC, 1965).

6.2.2 Legislation The evaluation of the safety of residues is based on the determination of the acceptable daily intake (ADI) on which in turn maximum residue limits (MRL) are based. The ADI is an estimate of the residue that can be ingested daily over a lifetime without a health risk to the consumer. The ADI is determined following

Detecting veterinary drug residues 93 the evaluation of pharmacological and toxicological studies. The basis for the calculation of the ADI is the no-observed-effect-level (NOEL) and the calculation includes an extremely large safety factor. In addition, to derive MRLs from the ADI it is assumed that the average person consumes, on a daily basis, 500 g of meat, 1.5 l of milk and 100 g of eggs or egg products (Grein, 2000). Veterinary medicinal products having a pharmacological action, as defined by the European Union (EU), are ‘substances capable of pharmacological action in the context of Article 4 of Directive 81/851/EEC and should be interpreted as substances which are pharmacologically active at the dose at which they are administered to the target animal by means of the veterinary medicinal product in which they are included’. (EMEA/CVMP/046/00-Rev. 3, 17 April 2002). Residues of veterinary medicinal products are ‘pharmacologically active substances (whether active principles, excipients or degradation products) and their metabolites which remain in foodstuffs obtained from animals to which the veterinary medicinal product in question has been administered’ (Council Regulation (EEC) No. 2377/90, 1990). An MRL means the maximum concentration of residue resulting from the use of a veterinary medicinal product (expressed in mg/kg or g/kg on a fresh weight basis) which may be accepted by the Community to be legally permitted or recognised as acceptable in food (Council Regulation (EEC) No. 2377/90, 1990). Once the MRL has been allocated it is necessary to determine the withdrawal period. This is the period after administration of the veterinary medicinal product during which the target animal must not be slaughtered or during which milk or eggs must not be taken for human consumption. This ensures that residues from the product concerned will not exceed the MRL (Grein, 2000). The responsibility for keeping residues under the MRL lies with veterinary surgeons and farmers using licensed animal medicines. Regulatory bodies and laboratories testing food products are required to ensure that regulations are met. Official samples taken by inspection services in the slaughterhouse or the farm need to be analysed in official laboratories for forbidden substances and for legally used veterinary drugs. In Belgium, if residues of a veterinary medicine are detected in a concentration higher than the MRL then the farm receives an Rstatus. This means that for eight weeks there will be one analysis for every ten slaughtered animals at the cost of the owner. If residues are found of a forbidden substance the consequences are more severe and the farm will receive an Hstatus (Okerman et al., 1999). This is implemented for 52 weeks. A sample from one of every ten animals slaughtered will be analysed at the cost of the owner.

6.3

Methods for detecting residues

6.3.1 Screening methods A screening test is an analytical method which will give a strong indication if there is some form of drug residue present in a sample. The residue present may

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be in the form of parent drug or metabolite. Screening tests are generally expected to provide inexpensive, qualitative and sometimes semi-quantitative test responses. Various rapid tests for detecting veterinary drugs in foods have been reported. They are classified as either microbiological and electrophoretic methods or immunological methods and receptor tests (Watanabe et al., 1998). Microbiological tests are considered as multi-residue screening tests, while immunological and receptor tests are more specific and can detect one substance or a group of related chemicals (De Wasch et al., 1998b; Okerman et al., 1998). Microbiological identification tests use bacterial strains with varying sensitivities to antibiotics on media of varying pH values and supplemented with substances blocking or enhancing the action of certain antibiotics or antibiotic groups (Myllyniemi et al., 2001). An example of such an inhibition test is the four plate test (FTP). As the name infers four plates are required with three using the organism Bacillus subtilis at different pH values and the fourth using Micrococcus luteus (Woodward and Shearer, 1995). The addition of a plate seeded with Escherichia coli to the system will facilitate the detection of quinolones (Okerman et al., 1998). From the size and the pattern of the annular zone on the various plates an idea can be formed of the identity and the concentration of the analyte (Woodward and Shearer, 1995). The composition and the properties of the medium used in a microbiological inhibition test influence the detection limits of antibiotics; in particular, the pH of the medium is an important factor. It is therefore possible that tissue components such as proteins change the composition of the medium and influence the inhibitory zone produced by an antibiotic residue present in the sample (Okerman et al., 1998). Next to the microbiological inhibition tests, electrophoretic systems can be used as an aid in identifying antibiotics. Discs of meat are placed on a gel nutrient media and high voltage electrophoresis is carried out after a short diffusion time. Depending on the pH and the nature of the gel, antibiotics have differing mobilities. After applying the voltage, the gel is overlaid with an organism-containing gel and incubated. Zones of inhibition indicate the presence of antibiotics and the position on the plate could indicate the nature of the antibiotic (Woodward and Shearer, 1995). The purpose of microbiological and electrophoretic tests is to select samples, which probably contain one or more analytes and which should be investigated with more sophisticated immunochemical and/or chromatographic methods. Microbiological tests should be simple, cheap, easy and fast. Multi-residue screening methods are preferred to methods detecting only one analyte (Okerman et al., 1998, 1999). Immunological (radioimmunoassay (RIA) and enzyme-linked immunosorbent assay (ELISA)) and receptor tests have similar working mechanisms. In both cases there is a reaction between an analyte and an antibody. Immunological tests are based on the competition between a labelled antigen (tracer) and an analyte. First a carrier is coated with a monoclonal or polyclonal antibody that recognises both the tracer and the analyte. The carrier is then brought in

Detecting veterinary drug residues 95 contact with the labelled antigen as well as with an extract of a sample. If the extract is residue free only the tracer will bind to the antibody. If some analyte is present in the extract some of the spaces on the carrier will be occupied with unlabelled analyte. The antigen is labelled with a radioactive isotope (RIA) or with an enzyme (ELISA). The reaction in ELISA is made visible afterwards by adding a substrate, which will be converted into a coloured product through reaction with the enzyme. So for both RIA and ELISA qualitative and quantitative results are obtained (De Wasch et al., 1998b; Okerman et al., 1999; Woodward and Shearer, 1995; Haasnoot and Schilt, 2000). Receptor tests make use of the reaction between an antibacterial compound and a bacterial receptor. Receptors are isolated from sensitive organisms and they are able to bind antibiotics (Okerman et al., 1999). Immunological and receptor tests are more specific than inhibition tests and provide faster results but the kits cost more and the reagents are not stable for a long time (De Wasch et al., 1998b). The objective of a qualitative screening method is to check whether a sample contains antibiotics. It is not the intensity of the signal, but the sensitivity that is important (Arts et al., 1998). Screening tests can only be used if no falsenegative results are possible, because negative samples are accepted without further confirmation analysis (Okerman et al., 1998). The semi-quantitative screening methods can often be used more efficiently and hence at lower cost than the more sophisticated quantitative chromatographic methods. Nevertheless the latter are necessary for confirmation of suspected samples obtained during screening (Arts et al., 1998).

6.3.2 Confirmation methods Numerous confirmation methods have been described for the detection of veterinary drugs in various matrices. Most techniques comprise a chromatographic separation and a detection method. A chromatographic system on its own does not provide unambiguous confirmation of the identities of the components because the signal obtained is devoid of structural information. The only criterion of identity is the retention time. Unfortunately any one of a given number of substances can have the same retention time (Rose, 1990). Liquid chromatography (LC) is often combined with ultraviolet detection (UV), fluorescence detection and mass spectrometry (MS) (Mellon, 1991). Gas chromatography (GC) can be combined with electron capture detection (ECD), infrared detection (IR) and mass spectrometry (Rose, 1990). The hyphenation of gas chromatography and mass spectrometry was first introduced in 1952. Because some products (with a molecular mass higher than 1500 and thermo labile molecules) cannot be separated with GC, the development of LC/MS technologies and applications started almost 20 years ago (De Brabander et al., 1998). To obtain better detection limits MS-MS was developed. MS-MS methods offer great advantages in three areas: structural studies, the characterisation of mixture compounds and trace analysis. The use

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of LC-MSn has led to a reduction of the sample extraction and clean-up (Scrivens and Rollins, 1990). Confirmation methods can be both qualitative and quantitative. Quantitative methods are necessary to detect veterinary products that are permitted in some matrices in a maximum concentration. The methods need to confirm if the concentration of an analyte is below or above this limit. Therefore such confirmation methods need to have a quantification limit lower than the MRL. The quantification limit should be approximately 0.5 times the MRL (see Section 6.4.3). Qualitative methods are used for forbidden substances and violative use of veterinary medicinal products. These methods are always semi-quantitative. To determine if an analyte is present or absent an action limit (AL) is used. An action limit separates a signal from the background noise. An action limit is not a legal value; it is an agreement between the laboratories and the inspection services and depends on the analytical possibilities at that moment. There are three main differences between an MRL and an AL: 1. 2. 3.

6.4

MRLs are situated in a higher concentration level than ALs MRLs are only used for registered veterinary drugs in edible products whereas ALs are used for forbidden substances MRL has a legal implementation, whereas AL is a convention between laboratories and inspection services (De Brabander et al., 1998).

Validating detection methods

6.4.1 Method validation Every regulatory analytical method should be validated for different parameters. The most important parameters are recovery, detection and quantification limits. Recovery means ‘the percentage of the true concentration of a substance recovered during the analytical procedure’. The limit of detection means ‘the smallest content of the analyte that may be detected in a sample after applying the appropriate identification criteria’. The limit of quantification means ‘the smallest measured content of the identified analyte in a sample that can be quantified with a specified degree of accuracy and precision’ (Council Directive 96/23/EC, 1996).

6.4.2 Identification Every confirmation method requires a ‘standard injection protocol’ (SIP) to guarantee the quality of the detection and quantification. An SIP is a logical succession of standards, blanks and samples (Table 6.1). When a sample reveals a ‘suspected’ mass spectrum, quality criteria are necessary for the qualification and quantification. These criteria are determined by the EU, but are constantly evaluated. Quality criteria for the identification of organic residues and contaminants are based on the use of identification points

Detecting veterinary drug residues 97 Table 6.1 The ‘standard injection protocol’ (SIP) for the detection and quantification of veterinary drugs Detection

Quantification

1. 2. 3. 4. 5. 6. 7.

1. 2. 3. 4. 5. 6. 7. 8. 9.

standard blank (mobile phase) samples in positive mode blank samples in negative mode blank (mobile phase) standard

standard blank (mobile phase) sample blank (mobile phase) blank spike at MRL connection spike at 10  MRL concentration blank mobile phase) standard

(IP). The system of identification points balances the identification power of the different analytical techniques and moreover has the advantage that new techniques may easily be incorporated in the procedure. The minimum number of IPs for forbidden compounds is set to four, for compounds with an MRL a minimum of three IPs are required for the confirmation of the compound’s identity. The most important limitations of these criteria are: they are not applicable to all compounds and they are ambiguous. Not all forbidden substances generate four IPs and diagnostic ions that appear at relatively high concentration may disappear when the concentration of the analyte decreases. All diagnostic ions must have a signal-to-noise ratio of at least 3:1. The relative intensities of the detected ions expressed as a percentage of the intensity of the most intense ion must correspond to those of the standard analyte or spiked matrix at comparable concentrations and measured under the same conditions, within the tolerances given in Table 6.2. If mass fragments are measured the system of identification points shall be used to interpret the data. LC-MSn precursor ions earn 1 IP, LC-MSn transition products earn 1.5 IP. Moreover at least one ion ratio must be measured and all measured ion ratios must meet the criteria described above. The criteria for identification are based on precursor and diagnostic ions, which give structural information. Retention time is another parameter that gives an indication of the identity of an analyte, but it contains no structural information. Moreover various substances have the same retention time (Andre´ et al., 2001).

Table 6.2

The allowed margins for the relative ion intensities for LC-MSMS

Relative intensity

Maximum permitted tolerance

>50% >20–50% >10–20% <10%

±20% ±25% ±30% ±50%

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6.4.3 Quantification The identification of compounds has to be completed before their quantification. A quantitative validation is required for each sample containing a permitted veterinary drug. Such a validation consists of determining the required validation parameters at three levels: ½MRL, MRL and 2MRL. This validation approach is very elaborate and time consuming. Because of the high concentrations of VMPs in injection sites an alternative validation is proposed for that matrix. De Wasch et al. (2002) described an alternative validation consisting of a comparison of the analyte concentration in the sample with the spike at MRL and 10 times MRL concentration. The alternative approach is performed as a mini-validation. A mini-validation consists of five blank matrices fortified with the MRL concentration of the analyte, five blank matrices fortified with 10 times the MRL concentration of the analyte and one blank matrix. This approach allows the analyst to meet the needs and requirements of the customer awaiting the results. The analysis is accurate, fast and the total cost of this approach is minimised in comparison with a traditional validation and analysis. In Section 6.5.2 this alternative validation is illustrated by the example of sulfadimethoxine. The conditions that need to be fulfilled before reporting a sample as violative are described by De Wasch et al. (2002). Traditional validation procedures require extensive studies to obtain all necessary data. Such a validation is time consuming and expensive. It is, however, not practical or necessary that all analytical methods are assessed at this ideal level. The alternative validation gives a rapid reporting to the customer and the costs are lower than for a traditional analysis. Identification can be performed within 48 hours and an extra 24 hours are necessary for the quantification. The choice between a traditional validation and an alternative validation depends on the analytical purpose and method.

6.5 Rapid on-line confirmation of different veterinary residues 6.5.1 General A Belgian research project financed by the Federal Ministry of Agriculture and the Institute of Veterinary Inspection started in January 2001 in which injection sites were collected at the slaughterhouse. In analysing these samples, an overview could be given of what is frequently used nowadays in practice. Another important aspect to consider is that injection sites very often contain high concentrations of the administered product. Injection sites are considered as meat by inspection services and therefore the MRL for meat applies, especially because of the possible consumption of an injection site. To develop and to use very specific confirmation methods takes time and is expensive. Because of the high concentrations there is no demand for the registered VMPs to be quantified in the concentration range of the MRL. A different validation can be used (Section 6.4.3).

Detecting veterinary drug residues 99 Since the beginning of 2001 different injectable or standard solutions of registered veterinary medicine products were collected. These solutions were subjected to infusion-MSn and LC-MSn. The collected data will function as a database for the identification of analytes present in an injection site. Injectable solutions are not the active compounds but the VMPs as used in veterinary practice. Additional impurities can therefore obscure the chromatogram and the spectrum, but this can also be expected in injection sites. The use of an ointment base such as polyethyleneglycol can mask the pure product when using direct infusion (De Wasch et al., 2002).

6.5.2 Examples For the identification of ‘unknown analytes’ two approaches can be used: infusion-MSn (example: flunixin) and LC-MSn (example: sulfadimethoxine). Electospray ionisation is preferred for both because it is a soft ionisation technique and fragmentation of the pseudo-molecular ion in full scan MS is not as intense as when using APCI. Possible molecular masses are calculated from MH‡ or MHÿ ions, Na‡, NH4‡ or Acÿ adducts. There are different identification strategies. It is possible to derive the molecular weight of the unknown analyte by complementary data from the positive and negative ions (MH‡ and MHÿ ions). Sometimes there appears only one pseudo-molecular ion either in positive or negative ion mode and an adduct in the other mode. Adducts are formed by reaction between the analyte and the solvent used in the mobile phase. Knowledge of the solvents used is therefore very important. For example when an acid is added to methanol or water acetate, adducts can be formed in negative ion mode, ammoniumacetate leads to NH4‡ adducts. It is also possible to detect only adducts in both ion modes. Some analytes, depending on their functional groups, cannot form positive or negative ions or adducts. The absence of ions in one ion mode also gives structural information about the analyte. In the presence of a second compound (a veterinary drug or a chemical product) different combinations can be formed leading to mass spectra in which these different combinations can be recognised. Different masses will appear in the spectrum at different retention times. De Wasch et al. (2002) demonstrated this with the example Penicillin G – benzathin. These different strategies demonstrate that some knowledge is required for the interpretation of mass spectra. First, knowledge about the chromatographic system, the mobile phase in particular. This mobile phase can lead to the formation of an adduct in positive or negative ion mode. Second, knowledge about the mass spectrometer. Electrospray ionisation is a soft ionisation technique and gives rise to the pseudo-molecular ion without extensive fragmentation. Using the collected data of the different injectable or standard solutions and the database of the Merck index the identity of the analyte can be elucidated. All possible compounds from the database are filtered based on their therapeutic category or intended use. Ion traces of ‘known’ compounds (collected injectable and standard solutions of registered veterinary medicinal products) can be more

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easily detected using LC-MSn by applying a layout. A layout is an option in the software in which mass traces of pseudo-molecular ions of injectable solutions are combined in a window. A layout can be added depending on the knowledge of injectable analytes at that time. In the next examples the identification of some veterinary medicinal products is described using infusion-MSn or LC-MSn. The identity of the analyte can be elucidated using the mass spectra in MS-full scan and/or MS-full scan and the collected data of different injectable or standard solutions and the database of the Merck index. The alternative validation consisting of a comparison of the analyte concentration in the sample with the spike at MRL and 10 times MRL concentration is illustrated in the first example of sulfadimethoxine. Such a validation is comparable for each compound. Identification of sulfadimethoxine Sulfadimethoxine (Fig. 6.1) is a sulfonamide antibiotic used in veterinary medicine to treat susceptible bacterial and coccidial infections. It is recommended in cattle for the treatment of bovine respiratory disease complex, bacterial pneumonia associated with Pasteurella spp., necrotic pododermatitis (foot rot) and calf diphtheria caused by Fusobacterium necrophorum. Sulphonamides are antibacterial agents widely used in veterinary practice to prevent infections in livestock. They have also been used in animal feeds to promote growth and to treat disease. Residues are often found in meat and milk

Fig. 6.1

Structural formulas of sulfadimethoxine, flunixine, prednisolone and oxytetracycline.

Detecting veterinary drug residues 101 Table 6.3

Identity and percentage of the analytes present in injection sites in 2002

Analyte

Group of VMPs

Number of violations

Percentage

Penicillin G Flunixin Oxytetracycline Sulfadimethoxin Enrofloxacin Trimethoprim Prednisolone Erythromycine Tilmycosine Tylosine Florfenicol Tolfenamic acid Lincomycin Dexamethasone Tetracycline Sulfadoxine Meloxicam

-lactam NSAID tetracycline sulfonamide quinolone diamine derivatives corticosteroid macrolide macrolide macrolide florfenicol NSAID lincosamide corticosteroid tetracycline sulfonamide NSAID

26 19 10 6 5 5 4 4 4 4 3 3 2 2 2 1 1

7.6 5.6 2.9 1.8 1.5 1.5 1.2 1.2 1.2 1.2 0.9 0.9 0.6 0.6 0.6 0.3 0.3

products where they enter the human food chain. The presence of sulphonamide residues in food is of concern because some of the compounds are carcinogenic and they enhance the risk of developing bacterial resistance, which makes the therapeutic use of this medicine inefficient (Dost et al., 2000) Most of the sulphonamides are combined with trimethoprim that potentiates the antibacterial effects of the sulphonamide. These combinations are believed to act synergistically on specific targets in bacterial DNA synthesis (Woodward and Shearer, 1995). Table 6.3 shows that trimethoprim was detected in five samples and this always in combination with sulfadimethoxine. Sulfadimethoxine has an MRL of 100 g/kg in muscle. Because injection sites are considered as meat by inspection services, the MRL for meat applies. Injection of an extract of an injection site revealed in MS-full scan an intensive negative ion with m/z 309 and an intensive positive ion with m/z 311. An analyte with molecular mass 310 can be expected from the complementary information of the positive and negative ion spectra. Using the Merck index a search is performed in the molecular weight range 309–311. Different possibilities were found: altrenogestagen (prostagen), mepazine (tranquilliser), methoprene (ectoparasiticide), sulfadoxine (antibacterial) and sulfadimethoxine (antibacterial). Mass spectra of the injectable and standard solutions of some of these compounds revealed that both sulfadoxine and sulfadimethoxine (MM 310.33) produce the same pseudo-molecular ions as the ‘unknown’ sample. There are no differences between the two components in MS-full scan, but the ion ratio in MS is different (sulfadoxine: 108 (±15%), 156 (100%), 218 (±2%), 245 (20%); sulfadimethoxine: 108 (±10%), 156 (100%), 218 (±40%), 245

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Fig. 6.2

MS2 fragmentation of the ion with m/z 311 in positive mode.

(±90%)). Therefore a second injection of the extract is performed in the positive ion mode, both in MS-full scan and MS2-full scan (Fig. 6.2). Also the standard solutions of sulfadoxine and sulfadimethoxine are injected (Fig. 6.3). Not only the ion ratio in MS is different, but also the retention time (sulfadoxine elutes around 10 min and sulfadimethoxine around 14 min). It can be concluded that the injection site contains sulfadimethoxine. Because sulfadimethoxine is a legally used veterinary medicinal product with an MRL of 100 g/kg a quantification is required (Fig. 6.4). This is performed by comparing the analyte concentration in the sample with the spike at MRL and 10 MRL concentration. The area ratio is calculated by dividing the area of sulfadimethoxine by the area of the internal standard desoximethasone. The area ratio of the sample is 34.732, the one of the spike at MRL concentration is 0.0066 and the one at 10 MRL concentration is 0.0618. Subsequently the conditions described by De Wasch et al. (2002) on which violation is based, are calculated. The ratio of the area ratio of the spike at 10  MRL concentration and the area ratio of the spike at MRL concentration is 9.36 (the prescribed ratio is 4) and the ratio of the area ratio of the sample and the area ratio of the spike at MRL concentration is 5262 (the prescribed ratio is 10). The sample is thus clearly violative for sulfadimethoxine, with a concentration higher than the MRL concentration.

Detecting veterinary drug residues 103

Fig. 6.3

MS2-full scan spectrum of sulfadoxine (top) and sulfadimethoxine (bottom).

Fig. 6.4 The chromatograms of sulfadimethoxine and desoximethasone (IS), each with the retention time and the area, of a spike at MRL concentration, a spike at 10 MRL concentration and the extract of an injection site. The calculation of the conditions for violation.

Fig. 6.4 Continued

Fig. 6.5

MS-full san of an injection site in positive and negative mode.

Fig. 6.5 Continued

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Fig. 6.6 MS2 fragmentation of the ion with m/z 419 in negative mode (top) and MS2 fragmentation of the ion with m/z 361 in positive mode (bottom).

Detecting veterinary drug residues 109 Identification of flunixin Flunixin (Fig. 6.1) is a nonsteroidal anti-inflammatory drug (NSAID) used to alleviate inflammation and pain in veterinary medicine. NSAIDs are used either alone or in combination with approved antibiotics to treat food-producing animals. Flunixine is a legally used veterinary medicinal product with an MRL of 20 g/kg in muscle. An extract of an injection site was infused in the mass spectrometer through a T-piece. In positive ion mode ions with m/z 297 and 279 were observed, in negative ion mode ions with m/z 295 and 251 (Fig. 6.5). The molecular mass 296 can be derived from the complementary information of positive and negative ions. MS fragmentation of the ion with m/z 297 in positive mode and the ion with m/z 295 in negative mode indicates that the other two ions observed after infusion are fragments of the pseudo-molecular ion. Using the Merck index the compounds with molecular weight 296 are filtered based on their use in veterinary medicine. Even then still a lot of possibilities remained. Making use of our database of mass spectra of injectable solutions of registered veterinary medicine products it can be concluded that the analyte present in the injection site was flunixin (MW 296.25). Identification of prednisolone Prednisolone (Fig. 6.1) is a glucocorticoid used for the suppression of inflammatory and allergic disorders in veterinary medicine. Besides the therapeutic use of glucocorticoids research demonstrated that these compounds are capable of increasing weight gain and reducing feed conversion, and they have a synergetic effect when combined with other molecules like -agonists or anabolic steroids. Thus corticosteroids are illegally used as growth promoters in cattle, administered through livestock food or by injection (Antignac et al., 2001). Prednisolone has an MRL of 4 g/kg in muscle. Injection of the extract of an injection site showed in MS-full scan in negative ion mode the ions with m/z 419 and 359 and in positive mode the ion with m/z 361. The molecular weight 360 can be derived from the positive and negative ions (positive ions: 361 ÿ 1 ˆ 360 and negative ions: 359 ‡ 1 ˆ 360, 419 ÿ 60 (acetate adduct) ˆ 359). In MS2-full scan in positive mode of the ion with m/z 361 a specific spectrum was revealed. A large number of product ions is observed decreasing in intensity with decreasing m/z. This pattern of decreasing ion peaks is typical for glucocorticoids (De Wasch et al., 1998a). In negative mode the spectrum in MSfull scan of the ion with m/z 419 reveals a loss of 60 due to the acetate, followed by a loss of 30 (Fig. 6.6). Using the Merck index a search is performed in the molecular weight range 359–361. Two glucocorticoids were found: cortisone and prednisolone. MS fragmentation of the extract corresponds to the standard of prednisolone (MW 360.44) that was already acquired in a different application.

Fig 6.7

MS-full scan of an injection site in positive and negative ion mode.

Fig. 6.7 Continued

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Identification of oxytetracycline Tetracyclines are broad spectrum antibiotics, used to treat respiratory disease in cattle, sheep, pigs and chicken and they may be given in feed or drinking water. Oxytetracycline (Fig. 6.1) is used to treat enteric disease in a number of species. It is a legally used veterinary medicinal product with an MRL of 100 g/kg in muscle. Infusion of an extract of an injection site showed in positive mode especially the ion with m/z 461 and in negative the ion with m/z 459 (Fig. 6.7). A search was performed in the Merck index for the molecular weight 460 (range 459– 461). There was only one compound that is used in veterinary practice: oxytetracycline. The spectrum of MS of the extract in positive mode corresponds to that of the standard solution of oxytetracycline collected in our database.

6.5.3 Conclusion In 2002, 342 injection sites were analysed for ‘unknown analytes’, 101 of them (29.5%) were reported violative with a concentration higher than the MRL. Table 6.3 gives a summary of the identity and the percentage of these analytes. In 17 injection sites (5%) the identity of a veterinary drug could be demonstrated, but with a concentration lower than the MRL. Penicillin G and flunixin are the most commonly used veterinary medicines in this study. Also oxytetracycline and sulfadimethoxin are frequently used.

6.6

Future trends

This chapter illustrates the advantages of using infusion-MSn and/or LC-MSn to detect and confirm the presence of highly concentrated compounds in injection sites. It is a multi-residue approach which is able to detect a wide range of administered products. Injection sites can contain a wide range of analytes in high concentrations. To develop specific confirmation methods and switching instruments to different applications for only one sample, is time consuming and expensive. An alternative approach was therefore necessary. For the examples illustrated in this chapter no specific method development for extraction or clean-up or confirmation was performed. There is no need for quantification of the registered VMPs in the concentration range of the MRL. An alternative validation is used comparing the analyte concentration in the sample with the spike at MRL and 10 times MRL concentration. The alternative approach is performed as a minivalidation. Identification is based on the collected data of the different injectable and standard solutions of registered VMPs and the database of the Merck index. Extraction and identification can be performed within 48 hours. If the identified compound needs to be quantified, an extra 24 hours are necessary before the result can be reported. To create a faster and on-line method the

Detecting veterinary drug residues 113 clean-up can be performed on-line. Due to the simplicity of the extraction and clean-up (only SPE) an online procedure should be possible. Extraction and clean-up is necessary because the injection sites are rather dirty. Such an on-line method will lead to faster and more homogeneous results. The detection method itself cannot be shortened because then there is a chance that some veterinary medicinal products would not be detected. The interpretation of the mass spectra can also be very time consuming. It is therefore important to collect as much mass spectra as possible of injectable and standard solutions of VMPs. Then the interpretation can be more automated using a library. A library contains all mass spectra of injectable and standard solutions and can be correlated to the interpretation of the data. The interpretation can so be simplified and even automated. There is a continuing need for reliable analytical methods for use in determining compliance with national regulations as well as international requirements in all areas of food quality and safety. The reliability of a method is determined by some form of validation procedure. Method validation needs to be carried out under an appropriate quality system. The final goal is to produce correct results, by means of a reliable quantitative validation, and minimise the costs and time doing so. This will lead to specialisation of laboratories and to alternative validations if the analytical goal permits it. It is important that correct results can be produced within a reasonable period of time.

6.7

Acknowledgements

This work was supported financially by the Belgian Federal Ministry of Agriculture and the Institute of Veterinary Inspection (S6044/S3).

6.8

References

ANDRE´ F, DE WASCH K K G, DE BRABANDER H F, IMPENS S R, STOLKER L A M, VAN GINKEL L, ¨ RST P, GOWIK P, KENNEDY G, STEPHANY R W, SCHILT R, COURTHEYN D, BONNAIRE Y, FU

and SAUER M (2001), ‘Trends in the identification of organic residues and contaminants: EC regulations under revision’, Trends Anal Chem, 20 (8), 435–445. ANTIGNAC JP, LE BIZEC B, MONTEAU F, POULAIN F and ANDRE´ F (2001), ‘Multi-residue extraction-purification procedure for corticosteroids in biological samples for efficient control of their misuse in livestock production’, J Chrom B, Biomed Appl, 757 (1), 11–19. ARTS C J M, VAN BAAK M J, ELLIOT C J, HEWITT A, COOPER J, VAN DE VELDE-FASE K and WITKAMP R F (1998), ‘Comparison of conventional immunoassays and the oestrogen radioreceptor assay for screening for the presence of oestrgenic anabolic compounds in urine samples’, Analyst, 123, 2579–2583. KUHN T, MORETAIN JP

COUNCIL DIRECTIVE 65/65/EEC OF 26 JANUARY 1965 OF PROVISIONS LAID DOWN BY LAW, REGULATION OR ADMINISTRATIVE ACTION RELATING TO MEDICINAL PRODUCTS

(1965)

114

Rapid and on-line instrumentation for food quality assurance Official Journal of the European Communities, no. L 22 of 9.2., 369.

COUNCIL REGULATION (EEC) NO. 2377/90 OF JUNE 1990 LAYING DOWN A COMMUNITY PROCEDURE FOR THE ESTABLISHMENT OF MAXIMUM RESIDUE LIMITS OF VETERINARY MEDICINAL PRODUCTS IN FOODSTUFFS OF ANIMAL ORIGIN

(1990), Official Journal of

the European Communities, no. L 67 of 7.3, 1. COUNCIL DIRECTIVE 96/23/EC OF 29 APRIL 1996 ON MEASURES TO MONITOR CERTAIN SUBSTANCES AND RESIDUES THEREOF IN LIVE ANIMALS AND ANIMAL PRODUCTS AND REPEALING DIRECTIVES 85/358/EEC AND 86/469/EEC AND DECISION 89/187/EEC AND 91/664/

(1996), Official Journal of the European Communities, no. L 125, 32–45. and BATJOENS P (1998), ‘Moderne analysemethodes voor additieven, contaminanten en residuen’, Vlaams Diergeneeskundig Tijdschrift, 67, 96–105. DE WASCH K, DE BRABANDER H, COURTHEYN D and VAN PETEGHEM C (1998a), ‘Identification of corticosteroids in injection sites and cocktails by MSn’, Analyst, 123, 2415– 2422. DE WASCH K, OKERMAN L, CROUBELS S, DE BRABANDER H, VAN HOOF J and DE BACKER P (1998b), ‘Detection of residues of tetracycline antibiotics in pork and chicken meat: correlation between results of screening and confirmatory tests’, Analyst, 123, 2737–2741. EEC

DE BRABANDER H F, DE WASCH K, OKERMAN L

DE WASCH K, VAN HOOF N, POELMANS S, OKERMAN L, COURTHEYN D, ERMENS A, CORNELIS M

and DE BRABANDER H F (2002), ‘Identification of ‘‘unknown analytes’’ in injection sites: a semi-quantitative interpretation’, Anal Chim Acta, accepted. DOST K, JONES D C and DAVIDSON G (2000), ‘Determination of sulfonamides by packed column supercritical fluid chromatography with atmospheric pressure chemical ionization mass spectrometric detection’, Analyst, 125, 1243–1247. GREIN K (2000), ‘The safe use of veterinary medicines and the need of residue surveillance’, in van Ginkel L A and Ruiter A, Proceedings of the Euroresidue IV conference, Veldhoven, The Netherlands. HAASNOOT W and SCHILT R (2000), ‘Immunochemical and receptor technologies’, in O’Keeffe M, Residue analysis in food – principles and applications, Singapore, Harwood Academic Publishers, 107–144. MELLON F A (1991), ‘Liquid chromatography/mass spectrometry’, in VG Monographs in Mass Spectrometry, 2 (1). ¨ CKMAN C (2001), ‘A MYLLYNIEMI A L NUOTIO L, LINDFORS E, RANNIKKO R, NIEMI A and BA microbiological six-plate method for the identification of certain antibiotic groups in incurred kidney and muscle samples’, Analyst, 126, 641–646. OKERMAN L, DE WASCH K and VAN HOOF J (1998), ‘Detection of antibiotics in muscle tissue with microbiological inhibition tests: effects of the matrix’, Analyst, 123, 2361– 2365. OKERMAN L, DE WASCH K, DE BRABANDER H, ABRAMS R, VAN HOOF J, CORNELIS M and LAURIER L (1999), ‘Oude en nieuwe opsporingstechnieken voor antibioticaresiduen in het kader van de huidige Belgische en Europese wetgeving’, Vlaams Diergeneeskundig Tijdschrift, 68, 216–223. ROSE M E (1990), ‘Modern practice of gas chromatography/mass spectrometry’, in VG Monographs in Mass Spectrometry, 1 (1). SCRIVENS J H and ROLLINS K (1990), ‘Tandem mass spectrometry’, in VG Monographs in Mass Spectrometry. WATANABE H, SATAKE A, MATSUMOTO M, KIDO Y, TSUJI A, ITO K and MAEDA M (1998), ‘Monoclonal-based enzyme-linked immunosorbent assay and immuno-

Detecting veterinary drug residues 115 chromatographic rapid assay for monesin’, Analyst, 123, 2573–2578. and SHEARER G (1995), ‘Antibiotic use in animal production in the European Union – Regulations and current methods for residue detection’, in Oka H, Nakazawa H, Harada K I and Macneil J D, Chemical Analysis for Antibiotics used in Agriculture, Arlington, AOAC International, 47–76.

WOODWARD K N

7 The rapid detection of toxins in food: a case study G. Palleschi, D. Moscone, L. Micheli, University of Rome ‘Tor Vergata’, Italy

7.1

Introduction

Filter-feeding molluscs such as clams, oysters and mussels can become toxic to humans during the so called ‘red tides’. The phenomenon of ‘red tide’ is caused by the fast growth of a kind of microscopic and single-celled algae, which are usually not harmful. Unfortunately, a small number of species (HAB = harmful algae) produce potent toxins that can be transferred throughout the food chain, affecting and even killing zooplankton, shellfish and even humans that feed on them either directly or indirectly. The growing threat of seafood intoxication has become significant in recent times. On-site refrigeration and transportation has removed the problem far from the incidence point. In Europe the economy of many coastal cities is linked to seafood production, hence toxin detection is of extreme importance. There are four human illnesses associated with shellfish and toxic blooms: • • • •

paralytic shellfish poisoning (PSP) neurotoxic shellfish poisoning (NSP) amnesic shellfish poisoning (ASP) and diarrhoeic shellfish poisoning (DSP).

Their occurrence is extremely rare, however regulations are imposed to effectively protect the consumers from shellfish toxins. The methods most widely used for the assay of naturally occurring toxins are high performance liquid chromatography (HPLC) and mouse time to death bioassay (MBA), but these methods are slow, expensive and not sufficiently robust for routine use or for analysis in the field. The need for rapid, on-site determination of seafood poisons favours the development of biosensors in this

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area. The toxins of major interest are non-protein molecules such as saxitoxin (STX), okadaic acid (OA) and domoic acid (DA). These molecules cannot be destroyed by normal cooking, freezing or smoking. In this context a European research project was approved, in which the University of Rome ‘Tor Vergata’ as co-ordinator (Prof. G. Palleschi, Italy), the University of Cork (Prof. G.G. Guilbault, Ireland) and the University of Lyon (Prof. P. Coulet, France) as scientific partners took part. The industrial partner (Domotek, Italy) was a company whose expertise was to construct electrochemical portable instrumentation useful for the immunosensors developed. The screen-printed electrodes (SPEs) have been developed by The Biosensors Laboratory of University of Florence (Prof. M. Mascini, Italy). The development, analytical evaluations and applications reported in this review, are the work of all the mentioned research groups involved.1 In this chapter, new analytical procedures based on the use of electrochemical disposable immunosensors for the detection of seafood toxins are discussed. Preliminary, spectrophotometric and electrochemical enzyme-linked immunosorbent assays (ELISA) have been optimised for the determination of toxins. Then, disposable immunosensors for the measurement of these toxins have been assembled using specific antibodies. A simple pocket instrument has been constructed and used for the ‘in situ’ determination of seafood toxins. Results, compared with those obtained using conventional instrumentation, indicated the possibility of measuring toxins in seafood collected in contaminated seawater, directly in the field. The development of electrochemical disposable immunosensors showed advantages in terms of sensitivity, rapidity and cost-effectiveness compared with previous analysis methods (such as MBA and HPLC), and were particularly useful for rapid screening tests.

7.2

Immunosensors

Immunosensors are analytical devices which selectively detect analytes and provide a concentration-dependent signal. Electrochemical immunosensors employ either antibodies or their complementary binding partners (antigens) as biorecognition elements in combination with electrochemical transducers.2 Immunoassay techniques are based on the ability of the antibodies to form complexes with corresponding antigens. The property of highly specific molecular recognition of antigens by antibodies leads to highly selective assays based on immune principles. The extreme affinity of antigen–antibody interactions gives rise to the high sensitivity of immunoassay methods. The most common type of immunoassay is known as enzyme-linked immunosorbent assay or ELISA. This method is a conventional solid phase immunoassay technique, where the antigen–antibody interaction occurs at a solid phase surface. There are different schemes of ELISA and the most popular is the competitive (direct and indirect) binding immunoassay method.3 Direct

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competitive assays are based on a competition between labelled antigens (added) with enzyme and unlabelled (sample) for binding sites of antibodies immobilised on the support, while in an indirect format the competition occurs between free and immobilised antigen versus the binding sites on the antibody labelled with enzyme. The amount of label associated with the solid phase is inversely related to the concentration of antigen. The spectrophotometric ELISA is performed with a 96-well microtitre plate in which samples can be processed simultaneously. All plates and wells are homogeneous, made of polyvinyl chloride (PVC). The method of detection for ELISA systems involves the addition of a chromogen to a completed assay and the coloured product formation is indicative of the amount of analyte present. In the electrochemical ELISA, the selectivity of immunological analysis is combined with the sensivity of electrochemical detection. Electrochemical sensors can make use of a number of different measurement techniques and different immunosensors are developed using potentiometry, amperometry or voltammetry.4,5,6 The most widely used systems have their immunoreactives located adjacent to the electrode, bound to a membrane or directly immobilised on their surface. Label enzymes used on such electrochemical immunoassays are usually oxidoreductases, such as horseradish peroxidase (HRP) or hydrolytic enzymes, such as alkaline phosphatase (AP) that yield electroactive species as products of the enzymatic reaction. Electroanalytical immunosensors provide an exciting and achievable opportunity to perform food analyses away form a centralised laboratory. The most common disposable biosensors are those produced by thick-film technology. Particular attention has been directed toward the screen-printed electrodes (SPE), because they can combine ease of use and portability with simple and inexpensive fabrication techniques. The modest cost of SPEs has further enhanced their desirability because it allows the device to become disposable. The use of disposable electrochemical immunosensors can be coupled with a portable instrument for the detection of seafood toxins. A prototype of this instrument was constructed and evaluated for test measurement using differential pulse voltammetry (DVP) and potentiostatic methods. The prototype was also compared with the bench laboratory instrumentation.

7.3

Detecting toxins: domoic acid

Domoic acid (DA) is a marine toxin (produced by phytoplankton species, Nitzschia pungens) and the main toxic agent associated with incidents of amnesic shellfish poisoning (ASP) on the east and west coasts of North America. This rare, naturally occurring amino acid is a member of a group of potent neurotoxic amino acids that act as agonists to glutamate, a neurotransmitter in the central nervous system. Pseudonitzschia phytoplankton species have been identified as a source of DA in toxic seafood incidents.7 Isomerisation of DA can occur photochemically and thermally, the latter being significant for cooked

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seafood. These isomers show a varying degree of toxicity.8 Reliable methods for the analysis of DA and its isomers in seafood products are vitally important for the protection of the public.

7.3.1 Analytical methods Mouse bioassay is the commonly used method for the detection of other seafood toxins but is not sufficiently sensitive to detect the regulatory level of 20 g DA/ g of edible tissue. Methods most commonly employed for determining the DA in contaminated samples involve high-performance liquid chromatography (HPLC) with a variety of sample extraction techniques.9,10 Liquid chromatography with ultraviolet absorbance detection (LC-UVD) is currently the preferred technique for the determination of DA in shellfish.11,12 DA may be extracted from shellfish tissues by AOAC hot acid method13 or by blending with aqueous methanol,14,15 the latter being most commonly used because it is better suited to trace analysis and combines well with a highly selective clean-up based on strong anion exchange. The detection limit of the toxin in an extract solution is 10–80 ng/mL and depends on the sensitivity of the UV detector. The detection limit in the original tissue is dependent on the method of extraction clean-up. A new rapid, sensitive and disposable amperometric immunoassay (ELISA) method has been employed for determination of DA in various marine samples, without the clean-up step which is necessary in the chromatographic assay. Spectrophotometric study The development of a spectrophotometric ELISA before the electrochemical study allowed the definition of the working ranges, limit of detection and crossreactivity evolution prior to the additional testing with the electrode. The test was performed in a 96-well microplate according to the details in Garthwaite.16 This immunoassay was performed in an indirect competitive assay involving antibodies against DA, DA conjugated to bovine albumin serum (BSA-DA) for coating, provided by Toxicology and Food Safety AgResearch (NZ), and horse anti-goat IgG alkaline phosphatase conjugate (IgG-AP) for the detection. The enzyme substrate was 4-nitrophenil phosphate. Figure 7.1 shows the results obtained using the applied indirect spectrophotometric ELISA test with all the analytical parameters optimised. The operative range was calculated with the four-parameter logistic model (7.1) given by the equation: Y ˆ d ‡ ‰…a ÿ d†=…1 ‡ …X =c†b Š

‰7:1Š

where d and a are the asympotic values for maximum and minimum (higher concentration and zero concentration), c corresponds to the analyte concentration (X), which gives Y ˆ …a ‡ d†=2, and determines the centre of the curve (IC50), b gives the slope of the curve.17 The working range, defined as the standard toxin concentration between 90% and 10% of maximum signal (A0)18 was comparable with the linear range and

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Fig. 7.1

Competitive curve for domoic acid with indirect test using spectrophotometric detection; AP as label.

was 0.2–20 ng/mL. The detection limit, defined as the concentration of toxin standard equivalent to three standard deviations A0 (no competition), was 0.6 ng/ mL and the errors involved were all below 10% (n ˆ 3). Electrochemical study A disposable amperometric biosensor, based on SPE coated with BSA-DA, has been prepared for measuring DA in mussel tissue following Kreuzer.19 Chessboard titrations were performed on electrodes to assess the optimum conditions yielding sufficient current (~1 A). Dilutions of both BSA-DA and DA sheep serum were prepared, followed by excess -sheep IgG-AP and SPEs’ measurement at +300 mV vs. Ag/AgCl. Once these conditions were optimised, indirect competitive analysis on SPEs was developed. A typical assay can be seen in Fig. 7.2, with an approach to the maximum signal seen at low DA concentrations. The inset of Fig. 7.2 also shows a linear range analysis of another assay between 10 and 160 ng mlÿ1 DA with an accompanying regression coefficient of 0.997. Errors associated with each standard were generally all below 6% (n ˆ 2). Recovery in real samples The ELISA assays were then applied to mussels. Mussel samples were collected and the extraction procedure performed following Garthwaite.16 The recovery of spiked mussel samples had to be determined with spectrophotometric and amperometric ELISA methods. As the calibration curves have been determined accurately, dilutions of extracted tissue containing the toxin were prepared such

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Fig. 7.2 Indirect competitive immunosensor for domoic acid with approach toward maximum control (zero analyte). Inset shows linear range of another assay between 10 and 160 ng mlÿ1 domoic acid (R 2 ˆ 0:997).

that they would fall within the range of the calibration curve. Percentage recovery tests were performed on established calibration curves to assess the accuracy of the unknown DA samples. The resulting values were obtained from equation (7.2):   [DA] in sample 1,2 ÿ [DA] in Blanks 1, 2  100 ‰7:2Š %Recovery ˆ [DA] in Original Sample The extraction efficiency was evaluated by the comparison of calibration curves (Fig. 7.3), constructed spiking blank mussels with known amounts of DA before and after the extraction. The spectrophotometric results of the recovery rate for the artificial contamination mussel samples ranged from 85 to 101 per cent. Repeatability and accuracy of ELISA assays were evaluated by means of six replicates of tissue from mussels bought in different days and stores. The sensor, however, lacked accuracy but this was attributed to the number, or lack, of electrodes per standard, clearly reflected in the spectrophotometric results. Spectrophotometric ELISA has a higher throughput due to convenience of standard preparation and measurement, whereas working with SPEs is more difficult in this matter. Nevertheless, the values were acceptable ranging from 91 to 125 per cent of the true [DA] concentration.

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Fig. 7.3 Effect of sample treatment (extraction efficiency) and matrix on competitive indirect ELISA for DA. Calibration curves, obtained spiking mussels, with known amounts of DA before (▲) and after (•) extraction were compared with the results in buffer. In both cases the extracts were diluted 1:250 w/v and assayed.

7.4

Detecting toxins: okadaic acid

Okadaic acid (OA) and its structural homologous are the toxins responsible for most human diarrhetic shellfish poisoning (DSP)-related illnesses. The acid was first isolated from two sponges, Halichondria okadai and H. melanodocia, and subsequently found in the dinoflagellates Prorocentrum lima20 and Dinophysis spp.21 OA is a cyclic fatty acid (C38) whose structure was first discovered by Tachibana.22 In 1982, Shibata and co-workers discovered that OA caused long-lasting contraction of smooth muscle from human arteries.23 OA also causes diarrhoea by stimulating the phosphorylation of proteins controlling sodium secretion by intestinal cells.24 Prolonged exposure and continuous uptake of sub-acute levels of okadaic acid and dinophysis toxins should be avoided due to their potent tumour-promoting activity.25 Acute toxicity of various toxins from the OA group after intraperitoneal injection in mice26 was 200 g/kg of mouse.

7.4.1 Analytical determination The most common DSP assays are biologically-based techniques. Non-specific toxin detection and the risk of false positives caused by fatty acids have led to

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the search for simpler and more reliable assays,27 not to mention the inherent dislike of using live animals. Immunoassays have also been developed including the RIA28 and ELISA29 methods. The latter has shorter assay time with good quantitation and little cross-reactivity to other DSP toxins. HPLC techniques, based on the methods of Lee30 and Aase,31 can detect OA and DTXs by the spectrophotometric measurement of 9–antharyldiazomethane derivatives. The level of sensitivity lies at approximately 15 g OA/100 g shellfish tissue (37.5 ng mlÿ1 or 4.6  10ÿ8 M OA). Methods combining HPLC and mass spectroscopy have proved to be sensitive (2 ng OA detectable).32 Nevertheless, it has been shown that up to 50 per cent of the toxin can be lost in the derivation and extraction procedure. More recent advances have led to a relatively rapid radioactive protein phosphatase (PP) assay, developed by Honkanen.33 It was used to detect OA in oyster extracts, and samples containing OA  0.2 ng/g were considered positive. Results correlated well with LC determination. Tubaro34 developed a colorimetric assay that could detect OA as low as 2 ng/g of digestive glands using PP2A. In 1997, a patent was granted for a fluorimetric PP assay that utilises 4–methylumbelliferyl phosphate as substrate with a 20–fold improvement in sensitivity.35 For economic and human health reasons, the presence of OA should be detected immediately at the production site and as quickly as possible, without pre-treatment and with an appreciable sensitivity regarding the EU critical limit, 40–60 ng OA/g of mussel tissue.13 In light of this, we report here the results from two different studies for a rapid and sensitive determination of OA in mussel tissues: a chemiluminescent immunosensor (Fig. 7.4) integrated in a flow injection analysis system and an immunosensor based on the use of SPEs. SPE immunosensor The electrochemical enzyme immunoassay for OA has been performed using disposable sensors whose signal transducer was the carbon working electrode, which was also used as solid phase for reagent immobilisation. It was important to determine the working range for the OA immunoassay before assembling the SPE. This was done using the spectrophotometric ELISA developed by Kreuzer.19 The results were then used for the electrochemical studies. The SPEs were prepared and stored desiccated at room temperature. Then the reagents were dropped onto the working electrode for the immobilisation. After all the steps relative to preparation of the immunoassay were performed, 100 L of the enzyme buffer solution containing the electrochemical substrate were added onto the electrode. Signal detection was performed by placing the SPE in a stirred electrochemical batch cell containing the substrate buffer solution and injecting p-APP to a final concentration of 1 mM. The current was monitored at + 300 mV (applied potential) vs. Ag/AgCl reference electrode. The indirect competitive ELISA format19 on SPEs yielded the best results in terms of linear range and limit of detection. The optimum conditions were used to obtain large signals and the final step of the assay involved the competition of

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Fig. 7.4

Free okadaic acid calibration curve in mussel homogenate. Each concentration has been assayed in duplicate.

labelled antibody (Ab*) with a dilution range of the free analyte (OA). Good accuracy, in terms of r2 (> 0.991), was obtained for all the assays performed in the linear range between 4 and 125 ng/mL of OA. Figure 7.5 reports results typical of such assays. The repeated measurement of each OA standard was somewhat hampered by the time necessary to measure each electrode. This could be overcome by using a multi-electrode potentiostat, which can limit the errors involved with each point. Another important aspect of these OA-SPEs was the detection limit. This parameter was again determined by several measurements and yielded a value between 1 and 4 ng/mL OA. This was in the range of nanomolar quantities of OA (1.24  109 mol/L) and when compared to fluorimetric assays showed more sensitive results with no need for prior derivatisation.36 The recoveries of OA from spiked mussel samples were studied using the OA immunosensor. Once the calibration curves had been determined, dilutions of organic solvent from the extraction procedure containing the toxins were prepared in such a manner that they would fall within the range of the calibration curve. Blank extracts were also prepared where zero toxin concentration should be present. These blanks would be indicative of the matrix effect resulting from the mussel tissue as well as fats also in the organic solvent used for the extraction. The recovery percentage using screen-printed electrodes for real samples yielded values generally ±10 per cent of the true value. Assays were generally completed in 60 minutes and measurements within five minutes. The incubation period could also be shortened to approximately 30 minutes, thus making the overall procedure no longer than 35 minutes.

The rapid detection of toxins in food: the case study

Fig. 7.5

7.5

125

Typical competitive immunoassay for okadaic acid using SPEs with amperometric detection at + 300 mV vs Ag/AgCl.

Detecting toxins: saxitoxin

Saxitoxin is one of the most lethal non-protein toxins known (LD50 9 g/kg37) and is one of the paralytic shellfish poisons (PSP) produced by several marine dinoflagellates and freshwater algae. Contamination of shellfish with saxitoxin has been associated with harmful algal blooms throughout the world. In humans, paralytic shellfish poisoning causes dose-dependent perioral numbness or tingling sensations and progressive muscular paralysis, which may result in death through respiratory arrest.38 The Food and Drug Administration has determined a maximum acceptable level for paralytic poison in fresh, frozen or tinned shellfish of up to 400 mouse units (MU) or about 40–80 g/100 g edible portion.13 This value is equivalent to twice the minimum detection level of the mouse bioassay, the first and still most common PSP toxin testing method, which is also the official AOAC method.39

7.5.1 Analytical methods The Mouse Bioassay (MBA) is the official method for the determination of PSP in seafood, but this is neither specific nor sensitive; it requires a continuous supply of mice and results are affected by test conditions such as animal strain and sample extract preparation. Other methods include fluorimetric assay40 and liquid chromatography.41,42 The latter requires expensive equipment for pre41 or

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post column42 analyte oxidation. Additionally, samples must be analysed one at a time, and so the method is unsuitable for routine on-site testing. Immunochemical methods have advantages in terms of both sensitivity and speed, and are therefore of increasing importance in food control as rapid screening tests. Because of the highly specific antigen–antibody interaction, several laboratories have attempted to develop an immunoassay for PSP.43,44 In the following section the optimisation and comparison of spectrophotometric and electrochemical competitive ELISA formats (direct and indirect) for the detection of saxitoxin (STX) are reported. Spectrophotometric study The tests were performed in a 96-well microplate using toxin-specific polyclonal antibodies produced in our laboratory.45 The antibodies were obtained from rabbits immunised with saxitoxin-keyhole limpet hemocyanin (STX-KLH). In indirect ELISA format saxitoxin, conjugated to bovine serum albumin (BSASTX), was coated onto the microtitre plate and incubated with standard toxin and anti-STX antibody. A goat anti-rabbit IgG peroxidase conjugate (IgG-HRP) was used to enable the detection. In the direct ELISA format, STX standard, STX conjugate to horseradish peroxidase (STX-HRP) and the enzyme substrate/ chromogen solutions were sequentially added to the microplate after antibody coating. The operative range was calculated using equation (7.1). The detection limit was 3 and 10 g/mL for direct and indirect ELISA formats, respectively (Figs 7.6 and 7.7). In both tests, the linear regression showed a range of 5  10ÿ3 to 4  10ÿ1 ng/mL (top right insert, Figs 7.6 and 7.7).

Fig. 7.6 Direct competitive ELISA for saxitoxin. Antibody against saxitoxin (10 g/ mL) was coated on the ELISA plate and STX-HRP (1:30) was used as competitor.

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Fig. 7.7 Indirect competitive ELISA for saxitoxin. BSA-STX (3 g/mL) was coated on the ELISA plate.

The stability of the coating reagents was evaluated using microplates coated with conjugated antigen or antibody, blocked and then stored at 4ºC. Assays were performed periodically using assessed protocols. Results showed that the plates coated with the antibodies (direct test) could be used up to three weeks after the coating step, while the antigen immobilised on the wells was stable for only 24 h (indirect test). The superior results obtained from the direct format could therefore be due to this higher antibody stability. The suitability of the assay for saxitoxin quantification in mussels was also studied. Sample extraction was carried out according to the AOAC method.13 Samples were spiked with saxitoxin before and after sample treatment to study the extraction efficiency and the matrix effect, respectively. After treatment, samples were analysed at 1:1000 v/v dilution in PBS to minimise the matrix effect and to detect the established limit of 40 g of saxitoxin in 100 g of mussels. The saxitoxin extraction efficiency was from 72 to 102 per cent (see equation 7.2). Repeatability and accuracy of ELISA assays were evaluated by means of six replicates of tissue. Blank controls fortified with saxitoxin at a concentration equal to twice (0.8 /g), half (0.2 g/g) and regulatory limit (0.4 g/g), were prepared and extracted on three days for each concentration (n ˆ 18). The precision was calculated by the relative standard deviation (RSD%) for the replicate measurements and the accuracy (relative error, RE%) was calculated by assessing the agreement between measured and nominal concentrations of the fortified samples. Results were confirmed by the analysis of the same extracts using a previously validated LC method.42 Values reported

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Table 7.1 Precision (RSD) and accuracy (RE%) for saxitoxin in mussel, determined by ELISA and LC (n ˆ 18) (a) Results with ELISA method STX added g/g

STX found g/g

RSD

0.20 0.40 0.80

0.19 0.43 0.83

4 1 3

RE% 3 7 4

(b) Results with LC method STX added g/g

STX found g/g

RSD

RE%

0.20 0.40 0.80

0.18 0.42 0.88

4 5 5

ÿ10 5 8

(c) Comparison of ELISA and LC methods STX added g/g

Direct ELISA/LC RE (%)

0.20 0.40 0.80

6 2 ÿ6

in Table 7.1, together with the accuracy of the spectrophotometric ELISA versus LC, showed a good agreement. In conclusion, ELISA assays were shown to be suitable screening tools for routine analysis of saxitoxin in mussels. In fact, compared to the LC method, spectrophotometric direct ELISA showed similar precision but better accuracy and speed, and at a lower cost. Additionally, this method does not require sample purification. SPE immunoassay The electrochemical enzyme immunoassay for STX has been performed using the carbon working electrode of the disposable sensors as solid phase for reagent immobilisation and as signal transducer. In a first phase, the spectrophotometric ELISA protocols were applied, but the results were unsatisfactory. In order to obtain the best signal-to-noise ratio and the highest sensitivity, several trials were performed for each test to optimise the analytical parameters, and all tests were repeated several times in order to confirm the data obtained. This immunosensor was employed in a direct competitive assay involving STX labelled with antibody. The enzyme substrate used was 3,30 ,5,50 -tetra-

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Fig. 7.8 Competitive curve with direct test for saxitoxin using SPEs.

methylbenzidine (TMB) plus hydrogen peroxide and the product of the reaction was detected by chronoamperometry at ÿ100 mV for 60 s. The calibration curve for STX was measured in the concentration range 0–103 ng/mL and the results showed a sensitivity in the range 1–103 ng/mL of the toxin (Fig. 7.8). STX levels determined by the proposed electrochemical immunoassay compared favourably with a spectrophotometric method.

7.6

Developing on-line applications

Miniaturisation is a growing trend in the field of analytical chemistry. Electrochemistry is particularly attractive for microscale analysis as its instrumentation can be miniaturised and multiplexed without compromising its capabilities. The portable nature and low power demands of electrochemical analysers46 satisfy many of the requirements for on-site and in-situ measurements. Modern microfabrication technologies allow us to replace the traditional electrodes and cumbersome cells with easy-to-use miniaturised electrochemical systems. A small portable and easy to use instrument (calculator size, microprocessor controlled with LCD display) was constructed for toxin measurement with disposable strips. This system was provided with one site for disposable strip connection. A battery applies a selected potential to the electrodes screen printed onto the strip, and the current due to the reaction occurring on the strip is recorded and displayed as the concentration of toxin measured. The instrument will be provided with a self-calibration to make it easy to use for unskilled personnel. This small instrument is able to perform DPV and chronoampero-

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Fig. 7.9

(a) DPV peaks obtained with the portable instrument prototype.; (b) DPV peaks obtained with AUTOLAB instrument equipped with GPES software

metric measurements in an interval range between ÿ1000–1000 mV, useful for the determination of 1–naphthol, as example, of enzymatic products, which is the product of the reaction of AP with 1–naphthyl phosphate. A calibration curve was obtained for 1-naphtol at different concentrations (0– 1 mM). The peaks obtained for each concentration were compared with the results attained with AUTOLAB and AMEL instruments (Fig. 7.9). An excellent agreement was observed among all results (the difference in current observed with the AMEL instrument is due to its software, which multiplies ten times the current when compared to the other instruments). Indirect competitive tests have been performed using the SPE’s working electrode also as solid phase for the immobilisation of the reagents. The same experiments were also carried out using the AUTOLAB instrument and the results obtained with the two instruments were comparable (Fig. 7.10).

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Fig. 7.9 (c) DPV peaks obtained with AMEL instrument (mod. 433). This instrument multiplied the current values by 10 automatically.

Fig. 7.10 Competitive curve for DA with indirect test using SPE; DPV detection between 0–600 mV; portable prototype (•) and AUTOLAB (▲) instruments; AP as label.

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7.7

Conclusions

This chapter reports the development of novel procedures based on cost effective electrochemical instrumentation for the detection of selected seafood toxins by the use of monoclonal and polyclonal antibodies, electrode strips and a portable instrument. The work has been carried out with a selection of the most common seafood toxins and the production of specific polyclonal and monoclonal antibodies. Biosensor development has been initiated and carried on in parallel with the setting up and comparison of ELISA procedures with spectrophotometric and then finally with electrochemical detection. The development of a procedure for measuring toxins with disposable strips was carried out first using bench electrochemical instrumentation, then a portable prototype constructed by the industrial partner. Validation of disposable strips using established reference procedures and the portable electrochemical instrument has been carried out. This research has successfully achieved the primary objective: detection of seafood toxins by use of disposable strips.

7.8

Acknowledgements

This work was supported by the EC project CT 96 FAIR 1092 and by the European Concerted Action QLK3-200-01311 ‘Evaluation/Valuation of Novel Biosensors in Real Environmental and Food Samples’.

7.9 1.

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and SCHMITZ F J, ‘Okadaic acid, a cytotoxin polyether from two marine sponges of the genus Halichondria’, J Am Chem Soc, 1981 103 2469–2471. SHIBATA S, ISHIDA Y, KITANO H, OHIZUMI Y, HABON J, TSUKITANI Y and KIKUCHI H, ‘Contractile effects of okadaic acid, a novel ionophore-like substance from black sponge, on isolated muscles under the condition of Ca deficiency’, J Pharmacol Exp Ther, 1982 223 135–143. COHEN P, HOLMES C F B and TSUKITANI Y, ‘Okadaic acid: a new probe for the study of cellular regulation’, TIS, 1990 15 98–102. SAKAI A and FUJIKI H, ‘Promotion of BALB/3T3 cell transformation by the okadaic acid class of tumor promoters, okadaic and dinophysistoxin-1’, Jpn J Cancer Res, 1991 82 518–523. GOPICHAND Y

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and KELLY S S, ‘Isolation of a new okadaic acid analogue from phytoplankton implicated in diarrhetic shellfish poisoning’, J Chromatogr A, 1998 798(1/2) 137– 145. BROWER D J, HART R J, MATTHEWS P A and HOWDEN M E H, ‘Non protein neurotoxins’, Clin Toxicol, 1981 18 813–865. USLEBER E, SHNEIDER E, TERPLAAN G and LAYCOCK M V, ‘Two formats of enzyme immunoassay for the detection of saxitoxin and other paralytic shellfish poisoning toxins’, Food Addit Contam, 1995 12(3) 405–413. GAZZETTA UFFICIALE DELLA REPUBBLICA ITALIANA (8–9–1990), serie generale no. 218. BATES HA and RAPOPORT H, ‘A chemical assay for saxitoxin, the paralytical shellfish poison’, J Agr Food Chem, 1975 23(2) 237–239. LAWRENCE J F and MENARD C, ‘Liquid chromatographic determination of paralytic shellfish poisons in shellfish after prechromatographic oxidation’, J Assoc Off Anal Chem, 1991 74(6) 1006–1012. OSHIMA Y, ‘Postcolumn derivatisation liquid chromatographic method for paralytic shellfish toxins’, J AOAC Int, 1995 78(2) 528–532. CHU F S and FAN T L, ‘Indirect enzyme-linked immunosorbent assay for saxitoxin in shellfish’, J Assoc Off Anal Chem, 1985 68(1) 13–16. M

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46.

8 Rapid detection methods for microbial contamination I. E. Tothill and N. Magan, Cranfield University, UK

8.1

Introduction

Today, quality assurance systems (QA) based on Hazard Analysis Critical Control Point (HACCP) principles are preferred to quality control systems (QC) which require careful monitoring and the withholding of contaminated material from the market place. Thus the rapid detection of spoilage microorganisms in the food production and processing chain is critical regardless of the system being used or implemented. There are however, different levels of detection, which may be required from a simple yes/no to quantitative data to meet legislative requirements. Thus there is a need for commercial instrumentation which is rapid but relatively inexpensive for the detection of microbial contaminants of food products. A range of methods has been developed relying on the biochemical and physical properties of micoorganisms. Conventional methods of microbial detection used in the food industry have a number of drawbacks including being labour intensive, time-consuming and sometimes expensive. Furthermore, within a HACCP framework corrective actions require real-time analyses. Figure 8.1 compares the time required for carrying out a range of tests and the time required obtaining a result. This shows clearly the difference between techniques, which can have a significant impact on the responsiveness within a QA system. This chapter will describe the range of conventional and new and novel technologies available and being developed for assessing microbial quality of food.

8.2

Conventional methods

Classical microbiological methods are usually based on several steps: isolation; identification and then if necessary colony forming unit counting and rely

Rapid detection methods for microbial contamination

137

Fig. 8.1 Comparison of the time required to carry out and the time to obtain a result for different types of tests. Arrow indicates the time for a test and time for a result which is needed today.

mainly on specific microbiological and biochemical markers. These methods take a long time to complete and need a skilled operator to interpret the results, but they are sensitive and inexpensive and give both qualitative and quantitative information. Since the occurrence of pathogens in food is usually at very low numbers, an initial enrichment step is needed to detect the contaminating microorganisms. The simple method used for biomass estimation is the dry weight method. Dry weight is widely used for the assessment of biomass in fermentation culture samples. By removing the required volume, the cells are washed free of the media components and dried to constant weight by heating in an oven at 105ºC, cooled and weighted. This method can only be applied to liquid samples that do not contain suspended solid. Other methods such as the viable count method (motility test) are used to estimate microbial populations. This method relies on the growth of the cells in either liquid culture medium, on an agar media or on membrane filters. Serial dilution of the sample is usually carried out before spreading the sample on the plate or filtering through the membrane. The method requires the plates or cultures to be incubated at an appropriate growth temperature for between 12–72 hours depending on the type of microorganisms being analysed. The number of colonies is counted and this calculated as a colony-forming unit (cfu mlÿ1) obtained in the original sample. The disadvantages of this method is the long incubation time required before the results can be achieved, which is hazardous in food testing since the foods may have already been displayed to the consumer. Standard methods such as the NF EN ISO 11290-1 for Listeria monocytogenes

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Fig. 8.2

(a) The HYCONÕ Dip Slide and (b) the HYCONÕ Contact Slides (Courtesy of Biotest Diagnostics Corporation, USA).

detection may require 7 days for visible colonies to be identified (Artault et al., 2001). Several products are on the market such as the HYCONÕ Dip Slides and the Contact Slides (Fig. 8.2) marketed by Biotest (New Jersey, USA). The HYCONÕ dip slides are used to monitor microbial contamination in liquid samples and contain different microbiological media on each side. This can either be dipped in the sample or streaked. The contact slides, which also contain selective growth media, are used for the detection of contamination on surfaces by bacteria, yeast and moulds. These tests require an incubation time between 2– 3 days. The use of highly porous cellulose acetate membrane filters (0.45 m) has also been implemented in microbial detection. Membranes that allow the passage of large volume of liquid sample but prevent the passage of bacteria or fungi have been used. Microorganisms retained on the membrane are incubated on a specific agar medium and the appropriate room temperature. The number of colonies is subsequently counted on the filter. The main advantage of this method is the large sample volume that can be applied on these types of filters. Turbidity is also widely used for the estimation of cells in suspensions by using a spectrophotometer. The ability of microbial cells to scatter lights and hence appear turbid in a solution is utilised in this technique to measure the concentration of the cells. The scattered light of a microbial suspension is proportional to the number of cells present. Measurements are usually carried out at 600 nm of bacterial analysis using a spectrophotometer. A standard calibration curve of log Io/I against either the total count or the dry weight is used (Singh et al., 1994). The calibration curve applies only to a particular microorganism grown under a particular set of growth conditions. But this

Rapid detection methods for microbial contamination

139

technique is unable to differentiate between viable and non-viable cells. Park and co-workers (1995) used spectrofluorometric assay to detect total and faecal Coliforms in water samples. Microscopy is also an important technique in the diagnosis of microorganisms, since it allows the view of the cells under the microscope. The most important stain procedure in microbiology is the Gram stain. Using this method bacterial cell morphology and Gram reaction may be examined via the use of Gram stain microscopy, which provide the information of whether the organism is a gram negative or gram positive based on the differences in bacterial cell walls. Gram staining is a rapid procedure that can be performed in minutes. Further details on the methods are given in Dart (1996).

8.3 Specialised techniques: epifluorescence (DEFT), bioluminescence and particle counting 8.3.1 Epifluorescence technique Fluorescent microscopy is a very rapid method for microbial enumeration and it does not require an incubation step. The direct epifluorescence filter method (DEFT) has been developed for microbial contamination detection in milk and other dairy products (Pettifer et al., 1980). The principle of the method is similar to membrane filtration and takes about 25 min to complete. The method involves the re-treatment of the sample with the enzyme trypsin and detergent (Triton X100) to break down the somatic cells and fat globule content of the milk, enabling it to be filtered through a polycarbonate membrane, retaining the bacteria present in the sample. The fluorochrome (acridine orange or diamidino2-phenylindole) is added to the filtered sample for a contact time of a few minutes and then filtered through a polycarbonate membrane. The membrane is rinsed with the same sample volume of distilled water and the microorganisms are counted using epifluorescence microscopy. Under UV light, acridine orange stains deoxyribonucleic acid (DNA) green and ribonucleic acid (RNA) orange. This method can distinguish between active from inactive microorganisms based on their higher RNA content (Allen, 1990). Systems such as the Bactoscan Automated Microbiology System (Foss Electric, Hillerød, Denmark) has been developed for the detection of bacteria in food and drink samples. This device is rapid, with an estimated 60 to 70 samples undertaken every hour. The MicroFoss is a user-friendly instrument and has been used especially in dairy and meat segments and gives rapid microbiological analysis based on microorganisms growth, pH change and dye indication. The products also include ready-to-use vials for enumeration of Total Viable Count, Enterobacteriaceae, Coliform, generic E. coli, and Yeast. The MicroFoss showed the ability to detect counts as low as 0.5 cfu mlÿ1 in milk. The use of the fluorescent indicators can discriminate between viable from non-viable cells (Matsunaga et al., 1995). Antibodies conjugated to fluorochrome such as fluorescein iosthiocyanate has been used for microbial detection. By applying tagged antibody for the

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target microorganism to the sample, the micoorganism will fluoresce due to the complex forming with its complementary tagged antibody. The number of fluorescing cells is then counted using an epifluorescence microscope. This method has had wider applications in the field of on-line estimation of fermenter biomass (Armiger et al., 1984). Nakamura et al. (1993) coupled E. coli separation using magnetic particles with detection by flourescein isothiocianate.

8.3.2 Bioluminescence Bioluminescence test based on adenosine 5’ triphosphate (ATP) is a rapid and sensitive method used for microbial detection. ATP is the energy molecule for all living cells (animal, vegetable, bacteria, yeast and mould cells). The measurement is based on the use of the firefly enzyme luciferase: Luciferase

Luciferin ‡ ATP ‡ O2 ÿÿÿÿ! Oxyluciferin ‡ AMP ‡ CO2 ‡ PPi ‡ Photon Light is produced depending on the concentration of ATP in the sample, which can be interpreted to the microbial content. Several instruments have been developed based on this principle for the estimation of microbial biomass and also as cleanliness and hygiene testing. The Clean-TraceTM products such as the Biotrace Uni-LiteÕ and Uni-LiteÕ XCEL instruments (Biotrace Ltd., Bridgend, UK) are instruments using the above principle. Other companies such as Celsis International plc (Suffolk, UK) and Biotest (New Jersey, USA) also market instruments based on this technology. ATP Instruments can usually detect low levels of contamination (103 cells mlÿ1) and are used for testing in food production plants and dairies. When testing milk samples, the samples need to be pre-treated to remove non-bacterial ATP present in somatic cells before the analysis can be carried out for microbial contamination in milk. Tests may take between 10ÿ20 minutes depending on the procedure to be implemented in the tests. Stanley (1992) has reviewed commercially available luminometers.

8.3.3 Particle counting Coulter counters are also used for microorganisms counting mainly algae and yeast. The principle of the technique is based on passing a suspension of cells through a small aperture separating two electrodes between which an electric current flows (sensing zone). The pulse generated by each cell is amplified and recorded electronically, giving a count of the number of cells flowing through the aperture. The Coulter method of sizing and counting particles is based on measurable changes in electrical impedance produced by nonconductive particles suspended in an electrolyte. In this case cells can be counted in the medium in which they are growing. A range of products are commercially available such as the COULTER COUNTERÕ Z1 Series, (Coulter International Corporation, Miami USA ), and MultisizerTM 3 Coulter CounterÕ (Beckman Coulter, UK). CellFacts I, developed and manufactured by CellFacts Instruments Ltd, UK, also uses electrical flow-impedance determination to

Rapid detection methods for microbial contamination

Fig. 8.3

141

CellFacts I (Courtesy of CellFacts Instruments Ltd, UK).

count and size particles in a sample (Gentelet et al., 2001). The analysing principle of CellFacts I is it counts and sizes every particle in a sample introduced to the instrument and provides detailed information on the microbiological status of that sample with applications in microbiological research and the food, biotechnology, water, cosmetics, and pharmaceutical industries. Its most powerful applications are in on-line monitoring of fermentation processes and cell cultures (Fig. 8.3). Devices based on acoustic resonance densitometry have been reported by Clarke et al. (1985), which could provide effective real-time and in situ determination of biomass in fermentation and downstream processes. The technique is based on the change in the acoustic resonance of a fixed volume of fermenter culture during the fermentation period due to the microbial growth.

8.4 Specialised techniques: flow cytometry, electron microscopy and immunoassay techniques 8.4.1 Flow cytometry Flow cytometry is a powerful technique that allows the user to measure several parameters in a sample and it is one of the most reviewed methods for bacterial detection (Jepras et al., 1995, Attfield et al., 1999). Parameters such as physical characteristics as cell size, shape and internal complexity can be examined. The principle behind the technique is that a thin stream of fluid containing the cells of interest is passed through a laser beam. Biomass is analysed by light scattering methods and by staining of chemical components such as DNA. The light energy is converted into an electrical signal by the use of photomultiplier tubes (Okada et al., 2000). Gunasekera et al. (2000, 2002), used flow cytometry for analysing the microbiological states of milk and dairy products.

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8.4.2 Electron microscopy The use of a scanning electron microscope for counting bacteria on membrane filters has been reported (Borsheim et al., 1990). However, this technique suffers from the high cost of the instrumentation and operator skills required.

8.4.3 Immunoassay techniques Immunodetection with antibodies has been successfully employed for the detection of microbial cells, viruses and spores (Iqbal et al., 2000) Antibodies can be easily produced for a range of microorganisms. Immunoassay techniques have been developed for microbial detection using different labels to generate the signal. Radioisotopes were the first to be used, but enzymes became more attractive due to cost and environmental issues. Lateral flow immunoassay tests such as the RapidChekTM for E. coli O157 marketed by SDI (Strategic Diagnostics Inc. Hampshire, UK) can detect one cell in 25 grams and has been approved by the AOAC RI for applications in ground beef, boneless beef, and apple cider (Fig. 8.4). The test has also been validated on carcass swabs and poultry and is available for an 8 hour enrichment time and 10 min test time. A RapidChekTM for Salmonella is also marketed by the SDI, with 24 hour enrichment time (10 min test time). ClearviewTM and REVEALÕ are a range of products marketed by Oxoid (Basingstoke, UK) and Adgen Ltd. (Ayr, UK) respectively for the detection of E. coli O157, Salmonella and also Listeria based on the same principle used by SDI. Tests for Campylobacter are also available on the market. Most of these tests are based on isolation and enumeration of the bacteria before detection. The reason being that concentration of these bacteria in food (raw vegetables, milk, soft cheese and ready prepared food) are usually very low to be detected directly by the common

Fig. 8.4

RapidChekTM (Courtesy of Strategic Diagnostics Inc. Hampshire, UK).

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plating techniques and it needs to be isolated and allowed to multiply to the detectable level (Scott, 1998). Enzyme-linked immunosorbent assay (ELISA) tests have been used for the detection of microbial infections in veterinary diseases, with detection limit in the range of 103 cells mlÿ1. ELISA tests for microbial detection in food samples are also marketed by several companies (e.g. Syva laboratories, S.A.; SDI; BioMe´rieux’s; Adgen). A range of test formats has also been developed such as the immobilisation of the antibody on a bead solid support. The beads are paramagnetic and can be separated from the food sample easily in a magnetic field. This separation step will remove the target contaminated microorganism for the food sample. The beads can be washed and either used to detect the microorganism following the Elisa procedure (Bennett et al., 1996) which takes 2 hours for the test or re-suspended and transferred to a selective agar plate, streaked and cultured, if the concentration is low for ELISA detection. Results in this case can be obtained in 24–48 h. The use of the paramagnetic beads have been applied for the detection of Salmonella (Cudjoe et al., 1995), E. coli 0157 (Cubbon et al., 1996) and Listeria in food samples. Immunoassay tests can take up to 2 hours to achieve the results, therefore it is not a ‘real-time’ procedure. However, complete assay automation can be carried out using the range of equipment available on the market for this application. Also the advances in recombinant antibodies and the emergence of phage-displayed peptide receptors (Goldman et al., 2000; Benhar et al., 2001; Goodridge and Griffiths, 2002) and their application in pathogens detection offer increasing possibility for rapid methods development.

8.5 Cellular components detection: API, metabolising enzymes and nucleic acids Methods of biomass estimation by measuring the concentration of a biochemical component of the target microorgansm are reported in the literature. However, the presence of these compounds needs to be detected with the appropriate precision if they are to be used for microbial estimation. A range of compounds such as lipids and their derivatives (Singh et al., 1994), cell carbon/phosphate (Galnous and Kapoulos, 1966) and total nitrogen/proteins (Garg and Neelkantan, 1982) have all been used for microbial quantitation. Some of these methods are dependent on the physiology of the cells and therefore their validity may be questionable. In this section the more applied methods will be covered in detail.

8.5.1 API The API test kits are the best known biochemical tests for microbial identification. API tests marketed by BioMerieux Inc (France) usually contain about 20 miniature biochemical tests, which may detect all bacterial groups and 550 species. The procedure involves the inoculation of each of the 20 mini test

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tubes with a saline suspension of a pure culture. The samples are then incubated for 18–24 hours before the colour change is read. An API system based on enzyme detection (Rapidec) is also marketed by BioMerieux Inc. The test is followed either by a colour change directly or following the addition of appropriate reagents (Batchelor, 1995).

8.5.2 Metabolising enzymes Specific enzymes have been used as indicator for the presence of specific microorganisms. For example total coliforms and E. coli contain the enzyme aˆgalactosidase which can be used as an indicator to their presence. E. coli also contain the enzyme glucuronidase, which is used to indicate the presence of E. coli in the samples. Different companies have implemented the analysis of these two enzymes to develop products for microbial detection. Palintest Ltd. (Tyne and Wear, UK) has produced the Colilert test, which is a colorimetric test based on the detection of these enzymes through their interaction with the substrate. The tests are capable of detecting 1 CFU 100 mlÿ1 in 24 hours of incubation (Hobson et al., 1996). The enzymes catalase and oxidase have also been used to analyse for bacterial contamination.

8.5.3 Nucleic acids The building blocks (nucleic acids) of DNA and RNA are present in all living cells and may be used as a general indicator of microbial biomass. The principle of the tests is based on the hybridisation of a characterised nucleic acid probe to a specific nucleic acid sequence in a test sample followed by the detection of the paired hybrid. The use of the polymerase chain reaction (PCR) has been frequently applied for microbial detection to enhance the sensitivity of nucleic acid-based method (Baker et al., 2003; Cook, 2003). The technique has been used for qualitative analysis, but quantitative measurements are important in food analysis and methods have been developed to make the test quantitative. The use of fluorometry for PCR product analysis has been implemented for rapid and sensitive tests. Quantitative PCR methods are shown to be very sensitive with a detection limit of 10 cells mlÿ1 and an analysis time of approximately 3 h has been reported (Paton et al., 1993). PCR methods have been used to detect viruses (Traore et al., 1998), bacteria (Fach and Popoff, 1997) and protozoa (Stinear et al., 1996) in food and water samples. However, these methods can be expensive, time consuming, require skilled workers and sometimes complicated. The GEN-PROBE hybridisation protection assay (HPA) technique uses a specific DNA probe, labelled with an acridinium ester detector molecule that emits a chemiluminescent signal. Two methods which have used this technology successfully are the AccuProbeÕ and the FlashTrak (Gen-Probe Incorporated, San Diego, USA). In these systems the DNA probe is targeted against the ribosomal RNA of the target organism. The nucleic acids are then hybridised to

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form a stable molecule. The chemiluminescence is then measured by the genprobe luminometer (Thomson, 2001). This method has been developed for Fungi and bacterial detection and takes 5 hours to achieve the results with 92– 100% sensitivity and specificity. Molecular Devices Corporation (Sunnyvale, USA), have developed products for total DNA assay which can be applied for microbial detection. DNA array marketed by Affymetrix (Santa Clara, USA), such as the GeneChip E. coli Antisense Genome Array is used for examining expression of all known E. coli genes. The GeneChip Yeast Genome S98 Array contains probe sets for approximately 6,400 S. cerevisiae (S288C strain) genes identified in the Saccharomyces Genome Database. The GeneChip technology can be used mainly for the broad spectrum of nucleic acid analysis applications including sequence analysis, genotyping and gene expression monitoring. Other companies such as MediGenomix GmbH (Germany) is also expanding into DNA-analysis for veterinary and food testing.

8.6 Electrochemical methods: impedimetry, conductivity and other methods Electrochemical methods have been developed for microbial detection (Paddle, 1996). The most reported techniques for food analysis will only be covered in this section.

8.6.1 Impedimetry and conductivity Changes to the ionic conductivity of the culture medium due to microbial growth have been used to measure microbial content (Richards et al., 1978; Colquhoun et al., 1995). Impedance measurements have been utilised in the development of microbial devices. The Bactometer marketed by BioMe´rieux (BioMerieux Inc., Marcy-’Etoile, France) (Fig. 8.5) is one of these instruments based on proven impedance technology. It provides a rapid and cost effective system for the detection of spoiled raw materials. It offers quantitative and qualitative tests including total counts of enterobacteriaceae, coliforms, yeast and mould. The Malthus system (Malthus Instruments Ltd, Bury, UK), is also based on the detection of microorganisms by measuring the changes in the flow of an electric current passing through a medium. This system can detect a range of microorganisms such as Coliforms, Salmonella, Yeast and Mould in foods.

8.6.2 Fuel cell technology The technology is based on the direct conversion of chemical to electrical energy (Hobson, 1996). The fuel cell device uses an anode, a cathode and a supporting electrolyte medium to connect the two electrodes, and an external circuit to utilise the electricity. The use of microorganisms for the generation of electric

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Rapid and on-line instrumentation for food quality assurance

Fig. 8.5

BactometerÕ (Courtesy of BioMe´rieux INDUSTRY, Missouri, USA).

currents has been reported to be enhanced by the incorporation of traces of potassium ferricyanide or benzoquinone in the solution (Davis, 1963). Devices based on the use of mediated systems (Benjamin et al., 1979) and non-mediated systems (Matsunaga et al., 1980) have been used for monitoring microbial growth. The analytical sensitivity of the fuel cell method has been increased by the use of the mediator phenosine ethosulphate for the detection of E. coli (detection limit of 4  106 cells mlÿ1) in 30 minutes assay time (Turner et al., 1983).

8.6.3 Amperometry Amperometric techniques have also been applied to the measurement of microbial biomass and the detection system is based upon the measurement of the current flowing through the working electrode of an electrical cell. Several devices based on amperometry have been applied for microbial sensing. Redox mediators (such as Potassium hexacyanoferrate (III), benzoquinone and 2,6dichlorophenolindophenol) have been used which are reduced by the micoorganisms as a consequence of substrate metabolism. Kala´b and Skla´dal (1994) have evaluated the use of different mediators for the development of amperometric microbial bioelectrodes. The reduced mediators diffuse to the working electrode where it is subsequently re-oxidized. The current flow measured has been shown to be proportional to the reduced mediator concentration and hence the microbial concentration. This device can detect 5  104 cells mlÿ1 E. coli in 15 minutes (Hobson, 1996). Several devices have been developed based on this principle and commercial instruments were also marketed, but were then withdrawn due to poor reproducibility when testing a range of microorganisms. Hitchens et al., (1993) measured bacterial activity using mediated amperometry in a flow injection system. The Medeci analyser (Medeci Developments Ltd, Harpenden, UK) is under development for medical application. The device is based on the use of screen-printed three-electrode

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configuration incorporated into a novel wall-jet flow-cell design, with electrochemical measurement of bacterial concentration by hydrodynamic coulometry. This process is similar to amperometry in that it measures current, but instead of current at a point in time it measures current over a period of time, hence total charge passed (Thomson, 2001). Devices based on the use of the Clark oxygen electrode have also been developed for microbial detection.

8.6.4 Cyclic and square wave voltammetry A three electrode system using working electrode, reference electrode (SCE or Ag/AgCl) and counter electrode (platinum) have been developed using cyclic voltammetry (CV) for the detection of yeast and bacteria. The application of dyes for bacterial detection using square wave voltammetry (SWV) has also been applied (Lafis, 1992). Dyes such as carbocyanine, 3,30 -dihexyloxacarbocyanine and safranin O have been investigated (Carino et al., 1991) with concentrations in the range of 104 ÿ 108 cells mlÿ1 being detected (Lafis, 1992).

8.7 Immunosensors: amperometric, potentiometric, acoustic wave-based and optical sensors Microbial detection using immuno- and affinity sensor configuration has been used for a range of microorganisms. Different types of transducers have also been applied in the development of these sensors for microbial detection.

8.7.1 Amperometric sensors Detection of microorganisms using the amperometric transducer is widely used and it involves the measurement of the current produced through an oxidation/ reduction mechanism catalysed by microbial enzymes. Amperometric transducers have also been applied in affinity sensors format to detect micoorganisms where the antibody marker produces an electrochemical signal. Devices based on a flow through, immunofiltration and enzyme immunoassay in conjunction with an amperometric sensor were used for the detection of E. coli (Abdel-Hamid et al., 1999; Ivnitski et al., 1999). An amperometric enzymechannelling immunosensor has been developed and was able to detect S. aureus cells in pure culture at concentrations of 1000 cells mlÿ1 (Rishpon and Ivnitski, 1997). Ivnitski et al. (2000) developed an amperometric immunosensor based on supporting planar lipid bilayer for the detection of Campylobactor. Sensors for E coli O157 using the paramagnetic beads have been developed coupled with electrochemical detection (P’erez, 1998). The system was based on flow injection analysis (FIA) detection of viable bacteria. Using a solution containing E. coli O157, the electrochemical response with different mediators (potassium hexacyanoferrate (III) and 2,6-dichlorophenolin-dophenol) was evaluated first in

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Fig. 8.6 Schematic model of E. coli detection method performed in three separate steps: (A) the selective capture of the bacteria, (B) the reaction of the bacteria with a mediator and (C) the electrochemical measurement of the reduced mediator using an amperometric method. (Pe´rez et al., 1998).

the FIA system. Antibody derivatized Dynabeads were used to selectively separate E. coli from the matrix. The immunomagnetic separation was then used in conjunction with electrochemical detection to measure the concentration of viable bacteria (Fig. 8.6). A calibration curve of colony-forming units (cfu) against the electrochemical response was obtained and a detection limit of 105 cfu mlÿ1 in 2 h assay time was achieved (Pe´rez et al., 1998). Coupling flow injection analysis with immunosensor configuration is very attractive for on-line detection system development and many researchers have used this system for food sensing (Bouvrette and Luong, 1995).

8.7.2 Potentiometric sensors A light addressable potentiometric sensor (LAPS) based on a field effect transistor (FET) has been used for the detection of microorganisms (Invitski et al., 1999; Leonard et al., 2003). The sensor is based on a silicon semiconductor

Rapid detection methods for microbial contamination

Fig. 8.7

149

The ThresholdÕ Immunoassay detection system.

and uses antibody as the receptor. A commercially available LAPS (ThresholdÕ Immunoassay System) marketed by Molecular Devices (USA) uses silicon semiconductor and an enzyme generates potentiometric signal (urease) as the enzyme marker. This system (Fig. 8.7) has been used by several researchers for microbial cells detection (Dill et al., 1997; 1999). A cell concentration of 2.5  104 cells mlÿ1 of E. coli O157:H7 was detected using this system (Gehring et al., 1998).

8.7.3 Aucoustic wave-based sensors Acoustic wave based devices have been applied for microbial detection. The mass sensitive detectors operate on the basis of an oscillating crystal that resonates at a fundamental frequency. Antibodies or receptor molecules are usually immobilised on the crystal surface and the sensor is then exposed to the sample containing the microorganism of interest. A change in the resonant frequency of the crystal surface related to the mass change is quantifiable and depends on the microbial concentration in the sample (Fig. 8.8). These type of sensors offer label free and on-line analysis of miccroorganisms (Bunde et al., 1998, Babacan et al., 2000). Mass balance acoustic wave transducers can be classified into: (a) bulk wave (BW) devices and (b) surface acoustic wave (SAW) devices. Piezoelectric crystal immunosensors for the detection of enterobacteria in drinking water have been reported by Plomer et al. (1992). A piezoelectric biosensor for the detection of Salmonella (Pathirana et al., 2000), Helicobacter pylori (Su and Li, 2001), Listeria monocytogenes (Vaughan et al., 2001), Legionella and E. coli (Howe and Harding, 2000) have also been developed using antibodies as the receptor.

8.7.4 Optical sensors Optical transducers are usually very attractive as they allow real-time and direct ‘label-free’ detection of microorganisms. Optical sensors based on surface

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Fig. 8.8

Piezoelectric microbial immunosensor.

plasmon resonance (SPR) detection and evanescent wave (EW) have shown promise in microbial detection (Haines et al., 1995; Watts et al., 1994). The BIAcoreTM (BIAcore AB, Uppsala, Sweden) has been applied for E coli sensing and also for Salmonella and Listeria detection (Haines et al., 1995). A large number of instruments are marketed today by BIAcore AB offering varying degrees of automation and cost and these include BIAcore 1000, 2000, 3000 and BIAliteTM, BIAcore XTM, BIAQuadratTM, BIAcore S51 and BIAcore J. In these devices binding events are monitored between two molecules, such as an antibody and its antigen (in this case it is the microbial cell) using SPR technology. Direct microbial detection using the BIAcoreTM achieved a detection limit for E.coli O157:H7 of 5  107 CFU mlÿ1 (Fratamico, 1998). The IAsysÕ systems (Affinity Sensors, Cambridge, UK), which is also an optical biosensor based on a resonant mirror has also been used for microbial detection by immobilising the antibody on the chip surface. The company markets several products today and these include IASys plusTM and IASys Auto + AdvantageTM. Optical devices as the ones listed above tend to use small samples and the presence of microorganisms in complex food matrices are problematic. Therefore, pre-enrichment (as in ISO 11290-1) and immunoseparation or a concentration step is needed to enhance the detection limit of the sensors for pathogen detection (Kaclikova et al., 2001, Quinn and O’Kennedy, 2001). Watts et al., (1994) reviewed optical biosensors for microbial cells monitoring. A new range of devices has emerged recently based on resonant mirror configuration and SPR and these are reviewed by Leonard et al., (2003).

8.8

Detection of moulds using biochemical methods

A wide variety of methods has been used to quantify the fungal activity in raw materials such as grain and processed food. Chitin, ergosterol, adenine triphosphate, immunofluorescence, immunoassays and DNA probes have all been developed (Magan, 1993; Fleurat-Lessard 2002). Since ergosterol is the predominant sterol in most spoilage fungi (ascomycetes and deuteromycetes) and not found in insect pests it has been utilised extensively as an indicator of

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whether deterioration has occurred in the food chain. The method was first described by Seitz et al. (1977) and can now be performed relatively quickly and routinely using simple extraction and HPLC. It has thus been used extensively for the in vitro quantification of biomass of spoilage fungi which demonstrated that this does change with culture age (Marfleet, et al. 1991). Cahagnier et al. (1991) suggested that the ergosterol content in storage fungi was not significantly affected by environmental factors such as aw. They thus suggested that ergosterol could be used as an ‘index’ of the level of fungal biomass and the length of storage of food raw materials. Tothill et al. (1993) determined the relationship between ergosterol content, CFUs and microscopic and visible moulding in both inoculated and natural grain under different aw and temperature regimes. These studies showed that there was a significant positive correlation between ergosterol content and total CFUs at 0.95 aw, while in drier grain of 0.85 aw there was no significant correlation. Grain inoculated with individual species (Alternaria alternata, Eurotium amstelodami, Penicillium aurantiogriseum) at 0.95/0.85 aw and 25ºC showed a significant correlation between CFUs and ergosterol although the content for an individual species varied considerably. Table 8.1 compares some of the available information on ergosterol levels in a raw material such as grain used for the bakery product industry with that when microscopic growth has occurred, with those suggested by Cahagnier et al. (1991) as a threshold for fungal spoilage. They suggested levels of < 5–6 g gÿ1 fresh wheat grain, with grain having microscopic growth about 7.5–12 g gÿ1. This correlated with a threshold of 105 CFUs gÿ1 grain as a spoilage threshold indicator. Fleurat-Lessard (2002) has suggested that perhaps modelling of ergosterol production rates under different environmental conditions using sigmoid curves similar to those used for insect population dynamics may enable the use of an ergosterol index in the future when correlation models become available. It may also be possible to use both ergosterol and the production of mycotoxins in predicting potential environmental factors over which spoilage/ Table 8.1 Comparison of ergosterol levels in dried, recently harvested grain (A) with concentrations at which microscopic fungal growth (B) was observed Grain type

Ergosterol (g gÿ1) A B

Reference

Wheat Maize Sorghum Wheat Barley Maize Wheat (Avalon) Wheat (Rendevous) Barley

0.7–3.5 0.2–2.0 0.2–4.0 3–4 4.0 0.5 4–5 5–6 3–7

Seitz et al. (1977) Seitz et al. (1977) Seitz et al. (1977) Cahagnier et al. (1991) Cahagnier et al. (1991) Cahagnier et al. (1991) Tothill et al. (1993) Tothill et al. (1993) Olsen and Schnurer (2002)

ND ND ND 10–12 10–12 5–8 8–9 10.13 > 10

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mycotoxins may be produced. Two-dimensional models for growth and fumonisin production have already been developed (Marin et al., 1999) and such information may be useful in further development of predictive models for risk assessment of spoilage and toxin contamination of food. Perhaps, modelling of cumulative ergosterol production by spoilage fungi and associated mycotoxins in relation to aw, temperature, time, gas composition (modified atmosphere storage) and time may allow more effective and precise risk assessment of mould contamination and mycotoxin occurrence to the consumer.

8.8.1 Enzyme changes as an indicator of deterioration of food by moulds Changes in food enzyme concentrations, e.g. amylases, due to fungal deterioration are important as they have an impact on processing and bread making quality of flour and dough. However, studies which examined amylase, -amylase and total amylases of wheat and tried to correlate these with the time to microscopic and visible moulding were found to be an inaccurate measure of mould activity in grain (Magan, 1993). Fleurat-Lessard (2002) has suggested that for a range of cereal raw materials enzyme changes are too small and occur too late as functions of storage conditions and duration, especially as a rapid indicator of spoilage. However, there is a quite large body of work which suggests the contrary. Fungi colonising the rich food raw materials such as grain under conducive environmental conditions produce a battery of hydrolytic enzymes for degrading food. Both Flannigan and Bana (1980) and Magan (1993) showed that aw and temperature affect the production of enzymes by fungi during grain colonisation, including cellulases, polygacturonase, pectin methyl esterase, 1-4- -glucanase, -glucosidase, -xylosidase and lipases. Jain et al. (1991) were the first to demonstrate that by using chromogenic 4-nitrophenol substrates in an ELISA well format, rapid quantification could be carried out for a range of hydrolytic enzymes, provided that substrates were available for them. They demonstrated that in both barley and wheat grain at different aw levels (0.85, 0.90, 0.95) significant increases in N-acetyl- -D-glucosaminidase were produced when compared to non-moulded dry harvested grain. Grain inoculated with the xerophile Eurotium amstelodami also showed marked increases in -Dgalactosidase. Magan (1993) extended this and examined stored dry grain with that at different aw levels and temperatures of incubation. This showed that significant change in the production of some enzymes was evident at times of microscopic and visible moulding. Of seven enzymes examined significant changes in -Dglucoaminidase, -D-glactosidase and -D-glucosidase were observed by the time microscopic growth had occurred. Work with maize-based food matrices under different temperature and aw regimes inoculated with fumonisinproducing Fusaria (F. verticillioides, F. proliferatum) demonstrated that both total and specific enzyme activity for -D-galactosidase, -D-glucosidase and N-acetyl- -D-glucosaminidase changed significantly (Martin et al., 1998).

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Changes could be monitored within 72 hrs of storage. They also suggested that these enzymes could be used as an early indicator of infection of food by such mould species and that these enzymes were important in enabling rapid colonisation over a wide range of environmental factors. Recent work by Keshri and Magan (1998) and Keshri et al. (2002) have also suggested similar hydrolytic enzymes are an early indicator of fungal activity in vitro on wheat flour-based media and in bread substrates. Thus potential does now exist for the use of such relatively simple enzyme assay formats to be used as a possible tool for early detection of fungal activity in food substrates.

8.9

Electronic noses

In recent years the rapid development of sensor technology has enabled the production of different sensor array formats which can interact with different volatile molecules. The resistance of the material is changed providing a signal which can be utilised effectively as a fingerprint of the volatiles produced. Metal oxide, conducting polymer and discotic crystals have all been utilised in different array formats to try and qualitatively and semi-quantitatively obtain information on, and differentiate between volatile production patterns produced by spoilage microorganisms in food matrices. However, it is important that the results are combined with multivariate data analysis systems to enable rapid intepretation of volatile patterns and for user-friendly answers to be obtained. Although most electronic nose systems are qualitative for QA, the level of detail required determines the use of the instrument. If a simple yes/no answer is needed then it can be very appropriate. Recent studies have demonstrated that real-time evaluation of food raw materials can be made in approx. 10 mins per sample for discrimination of mouldy from good grain (Magan and Evans, 2000; Evans et al., 2000). Recent studies have also demonstrated that discrimination between mould contaminated and non-contaminated bread was possible within 24–30 hrs after inoculation, prior to any visible growth and more sensitive than enzyme assays or CFU population measurements (see Fig. 8.9; Keshri et al., 2002). Studies have also suggested that since the biochemical pathways for mycotoxin producing strains of a species may differ from non-producing strains, the volatiles produced may also differ (Olssen et al., 2002). Keshri and Magan (2000) demonstrated that mycotoxigenic and nonmycotoxigenic strains of Fusarium species could be discriminated using volatile production patterns with an electronic nose using conducting polymer sensor array. Recent studies have also suggested that changes in bacterial populations in milk and water can be detected at between 103ÿ104 CFUs per ml (Magan et al., 2001; Canhoto and Magan, 2003). Indeed, detection of medically important aerobic and anaerobic bacteria has also been successful (Pavlou et al., 2002). The take up of this technology had been slow because of problems with consistency and the price of the technology. However, as the development of sensor arrays becomes cheaper the potential for exploitation of this technology should improve rapidly.

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Fig. 8.9 Dendrogram which shows discrimination between bread inoculated with different mould species and the control (Keshri et al., 2002). Example of differentiation between spoilage moulds using electronic nose technology.

8.10

Conclusions: commercial products

Due to the increased need for detecting micoorganisms in food and other applications many devises have been developed and marketed. The success of any instrument is based on the level of detection and cost. Table 8.2 lists some of the products on the market today. Most of the instruments developed for laboratory analysis are large with detection limits of 103ÿ105 cells mlÿ1 with analysis time ranging from 10 min to 8 hours. Smaller instruments are in demand especially for on site testing. On-line methods of microbial testing have been Table 8.2

Examples of commercial instruments available for microbial detection.

Detection method

Detection limit (cells mlÿ1 )

Time of analysis

Bioluminescence

103

10–20 min

Electronic particle analysis Coulter counter Enzymes

105

20 min

5104 1 cfu 100mlÿ1

30 min 24 h

Surface plasmon resonance (SPR) Epifluorescence Impedance

105

1–2 h

0.5 cfu 105

10–20 min 2.5–8 h

Electronic nose

103ÿ104

1 hr

Commercial instrument Clean-TraceTM (Biotrace Ltd., Bridgend, UK) Ramus 265 (Orbec Ltd., Surrey, UK) Coulter Counter Inc., Canada Colilert (Palin test Ltd., Gateshead, UK) BIAcore (Pharmacia, Uppsala, Sweden) FOSS Electric, Hillerød, Denmark Bactometer (BioMerieux Inc., Marcy-’Etoile, France) AlphaMoss, France

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developed by many researchers (Ashley, 1991; Chunxiang et al., 1993; Silley, 1994). For food samples, which are usually coloured and contain particulate matter, sample preparation is important to reduce interferences. Ideal methods of analysis should be capable of real time and in situ analysis. Microbial contamination detection and analysis is a very important area in food safety. Classical methods usually have a long time of analysis but give good sensitivity. The other more recently developed methods may give faster results but usually suffer from low sensitivity. However, no single method, to date, has been completely satisfactory in the determination of microbial contents. Drawbacks encountered by the various detection techniques include long response time, lack of sensitivity and high cost of the instruments. Lack of discrimination between viable and non-viable biomass can result in errors. A range of contract laboratories provide a complete microbial testing with testing regimes for physico-chemical characterisation of ingredients and their application in food and drink products from bench to pilot plant to factory sites.

8.11

Sources of further information and advice

http://www.sdix.com/ http://www.oxoid.com/uk/index.asp http://www.ciberis.com/syva/ http://www.leatherheadfood.com/lfi/index.htm http://www.mcsdiagnostics.com/doc-e/e-cf1-1.htm http://www.mcsdiagnostics.com/index.htm http://www.microcheck.com/ http://www.rocheuk.com/html/products/default.asp http://www.biomerieux-usa.com/clinical/immunoassay/index.htm http://www.foss.dk/c/p/default.asp?width=1024 http://www.adgen.co.uk/ http://www.gen-probe.com http://www.affymetrix.com/index.affx http://www.biacore.com http://www.affinity-sensors.com

8.12

References

and WILKINS, E. (1999). Flow-through immunofiltration assay system for rapid detection of E. coli O157: H7. Biosensors & Bioelectronics, 14, 309–316. ALLEN, M.E. (1990). Applications for mediated amperometric biomass sensor technology. M.Phil. Thesis, Cranfield Biotechnology Centre, Cranfield University Bedford, UK. ARMIGER, N.B., ZABRISKI, D.W., MEANNER, G.F. and FORRO, T.F. (1984). Analysis and process control of feed batch production of E. coli culture fluorescence. Presented at Biotech. 1984, Washington, DC. On-line publications, Pinner, UK. ABDEL-HAMID, I., IVNITSKI, D., ATANASOV, P.

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9 Rapid analysis of microbial contamination of water L. Bonadonna, Istituto Superiore di Sanita` – Rome, Italy

9.1

Introduction

The presence of enteric pathogens in drinking and recreational waters is of great public concern. As a result of the risk to public health due to the presence of pathogens, it is extremely important to determine the microbiological safety of these waters. The ideal manner for doing this would be to analyse the waters for the presence of specific pathogens of concern. However, hundreds of different micro-organisms have been shown to be involved in waterborne disease outbreaks; thus, it would be impractical to look for every pathogen potentially present in water. In addition, traditional methods used routinely in the control for enteric pathogens are often time-consuming and scarcely selective. Thus, indicator organisms of faecal contamination are used globally as a warning of possible contamination. They are considered as an index of theoretical risk for public health and of water quality deterioration. Heavy reliance has been placed on the coliform and enterococci groups of bacteria to determine the safety of drinking water, recreational water and shellfishharvesting water. However, the presence of the indicators is not an absolute indication of the presence of pathogens and, on the other hand, their absence is not a guarantee that other, more resistant microbial forms are not present. Furthermore, their presence has no diagnostic value for biological agents deliberately introduced in water. Ideally, microbial indicators should provide a measure of health risk associated with the exposition to contaminated water (ingestion or contact). Nevertheless, these groups of micro-organisms have many limitations as predictors of risk of waterborne disease. In fact, the bacterial indicators tend to be poor models for enteric protozoa and viruses because of their shorter survival

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times in water and their greater susceptibility to water treatment processes. Moreover, there are non-faecal sources for these indicator organisms, and in contrast to most enteric pathogens, coliforms may multiply in aquatic environments with sufficient nutrients and optimal temperatures. Such characteristics may result in false-positive reports of water contamination. One of the requirements of choice of an ideal indicator of faecal contamination is to be easy to identify, isolate and enumerate. Thus the monitoring and the statutory assessment of the hygienic quality of drinking water are based on the determination of bacterial indicators of faecal contamination.

9.2

Current techniques and their limitations

Classically and routinely, the detection and the enumeration of indicator microorganisms of faecal pollution is based on cultural methods. In these methods, the micro-organism is grown on either a solid (agar) or liquid (broth) medium, which supplies the nutritional requirements of the organism. Once a microorganism has been grown and isolated as a pure culture, the identification is generally based on biochemical characteristics of the isolate; sometimes immunological (serological) and genetic characteristics are also determined. In many instances, specific and selective compounds are incorporated into the primary media, which allow for selection and differentiation of the target organisms and, contemporaneously, inhibit the growth of background bacterial flora (non-target organisms). This detection system can be based on fermentation of specific sugars, enzymatic degradation of specific substrates, mobility, reduction of hydrogen acceptors, etc. and will usually result in recognisable colour changes, gas production, etc. Cultivation of micro-organism needs the growth/multiplication of micro-organisms. However, the viability of a micro-organism may affect detection and for a long time the failure of some bacteria to grow on solid media has been recognised. Conventional methods for detecting indicators and pathogenic bacteria in water may indeed underestimate the actual microbial population due to sublethal environmental injury, inability of the target organisms to take up nutrients and other physiological factors which reduce bacterial culturability. In fact, it is recognised that only a small proportion, possibly less than 1%, of the number of viable bacteria may be enumerated in water (McFeters, 1990). A requirement for reproductive ability is for the cell to be metabolically active and possess intact cell membrane and cellular components. An intact, metabolically active cell may not necessarily grow, however, due to non-lethal injury. The concepts of bacterial injury (McFeters, 1990) and ‘viable but non-culturable’ cells (VBNC) (Roszak and Colwell, 1987; Desmonts et al., 1990, 1992) have been demonstrated by molecular techniques. Besides, stressed micro-organisms, even able to multiply, can lose the ability to express some metabolic characteristics. For example, it is the case of stressed

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Escherichia coli strains: non-gas producing strains of E. coli have been reported to approach 10% of the E. coli population in water (Dufour et al., 1981). Appropriate sampling procedure is the first step at obtaining good analytical results from an analysis. The sample should be representative of the water to be analysed. Additionally, the amount of elapsed time allowed between sample collection and analysis should not exceed 24 hours for most micro-organisms and less than 18 hours for some pathogens. All microbiological methods are designed to detect and/or enumerate particular types of micro-organisms, the target-organisms. Thus the detection of specific groups (e.g., coliforms) or species (e.g., Escherichia coli) of microorganisms should be carried out using selective media. Interfering flora that may be present in the sample should go undetected and should not interfere with the analytical process. The often harsh conditions needed to suppress the non-target groups may however reduce the recovery of the target population as well. Besides, non-target organisms will not always be totally eliminated and the characteristic appearance of the target colonies may not be unequivocally distinct. Consequently, methods/media are not completely selective for the specific micro-organisms to be determined and few selective methods can be trusted to function so well that further confirmation of the primary colonies is unnecessary in all sample types. Moreover different methods will recover different proportion of the bacterial population. It is also important to outline that because the growth medium and the conditions of incubation, as well as the nature and age of the water sample, can influence the species isolated and the count, microbiological examinations may have variable accuracy. This means that the standardisation of methods and of laboratory procedures is of great importance if criteria for microbiological quality of water are to be uniform in different laboratories and internationally.

9.3

Identifying indicator organisms

The following paragraphs give a brief overview of the most common methods used for detection of bacteria in water. In liquid enrichment methods, a test portion is inoculated into a growth medium that has been formulated to stimulate growth of the target organisms and to suppress growth of the background flora. The selective nature of the enrichment medium is enhanced by choosing an appropriate incubation temperature and time. If the target organism is present in the test portion, this will usually result in a positive signal, irrespective of the original number. In its simplest form, a liquid enrichment method therefore gives a Presence/Absence type of information. In order to obtain (semi-) quantitative information, a series of different volumes (e.g., 100, 10, 1 and 0.1 ml) may be examined to produce an end-point type of result. If a series of different volumes is examined in replicate, e.g. three- or five-fold, it is possible to use a method known as the Most Probable

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Number (MPN) technique to estimate the original concentration of the target organism. MPN analysis is a statistical method based on the random (Poisson) dispersion of micro-organisms in a given sample. The results are expressed in terms of the MPN of micro-organisms detected per volume of sample. Classically, this assay has been performed as a multiple-tube fermentation test. The precision of this estimate is low (e.g., the 95% confidence interval of a fivefold MPN estimate is roughly between one-third and three times the analytical results) (Lightfoot and Maier, 1998). However, if the number of parallel test portions is increased to 100 or more, the MPN technique will surpass the conventional plating technique in precision. In fact, precision of single-dilution or multi-dilution MPN estimates is inversely related to the square root of the number of parallel tubes. A convenient way to increase precision is to use the miniaturised enzymatic MPN method at multi-well (Section 9.4). The classic MPN technique is also time-consuming: to perform the presumptive, confirmed, and sometimes completed steps, 48–72 hours or more are necessary. Besides, only little volumes of the sample can be analysed. However, it still remains a good technique for the analysis of samples characterised by high turbidity and for the analysis of environmental solid matrixes (sediments, sands, sludge, compost). The other traditional method is the colony count technique. A test portion is inoculated onto the surface of a selective or not selective growth medium that has been solidified by addition of agar-agar (spread-plate method). Each individual cell of the target organism will multiply into a colony visible to the naked eye. If several cells of the target organism are physically connected or laid upon, this will result in one colony. The result of the plate count technique are therefore expressed as the (number) concentration of Colony-Forming Units (CFU) per unit volume. Each CFU represents one or more cells of the target organism in the original sample. Variations are the pour-plate method where the test portion is mixed with the liquefied agar medium, poured into Petri dishes and incubated after solidification. The more commonly used colony count procedure for the detection of indicator organisms is the membrane filtration method where the test portion is filtered through a membrane filter (usually of 0.45 m pore size) and the filter is placed on the growth agarised medium. Results of analyses performed by the membrane filtration method are generally obtained in primary isolation after 24–48 hours; if a confirmation or biochemical tests have to be carried out final results can be reached also after 96 hours. A procedure of resuscitation, necessary to revive micro-organisms before placing them on the selective growth medium, may be an integral part of the test method and usually involves incubation in a less selective medium and/or at a less restrictive temperature. The use of membrane filtration methods can exhibit some issues due to inhibition of growth on filter grid-lines, abnormal spreading of colonies, hydrophobic areas, poor colony sheen, decreased recovery and wrinkling

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(Brenner and Rankin, 1990). Different brands of membrane filters also produce discrepancies in the enumeration of micro-organisms from water (Presswood and Brown, 1973; Sladek et al., 1975; Brenner and Rankin, 1990). Turbidity in water samples may preclude the use of filtration as high sediment concentration on the membrane surface may interfere with colony growth. Besides, bacterial colonies on plates or membrane filters may influence each other and in general there will be a tendency to lower test results if more colonies are found on the same plate. To some extent these so-called ‘crowding effects’ will influence the final test result. Furthermore, the linearity of the method can be affected by the ability of the analyst to distinguish ‘typical’ from ‘atypical’ colonies. Experience has shown that misinterpretation and differences between several analysts are more likely to occur at higher colony densities, particularly with methods that require subjective interpretation of colours or dimensions of colonies. Advantages of the current methods have outweighed the limitations for decades. The low cost per sample and low complexity of the procedure makes the techniques universally applicable to laboratories. The selectivity of the media commonly used and the time for obtaining confirmed results remain major limitations.

9.3.1 Microbiological detection methods A broad variety of micro-organisms can be found in aquatic environments. Since the pathogen micro-organisms appear intermittently in natural waters at low concentrations, and the techniques available for their selective recovery and enumeration are, generally, complex, the use of surrogate (indicator) bacteria has been standard practice in water quality monitoring. Historically the heterotrophic plate count (HPC), the coliform group, the enterococci have been the bacterial indicators of choice. The former parameter is generally used as indicator of the effectiveness of the water treatment processes and as a measure of numbers of regrowth organisms that may or may not have sanitary significance. The latter two indicator bacteria are excreted in high numbers by healthy humans and animals, and thus their presence in environmental samples is indicative of faecal contamination. By contrast, specific enteric pathogens are voided only by infected individuals, and their numbers in aquatic environments depend on the excretion level of each particular pathogen and on the number of infected individuals in the community. The great diversity of micro-organisms in water, and associated variety of required growth conditions, hamper attempts to isolate, identify, and enumerate most organisms’ members of this microcosm. Thus the presence of indicator organisms will likely continue to be used as a criterion of water quality that will be of value if attention is given to the development and use of optimal and more rapid methods for the recovery of these micro-organisms. The commonly used indicator organisms belong to the groups of coliforms and streptococci/enterococci. The traditional definition of the coliform group of bacteria (family of Enterobacteriaceae) specifies that they are aerobic and

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facultatively anaerobic, gram-negative, non spore-forming, rod-shaped bacteria, able to ferment lactose with gas and acid production in 24 to 48 h at 35–37ºC (total coliforms) or at 44–44.5ºC (faecal or thermotolerant, or more correctly thermotrophic coliforms). However, the more recent advent of enzyme-specific media and tests has allowed the application of cytochrome oxidase (negative) and -galactosidase (positive) as additional criteria for their characterisation. Adoption of DNA-DNA hybridisation has also recently allowed a substantially improved grouping of Enterobacteriaceae in general and of coliforms in particular. The coliform group, now defined as ONPG+ Enterobacteriaceae (ortho-nitrophenyl- -D-galactosidase-positive), is distributed among 19 genera and 80 species. Among the coliform group, Escherichia coli deserves further discussion. In particular, E. coli has been demonstrated to be a more specific indicator for the presence of faecal contamination than the other coliform bacteria. In addition, E. coli conforms to taxonomic as well as functional identification criteria and is enzymatically distinguished by the presence of -Dglucuronidase, while possession of the gene coding for the -galactosidase enzyme is the most fundamental characteristic of the coliforms. Moreover, these enzymes form the basis for recently developed differential methods that will be discussed later in this section. The coliform bacteria have been for a long time the primary standards for drinking water in Europe and North America. Now, among the microbiological parameters for the control of water for human consumption, the European Directive 98/83 (European Directive, 1998) indicates, in substitution to faecal coliforms, Escherichia coli as specific indicator of faecal contamination. The group of the faecal streptococci/enterococci, gram-positive bacteria, is useful as indicator of microbiological water quality since these micro-organisms are common inhabitants of the intestinal tracts of humans and lower animals. Some of these organisms have persistence patterns that are similar to those of a range of potential waterborne pathogenic bacteria. As to the coliform group, recent molecular approaches have markedly changed traditional classification of the group. Two genera, Streptococcus and Enterococcus, have been recognised, and the different species have been reassigned to them. Whether testing for indicator organisms or directly for pathogens, there is a common need for rapid analyses. Typically, the drinking water treatment process is a continuous process and water is consumed within a few hours after treatment. Real-time analysis would be ideal for the management and control of microbial water quality and the safeguard of public health. At the present, with the use of conventional cultural methods, the assessment of the hygienic quality of drinking water is only available after a minimum of 18 to 24 hours. If results have to be confirmed, another one to two days may be required. The detection of bacterial pathogens in water can take even longer. Analytical procedures have often low selectivity and are complex and time-

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consuming. Testing water for the presence of specific viruses or Giardia and Cryptosporidium might even take as long as a few weeks for a complete determination (Keswick et al., 1984; Gerba et al., 1989; Girones et al., 1993; Fricker, 2002). Consequently, information on the microbiological quality of the water supplied to consumers is often available long after the water has been utilised. Nevertheless different causes could change water quality: sudden failure of drinking water treatment plants, contamination of distribution networks or disruption of water supply services, water contamination by natural disasters or by intentional introduction of microbial pathogens, regrowth phenomena. That could constitute a risk to consumer health. So water treatment facilities should have the capability of detecting water quality changes rapidly in order to adjust the treatment process. In all these cases there is an urgent need for rapid and reliable information on the microbiological quality of drinking water.

9.4

The development of more rapid detection methods

A variety of analytical approaches have been proposed for the rapid detection of bacteria in water, although most are limited by sensitivity with respect to analysis of water of good microbiological quality. An essential requirement for rapid methods should be the availability of data in the shortest time possible, that means that these methods should be faster than the standard methods currently used. For bacterial indicators, the ideal for rapid methods should therefore be to have results within the same working day. Rapid methods should ideally have sensitivity and specificity at least equal to those of the standard methods used regularly. Until now, sensitivity remains a major drawback for many of the rapid methods in development. Moreover, these techniques should have the ability to distinguish between viable and dead microorganisms and results should be robust, repeatable and reproducible. In order for the development of rapid detection methods to be used on a routine basis, other logistical and economic factors should also be considered. Thus attention should be given to the cost and availability of reagents, the need for special handling of samples, the need for dedicated and expensive apparatus, the ease of performance and interpretation of results, and the training needs of the analyst. A great variety of methods, based on different principles, are available for recovery and characterisation of micro-organisms in water. Some of them, however, have a more meaningful application and are more suitable for pathogens detection rather than indicator micro-organisms. In fact, the costsbenefits ratio is still unfavourable for some methods because of their low sensitivity and specificity, interference problems, need of skilled personnel, high cost of instruments and reagents. Therefore, some rapid methods for the indicators detection in water are described with consideration to their current and wider actual application.

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Among these methods, some techniques are referred to as rapid methods although they may not be faster than some of the membrane filtration methods using selective media. Nevertheless, as the results are available within 18 to 24 hours, they are faster than those of the standard methods where confirmation of the results is required, which take 48 to 72 hours. Methods for the detection of micro-organisms in water can be roughly divided into two principal categories: cultivation techniques and techniques in which the micro-organisms are detected directly without first culturing them. Within these two big categories, methods, sometimes combined, can be arranged at different levels: qualitative or semi-quantitative methods, quantitative methods and methods for identification and characterisation of micro-organisms.

9.4.1 Methods based on specific enzyme activities Over the last twenty years, new membrane filtration and MPN techniques, and Presence-Absence tests, using the metabolic activity of cellular enzymes for the detection of total coliforms, E. coli and enterococci have been developed and now currently applied (Edberg et al., 1988; Manafi and Kneifel, 1989; Hernandez et al., 1991; Budnick et al., 1996). The tests rely on the detection of specific enzyme activities ( -Dgalactosidase, -D-glucuronidase, -D-glucosidase) associated with the targeted indicator organism and no further confirmation tests are needed (Manafi et al., 1991; Frampton and Restaino, 1993). The specific substrates allow the expression of these enzymes and their hydrolysis by the specific enzymes releases fluorophores or chromophores, providing a signal for detection. However, these assays can be affected by the incidence of enzymes positive interfering organisms (e.g., Flavobacterium spp., Aeromonas, some Shigella strains) and E. coli strains (E. coli O157:H7) that do not express the specific enzyme. For use in membrane filtration procedures, with the aim to obtain faster results compared to those of traditional media, many new selective media based on enzymatic activity have been proposed for the recovery of indicator organisms, particularly E. coli. Nevertheless, over the last ten years, enzyme substrates used in semi-quantitative methods have received more concern. Among these techniques, based on the MPN procedure, ColilertÕ QuantyTrayTM and Enterolert Quanty-TrayTM (IDEXX, Westbrook, Maine) are the recommended methods for drinking water analysis in the United States and for recreational water analysis in Australia and New Zealand. Moreover ColilertÕ Quanty-TrayTM is approved by USEPA and by UBA, the Agency for the environment, as an alternative method in Germany to the ISO 9308-1 reference method for drinking water analysis. The ColilertÕ Quanty-Tray, developed by Edberg et al. (1988), enables simultaneous detection and enumeration of total coliforms and E. coli within 18–24 hours. Sample preparation is minimal, requiring direct addition of the sample to the powdered medium containing ortho-nitrophenyl- -Dgalactosidase (ONPG) and 4-methylumbelliferyl- -D-glucuronidase (MUG). A

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yellow colour is considered a positive reaction for total coliforms, whereas a blue-fluorescent colour under UV (ultraviolet) light is considered a confirmation of E. coli presence. A European trial among twenty laboratories belonging to thirteen European countries, held in 1999–2000, showed the ColilertÕ to be at least equivalent to the ISO standard reference method 9308-1 for the detection of E. coli and coliforms (Fricker et al., 2000). The EnterolertÕ Quanty-TrayTM uses the same principle of the former method. Its powdered medium contains as a substrate the 4-methylumbelliferyl- -D-glucoside (MUD) for the detection and enumeration of enterococci in 24 hours. All these methods require little skill to perform and interpretation of the results is easier and less prone to error, especially for high colony concentrations, than from a membrane filter plate. Miniaturised enzymatic MPN methods for E. coli (MU/EC) and for enterococci (MUD/EN) are based on the same criteria of the previous mentioned methods: positive wells in a 96 wells microtitre plate result fluorescent under UV light. During two European Projects both the methods were compared to selected and representative analytical methods used in different laboratories in Europe. Even if they give results in 36–72 hours, no confirmation has to be done and a higher specificity was found compared to the membrane filtration methods (Hernandez et al., 1995). Both the methods have recently been recommended by the ISO (International Organisation for Standardisation). The revised Bathing Water Directive, at the present time under discussion at the European Parliament, include them as reference methods for bathing water analysis. Among the Presence/Absence tests, ColifastÕ (Norway) has proposed the ColifastÕ Analyser for the detection of total and thermotolerant coliforms, E. coli, faecal streptococci, Pseudomonas aeruginosa and TVO (Total Viable Organisms) in water samples. It is a semi-automated instrument with customised software and ColifastÕ reagents and media incorporating enzyme substrates. The method is automated after sample concentration and registration, and contemporaneously allows the analysis of 80 samples. Fluorescence is detected by the ColifastÕ Analyser providing Presence/Absence results. Semiquantitative information can be obtained by determining the Time To Detect (Samset et al., 2000). The speed of detection depends upon the level of contamination within the sample. In raw water samples detection times ranged from 1 hour with >1000 CFU to generally under 8 hours for one thermotolerant coliform and 9.5–13 hours for one total coliform (Eckner et al., 1999). The ColifastÕ Analyser has been applied as an early warning operational tool (Tryland et al., 2000), in MPN format (Samset, 2000), Presence/Absence format (Samset et al., 2000) and direct addition or membrane filtration (Angles d’Auriac et al., 2000). Another application is an on-line, auto-sampling/auto-reporting instrument (CALM) for routine assessment of the quality of incoming raw water being used for drinking water production. At the present time, the performance of the ColifastÕ method is evaluated into a European Project (Section 9.6).

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9.4.2 Biosensors Among techniques used for the detection of active micro-organisms in water, biosensors can be included (Hobson et al., 1996). They provide an indication of the level of active micro-organisms in the sample and are, therefore, of only limited use in controlling the microbiological quality of water. Biosensors are analytical devices which yield a measurable signal proportional to the concentration of micro-organisms in the sample. Biosensors can provide direct detection of a biological reaction by measuring physical changes in pH, potential difference, oxygen consumption, ion concentrations, current, resistance or optical properties occurring as a direct result of the analyte-receptor complex formation on surfaces of a physical or chemical transducer. Biosensors are of use as screening tools and give a rapid indication of poor water quality in hours. There is a wide range of biosensors available on the market, some of them are portable (e.g., the GeneChip); others require substantial power inputs and software appliances. Among the electrometric biosensors, the impedimetric methods, widely known for a long time, measure the electrical resistance (impedance) to a flow of alternating current through a conducting medium where the microbial growth results in electrochemical changes, increasing the conductivity of the medium. The number of micro-organisms present in the inoculum can be estimated from the rate of change of the impedance. The success of impedimetric methods depends entirely on the selective properties of the growth medium. The first uses of impedimetry were to replace general parameters, such as total plate counts, sterility testing, yeasts and moulds. Over the last years, systems such as the Malthus (IDG, UK), the Bactometer (BioMe´rieux, France) and the RABIT (Don Whitley Scientific Ltd, UK), with selective media, have been developed for detection of microbial groups such as coliforms and enterococci.

9.4.3 Direct detection techniques In recent years, technological advancements have developed a variety of direct detection techniques for recovery of micro-organisms in water. Moreover, with the rapid development of molecular methods, several techniques with high specificity have been developed for the direct micro-organisms characterisation. In order to utilise the major advantages of both groups of techniques, combinations have been developed. Immunoassays may be competitive or non-competitive, based on the principle of antibody presence (Ekins, 1997). Commonest techniques for the labelling of antibodies include the conjugation of an immuno-fluorescent dye (IF), immuno-magnetic bead (IMS), secondary enzyme-linked antibody (ELISA), increasing the signal for detection of fluorescence, magnetism or enzyme activity. Polyclonal and monoclonal antibodies can be used, and these latter, more specific, produce a more reproducible and standardised immunoassay response. Nevertheless, the sensitivity and specificity required

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for water quality determination and the abundance of non-specific substances and interfering organisms producing false-positive results hamper the routine application of immunoassay to water analysis. The application for the detection of coliforms, E. coli and enterococci is further limited by the lack of commercially available antibodies specific for the required target. Cells to which the antibodies attach can be detected by epifluorescent microscopy, flow cytometry, optical density or by the ChemScanÕ RDI (Chemunex, France). In fact, epifluorescent microscopy is widely used for the detection and enumeration, by operator, of auto-fluorescent or labelled by fluorescent compounds organisms. Alternative detection techniques, such as flow cytometry involve counting blind to the operator. Flow cytometry measures the physical (size and length) and biochemical (DNA, photosynthetic pigments and proteins) characteristics of individual cells as they pass through a sharply focused, high intensity light beam derived from an arc lamp or laser (Shapiro, 1990). Cells can be detected by the effect of their physical status upon light scatter, or by auto-fluorescence or other fluorescent compounds conjugated to cell markers. The instrument detects cell presence when the signal strength exceeds a threshold set by the operator, which triggers a measurement. A constant, known flow rate enables the user to obtain an absolute count of cell number per unit volume injected. This is achieved by a constant sheath flow rate, maintained by pressure or pumps (Shapiro, 1995). Although it is assumed that measurements result from the detection of single cells, an underestimation of the real cells number has been obtained whether cellular aggregation occurs or not. Flow cytometry relies upon the strength of the signal for detection; therefore most target bacterial cells are labelled with fluorescent stains, antibodies or nucleic probes. Stains can be used to identify cell viability and taxonomic identification to some extent, although antibody labelling and nucleic probe and in situ hybridisation provide a more specific identification method. The application of flow cytometry for the detection and enumeration of indicator bacteria is limited (Porter et al., 1993; Davey and Kell, 2000) because of the high microbial density required (102–103 cells for optimal detection), the need for skilled operators, the high cost of the instrument. A portable, battery operated flow cytometer is the Microcyte (Optoflow, Norway) that has the advantage to reduce interference from auto-fluorescence of non-target organisms and particles. Flow cytometric analysis is completed in under 10 seconds at a fixed flow rate, therefore absolute cell numbers per unit volume are obtained (Davey and Kell, 2000). The detection limit is approximately 101–102 cells/ml, although 104–106 cells are required for optimal signal detection. The instrument requires little training for successful utilisation and provides the opportunity to screen biological from non-biological particles using fluorescent dyes. The simplicity, low cost and portability of the instrument are definite advantages when compared to the large-scale flow cytometers. Applications include the analysis of micro-organisms in environmental samples (river and

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drinking water, soil) and clinical specimens. The instrument has been extensively evaluated, particularly for the application to biowarfare. The ChemScanÕ RDI instrument (Chemunex, France) is a laser-scanning instrument designed for the detection and enumeration of fluorescently labelled micro-organisms. The instrument has been developed specifically for the detection of Cryptosporidium, Giardia, coliforms or E. coli. A three and a halfhour assay is required for the induction and labelling of target cells prior to laser scanning. Organisms are captured by membrane filtration, labelled and the filter subsequently scanned with a laser. The laser scanner detects and locates the position of all fluorescent organisms within three minutes. During the analysis, fluorescent events, including labelled organisms are detected by a series of detection units. Finally the signals generated undergo a sequence of computer analyses which distinguish between labelled organisms and fluorescent debris. A visual validation of all results can be made by transferring the membrane to an epifluorescence microscope which is fitted to a motorised stage. This stage, which is controlled by the ChemScan RDI, can be driven to the location of each fluorescent event for a rapid confirmation of all results.

9.4.4 Molecular techniques Hybridisation techniques using various types of probes have been used for the detection of specific pathogenic bacteria, viruses and parasites in water (Abbaszadegan et al., 1991; Dubrou et al., 1991; Knight et al., 1991). Because of their low sensitivity, these techniques have been used mainly for the identification of micro-organisms in polluted water and have to a great extent been replaced by PCR-based techniques. In situ hybridisation has been used for the direct detection of bacteria in water samples. Only active bacteria should be detected because the oligonucleotide probe is directed at the rRNA of the bacterium. After hybridisation, the organisms can be detected with a microscope or flow cytometer (Manz et al., 1993; Manz et al., 1995). The polymerase chain reaction (PCR) technique has recently received most of the attention in the development of rapid detection methods. This is due mainly to the excellent specificity, improved sensitivity, applicability to any group of micro-organisms and ease of detection of results. It is used mostly for the detection of pathogens in water but can also be used for indicator bacteria (Alvarez et al., 1993). PCR can be used as a screening, quantitative or characterisation technique. By using the PCR, a selected gene sequence specific to a group of organisms or a single species can be selectively amplified, increasing the chance of detection of low numbers of organisms within a complex mixture of micro-organisms and particulate. The replication process involves purification and extraction of cellular DNA, followed by melting of the DNA to break down double stranded DNA to single strands. Oligonucleotide primers (commonly Taq) hybridise to regions of the DNA flanking the target sequence using DNA polymerase enzyme in the presence of free deoxynucleotide triphosphates, resulting in duplication of the

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target sequence (Steffan and Atlas, 1991). This procedure is repeated over a number of cycles to exponentially increase the quantity of target DNA. High temperature cycles, to melt the DNA, are alternated with cool cycles, which provide the optimal temperature for hybridisation. PCR amplification time is limited by the length of time required for heating and cooling cycles. The more recent PCR instruments utilise rapid heating and cooling to reduce PCR cycling times. Successively the amplified sequence can easily be detected by means of techniques such as electrophoresis, hybridisation, high performance liquid chromatography or ELISA. PCR has been used for the direct detection of bacteria, protozoan parasites and viruses in water (Bej et al., 1991; Mahbubani et al., 1991; Toranzos and Alvarez, 1992; Abbaszadegan et al., 1993; Graff et al.,1993; Mayer and Palmer, 1996). The main concern with the use of PCR-based techniques for the direct detection of all types of micro-organisms in water is about infectivity and viability. In particular, no PCR method has been proved reliable for the detection, enumeration and examination of viable cells, because nucleic acid fragments from cells which may have been alive or dead, metabolically active or inactive, or even from previously lysed cells, may be amplified. Nevertheless, recent studies have suggested that methods to detect mRNA represent a promising approach for distinguishing bacterial viability (Sheridan et al., 1998). Furthermore, the use of reverse transcriptase PCR (rt PCR) can be used for the detection of viable organisms (Kaucner and Stinear, 1998). The technique also needs to be combined with concentration steps. Another problem with PCR-based techniques is that they only supply presence-absence data. Nevertheless, at present, different methods for the quantification of PCR products have been developed by adding known quantities of competitive DNA or by Most Probable Number PCR or by utilising fluorescence to quantify amplified DNA products.

9.5

Developing online monitors

Historically, physical, chemical, and biological parameters associated with producing drinking water have been monitored using routine grab sampling in source waters, treatment plants, and distribution systems, followed by analysis in the laboratory and manual or computer-assisted data handling. This approach collected only a relatively small data set to describe the sample variance. Automation has increased the amount of data produced and the ease of data handling and analysis. Online monitor development has included automation of sampling, analysis, and reporting functions. The online monitoring can be defined as unattended (except for routine maintenance) sampling, analysis, and reporting of a parameter; it produces data at a greater frequency with respect to the traditional grab-sample monitoring. It also allows real-time feedback for water quality characterisation for operational

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and regulatory decisions. Online monitoring may also be used as a screening or warning tool rather than a replacement for grab sampling of health-related parameters. In France, Germany, Italy, the Netherlands, Spain, and the United Kingdom, a relatively small number of large water companies supply a large portion of the population on a National or international basis. These companies perform online monitoring throughout the entire water cycle (from source to distribution) because facilities are operated remotely or because certain analytes require frequent data collection in order to ensure adequate treatment. Nevertheless online monitors are not available for measuring all parameters for which a critical demand exists in the drinking water sector. In fact, while automated, online measurement technologies have been developed for various physico-chemical parameters such as turbidity, particles, etc., automated online or in situ microbiological testing remains largely unrealised. On the biological point of view, two kinds of monitors can be distinguished: monitors that use biological species as sentinels for in water presence of contaminants of concern, such as toxic chemicals, and monitors that screen for the presence of specific biological species, such as pathogenic protozoa (Giardia and Cryptosporidium) and bacterial indicators. At the present time, generally speaking, automated online monitors for microbiological testing are indeed not in widespread use and most are still in development. In fact, until now there are still difficulties for setting up methods with an online capability for continuous monitoring. The biggest issues to be resolved include detection sensitivity and specificity, analysis time, data storage and transfer, and system cost. In this group of online monitors some biosensors (Section 9.4) could be included. In fact, an ideal biosensor would enable in situ, real-time detection of viable target micro-organisms, at concentrations as low as 1-10 cells/100 ml, by relatively untrained staff. However, biosensors are not currently the best choice for the microbiological evaluation of water samples containing low and moderate contamination, such as those characterising water for human consumption. Nevertheless industrial developments in this field have prospects of success. Now, an online instrument is utilised and its performance is evaluated for the detection of indicator organisms in the course of a European project (Sections 9.4 and 9.6). These technologies could have high operating relevance for the indicator bacteria because their presence, detected through a continuous monitoring, could be an early warning of failure or cross-connection with sewerage lines in drinking water distribution systems.

9.5.1 Automated devices for the detection of Giardia and Cryptosporidium Currently, automated devices for the detection of Giardia and Cryptosporidium are in a quite advanced development. In fact, the importance of automated methods being developed to detect and quantify these protozoans in waters is

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related to two orders of problems: these protozoans are known as human pathogens and are resistant to conventional treatment practices. As such, their presence can justify an immediate operational response. The two protozoan parasites are ubiquitous in the aquatic environment worldwide and have been implicated as the causative agents of outbreaks of waterborne enteric disease in humans. Their transmissive stages, oocysts and cysts, are voided by infected persons or animals and enter surface water through direct input, discharges of treated or untreated sewage, run-off or discharges of manure from agricultural lands and in pristine waters. The persistence of (oo)cysts in the aquatic environment, their infectivity and resistance to chemical disinfectants make the (oo)cysts of the parasites critical pathogens for drinking water production from surface water source and for recreational water (LeChevallier et al., 1991; Rose et al., 1991; De Abramovich et al., 1996; Graczyk et al., 1997; Fricker, 2002; Bonadonna et al., 2002). Recently, several systems have been described that may be appropriate for online measurement of Giardia and Cryptosporidium. These systems are still under development. The online innovations are mainly linked with new detection technologies. The systems described in the following paragraphs can be operated automatically and are claimed to be specific enough to eliminate the need for human verification. Multiangle, multiwavelength particle characterisation The multiangle, multiwavelength approach uses UV-visible light absorption and scattering at several observation angles. The absorption spectrum is used to estimate particle concentration, density, and chemical composition. The scattering data are used to measure particle size and molecular weight distributions. These spectral characteristics allow the identification and quantification of particles having a size range of 10 nm to 20 m, including Cryptosporidium oocysts (4 to 6 m) and Giardia cysts (8 to 15  10 m). Some technical limitations are yet to be resolved, especially those associated with the interpretation of spectral characteristics. In fact, some changes in diffusion and absorption characteristics have been observed in relation to quantity and chemical nature of the interfering particles. These spectral changes can result in a high number of false-negative or positive results. The spectral characteristics of Cryptosporidium and Giardia, for instance, are speciesspecific, and flow cytometry studies have shown that the light diffusion characteristics of Cryptosporidium oocysts change with the stage age (Compagnon et al., 1997). Multiangle light scattering (MALS) The Multiangle light scattering (MALS) technique has been used to detect various types of microscopic particles, including but not limited to E. coli, phytoplankton, and algae. This technique’s ability to detect Cryptosporidium parvum has been successfully tested in selected waters in a laboratory environment (Gregg, 2000). MALS relies on simultaneous measurement of

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various scattered light angles. The light source is a solid-state laser that provides a coherent monochromatic light source, typically using red wavelength (e.g., 632.8 nm). Water flows freely through the sensing device and particles pass through the laser beam and generate unique light-scattering fingerprints. They automatically and electronically are compared to an existing library of optical fingerprints. Identification is essentially instantaneous, and about 1 L of water can be completely scanned in 1 hour. Currently, the studies focus on the sensitivity and accuracy of the MALS technique in various water types and its ability to detect morphological changes of Cryptosporidium parvum oocysts. These approaches remain interesting but require further progress before onsite application can be considered.

9.6

Future trends

The industrial market for microbiology quality testing in environmental, food, beverage and pharmaceutical sectors, comprises approximately 800 million tests world-wide, only considering the ‘indicator’ tests and it is growing due to public health concerns and increased attention to food and water safety, increasing regulatory controls, economic pressures on producers to reduce delivery times, stock levels and wastage as well as protecting brand values. The European water industry produces some 900 000 000 tonnes of water per hour for domestic and industrial use. Fifty to sixty organisations are supplying water in the European Union and all have central and local laboratories where routine analyses are performed. It is estimated that there are some 1100 waterworks in Europe where large numbers of samples are tested for microbial contamination. About 1.2–1.5 million compliance tests are carried out in the European Union each year. This shows how appropriate water quality controls are important for this sector, and not only for the safeguard of public health but even for the economical perspectives. All the commonest methods for the microbiological examination of water are retrospective. There have been considerable efforts to try to develop new methods for the rapid detection of micro-organisms in water but most are complex and require specialised equipment and highly trained laboratory staff. At present the ideal of real-time analysis cannot be achieved, but developments during the last ten years have made it possible to detect many indicator organisms and pathogens in water within the same day. Sensitivity is still a major concern, especially when monitoring drinking water. To improve sensitivity, attention should be given not only to culture and direct detection techniques, but also to concentration and separation methods. The methods described in this chapter are by no means inclusive of all technologies in this area; in Table 9.1 some characteristics of the described technologies are reported. Most of them are still in the research and development phase or have only very recently been made available publicly, and questions remain about their utility and feasibility for early warning purposes.

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Table 9.1 Some features of technologies currently available for the detection and the enumeration of micro-organisms in environmental samples Technology

Speed of detection

Limit of detection

Screening Enzyme assay Bioluminescence Biosensors

< 1–12 hours 1 min–48 hours < 1 min–30 min

1 organism/sample 103–104 organisms 104–109 organisms

Quantitative methods Plate culture Enzyme activity (MF) Enzyme activity (MPN) Impedance

24–72 hours 18–24 hours 18–24 hours 6–48 hours

1 organism/sample 1 organism/sample 1 organism/sample 105–106 organisms

2–4 hours

–

15–30 min 10 min–1 hour+

– –

depending upon stain few min (flow injection) 2–3 hours (solid phase) < 1 hour depending upon probe

organism/sample 104–105 organisms

3–10 min 3 min depends on the operator

10–102 organisms 1 organism/sample 1 organism/sample

< 1 min

102–104 organisms

Pre-enrichment methods Plate culture Immuno-magnetic Separation (IMS) PCR Labelling technologies Staining Immunoassay Nucleic probe Detection Instruments Flow cytometry Laser scanning Epifluorescence microscope Luminometer

104–105 organisms

There are currently two commercially available products that may be applied to monitoring water quality, the ColifastÕ Analyzer and the ColilertÕ 3000. Both methods convey the drawbacks of enzymatic assays involving non-specific organism expression of enzymes and non-expression by certain E. coli strains, and the growth rate of organisms limits the time to detection. In this context the ‘Demonstration of a rapid microbial monitor for operations and quality monitoring in the water industries’ project was proposed to the European Commission and at the present time it is in progress. Its main global objective is to demonstrate in water industries that a sensitive and rapid micro-organisms atline monitor or laboratory system, the ColifastÕ Analyzer, is comparable to the relevant reference methods, and enables ‘Early Warning’ of various indicators and semi-automation of the methods. Among non-cultura1 methods for the detection of micro-organisms, some molecular methods could revolutionise water testing. The DNA microchip array is

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currently under development, and holds promise for the rapid, sensitive detection of micro-organisms in water. This method is a new technology that allows for the detection and identification of multiple organisms within four hours. Up to 400,000 oligonucleotides are synthesised in situ on a miniaturised glass substrate. DNA fragments that are labelled with a fluorescent marker are then floated across the chip array. A positive hybridisation result is detected by an intense fluorescence reaction. Because the oligonucleotide sequence for each position on the chip is known, micro-organisms in the water sample can be identified. Despite the numerous shortcomings of traditional growth-dependent methods, they still remain the cornerstone of microbiological quality assessment in most applied fields. Nevertheless, using image analysis, robotics, biosensors and molecular technologies, the possibility of rapid, automated monitoring for specific micro-organisms may be possible in the foreseeable future.

9.7

Sources of further information and advice

To ensure the full protection of drinking water, a technology-based early warning monitoring system should be the first component of a comprehensive programme to protect public health. Technological advancements are gathering speed to produce the ultimate rapid microbiological analysis system. The ideal technique for the enumeration of faecal indicators from water should be portable, flexible, speed, economic and ease to use. Its performance should be at least as accurate and precise as reference methods and it should provide cost benefits through time and labour savings. The current market for rapid analysis technologies provides a number of techniques which may fit into one or more of these categories and some of these may have an important application for the automation in aquatic microbiology in the future. However, it has to be outlined that the development and the choice of a new technology need the method performance to be evaluated with specially designed experiments. In this context, validation programmes must be prepared and acceptance criteria established. The new method, which is found acceptable after the validation steps have been completed, should then be tested against the reference method. This, as a final step before the routine use, should then demonstrate that there are no major differences between the two methods. Here some references are reported that can help in the application of the procedures for validation and comparison of methods: COMMISSION OF THE EUROPEAN COMMUNITIES, COMMUNITY BUREAU OF REFERENCE. MOOIJMAN KA, IN’T VELD PH, KOEKSTRA JA, HEISTERKAMP SH, HAVELAAR AH, NOTERMANS SHW, ROBERTS D, GRIEPINK B AND MAIER E,

Development of microbiological reference materials., Report EUR 14375 EN, 1992, ISSN 10185593. DRINKING WATER INSPECTORATE FOR ENGLAND AND WALES. Comparison of microbiological methods of analysis, organisation and supervision of performance tests. DWI Contract 70/2/128, 2001.

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DRINKING WATER INSPECTORATE,

Ashdown House, 123 Victoria Street, London, SW1E 6DE Comparison of Methods for Drinking Water Bacteriology – Cultural Techniques. www.dwi.detr.gov.uk/regs/infolett/2000/info500.htm. ISO CD 17994. Water Quality – Criteria for the establishment of equivalence between microbiological methods. Committee draft 2001-06-15. LIGHTFOOT NF and MAIER EA (1998), Microbiological Analysis of Food and Water: Guidelines for Quality Assurance, Amsterdam, Elsevier Science. LIGHTFOOT NF, TILLETT HE, BOYD P. and EATON S (1994), ‘Duplicate split samples for internal quality control in routine water microbiology’, Lett Appl Microbiol, 19, 321–4. ¨ SI (2001), ‘Comparison of methods NIEMI RM, HEIKKILA MP, LAHTI K, KALSO S and NIEMELA for determining the numbers and species distribution of coliform bacteria in well water samples’, J Appl Microbiol, 90, 850–8. STEERMAN, R L (1955), ‘Statistical concepts in microbiology’, Bacteriol Rev, 19, 160–215. WATER QUALITY – GUIDANCE ON VALIDATION OF MICROBIOLOGICAL METHODS, TECHNICAL REPORT ISO TR 13843:2000 INTERNATIONAL STANDARDS ORGANISATION, GENEVA.

9.8

References

and ROSE JB (1991), ‘Detection of Giardia cysts with a cDNA probe and applications to water samples’, Appl Environ Microbiol, 57, 927– 931. ABBASZADEGAN M, HUBER MS, GERBA CP and PEPPER IL (1993), ’Detection of Enteroviruses in groundwater with the polymerase chain reaction’, Appl Environ Microbiol, 59, 1318–1324. ALVAREZ AJ, HERNANDEZ-DELGADO EA and TORANZOS GA (1993), ‘Advantages and disadvantages of traditional and molecular techniques applied to the detection of pathogens in water’, Wat Sci Tech, 27, 253–256. ANGLES D’AURIAC MB, ROBERTS H, SHAW T, SIREVAG R, HERMANSEN LF and BERG JD (2000), ‘Field evaluation of a semi-automated method for rapid and simple analysis of recreational water microbiological quality’, Appl Environ Microbiol, 66, 4401– 4408. BEJ AK, MAHBUBANI MH, DICESARE JL and ATLAS RM (1991), ‘Polymerase chain reactiongene probe detection of microorganisms by using filter-concentrated samples’, Appl Environ Microbiol, 57, 3529–3530. BONADONNA L, BRIANCESCO R, OTTAVIANI M and VESCHETTI E (2002), ‘Occurrence of Cryptosporidium oocysts in sewage effluents and correlation with microbial, chemical and physical water variables’, Environ Monit Assess, 75, 241–252. BRENNER KP and RANKIN CC (1990), ‘New screening test to determine the acceptability of 0.45 lm membrane filters for analysis of water’, Appl Environ Microbiol, 26, 332– 336. BUDNICK GE, HOWARD RT and MAYO DR (1996), ‘Evaluation of Enterolert for enumeration of enterococci in recreational waters’, Appl Environ Microbiol, 62, 3881–3884. COMPAGNON B, ROBERT C, MENNECART V, DE ROUBIN MR, CERVANTES P and JORET JC (1997), ‘Improved detection of Giardia cysts and Cryptosporidium oocysts in water by flow cytometry’, in Proceedings of the AWWA Water Quality Technology Conference, Denver, Co, Am Water Work Ass. ABBASZADEGAN M, GERBA CP

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and KELL DB (2000), ‘A portable flow cytometer for the detection and identification of micro-organisms’, in Stopa PJ and Bartoszcze MA Rapid methods for analysis of biological materials in the environment, The Netherlands, Kluwer Academic Publishers, 159–167. DE ABRAMOVICH BL, LURA DE CALAFELL MC, HAYE MA, NEPOTE A and ARGANARA MF (1996), ‘Detection of Cryptosporidium in subterranean drinking water’, Rev Argentina Microbiol, 28, 73–77. DESMONTS C, MINET J, COLWELL R and CORMIER M (1990), ‘Fluorescent-antibody method useful for detecting viable but non-culturable Salmonella spp in chlorinated wastewater’, Appl Environ Microbiol, 56, 1448–1452. DESMONTS C, MINET J, COLWELL R and CORMIER M (1992), ‘An improved filter method for direct viable count of Salmonella in seawater’, J Microbiol Methods, 16, 195–201. DUBROU S, KOPECKA H, LOPEZ PILA JM, MARE´CHAL J and PRE´VOT J (1991), ‘Detection of Hepatitis A virus and other enteroviruses in wastewater and surface water samples by gene probe assay’, Wat Sci Tech, 24, 267–272. DUFOUR AP, STRICKLAND ER and CABELLI VJ (1981), ‘Membrane filter method for enumerating Escherichia coli’, Appl Environ Microbiol, 41, 1152–1158. ECKNER KF, JULLIEN S, SAMSET ID and BERG JD (1999), ‘Rapid, enzyme-based, fluorometric detection of total and thermotolerant coliform bacteria in water samples’ in Rapid Microbiological Monitoring Methods, Proceedings IWSA/AISE specialised conference, 23–24 February, Warrington, UK. EDBERG SC, ALLEN MJ and SMITH DB (1988), ‘National field evaluation of a defined substrate method for the simultaneous detection of total coliforms and Escherichia coli from drinking water: comparison with the standard multiple-tube fermentation method’, Appl Environ Microbiol, 54, 1559–1601. EKINS J (1997), Principle and Practice of Immunoassay, (2nd edn) Price and Macmillan DJ. ` Europee IT, L EUROPEAN DIRECTIVE 98/83/EC (1998), Gazzetta Ufficiale delle Comunita 330/32, 5 December 1998. FRAMPTON EW and RESTAINO L (1993), ‘Methods for Escherichia coli in food, water and clinical samples based on beta-glucuronidase detection’, J Appl Microbiol, 60, 1581–1584. FRICKER CR (2002), ‘Protozoan parasites (Crytosporidium, Giardia, Cyclospora)’, in World Health Organisation, Guidelines for drinking-water quality. Addendum Microbiological agents in drinking water, Geneva, World Health Organisation, 129–143. FRICKER CR, NIEMELA SI and LEE JL (2000), European method comparison trial: Final Report (unpublished). GERBA CP, MARGOLIN AB and HEWLETT MJ (1989), ‘Application of gene probes to virus detection in water’, Wat Sci Tech, 21, 147–154. GIRONES R, ALLARD A, WADELL G and JOFRE J (1993), ‘Application of PCR to the detection of Adenoviruses in polluted waters’, Wat Sci Tech, 27, 235–241. GRACZYK TK, FAYER R, AND CRANFIELD MR (1997), ‘Zoonotic transmission of Cryptosporidium parvum: implication for waterborne transmission’, Parasitology Today, 13, 348–351. GRAFF J, TICEHURST J and FLEHMIG B (1993), ‘Detection of Hepatitis A virus in sewage sludge by antigen capture polymerase chain reaction’, Appl Environ Microbiol, 59, 3165–3170. GREGG M (2000), ‘Real-time on-line monitoring for protozoa in drinking water’, in DAVEY HM

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Proceedings 2000 of the AWWA Water Quality Technology Conference, Denver, Co, Am Water Work Ass. HERNANDEZ JF, DELATTRE JM and MAIER EA (1995), BCR Information. Sea Water Analysis Sea Water Microbiology. Performance of methods for the microbiological examination of bathing water, Part 1. EUR 16601 EN. Directorate General, Science, Research and Development. Commission of the European Communities, Bruxelles. HERNANDEZ JF, GUIBERT JM, DELATTRE JM, OGER C, CHARRIERE C, HUGHES B, SERCEAU R and SINEGRE F (1991), ‘Miniaturised fluorogenic assays for enumeration of Escherichia coli and enterococci in marine water’, Wat Sci Tech, 24, 137–141. HOBSON NS, TOTHILL I and TURNER AP (1996), ‘Microbial detection’, Biosensors and Bioelectr, 11, 455–477. KAUCNER C and STINEAR T (1998), ‘Sensitive and rapid detection of viable Giardia cysts and Cryptosporidium parvum oocysts in large-volume water samples with wound fiberglass cartridge filters and reverse transcription-PCR’, Appl Environ Microbiol, 64, 1743–1749. KESWICK BH, GERBA CP, DUPONT HL and ROSE JB (1984), ‘Detection of enteric viruses in treated drinking water’, Appl Environ Microbiol, 47, 1290–1294. KNIGHT IT, DI RUGGIERO J and COLWELL RR (1991), ‘Direct detection of enteropathogenic bacteria in estuarine water using nucleic acid probes’, Wat Sci Tech, 24, 262–266. LECHEVALLIER MW, NORTON WD and LEE RG (1991), ‘Occurrence of Giardia and Cryptosporidium spp. in surface water supplies’, Appl Environ Microbiol, 57, 2610–2616. LIGHTFOOT N F and MAIER E A (1998), Microbiological Analysis of Food and Water: Guidelines for Quality Assurance, Amsterdam, Elsevier Science. MAHBUBANI MH, BEJ AK, PERLIN M, SCHAEFFER FW, JAKUBOWSKI W and ATLAS RM (1991), ‘Detection of Giardia cysts by polymerase chain reaction and distinguishing live from dead cysts’, Appl Environ Microbiol, 57, 3456–3461. MANAFI M and KNEIFEL W (1989), ‘A combined chromogenic-fluorogenic medium for the simultaneous detection of total coliforms and Escherichia coli in water’, Zentralbl Hyg, 189, 225–234. MANAFI M, KNEIFEL W and BASCOMB S (1991), ‘Fluorogenic and chromogenic substrates used in bacterial diagnostics’, Microbiol Rev, 55, 335–348. ¨ M TA, HUTZLER P and SCHLEIFER K-H MANZ W, AMANN R, SZEWZYK R, SZEWZYK U, STENSTRO (1995), ‘In situ identification of Legionellaceae using 16S rRNA-targeted oligonucleotide probes and confocal laser scanning microscopy’, Microbiol, 141, 29–39. ¨ M T (1993), ‘In MANZ W, SZEWZYK U, ERICSSON P, AMANN R, SCHLEIFER K-H and STENSTRO situ identification of bacteria in drinking water and adjoining biofilms by hybridization with 16S and 23S rRna-directed fluorescent oligonucleotide probes’, Microbiol, 59, 2293–2298. MAYER CL and PALMER CJ (1996), ‘Evaluation of PCR, nested PCR and fluorescent antibodies for detection of Giardia and Cryptosporidium species in wastewater’, Appl Environ Microbiol, 62, 2081–2085. MCFETERS GA (1990), ‘Enumeration, occurrence and significance of injured indicator bacteria in drinking water’, in McFeters GA, Drinking water Microbiology: progress and recent developments, New York, Springer-Verlag, 478–492. PORTER J, EDWARDS C, MORGAN JAW and PICKUP RW (1993), ‘Rapid, automated separation of specific bacteria from lake water and sewage by flow cytometry and cell sorting’, Appl Environ Microbiol, 59, 3327–3333.

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and BROWN LR (1973), ‘Comparison of Gelman and Millipore membrane filters for enumerating faecal coliform bacteria’, Appl Environ Microbiol, 27, 129– 137. ROSE JB, GERBA CP and JAKUBOWKI W (1991), ‘Survey of potable water supplies for Cryptosporidium and Giardia’, Environ Sci Tech, 25, 1393–1400. ROSZAK DB and COLWELL RR (1987), ‘Metabolic activity of bacterial cell enumerated by direct viable count’, Appl Environ Microbiol, 53, 2889–2983. SAMSET ID (2000), ‘Faster results on microbiological water quality’, Vannforening Mag water 4. SAMSET ID, HERMANSEN LF and BERG JD (2000), ‘Development of a surveillance system for water treatment processes and hygienic quality of drinking water’, in Drinking water research towards year 2000 Conference, Trondheim 5–7 January 2000, Norway SHAPIRO HM (1990), ‘Flow cytometry in lab microbiology new directions’, Am Soc Microbiol News, 56, 584–588. SHAPIRO HM (1995), Practical flow cytometry, (3rd edn), New York, Wiley Liss Inc. SHERIDAN GEC, MASTERS CI, SHALLCROSS JA and MACKEY BM (1998), ‘Detection of mRNA by reverse transcription-PCR as an indicator of viability in E. coli cells’, Appl Environ Microbiol, 64, 1313–1318. SLADEK KJ, SUSLAVICH RV, SOHN BI and DAWSON FW (1975), ‘Optimum membrane structures for growth of coliform and faecal coliform organisms’, Appl Environ Microbiol, 30, 685–691. STEFFAN RJ and ATLAS RM (1991), ‘Polymerase chain reaction: applications in environmental microbiology’, Ann Rev Microbiol, 45, 137–161. TORANZOS GA and ALVAREZ AJ (1992), ‘Solid-phase polymerase chain reaction Applications for direct detection of enteric pathogens in waters’, Can J Microbiol, 38, 365–371. TRYLAND I, SAMSET ID, HERMANSEN L, BERG JD and RYDBERG H (2000), ‘Early warning of faecal contamination of water: a dual mode, automated system for high (< 1 hour) and low levels (6–11 hours)’, in 1st World Water Congress of the International Water Association, Paris, 3–7 July, 2000. PRESSWOOD WP

Part II Product quality

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10 Rapid techniques for analysing food additives and micronutrients C. J. Blake, Nestle´ Research Centre, Switzerland

10.1

Introduction

Today more than 2500 different additives are added to food products. Branen and Haggerty (2000) have classified these additives into six major categories: preservatives, nutritional additives, flavouring agents, colouring agents, texturising agents and miscellaneous additives. Several lists of additives are available. In Europe and other parts of the world the E system developed by the European Union provides a comprehensive list. Nutrients are not included in the E system. An alternative international numbering system (INS) has been developed by the Codex Alimentarius Commission. Micronutrients are also added to food products and may in some cases also be classified as additives. In the food industry analytical methods for these compounds can be divided into reference methods and rapid methods. However the borderline between these classes of methods is often indistinct. The ‘reference methods’ are often used to calibrate or to check the performance characteristics of rapid methods. Blake (2002) has recently reviewed reference methods for food additives while analyses of vitamins by HPLC, microbiological assay and other techniques are described by De Leenheer et al. (2000), Ball (2000) and Song et al. (2000). Many of the reference methods have been issued by international organisations such as the Association of Official Analytical Chemists International (AOAC International), Commission European Normalisation (CEN), International Dairy Federation (IDF) and the International Standardisation Organisation (ISO) and have been validated through collaborative studies. This type of method is of great importance as analytical laboratories are seeking accreditation via ISO, EN or related systems where the use of official or well-validated methods is mandatory.

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Rapid (or alternative) methods provide faster analyses that can be performed at or near the food production line. Thus more rapid action can be taken to correct for incorrect addition of micronutrients or additives or to check nutrient premixes before use. The ideal situation would be to develop on-line measurement techniques but this is still a long-term aim for the future. However, a range of near-line techniques are available. Rapid methods are not always simple or cheap, but the aim is that they can be performed either by an unskilled line-operator or, if this is not possible, by a trained technician in a line-laboratory. For the latter case, this may involve relatively sophisticated techniques like high performance liquid chromatography (HPLC), gas chromatography (GC), inductively coupled plasma atomic emission spectrometry (ICP-AES), near infrared (NIR) or X-ray fluorescence (XRF) techniques, but may also involve simpler test-kit or sensor/biosensor procedures.

10.2

The range of rapid methods

As mentioned above the methods fall into several categories: • chromatographic methods which are normally off-line, demanding skilled technicians • indirect methods like NIR, Fourier Transform Infrared (FTIR), energy dispersive–X-ray fluorescence (ED-XRF) or wavelength dispersive–X-ray fluorescence (WD-XRF) which are usually near-line, but which can be operated by less-skilled personnel once calibrated • sensors/biosensors which can be used off-line or near-line with some potential to be used on-line in the future • various enzymatic, test-kit or simple colorimetric procedures which need to be performed in a line laboratory • ICP-AES or F-AAS (flame atomic absorption spectrophotometry) techniques for mineral analyses which also need to be performed in a line-laboratory.

10.3

Chromatographic techniques

HPLC is the most useful and widely applied chromatographic method for analysis of additives and certain vitamins in food products. Its theory and application is described in numerous publications but Nollet (2000a) focuses on food analysis by HPLC. In recent years the technology of HPLC columns has markedly improved enabling more reproducible and robust separations. The range of detectors has also been improved with developments in diode-array detection, post-column reaction with fluorescence detection, evaporative light scattering detection and mass spectrometry, improving the selectivity and specificity of analysis.

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187

Blake (2002) thoroughly reviewed HPLC methods for additives including: colours, sweeteners, sugars, antioxidants, preservatives, emulsifiers and stabilisers and the reader will find much useful information. Direct methods of analysis of food samples by HPLC were reviewed by Bovanova and Brandsteterova (2000). Key steps are off-line and on-line solid-phase extraction (SPE) and on-line dialysis methods to speed up sample preparation. HPLC is generally accepted as the reference method for the individual analysis of the fat-soluble vitamins A, E, D3, and K1 as well as tocopherols. Internationally accepted methods have been published by the AOAC International, CEN and other organisations. This technique generally involves the following steps: • saponification followed by solvent extraction or direct solvent extraction • HPLC analysis using UV or fluorescence detection. Efforts have been made to reduce the time-consuming sample preparation steps. One approach involves direct solvent extraction avoiding saponification (Ye et al., 1998, 2001), for vitamin E in margarine and reduced fat products and for total vitamin E and -carotene in reduced-fat mayonnaise. A similar approach was also reported for extraction of all-trans-retinyl palmitate, -carotene and vitamin E in fortified foods (Ye et al., 2000). A recent advance is in the use of an on-line supercritical fluid extraction (Turner et al., 2001) with immobilised lipase hydrolysis for the extraction of vitamin A and E esters in dairy and meat products prior to HPLC analysis. This procedure was recently collaboratively studied for vitamins A, E and -carotene (Mathiasson et al., 2002) for a wide variety of food matrices. It was reported that sample throughput was at least 12 per day, about double that of the conventional HPLC methods. Since the classical saponification/extraction methods are similar for the vitamins A, D and E, it would be advantageous to determine several vitamins in one chromatographic run. Multi-analyte methods for fat-soluble vitamins were reviewed by Eitenmiller and Landen (1999). A novel approach was described by Gomis et al. (2000) in which fat-soluble vitamins (A, D2, D3, E, K1, retinyl acetate, retinyl palmitate, tocopherol acetate) and provitamins D2 and D3 in milk were separated simultaneously by reversed-phase fused-silica microcolumn chromatography with UV detection. Recoveries of each vitamin spike were in the range 89–107 per cent; however, this method needs further validation. HPLC methods have also been reported for analysis of water-soluble vitamins in certain matrices (see Table 10.1). Some of these methods are in the process of approval by the CEN. These HPLC methods are often complex, requiring pre- or post-column reactions to improve the specificity of detection and often using fluorescence detection. A multi-method for several B-vitamins was reported by Albala-Hurtado et al. (1997). However, it needs a more complete validation. Related micronutrients like 50 -mononucleotides, added to dietetic and clinical nutrition products, can be analysed by HPLC-UV (Perrin et al., 2001).

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Table 10.1

Examples of HPLC methods for analysis of water-soluble vitamins

Vitamin C Biotin Folic acid Niacin Pantothenate Thiamine and riboflavin Vitamin B6

Furusawa (2001) Lahe´ly et al. (1999) Ndaw et al. (2001) LaCroix et al. (1999), Rose-Sallin et al. (2001) Woollard et al. (2000) Arella et al. (1996) Bergaentzle´ et al. (1995), Reitzer-Bergaentzle´ et al. (1993)

Since different extraction methods are used for each water-soluble vitamin, multi-vitamin sample preparation methods are needed. Some progress is being made by the group of Professor Hasselmann (Ndaw et al., 2000) using enzymatic hydrolyses to extract several vitamins (B 1 , B 2 and B 6 ) simultaneously. More work is needed in this area. Some attempts have been made to automate sample preparation and HPLC extraction using robotics. Russell et al. (1998) described a robotic system for automated determination of riboflavin in foods while Gamiz-Gracia et al. (1999) described an automated analysis of vitamins A and E. Liquid chromatography-mass spectrometry (LC-MS) and tandem LC-MS/MS is a promising multi-analyte technique for the future. Several papers describe the analysis of individual vitamins: folic acid (Stokes and Webb, 1999), tocopherols and carotenoids (Rentel et al., 1998) and vitamin E (Stoeggl et al., 2001). A multi-analyte approach would be more useful and cost effective. Ion chromatography is another useful and robust separation technique involving separation of anions or cations on an ion-exchange column (Henshall, 1997). The column packings for ion chromatography consist of ion-exchange resins bonded to inert polymeric particles (typically 10 m diameter). Detection is usually made by conductimetry or electrochemistry and occasionally by UV. Suppressors reduce eluent background conductivity to facilitate optimum conductivity detection. Some applications are given in Table 10.2. Another traditional but still useful technique is thin-layer chromatography (TLC), which is particularly useful for separation of natural and synthetic food colours (Hirokado et al., 1999). Sherma (2000) reviewed the use of TLC in food and agricultural analysis. Capillary electrophoresis (CE) is a fairly recent analytical technique that allows the rapid and efficient separation of sample components based on differences in their electrophoretic mobilities as they migrate or move through narrow bore capillary tubes (Frazier et al., 2000a). In spite of considerable developments CE still suffers from problems of poor peak shape, lack of sensitivity and poor robustness. It has not yet been accepted in the food industry due to the absence of well-validated analytical procedures applicable to a broad range of food products. Some recent examples of applications include: eight colorants in milk beverages (Huang et al., 2002) and the simultaneous analysis of artificial sweeteners, preservatives and colours in soft drinks (Frazier et al.,

Rapid techniques for analysing food additives and micronutrients Table 10.2

189

Examples of ion-chromatographic methods for analysis of food additives

Aspartame Carrageenans and agars containing 3,6-anhydrogalactose Choline Inositol Nitrates and nitrites Polyphosphates Sugars Sugar alcohols Sucralose Sulfites Synthetic food colours Artificial sweeteners, preservatives, caffeine, theobromine and theophylline

Qu et al. (1999) Jol et al. (1999) Laikhtman and Rohrer (1999) Tagliaferri et al. (2000) Siu and Henshall (1998); Kaufman et al. (2000) Sekiguchi et al. (2000); Kaufman et al. (2000) Cataldi et al. (2000) Corradini et al. (1997) Ito (1999) Wygant et al. (1997) Chen et al. (1998) Chen and Wang (2001)

2000b). Sa´decka´ and Polonsky´ (2000) reviewed electrophoretic methods in the analysis of beverages. The principles of gas chromatography (GC) applied to food analysis are well covered in numerous publications (Cscerha´ti and Forga´cs, 1999). Capillary GC or GC-MS are widely used for flavour analysis. However, few additives are sufficiently volatile for direct analysis by GC without prior derivatisation. Thus the application of GC for food additives is much less widespread than that of HPLC. Some recent examples of applications are given. Gonza´lez et al. (1999) described a GC method for the preconcentration and simultaneous determination of antioxidant and preservative additives in fatty foods. Ochiai et al. (2002) described the use of stir-bar sorptive extraction for the simultaneous and quantitative determination of several preservatives in soft drinks, wines, vinegar, soy sauce and quasi-drug drinks with thermal desorption GC-MS.

10.4

X-ray fluorescence and other indirect methods

10.4.1 X-ray fluorescence This non-destructive technique is very useful for rapid in-line analysis of inorganic additives in food products (Price and Major, 1990; Anon, 1995). It includes a family of related techniques, WD-XRF or ED-XRF. The principles of this technique related to food analysis are described by Pomeranz and Meloan (1994). The main components of a laboratory ED-XRF are the X-ray source (normally an X-ray tube) and the detector e.g. liquid nitrogen or Peltier cooled Si(Li) detectors. Some benchtop instruments have proportional counters, or newer Peltier cooled PIN diode detectors. The most recent and fastest growing detector technology is the Peltier cooled silicon drift detector (SDD). Another important component is the X-ray tube filter whose function is to absorb and transmit some energies in order to reduce the background counts in the region of

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Rapid and on-line instrumentation for food quality assurance

interest while producing a peak that is well suited to exciting the elements of interest. Secondary targets are an alternative to filters. A secondary target material is excited by the primary X-rays from the X-ray tube, and then emits secondary X-rays that are characteristic of the elemental composition of the target. One specialised form of secondary target is the polarising target which provides high quality measurements. The second major technique is WD-XRF which also uses an X-ray tube source to directly excite the sample. Because the overall efficiency of the WDXRF system is low, X-ray tubes in larger systems are normally rated at 1–4 kilowatts. There are some specialised low power systems that operate at 50 to 200 watts. A diffraction device, usually a crystal or multi-layer, is positioned to diffract X-rays from the sample toward the detector at a specific angle. Collimators are normally used to limit the angular spread of X-rays, to further improve the effective resolution of the WDX system. Because the detector is not relied on for the system’s resolution it can be a simple proportional counter or other low-resolution counter. A simultaneous WD-XRF analyser will have a number of fixed single channels for individual elements usually formed in a circle around the sample with the X-ray tube facing upward in the middle. Other sequential WD-XRF analysers use a goniometer to allow the angle to be changed, so that one element after another may be measured in sequence. There are also combined sequential/simultaneous instruments. XRF is a comparative measurement technique, thus a calibration needs to be established involving the intensity measurements for each element against the concentration of the element. The key step is to establish a calibration covering the elemental concentration ranges to be met in practice and using a set of samples of the same matrix as those to be analysed routinely. The ‘reference set of samples’ must be analysed by a reliable method like ICP-AES or F-AAS. Once the instrument is calibrated with a set of ‘reference products’, and the calibration validated with a second set of similar products, it can be readily used by non-skilled operators. Dry materials like powders can be pressed into a pellet or simply poured into a sample cup. Semi-liquid or wet products can also be placed in the sample cups. Calibrations can be stored by the instrument software and restandardised on a daily basis with reference materials. Some typical applications include: determination of salt content in snack foods via analysis of chloride, and analysis of fortified minerals like iron and calcium in infant formula or petfoods and calcium in fortified fruit juices. Another application is the determination of the food colour titanium dioxide in bakery products and confectionery. Because no reagents or chemicals are involved, it can be used for in-line quality control and its main advantages lie in its simplicity of use and speed of analysis, typically 5–10 minutes per sample. One of the main difficulties is to apply the technique to broad classes of food products, and often individual calibrations are required per food product. Another disadvantage is the high cost of XRF equipment.

Rapid techniques for analysing food additives and micronutrients

191

10.4.2 Near infrared and Fourier Transform infrared spectroscopy Applications of NIR and FTIR techniques for food and agricultural analysis have been reviewed recently by Reh (2001) and Williams and Norris (2001). Very few applications have been described for analysis of additives or vitamins in food products. One interesting application is for controlling vitamin premixes used for fortification of food products by attenuated total reflectance (ATR) accessory with FTIR. Four vitamins were analysed – B1, B2, B6 and niacin (nicotinic acid) – in about ten minutes (Wojciechowski et al., 1998). The partial least squares technique was used for calibration. The precision of measurements was in the range 4–8%, similar to those obtained for the four vitamins by the reference HPLC method. Another publication (Shi et al., 2000) described the quantitative determination of vitamin E by NIR. Coupled FIA-FTIR has been used to determine caffeine in soft drinks (Daghbouche et al., 1997). The sample was passed through a C18 SPE cartridge and extracted with chloroform prior to FTIR analysis. A more direct determination of caffeine content in soft drinks by FTIR-ATR spectroscopy was reported by Paradkar and Irudayaraj (2002). The spectral region between 2800 and 3000 cmÿ1 was used with a correlation coefficient (R2) between 0.97 and 0.99 for different drinks. The method could predict caffeine content in about five minutes.

10.4.3 Conductimetry Conductimetry-based analysers are often used for rapid salt determination. The digital salt analysers need periodic checking against a reference method for chloride e.g. potentiometry (AOAC Int., 2000).

10.5

PCR, immunoassays and biosensors

A new method of analysis for specific additives involves the polymerase chain reaction (PCR) for rapid amplification of specific DNA fragments which can then be analysed (Schwagele, 1999). The advantage is that DNA is present in every cell of the subject, unlike specific proteins. PCR for food analysis is limited by the lack of reference materials, and by bioactive substances in foods. A recent example was published by Meyer et al. (2001). A polymerase chain reaction (PCR) was developed to differentiate the thickening agents locust bean gum (LBG) and the cheaper guar gum in finished food products. The presence of <5% (w/w) guar gum powder added to LBG powder was detectable. A second PCR method for the specific detection of guar gum DNA was also developed. This assay detected guar gum powder in LBG in amounts as low as 1% (w/w). Both methods successfully detected guar gum and/or LBG in ice cream stabilisers and in several food products but not in products with highly degraded DNA, such as tomato ketchup and sterilised chocolate cream. Both methods detected guar gum and LBG in ice cream and fresh cheese at levels <0.1%.

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10.5.1 Immunoassay/ELISA A recent handbook (Jones, 2000) describes the principles of immunoassay/ ELISA including sandwich ELISA, indirect ELISA, competitive ELISA, immunodiffusion, latex agglutination, immunoaffinity columns and immunomagnetic beads. Immunoassay methods have been reviewed for vitamin analysis by Finglas and Morgan (1994) including pantothenic acid, biotin, folate, vitamin B6 and B12. These assays were developed for the 96-well microtitre plates, and involve reactions with either antibodies or naturally occurring vitamin binding proteins. Despite the obvious potential of this technique, in routine use it was not sufficiently robust or selective enough to become an accepted procedure. The technique has been revisited and Arcot et al. (2002) recently evaluated an enzyme protein binding assay for determining folic acid in fortified cereal foods and results were similar to those found by microbiological assay (r ˆ 0:89, p < 0:001). Pettolino et al. (2002) reported the development of an ELISA competitive binding assay involving a monoclonal antibody that specifically binds the (1!4)- -linked mannan backbone of galactomannans. Carrob and guar galactomannans additives in commercial food samples could be detected at very low concentrations. The analysis time was about 24 h.

10.5.2 Biosensors A biosensor is an analytical device characterised by a biological sensing element intimately connected or integrated within a transducer, which converts the biological event into a response that can be further processed. The biological sensing element can be either catalytic (e.g. enzyme, microorganism) or noncatalytic, also denoted ‘affinity sensing element’ (e.g. antibody, a nucleic acid or a hormone receptor). Transducers (Eggins, 2002) can be divided into 4 main types: • electrochemical transducers (potentiometric, voltammetric, conductometric and field-effect transistor-based sensors) • optical transducers (absorption spectroscopy, fluorescence spectroscopy, luminescence spectroscopy, internal reflectance spectroscopy, surface plasmon spectroscopy and light scattering) • piezoelectric devices • thermal sensors. The development of biosensors for the food industry has historically been one of the most promising yet frustrating areas of development. A number of excellent reviews have been made in biosensor technology (Eggins, 2002; Kress-Rogers and Brimelow, 2001; Mello and Kubota, 2002; Scott, 1998; Wolfbeis, 2002). Numerous developments of biosensors for analysis of additives and vitamins have been published in the literature, and some examples are given in Table 10.3. Very few of these applications have been developed commercially.

Rapid techniques for analysing food additives and micronutrients Table 10.3

Some applications of biosensors for analysis of additives and vitamins

Parameter

Type of biosensor

Reference

aspartame fructose glucose

optical amperometric, enzymatic enzymatic, oxygen electrode microbiological, E. coli enzyme, silver electrode

Xiao (2002) Stredansky et al. (1999) Wu et al. (2000)

nitrate sucralose sulfite biotin choline carnitine vitamin C

193

Prest et al. (1997) Nikolelis and Pantoulias (2000) Matsumoto et al. (1996) Lu et al. (1997) Panfili et al. (2000)

microbial biosensor amperometric enzyme, electrochemical biosensor platinum electrode, enzyme Comtat et al. (1988) electrochemical O’Connell et al. (2001) biosensor

Enzyme biosensors are commercially available from several companies for food analysis (Woodward et al., 1998). Bucsis (1997) noted a few applications of the use of biosensors in quality control. Warsinke et al. (2001) described applications of the Yellow Springs Incorporated, YSI 2700 instrument for analysis of sugars, starch, glutamate and choline. An optical biosensor has been commercialised by Biacore AB. It is a fully automated continuous-flow system, which exploits the phenomenon of surface plasmon resonance (SPR) to detect and measure biomolecular interactions. The recent model Biacore Q is particularly suitable for large series of routine analyses of vitamins. The technique has been validated for determination of folic acid and biotin in certain fortified foods (Bostrom and Lindeberg, 2000; Indyk et al., 2000; Haines et al., 2001) and more recently for vitamin B12 in milk-based products (Indyk et al., 2001) and cereals (Grace and Stenberg, 2002). Results are similar to those obtained by the reference microbiological assays but further experience and validation is required before the technique can become accepted as an official method.

10.6

Other rapid methods

10.6.1 Ion selective electrodes The well-known ion selective electrodes (ISE) are useful for the direct determination of added iodide (AOAC Int., 2000) and fluoride (LMBG, 2000) in fortified milk-based products. Salt, via analysis of chloride, is often analysed by potentiometry using a silver nitrate solution and silver electrode (AOAC Int., 2000). This technique can easily be automated using an autosampler with a titration station.

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10.6.2 Flow injection analysis FIA systems usually consist of a high-quality multichannel peristaltic pump, an injection valve, a coiled reactor, and a detector such as a photometric flow cell. The reagent(s) are added continuously to the carrier stream near (after) the injection zone, allowing the sample/reagent(s) to mix. The resulting reaction product forms a concentration gradient corresponding to the concentration of the analyte throughout the entire sample zone length. Full software automation allows for complete integration of an autosampler, standards for calibration, multichannel spectrometer-based detectors, and easy plug/play additional peripheral components such as heaters, alarm systems (useful for on-line monitoring), and additional pumps and valves. Another version of FIA is sequential injection analysis (SIA). It is capable of reducing reagent usage and waste generation, and is robust and reliable. Lenehan et al. (2002) described the principles of SIA, software for SIA operation, and applications for food and beverage analysis, e.g. iron in infant formulas. SIA has been used with various detectors including UV and visible absorbance, fluorescence, turbidimetry, electrochemistry, atomic spectroscopy, chemiluminescence, and inductively coupled plasma mass spectrometry. FIA is particularly suitable where a large number of analyses are required for quality control or monitoring purposes. FIA is a common method for on-line and laboratory processing of waste water, soil testing and other environmental applications where measurements of, e.g. phosphate, ammonium, and nitrite/ nitrate, are required but is less used for analysis of vitamins or food additives. Smith and Hinson-Smith (2002) reviewed FIA instrumentation and applications. Yebra-Biurrun (2000) reviewed the determination of artificial sweeteners (saccharin, aspartame and cyclamate) by flow injection. A number of applications for food analysis are described in Table 10.4. There is increasing interest in the use of specific sensor or biosensor detection systems with the FIA technique (Galensa, 1998). For example an electrochemiluminescence-based fibre optic biosensor for choline with FIA was reported (Tsafack et al., 2000) while sulphite in wines and fruit juices was detected using a bulk acoustic wave impedance sensor coupled to a membrane separation technique (Su et al., 1998). Pasco et al. (1999) developed a thermophilic L -glutamate dehydrogenase biosensor for amperometric determination of L-glutamate by FIA. Table 10.4

Some examples of application of flow injection analysis for additives

aspartame citric acid chloride, nitrite and nitrate cyclamates additives ascorbyl 6-palmitate sulphites carbonate, sulphite and acetate

Fatibello et al. (1999), Prodromidis et al. (1997) Ferreira et al. (1996) Cabero et al. (1999); Gouveia et al. (1995) Buratti et al. (2001) Huang et al. (1999); AOAC Int. (2000) Shi et al. (1996)

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195

10.6.3 Enzymatic methods The principles of enzymic analyses and some food applications of enzymic analyses are described by Powers (1998). Enzymatic procedures are widely used for analysis of sugars, nitrate or sulfite in food products (AOAC Int., 2000). This technique can also be used for the determination of the nutritional additive, choline by a coupled enzymic-colorimetric assay (Woollard et al., 1997; AOAC Int., 2000). A similar procedure is used for determination of free carnitine. The first step involves its acid extraction from the food matrix, followed by an enzymic spectrophotometric assay via its reaction with carnitine acetyltransferase coupled with acetyl coenzyme A and dithiobenzoate (Indyk and Woollard, 1995). The relatively simple measurement chemistry involved is suitable for automation. An enzymatic procedure for analysis of added citric acid in cheese was published by ISO (1997). Commercial enzymatic kits (BIOQUANTÕ), are manufactured by several companies. One example is Merck KGaA who have kits available for the determination of aspartame (intense sweetener), sugars and nitrate (preservative). The Bioquant kit for aspartame was evaluated for yoghurt, quark and confectionery (Gromes et al., 1995). For low concentrations of aspartame a blank correction procedure was necessary. Recoveries of aspartame were in the range 93–102%. The principle involves the enzymic hydrolysis of aspartame with Pronase E which results in the formation of L-asparginic acid and Lphenylalanine methyl ester. The L-asparginic acid is further degraded with 2– oxoglutarate in the presence of glutamate oxalacetate transaminase to oxalacetate and glutamic acid. The oxalacetate is then converted to malate and NAD cation by treatment with NADH and malate dehydrogenase. The decrease in extinction at 340 nm, under the prescribed test conditions, is directly proportional to the aspartame concentration in the sample. Another related technique is the differential pH technique which also uses enzymatic reactions, and shows some potential for analysis of various food additives including ascorbate, lactate, malate, citrate (Bucsis, 1999) and sugars (Luzzana et al,. 2001). However, validated procedures applicable to analysis of additives in food products have not yet been published.

10.6.4 Spectrophotometry A simple colorimetric determination for inorganic iodine was reported for fortified culinary products by optimising the well-known Moxon and Dixon reaction (Perring et al., 2001). This reaction is based on the destruction of a ferric-thiocyanate complex by the nitrite ion catalysed by iodide with measurement at 454 nm. The main advantages of this procedure are that the sample preparation is minimal, avoiding the time-consuming dry-ashing step, and the reaction determines both iodide and iodate. A rapid (1 h) spectrophotometric method for the determination of iron in fortified foods, which does not need expensive equipment, was described by Kosse et al. (2001). Iron was extracted by boiling the sample for 15 minutes with

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a mixture of hydrochloric acid, trichloroacetic acid and hydroxylamine hydrochloride. The filtrate was mixed with a chromogen (bathophenanthroline disulphonic acid in sodium acetate), and the iron concentration measured by absorbance at 535 nm. The iron content in a powdered drink, iron-enriched (NaFeEDTA, electrolytic Fe, ferrous sulphate, ferrous fumarate) and unenriched flour, and enriched and unenriched cornmeal and rice was determined. For the majority of the samples, good agreement with the official (AOAC) method was obtained. However, the rapid method gave significantly lower results than the official method for enriched cornmeal and enriched flour.

10.6.5 Inductively coupled plasma atomic emission spectrometry and atomic absorption spectrophotometry These well-known atomic absorption and emission techniques are available in many laboratories and usually employed for analysing a range of minerals in food products. The common food colour titanium dioxide can be determined in foods using ICP-AES (Lomer et al., 2000). Samples are digested with 18 M sulphuric acid for 1 h at 250ºC to remove the organic matrix and dissolve the titanium dioxide. Titanium is determined, as a marker for titanium dioxide, at a wavelength of 336.121 nm. An atomic absorption spectrophotometric (AAS) method was published for determination of the anti-foaming agent polydimethylsiloxane in pineapple juice. It is based on extraction with 4-methyl-2-pentanone and aspiration into the nitrous oxide/acetylene flame of the AAS. A silicone lamp was used for detection (AOAC Int., 2000).

10.7

Future trends

Biosensors have great potential for application to a wide range of vitamins and food additives but any advances will be linked to the availability of specific antibodies or specific binding proteins for the various additives and micronutrients. It should be possible to analyse a whole range of additives and vitamins by the latest multichannel continuous flow systems with further miniaturisation. New reporter molecules are continuously being developed including: aptamers (O’Sullivan, 2002; Lee and Walt, 2002), DNA conformational switches (Fahlman and Sen, 2002), and molecularly imprinted polymers (Yano and Karube, 1999; Sellergren, 2001). Another technique with considerable potential is LC-MS or LC-MS/MS which should enable the multi-analyte analysis of many vitamins or additives simultaneously, and has the advantages of high selectivity and sensitivity. The main difficulty will be the commercial availability of suitable internal standards at a reasonable cost closely matching the analytes of interest and the development of automated methods of sample preparation suitable for a wide range of analytes. Capillary electrophoresis (CE) is another technique of

Rapid techniques for analysing food additives and micronutrients

197

potential which is widely accepted in the pharmaceutical industry but has not yet been applied to any great extent within the food industry. A short review of the application of CE for food analysis was reported by Treihou et al. (2001). It may be possible to increase the sensitivity of CE by coupling it with biosensor detection (Bossi et al., 2000). Miniaturised FIA systems may also have some potential for process control or as sample preparation systems for LC-MS (Smith and Hinson-Smith, 2002).

10.8

Sources of further information and advice

(2000), Approved methods of analysis, St Paul, American Association of Cereal Chemists. AOAC (2000), AOAC Official Methods of Analysis 17th edn, Association of Official Analytical Chemists International, Gaithersburg, USA. AMERICAN OIL CHEMISTS SOCIETY (1998), AOCS official methods, American Oil Chemists Society. BLAKE C J (2002), ‘New methods in detecting food additives’, in Watson D H (ed.), Food chemical safety, Vol. 2, Additives, chapter 11, CRC Press Woodhead Publishing Ltd. BRANEN A L, DAVIDSON P M, SALMINEN S and THORNGATE J H (2002), Food additives, 2nd edn, New York, Marcel Dekker Inc. CLARK S, THOMPSON K C, KEEVIL C W and SMITH M (2001), Rapid detection assays for food and water, The Royal Society of Chemistry, London. DE LEENHEER A P, LAMBERT W E and VAN BOCXLAER J F (2000), Modern chromatographic analysis of vitamins. 3rd edn. Chromatographic science series vol. 84, New York, Marcel Dekker Inc. DIEFFENBACHER A (1998), ‘The analysis of emulsifiers in foods’, in Berger K and Hamilton R J (eds), Emulsifiers: functionality and applications: proceedings of a conference, London, 1998, Society of Chemical Industry. D’ SOUZA S F (2001), ‘Microbial biosensors’, Biosens Bioelectron, 16 (6), 337–353. EGGINS B R (2002), Chemical sensors and biosensors, Chichester, John Wiley and Sons Ltd. FRAZIER R A, AMES J M and NURSTEN H E (2000a), Capillary electrophoresis for food analysis – method development. Cambridge, Royal Society of Chemistry HASENHUETTL G L and HARTEL R W (1997), Food emulsifiers and their applications, London, Chapman and Hall. KRESS-ROGERS E and BRIMELOW C J B (2001), Instrumentation and sensors for the food industry, 2nd edn, Cambridge, Woodhead Publishing Ltd. LA CHANCE G R and CLAISSE F (1995), Quantitative X-Ray Fluorescence Analysis: Theory and Application, Chichester, John Wiley and Sons, Inc. MARAZUELA M D and MORENO-BONDI M C (2002), ‘Fiber-optic biosensors – an overview’, Anal Bioanal Biochem, 372, 664 - 682. MELLO L D and KUBOTA L T (2002), ‘Review of the use of biosensors as analytical tools in the food and drink industry’, Food Chem, 77 (2), 237–256. NOLLET L M L (2000a), Food analysis by HPLC, 2nd edn, New York, Marcel Dekker Inc. NOLLET L M L (2000b), Handbook of food analysis, 2 volumes, New York, Marcel Dekker Inc. AMERICAN ASSOCIATION OF CEREAL CHEMISTS (AACC)

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and HANSEN EH (1998), Flow Injection Analysis, 2nd edn., New York, Wiley, Interscience. SONG W O, BEECHER G R and EITENMILLER R R (2000), ‘Modern analytical methodologies’ in Fat- and water-soluble vitamins, Chichester, John Wiley and Sons Ltd. VALCARCEL M and LUQUE DE CASTRO M D (1987), Flow Injection Analysis. Principles and Applications, New York, J. Wiley and Sons Ltd. RUZICKA J

10.9

References

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11 Detecting genetically-modified ingredients M. Pla, T. Esteve and P. Puigdome`nech, Instituto de Biologia Molecular de Barcelona, Spain

11.1

Introduction

During the last decade, techniques have been developed to allow the introduction of previously isolated genes into plants. These techniques are based on the regulated expression of new sequences in different plant tissues. Our knowledge of the molecular basis of plant physiology has increased greatly since these approaches became available. They have also led to the production of genetically-modified organisms (GMO) with characteristics of agronomical interest. These organisms were first introduced into the food market in 1994 (Food and Drug Administration, 1994) and they have since been extensively cultivated reaching 44 million hectares in 2000 and their derived products have reached the global marketplace. However, acceptance of these GMO derived products by the public has been controversial. Some reactions have been positive, especially among farmers and manufacturers, but most of the general public has reacted with incomprehension towards a technology regarded as unnecessary and risky. The risks attributed to GMO do not compensate for their perceived benefits in most European societies. Such negative public response highlights the importance of providing complete information on GMO presence in food as studies have shown that, in general, informed consumers have more confidence in new products (Swords, 1999). The United States is the country where most GMOs have been cultivated and consumed. As in Europe, they are not authorized until the absence of detectable adverse effects on health and environment have been proved. The parameters analysed both in the United States and in Europe as well as in most other countries are very similar. However, public perception is very different. For instance in the United States, no specific regulation on labelling has been

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approved. In fact, the principle of substantial equivalence is applied in this case. In this sense if no data on an alteration of the composition can be found it is considered in the United States that there is no need to label the product. As Europe is an important importer of soybean and maize from the United States and Argentina, these different regulations have produced a conflict between Europe and the United States and has increased the need for developing methods for detecting GMOs in food.

11.1.1 GMO labelling In the European Union (EU), marketing and distribution of GMO-containing foodstuffs is controlled by EU Regulations 258/97, 1139/98, 49/2000 and 50/ 2000 (concerns food additives), as well as by National Food Ordinances. These regulations establish that food or food ingredients containing GMO in concentrations above 1 per cent must be labelled properly. Therefore, it is expedient to develop GMO detection and quantification methods which are adapted to each product, from raw materials to processed products, and are able to identify the different authorized GMO. At present, only maize and soybean GMO are affected by the EU Regulations. In consequence methods for GMO detection in food have been developed on these two species, but similar work is being carried out on several other species, such as rapeseed, tomato, potato, rice, cotton, wheat, sunflower, etc., as transgenic varieties are either approved in other countries, or being experimented on in these species, and they may arrive to the market. Detection methods should also be specific, reliable and sensitive, to respond to the legislation and to the quality demanded by consumers. There is a movement towards collaboration between European laboratories to standardize and validate the best methods (which will be proposed to the European Normalization Committee, CEN, as standard protocols), to guarantee the results (cross-tests) and to update protocols and authorized GMOs.

11.2

Principles of analysis

The difference between a non-GM plant and a GMO is a small modification in the DNA. As is known, DNA molecules form a long polymer present in all cells and composed of four different nitrogenous base pairs in a double helix. The number and order of these base pairs (their sequence) determine the information contained in this DNA and, finally, the characteristics of the organism. In the case of GMOs the introduced modification normally consists of a DNA fragment from a different source (called ‘transgene’) although in some cases the source of the gene may be the same species but modifications have been introduced in vitro. Such a transgene allows the synthesis and accumulation of a protein which would not normally be present in this species. This protein provides the desirable characteristic to the GMO. This is the case with the CP4EPSPS gene in Roundup Ready soybean (RR-soybean, Monsanto), which gives

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herbicide resistance; or the Bt genes in the ‘Maximizer’ maize Bt176, Bt-11 maize (Novartis), ‘Liberty-Link’ T25 maize (Ho¨echst, Schering & AgrEvo), or the ‘Yield Gard’ maize (MON810, Monsanto), which gives insect resistance to the GMO plants. In other types of GMO (e.g. ‘FlavrSavr’, Calgene, a tomato with delayed maturation) where the introduced DNA fragment comes from the same species, the objective is to modify the amount of the protein derived from this gene. In this case the gene has been constructed in such a way that the RNA is expressed in a different direction (antisense), and it has been shown that in this case the amount of protein produced by the gene is decreased. If the function of the protein is to be involved in maturation of the fruit, this process is delayed. GMO detection methods are based on the detection of the modification introduced into the plant, either at DNA or protein level. PCR techniques are particularly useful for the identification and quantification of transgenic DNA in GMO food products because of their simplicity, specificity and sensitivity (Saiki et al., 1988; Meyer, 1995; Pietsch et al., 1997). In addition, the high stability of DNA under the adverse conditions to which some foods are subjected during processing makes PCR-based methods particularly useful.

11.2.1 Sample preparation Sampling must be done to ensure that the sample is representative of the whole. Sample size ranges preferably from 0.1 to 1 kg. Milling, hydration and homogenization of the sample must be performed carefully. DNA analysis requires its extraction, purification and concentration from the sample. In general, DNA extraction from plant tissues (i.e. seeds or leaves) and from raw or little-elaborated material (i.e. flours, semolina or sweet corn) do not present big problems. However, some intermediate or elaborated products may have small DNA content or may be enriched in components making it difficult to obtain DNA from the species of interest. Consequently protocols must be adapted to each type of sample, especially the most elaborated ones. At present, DNA may be extracted efficiently from a large variety of semitransformed or final products, such as polenta, baby food, bread, biscuits, minced meat, sausages, lyophilized soups, cocoa, beers, crude oils, cornflakes, dietetic complements, protein concentrates, starches, maize fecula, lecithins, colourings, gluten, glucose, sorbitol, dextrose, fructose, etc. Nevertheless it is extremely difficult to obtain DNA from a number of additives, natural extracts, alcoholic drinks, marmalade, maize margarine, fatty acids concentrate, cocoa creams, refined oils, tomato ketchup, etc. The DNA obtained is a mixture of DNA coming from each food ingredient, including plant, animal and/or microorganism. Moreover, and only considering the DNA from the plant of interest (i.e. maize and soybean), most of the DNA does not make any difference between GMO and non-GM plant. The objective is to detect and quantify the introduced transgene.

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11.3

Polymerase chain reaction (PCR) techniques

Polymerase chain reaction (PCR) amplification has proved to be one of the most powerful techniques for the detection of a specific DNA sequence. In PCR conditions, the enzyme Taq polymerase is able to copy again and again (around 240 times) a specific DNA fragment defined by two sequences. This is known as ‘DNA amplification’. It requires a pair of primers, very short DNA fragments which match the ends of the DNA fragment of interest. The primer pair of oligonucleotides determines which exactly will be the amplified DNA fragment. The design of an adequate primer pair requires the knowledge of the DNA sequence to be amplified, in this case the transgene. In the easiest case, that is, the analysis of presence or absence of GMO, the PCR product is visualized through a conventional agarose gel. Along the PCR reaction such a large number of copies of the transgene have been generated that they can easily be distinguished in the agarose gel. This result will only be positive if the DNA submitted to PCR (i.e. extracted from the food sample) did contain the transgene, in other words, it contains GMO material. Conversely, a non-GM plant shows a negative result since it does not contain the transgene in its genome (Fig. 11.1). Together with GMO positive and negative controls performed on certified material, this type of analysis also requires a control to determine whether the DNA extracted from the sample has a quality and concentration adapted to PCR. This control consists of what is known as an ‘endogenous control’, a speciesspecific gene, present in any variety of this particular species (either GMO or not), and absent in any other species. Amplification of such reference sequence will allow the detection of DNA from the plant species of interest in food samples, an assay for the quality of the extracted DNA, and provide a means to quantify the amount of GMO in the processed food sample. Much effort has been expended in order to obtain reference genes for the analysis of genetically modified soybean and maize due to the economic

Fig. 11.1 Agarose gel showing the PCR amplification of (A) RR-soybean transgene, and (B) soybean endogenous control. Lanes: GMO, RR-soybean; NO: non-GM soybean.

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importance of these food crops. Eighty-two per cent of the transgenic crops grown during the year 2000 were represented by these two plant species. The next two most important transgenic crops were cotton and rapeseed. Eleven per cent of the total cultivated production of rapeseed worldwide was transgenic.

11.3.1 GMO quantification: real-time PCR The use of real-time quantitative PCR detection methods is a very accurate and fast system for quantitative detection of GMO in processed food samples. These methods also rely on the amplification of transgenic-specific sequences and their quantification relative to an endogenous reference gene that gives an estimation of the total amount of target DNA in the sample. With this technology, the amount of GMO is calculated as a function of total plantspecific DNA in the food product (i.e., the soybean contained in a certain food is 3% GMO soybean). The difference between conventional PCR and real-time PCR reaction is that in this case the amount of amplified DNA is followed along the time of PCR reaction (‘real time’). This reaction can be divided into three phases: the initial one where the product cannot yet be detected, followed by an exponential increment of the amplified product, and a final plateau due to depletion of the reagents. It has been observed that the initial point of the exponential phase directly depends on the amount of specific DNA in the test tube being used for quantification, through comparison with a standard curve prepared with prequantified certified material.

Fig. 11.2 GMO RR-soybean detection and quantification in real-time PCR. (A) Amplification plot generated by DNA from certified RR-soybean containing 5%, 0.5%, 0.05% and 0.005% GMO. (B) Standard curve generated from the amplification data given in (A).

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Fig. 11.3 Amplification plot generated by DNA from a GMO-containing sample (GMO) and a GMO-free sample (NO). (A) RR-soybean transgene amplification. (B) soybean endogenous control amplification. Not shown: certified RR-soybean to generate standard curves, performed in parallel.

The DNA amount is measured by fluorescence, either with an unspecific method (through fluorochromes that specifically label double stranded DNA, i.e. SYBR-Green), either with a sequence-specific labelling (by adding to the PCR reaction a probe: a third primer which is labelled in such a way that it emits fluorescence each time a copy of the specific DNA is generated). The real-time PCR device contains a detector able to quantify the fluorescence in the reaction tube. Technical details concerning real-time PCR can be found in Sninsky (1999).

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This technique is highly specific and sensitive: the detection limit goes theoretically down to a single molecule of specific DNA in the reaction tube, and quantification can be performed with a minimum of 10 to 100 specific DNA molecules in the reaction tube. This indicates that 0.01% GMO in a sample can be quantified reliably. Moreover, this method is suitable for use in processed food samples with very low quantities of target DNA. The methodology is compliant with the four basic principles of laboratory practices (GLPs) as established in the European legislation: 1

2 3 4

Specificity: the detected fluorescent signal is generated on amplification of the amplicon containing the hybridized TaqMan probe. This highlights the importance of proper primers and probe design. Sensitivity: the dynamic range covers up to five orders of magnitude and our detection sensitivity is above 0.01%. Reproducibility: every analysis is in duplicate. From each replica three PCR tests are performed, making a total of six tests per sample. Precision: certified standards used in parallel under identical conditions to obtain a linear curve to compare the data obtained with the sample. Two parameters determine the validity of the reaction: • regression curve coefficient between 0.97 and 1.0 • the slope of the calibration curve must be between ÿ3.0 and ÿ4.0.

11.4

Identifying genetically-modified ingredients in practice

Any transgene is composed of different DNA fragments which cover specific functions in the construction. Some of them are present in most of the GMO presently approved (Pietsch et al., 1997). This is the case with the CaMV35S promoter. It comes from cauliflower mosaic virus and it is present for instance in RR-soybean, Bt176, Bt11, MON810, MON809, T25 maizes, etc., but not in RM3-3, RM3-4, RM3-6 chicory, or MS1, RF2PGS rapeseed, or Flavr Savr tomato, etc. A number of kits to detect these sequences qualitatively have recently appeared on the market, and some of these methods have been validated by ring trials among European laboratories (Lipp et al., 1999). They provide general information on GMO presence in a certain sample, but they do not allow either the identification of the precise GMO in that sample or the GMO species (i.e. a positive may be due to the presence of GMO soybean, or GMO maize, or both, in a complex sample). Moreover, these are methods with low sensitivity (around 1% or 0.1% GMO) and false negative and false positive results have been described, due at least in some cases to the presence of this type of sequence in the genome of some non-GM varieties. Different GMOs contain different numbers of copies of promoter CaMV35S, and that makes it difficult or even impossible to quantify GMO correctly by realtime PCR through CaMV35S (e.g. in samples which may contain at the same time different types of GMO). On the other hand, this method may permit the

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analysis of GMO, if species-specific endogenous control is available, which is the case for maize and soybean (Vaı¨tilingom et al., 1999) but also for species such as rapeseed (Herna´ndez et al., 2001), the third most important transgenic crop (11 per cent of the total cultivated production in 2000 was GMO). Each GMO contains a specific modification in its DNA, either the transgene itself (which gives the plant the desired characteristic) or the site in the genome where the transgene has been inserted. Analysis of these differential sequences allows a proper GMO identification and quantification. This method is extremely sensitive (allows the detection and quantification of 0.01% GMO) and reliable. Examples of these methods are those based on the amplification of the CP4-EPSP gene in RR-soybean, the Bt genes in Bt176, Bt-11, T25 or MON810 maizes (Meyer and Jaccaud, 1997; Ehlers et al., 1997; Meyer, 1999; Vaı¨tilingom et al., 1999; Zimmermann et al., 2000; Windels et al., 2001).

11.4.1 Protein detection In a number of cases the detection of the protein derived from the transgene may be indicated. There are on the market some kits for immune detection of RRsoya and Bt-maize proteins. The sensitivity is enough to cover the European Regulations, but those methods present some disadvantages: • in each GMO, protein derived from the transgene is expressed with a particular pattern: that means, in certain parts of the plant it may not exist • proteins may be lost during food processing • these methods do not allow the distinction between different GMO which express the same or a similar protein, i.e., the insect-toxic Bt protein is expressed in Bt176, Bt11, MON810, T25, etc., maizes.

11.5

Future trends

It is difficult to predict what will happen in the near future as these methods depend on the regulations officially established, and these regulations may vary with time and between different countries. At present, the need to establish standard methods in particular to quantify the presence of a certain percentage of a transgenic component implies the development of quantitative methods based, for instance, on real-time PCR. With the appearance of new GMO varieties and species it will be necessary to develop new analysis methods. Biochip and microarray technologies may be the answer. GM plant analysis methods are in development in Europe due to pressure from consumers. In the USA it has, in general, been considered that information for consumers must be based on food content, not in the way food ingredients have been developed, while in Europe authorities have followed the pressure from consumers who wish to be informed of the presence of GMO products in food. The present methods allows transparency in food composition when the

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genes or gene products are detectable. In any case these methodologies will also be useful for other applications such as the analysis of food authenticity and pathogen presence in food, a field expanding in importance all over the world. For that reason, the methods being developed will have a utility that will last long into the future.

11.6

References

EHLERS, V.B., STRAUCH, E., GOLTZ, M., KUBSCH, D., WAGNER, H., MIADHOF, H., BENDIEK, J.,

Nachweis gentechnischer vera¨nderugen in mais mittels PCR. Bundesgesundhbl. 1997, 4, 118–121. FOOD AND DRUG ADMINISTRATION (FDA). Fed. Reg. 1994, 59, 26700–26711. ´ NDEZ, M., Rı´O, A., ESTEVE, T., PREAT, S., PLA, M. A rapeseed-specific gene, Acetyl-CoA HERNA Carboxylase, can be used as a reference for qualitative and real-time quantitative PCR detection of transgenes from mixed food samples. J. Agric. Food Chem. 2001, 49, 3622–3627. LIPP, M., BRODMANN, P., PIETSCH, K., PAUWELS, J., ANKLAM, E. IUPAC collaborative trial study of a method to detect genetically modified soy beans and maize in dried powder. J. AOAC Int. 1999, 82, 923–928. MEYER, R. Nachweis gentechnologisch vera¨nderter Pflanzen mittels der Polymerase Kettenreaktion (PCR) am Beispiel der Flavr SavrTM-Tomate. Z. Lebensm. Unters. Forsch. 1995, 201, 583–586. MEYER, R. Development and application of DNA analytical methods for the detection of GMOs in food. Food Control. 1999, 10, 391–399. MEYER, R., JACCAUD, E. Detection of genetically modified soya in processed food products: development and validation of a PCR assay for the specific detection of Glyphosate Tolerant Soybeans. Proceedings of the ninth European Conference on Food Chemistry (EURO FOOD CHEM IX), FECS – Event Nº 220. 1997, 1, 23–28. OFFICIAL JOURNAL OF THE EUROPEAN COMMUNITIES, No. L 43/1-7 Regulation (EC) No. 258/ 97 of the European Parliament and the Council of 27 January 1997 concerning novel foods and novel food ingredients. (1997). OFFICIAL JOURNAL OF THE EUROPEAN COMMUNITIES, No. L 159/4-7 Regulation (EC) No. 1139/98 of the Council of 26 May 1998 concerning the compulsory indication of the labeling of certain foodstuffs produced from genetically modified organisms of particulars other than those provided for in Directive 79/112/EEC.(1998). OFFICIAL JOURNAL OF THE EUROPEAN COMMUNITIES, No. L 6/13-14 Regulation (EC) No. 49/ 2000 of the Commission amending Council Regulation (EC) No. 1139/98 of 10 January 2000 concerning the compulsory indication on the labeling of certain foodstuffs produced from genetically modified organisms of particulars other than those provided for in Directive 79/112/EEC. (2000). OFFICIAL JOURNAL OF THE EUROPEAN COMMUNITIES, No. L 6/15-17 Regulation (EC) No. 50/ 2000 of the Commission of 10 January 2000 concerning the labeling of foodstuffs and food ingredients containing additives and flavourings that have been genetically modified or have been produced from genetically modified organisms. (2000). PIETSCH, K., WAIBLINGER, H.U., BRODMANN, P., WURZ, A. Screeningverfahren zur Identifizierung ‘gentechnisch vera¨nderter’ pflanzlicher Lebensmittel. Dtsch. APPEL, B., BUHK, H-J.

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SAIKI, R.K., SCHARF, S.J., FALOONA, F., MULLIS, K.B., HORN, G.T., ERLICH, H.A., ARNHEIM, N.

Primer-directed enzymatic amplification of DNA with a thermostable DNA polymerase. Science. 1988, 230, 1350–1354. SNINSKY, J.J. PCR Applications: protocols for functional genomics, San Diego: Academic Press, 1999. SWORDS, K.M.M. Beyond breeding: pest-resistant plants and public perception. Trends Biotecnol. 1999, 17, 261–262. VAI¨TILINGOM, M., PIJNENBURG, H., GENDRE, F., BRIGNON, P. Real-time quantitative PCR detection of genetically modified Maximizer maize and Roundup Ready soybean in some representative foods. J. Agric. Food Chem. 1999, 47, 5261–5266. WINDELS, P., TAVERNIERS, I., DEPICKER, A., VAN BOCKSTACLE, E., DE LOOSE, M.

Characterisation of the Roundup Ready soybean insert. Eur Food Res Technol. 2001, 213, 107–112. ZIMMERMANN, A., LU¨THY, J., PAULI, U. Event specific transgene detection in Bt11 corn by quantitative PCR at the integration site. Lebensm.-Wiss. U.-Technol. 2000, 33, 210–216.

12 In-line sensors for food process monitoring and control P.D. Patel and C. Beveridge, Leatherhead Food International Ltd, UK

12.1

Introduction

Traditionally, within the food manufacturing industries, process performance assessment, fault detection and achievement of consistently high quality safe product have focused on off-line monitoring of quality and safety parameters such as sensory attributes, colour analysis, rheological measurements and chemical and microbiological analysis. There is now an increasing need for integration of real-time sensors in industrial process monitoring and control, that is attributed to a number of factors including legislative drive, consumer pressure for safe and wholesome food and companies’ policies for enhancing internal QA programmes. It is necessary to define some terminologies when referring to sensors and process monitoring. • The term sensor has been defined as a device or system – including control and processing electronics, software and interconnection networks – that responds to a physical or chemical quantity to produce an output that is a measure of that quantity (e.g. pH and ionic strength measurement). A biosensor comprises two distinct elements: a biological recognition element (e.g. antibodies, cell, receptor or nucleic acids) and, in close contact, a signal transduction element (e.g. optical, amperometric, acoustic or electrochemical) connected to a data acquisition and processing system. Thus, the signal resulting from the interaction of the biological element with the corresponding analyte (e.g. antibody-antigen interaction) is converted to a quantifiable signal (e.g. electrical). The usual aim of a biosensor is to produce either discrete or continuous digital electronic signals which are proportional to a single analyte or a related group of analytes. Because of the ability to be

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calibrated repeatedly, a biosensor is distinguished from a bioanalytical system, which requires additional processing steps, such as reagent addition. For further details concerning biosensors in relation to food safety, the reader is referred to the review by Patel (2002). • The terms on-line, in-line, at-line and near-line measurement in relation to process line application are defined as follows. – On-line: a measurement device that is truly part of the main process operation. This could be exemplified by a probe within the line or by an observation made through a transparent window in the line. – In-line: a sampling branch to the process line takes a portion of material for analysis, using the techniques above. – At-line: a portion of material from the sampling branch is isolated to allow sample conditioning to take place (e.g. filtration, pH adjustment, dilution) prior to measurement. Clearly, such an operation mode allows for reagent addition as part of the analysis (e.g. titration) – Near-line: the above sample is transported (manually or automatically) to a work station not connected to the process line, where a wide range of analytical operations can take place. The overall aim of this chapter is to provide an insight into the availability of recent technologies that have the potential to be integrated to provide process line sensors (and biosensors) for real-time measurement of analytes or groups of analytes of direct importance in food quality and safety. The chapter provides a brief overview of the principles and criteria for in-line sensors, points to the main drivers in the area, considers examples of recent commercial analytical technologies that potentially can meet the industrial criteria for process line sensors, explores examples of sample conditioning systems of potential value in line applications and, finally, considers combination technologies (sample conditioning, sensor-based detection) that could provide a basis for development of process line sensors.

12.2

Principles of in-line sensors

Figure 12.1 shows a schematic diagram of how the individual elements of the system go together to produce a feedback loop for plant and equipment control. The system would be valid for both continuous and batch processing. Multiple sensing heads with different functions can be accessed and correlated to provide a specific output. The product conditioning is a vital part of the overall sensor system. Without this, the heterogeneous nature of the flowing particulate food stream is likely to give a high noise-to-signal interference in the subsequent sensor-based analysis. This may be attributed to high background fluorescence and signal quenching due to the turbidity and components of the matrix, and the general presence of food particulates. A proper sample conditioning system will help reduce or eliminate the interference, thus allowing higher signal-to-noise

In-line sensors for food process monitoring and control

Fig. 12.1

217

The feedback loop.

detection and analysis of the desired signal in the subsequent sensing element. Following the information processing stage, which resolves and collates the desired information from the background interference, the final data is converted to the form that is recognised by the subsequent plant components, e.g. a process valve control device to control the level of the desired raw material in the process line or a computer to alert the process controller of the presence of undesired contaminants.

12.2.1 Operational characteristics The instrument must be capable of withstanding the rigours of the production line such as resistance to water of at least IP66 or IP67. Operation and commissioning should be simple and where possible self-calibrating. It should have easy communications to display and operating computers. This generally involves signal conditioning to provide a simple 4–20 mA signal or 0–10 V signal. To have maximum utility the unit should be capable of retrofitting to

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existing plant and plant control systems with minimal installation and commissioning.

12.2.2 Industrial considerations The majority of the food industry consists of small and medium size enterprise (SME) scale of companies and as such has limited financial resources. Instrumentation needs are basic and only that which is absolutely necessary. Highly sophisticated instruments will generally be rejected on the basis of cost and functionality. That being said, an advanced instrumentation system that is cost-effective and very simple to operate can be very sophisticated, but its complexity will be hidden from the operator/user (inside a ‘black box’). At present, the acceptable cost of a sensor-based instrumentation system will vary significantly depending on the type of information required, but in general this should average at about £1000 per installed unit. More realistically, however, the final anticipated cost is likely to vary between £5,000 and £10,000 per installed unit. These instruments will normally be applied to matters of food safety and or quality, identification, qualification and quantification. The processes to be measured for example can be the cleaning quality, natural or inadvertent contamination of food products (e.g. by allergens, food-spoilage/poisoning bacteria and metabolites, and toxins), chemical analysis, recipe formulation, and ingredient and moisture analysis.

12.2.3 Drivers for process line sensors There is no doubt that industrial specification-based sensors will increasingly play a vital role in enhancing the efficiency of a process in order to deliver safe products that are of a consistent quality. Some of the drivers in this regard are summarised briefly. • Legislation: This includes the safety and quality of food, and labelling legislation. • Consumer: Today’s consumers are increasingly aware of the potential risks and benefits associated with consumption of varied food types. The demand for wholesome safe food will continue. • Global competition: Food is no longer confined to national boundaries and manufacturers are striving to produce high-quality products with minimum costs that can be distributed widely. One of the major common limitations is the retrospective nature of the laboratory-based analytical results. • Environment: Environmental issues are another significant driver and acceptance is more likely through waste minimisation and reduction in energy consumption during food production. • Enhanced QA procedures: The manufacturers’ constant desire to produce high-quality consistent product right-first-time in order to prevent time-

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consuming and additional costs related to product re-work, give-away or discard all together.

12.3

Current commercial sensor systems

This section considers three categories of commercially available sensor systems, namely those currently used in industrial process applications, examples of some new instrument systems developed for in-line process monitoring and, finally, examples of low-cost analytical instrumentation of potential value for in-line applications.

12.3.1 Commercial in-line instrumentation Industrially used sensors A number of relatively expensive spectroscopic and imaging techniques, including NIR, NMR and MRI, have been covered in detail elsewhere. In this section, a brief overview of some of the commercial techniques used industrially for in-line measurement of food constituents has been considered. The most widely used in-line techniques are based on spectroscopy. These use the absorbency, emission/reflectance and fluorescent characteristics of the components being examined to defined parts of the electromagnetic spectrum, including UV, visible and infrared and microwave. These systems are used largely for the identification and quantification of groups of chemical species (e.g. proteins, fats and ions) and measurement of particle size and molecular interaction of components. Reliable monitoring of temperature and humidity is also important to food safety and quality. The control of humidity conditions is important to prevent refrigerated products such as fresh products from losing moisture. Dual-sensor data loggers are used to monitor the temperature and humidity in many applications (e.g. smoking processes for gourmet sausages and environmental conditions in retail stores). The data can be used to optimize energy use and provide a means of traceability. Table 12.1 summarises examples of the technologies available and their industrial applications.

12.3.2 Recent new instruments for process monitoring Visual Process Analyser (ViPA) – Image Analysis The Jorin ViPA (www.jorin.co.uk) is a particle size analysis system that is designed to operate continuously on-line (Fig. 12.2). It uses a video microscope to capture images of the particles in a process flow. The technology has largely been used for water monitoring (e.g. quality, filter efficiency, oil and reservoir), whilst other applications are being reported continually (e.g. chemical dosing monitoring and polymer manufacturing). The image analysis equipment and software has the potential to examine and characterise particles during food

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Table 12.1 Examples of some commercial in-line sensor instrumentation and their applications System

Measurement

Application

FTIR/NIR

Protein, fat, fibre, moisture, starch, sugar, salt and solids

NIR MRI/NMR Dual sensor

Oil and moisture Oil and moisture Temperature and humidity Hydrogen ion concentration Low level particulates

Dairy process control for low moisture products: whey protein concentrate (WPC), cream cheese and skim milk Snack foods Snack foods Smoking process for gourmet sausage Control acidification in yoghurt fermentation Waste water and chemical processes Filtered beer, juices and cream

Glass-free pH sensor Forward scatter turbidity sensor FSC402 optical density analyser Guided microwave spectrometry

Concentration of dissolved matter Total ionic chemical species (dielectric constant/conductivity)

High resolution ultrasonic spectroscopy

Particle size, monitoring gelation and coagulation processes

UV analyser

Fluorescent compounds

Analysis of moisture and salt in cheese, moisture in cereals/ snack-food, sugar and acid level in orange juice and percentage of fat in milk Particle size analysis of milk, coagulation point in calciumfortified milk and droplet concentration of salad dressings Flavoured mineral water

production. Its value in this important area, which includes microbial particles, has yet to be determined. Two typical images, saved directly from the ViPA analyser, are shown in Fig. 12.3. The image on the left shows oil droplets in water and the image on the right shows solid particles in water. In the above images it is clear that the solid particles have a very different shape to the oil droplets. The ViPA can use this difference to distinguish between the particle types and categorise them separately. In this way, and using any or all of the 17 parameters, the ViPA can differentiate between up to eight particle types in a single liquid flow. Information on the size distribution, concentration, etc., of each these particle types is then reported on screen and on the optional 4–20 mA ouput. Impedance analyser (‘Intelligent pipe’) This technology, developed by Kaiku (www.kaiku.co.uk), is based on resonance frequency interrogation of flowing streams allowing for real-time, in-line, noninvasive analysis of the fluid components (Fig. 12.4). The food applications

In-line sensors for food process monitoring and control

Fig. 12.2

Fig. 12.3

Schematic showing ViPA components.

Images of particles in flowing streams (from www.jorin.co.uk).

221

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Fig. 12.4

The Kaiku ‘intelligent’ pipe.

demonstrated by the manufacturer include determination of water addition to orange juice, starch addition to tomato base, determination of CIP, addition of salt to beers, authenticity testing of orange juices and colas (Fig. 12.5).

12.3.3 Portable analytical instrumentation in process monitoring Spectroscopic instrumentation Table 12.2 shows examples of some relatively low-cost fibre optic instrumentation available for spectroscopic measurement (e.g. Raman, NIR, UV-Vis and fluorescence) of samples. It also includes additional information on the potential applications of the systems and the company addresses. Most of the instruments are computer linked and hence amenable to process line

Fig. 12.5

Authenticity of colas determined using the Kaiku ‘intelligent’ technology.

Table 12.2

Some commercial portable systems that have potential for in-line applications

Basis

Type

Potential application

Company

Fibre optic (FO) spectroscopic systems: (UV/VIS/NIR/RAMAN/FLUORESENCE)

1. Avantes FO spectrometer

Remote spectral measurement of components in industries (e.g. chemical food processing, biomedical)

Avantes Inc,. USA: E-mail: [email protected]; www.avantes.com Thermo Nicolet, USA. E-mail: [email protected]; www.nicoletindustrial.com Clairet Scientific Ltd., UK; e-mail: [email protected] www.clairet.co.uk Ocean Optics, Inc. USA. E-mail: [email protected]

2. Antaris FT-NI FO spectrometer

3. NetworkR FO spectrometer

Fibre optic immersion probes for remote coupling to instrumentation (e.g. UV/Vis and NIR spectrophotometers, fluorimeter, diode array) Planar Waveguide Attenuated Total reflectance (ATR) NIR or Vis spectrometer – Computer linked Fluorescence lifetime sensor (FLS) detector – Computer linked

4. S2000 miniature FO spectrometer NIR512 miniature FO spectrometer SF2000 FO fluorescence spectrometer SF2000 FO fluorescence spectrometer Raman Systems R-2001 FO spectrometer Hellma ruggedized immersion probes

On-line rugged fibre optic probe At-line PS-1 Portable Spectrometer LifeSense LFS for fibre optic biosensors, and on-line, real-time process analysis

Liquid waveguide capillary cells (LWCC) coupled to fibre optic cable and remote instrumentation (e.g. UV/Vis/NIR spectrometers, etc.)

The WPI Liquid waveguide capillary cell

Multiwavelength ellipsometer for realtime process monitoring

Submonolayer-Ellipsometer EL X-1 and M-55/M-88 ellipsometer

Remote monitoring of analytes/chemical composition of process streams under harsh environments (e.g. temperature <200ºC, Pressure <25 bar) Non-invasive chemical spectral measurement (e.g. hydroxil, isocyanate, rhodamine) in cloudy, viscous liquids High-sensitivity detection of popular fluorophores (e.g. FITC, rhodamine, Cy-5) and novel NIR dyes (e.g. Cy-7, IRD-25) which are particularly suited to high background interference in liquids/slurry LWCC offers increased optical pathlength (50 cm) compared to standard cuvette (1 cm) and a small sample volume for sensitive measurement of analytes/chemical composition of liquid streams For fast (10 min) detection of immunological reaction. Also, in-situ multiwavelength, spectroscopic measurement for electrochemistry application

Hellma (England) Ltd; www.hellma.demon.co.uk

Optical solutions, Inc., E-mail: [email protected] www.oriel.com As above

World Precision Instruments (WPI), Inc, USA www.wpiinc.com

L.O.T.-Oriel, UK. www.Iotoriel.co.uk

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applications. The value (or otherwise!) of applications of these types of systems has yet to be determined, particularly in relation to set industrial (e.g. ruggedness, line-compatibility, data acquisition and analysis) and system performance (e.g. sensitivity, selectivity, specificity and data variability) specifications for a given test analyte. Electronic nose An electronic nose can be regarded as a modular system comprising a set of active materials which detect the odour. Associated sensors transduce the chemical quantity into electrical signals, followed by appropriate signal conditioning and processing to classify known odours or identify unknown odours. A range of instruments is commercially available (e.g. 4440B; Agilent technologies and Prometheus; Alpha Mos), including the portable A320 (Cyrano Sciences) and Kore MS-200 mass spectrometer used as an e-nose. Research has been carried out into the use of thin and thick film semiconducting (inorganic and organic) materials for odour sensing, e.g. the use of metal oxide thick films for sensing alcohols and ketones, and metal oxide thin films for ammonia and hydrogen. Research effort is now centred on the use of arrays of metal oxide and conducting polymer as odour sensors. The latter are particularly interesting because their molecular structure can be engineered for a particular odour-sensing application. There are various applications in which an electronic nose may be used. For example, to monitor the characteristic odour generated by a manufactured product (e.g. drink, food, tobacco, soaps). The electronic nose research group has considerable experience in the analysis of coffee odours (e.g. roasting level and bean type), lager beer odours (lager type and malodours) as well as having analysed tobaccos, spirits, wines, transformer oils, plastics and drinking water. More recent work is on the use of e-noses for medical diagnostics and biotechnology. The two main limitations of the e-noses are: 1.

2.

Sensitivity: Most e-noses have limits of detection in the low ppm range. For practical use, however, limits of detection in the low ppb range or lower are required. Matrix suppression: Variable matrices can cause problems. Isolating a known pattern from a highly variable background will require advanced combination of pattern classification (e.g. PCA analysis, see Section 12.4.7) and hardware approaches.

12.3.4 Biosensors for process line applications Tables 12.3 and 12.4 show examples of commercial biosensor-based analytical instrumentation, together with a summary of their description and applications. For details of the different types and formats of biosensors and their applications in food contaminant analysis, the reader is referred to Patel (2002). The basis of many of these techniques is amenable to development of dedicated

In-line sensors for food process monitoring and control Table 12.3

225

Examples of commercially available SPR-based biosensor instruments

Biosensor type

Biological element Analyte

BIAcore Q based on immobilisation on a sensor chip

Antibody (also other binding agents can be immobilised (e.g. enzyme, DNA, lectin and receptor)) As above

Biacore 1000, 2000, 3000, X

BIOS-1 biosensor As above based on immobilisation on a waveguide sensor chip As above IBIS biosensor based on immobilisation in a cuvette

Plasmoon biosensor based on immobilisation in a cuvette

As above

Spreeta miniature As above biosensor based on immobilisation in a flow-through cell

Folate, biotin, vitamin B12 (possible kits for mycotoxins and antibiotics)

Company Biacore AB, Rapsgatan 7, S-754 50 Uppsala, Sweden www.biacore.com

As above Generic sensor for studying binding interactions (e.g. protein-protein, DNAprotein, lectincarbohydrate) in real time As above Artificial Sensing Instruments, AG, PO Box 120, Schaffhauserstr. 550, 8052 Zurich, Switzerland As above Intersens Instruments, BV, Scheltussingel 156, 3814 BH Amersfoort, The Netherlands www.windsorltd.co.uk As above BioTul AG, Gollierstrasse 70B, D-80339 Munich, Germany www.biotul.com As above Texas Instruments Inc., 12500 TI Boulevard, Dallas, TX 75243-4136, USA www.ti.com

instrumentation for process line applications. It would not be possible or, indeed, cost-effective for analytical instrumentation to be simply ‘bolted’ onto process lines for monitoring specific components or contaminants in flowing food streams. The logical way forward is to develop fit-for-purpose instruments that can be integrated into process lines. This type of development will inevitably involve bringing together multidisciplinary skills and expertise from different industrial sectors (e.g. defence, medical, microelectronics and agrofood).

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Table 12.4

Examples of other commercially available biosensors (from Patel, 2000a)

Biosensor type

Biological element Analyte

Company

Membrane bound enzyme

Enzyme

YSI Inc., Yellow Springs, Ohio, USA www.ysi.com

Glucose, sucrose, lactate, lactose, ethanol, methanol, glutamate Staphylococcal enterotoxin B, E. coli O157:H7, viruses, spores

Research International, 18706 142nd Ave, N.E., Woodinville, WA 98072, USA www.resrchintl.com As above (Raptor) Antibody Biological and warfare As above agents: Ricin, B. anthracis, Y. pestis Antibody, enzyme, Generic research and Abtech Scientific, Electrochemical Inc., biosensor based on receptors and DNA development P.O. Box 376, hybridisation instrumentation for electroconductive Yardley, PA 19067chemical and polymer 8376, USA biological analytes transducers, which www.abtechsci.com respond to specific analytes (EPSIS) As above As above IME inert, array Interdigitated microelectrodes for microsensor electrochemical and electrode array optical chemical and device (IME), biological sensor similar basis to development above Inventus BioTec NS Ascorbic acid Hand-held GmbH & Co, KG, (AscoSens) in food instrument Nottulner Landweg applications, e.g. (NanoStat) 90, D-48161 quality control of consisting of a Munster, Germany juices, fruit and potentiostat for vegetables; also, uric www.inventuselectrochemical biotec.com acid (UroSens) in detection on a clinical samples disposable sensor chip Small molecules, e.g. Sycopel In vivo and in vitro Enzyme International, Rolling glucose x2 amino microdialysis Mill Road, Viking acids, glycerol and enzyme biosensor Industrial Park, ascorbate, in clinical with Jarrow Tyne & Wear matrices electrochemical NE 32 3 DT, UK detection www.biotech products.com Antibody Evanescent-wave fluoroimmunoassay using tapered fibre optic waveguide (Analyte 2000)

In-line sensors for food process monitoring and control

Fig. 12.6

227

Tapered FO immunosensor (from Patel, 2000a).

One possible approach for developing (bio)sensor-based process line monitoring instrumentation is to use a fibre optic (FO) system linked to optical detection (Fig. 12.6). In the FO (bio)sensor a receptor (e.g. antibodies, nucleic acid, natural binding proteins and plastic molecular imprinted polymers (MIPs) (Patel, 2001)) is immobilised to the distal end of an optical fibre. Light is then introduced at the proximal end, which travels to the distal tip by total internal reflection. The emission resulting either directly from the analyte bound to the corresponding receptor or subsequent binding of a fluorescent-labelled reactant (e.g. competitive immunoassay format) is measured by the detector and correlated to the concentration of the test analyte.

12.4

Dealing with complex food matrices

One of the main factors to consider when developing an in-line sensor for process application is the composition of sample matrix, in particular potential interference (e.g. due to background components, general turbidity, homogeneity, etc.) that can seriously affect the sensor response and reduce both sensitivity and specificity of the measurement. Other major factors that affect sensor response include effects of process variables (e.g. temperature, pH, Aw, ionic strength, flow rate and pressure). A schematic of a typical meat homogenate is shown in Fig. 12.7. In-line measurement of trace levels of specific components (e.g. functional protein, micronutrients) or contaminants (e.g. allergen, mycotoxin or bacterial toxin) present in such a complex matrix would almost certainly require the use of physicochemical techniques for the separation, concentration and resolution of the target analyte prior to measurement. The following arbitrary definitions are used: • Separation: gross isolation of the target analyte from complex matrices

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Fig. 12.7

Schematic diagram of a typical meat homogenate.

• Concentration: collection of the target analyte from large volume into smaller volumes • Resolution: a specific analyte from a group of analytes and cross-reacting non-target compounds. The issue of food matrix interference in relation to food microbiological analysis, and implementation of physicochemical and immunological procedures that can reduce interference and enhance the signal-to-noise are covered in detail in Patel (2000b). Examples of some physicochemical techniques and devices that have the potential for use as in-line sample conditioning systems are described below.

12.4.1 Ultrafiltration and dialysis probes Macromolecules (e.g. proteins and polysaccharides) in solution can be separated from low M.Wt. solutes (e.g. salts, amino acids) by dialysis which utilises a semipermeable membrane to retain macromolecules and allow small solute molecules to pass through (Fig. 12.8). An alternative way of separating macromolecules from low M.Wt. components is by ultrafiltration, in which pressure, vacuum or centrifugal force is used to filter the aqueous medium and low M.Wt. solutes through a semipermeable membrane, which retains the macromolecules (Fig. 12.9). Both of these techniques have been widely exploited in academic

In-line sensors for food process monitoring and control

Fig. 12.8

229

Example of a dialysis cell (Disc hemofilter).

settings and by the food industry (e.g. preparation of low lactose milk and hypoallergic foods). A biosensor probe integrating dialysis and enzyme-based potentiometric detection (Fig. 12.10), and an ultrafiltration probe (UF, Fig. 12.11) have been used in the medical research field for continuous in vivo isolation and detection

Fig. 12.9

Principle of ultrafiltration.

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Fig. 12.10

Sycopel microdialysis biosensor.

of low M.Wt. components from flowing blood steams (e.g. glucose, glutamate and acetylcholine). These probes use membranes of 30,000 M.Wt. cut-off value. These types of separation and detection devices with appropriate modifications, including addition of a chemometrics system, can be exploited as novel sample conditioning systems in the development of in-line sensor systems.

Fig. 12.11

The BAS in vivo UF probe.

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231

12.4.2 Continuous centrifuge A separation and concentration method based on batch centrifugation has been widely used in both academia and in industrial plants. A variation to batch method is the use of continuous centrifugation to separate particles from aqueous media from flowing streams. A recent introduction is the continuous flow centrifugation (CentrifugeTM Stratos, Kendro Laboratory Products Ltd), that has been used for the separation of suspended matter from river water. In this case, the table-top centrifuge connected through a pipe directly linked to a river was shown to achieve a rotor speed of 17,000 rpm with a degree of separation of just over 90 per cent. The system could be valuable in food processing applications, e.g. separation of sugar from molasses. A custom-made centrifuge that addresses industrial criteria (e.g. cost, ruggedness, efficiency, calibration) for an integrated in-line sensor could be invaluable as a novel sample conditioning system in the development of a process line sensor.

12.4.3 Ultrasonic standing waves When particles in suspension are placed in a stationary acoustic field (e.g. 1– 3 MHz), they move towards and concentrate at half-wavelength intervals where there is minimum acoustic potential energy (Coakley, 1997). A number of different configurations have been used in order to separate particles and measure their efficiencies. These include the static banding cell, ultrasonic flow cell and ultrasonic centrifuge. The reported efficiencies for the yeast Rhodotorula glutinans in the flow through design were 49–65 per cent retention rate at a concentration of 1  107 cfu/ml in aqueous suspensions to 86–96 per cent at 1  103 cfu/ml (Zamani et al., 1993). However, this rate was shown to be reduced (>60%) with smaller size bacterial cells at a concentration of (109–1010 cfu/ml; Coakley, 1997). Thus, the higher the biomass the greater the efficiency of retention with the implication that ability to form clumps at high biomass is an important aspect of harvesting prokaryotes. The BioSep (AppliSens) is an acoustic device that utilises MHz range ultrasonic waves to separate suspended cells from cell culture medium (Fig. 12.12). Cell separation takes place within a defined volume of the BioSep – the resonator. Basically, the resonator is composed of two opposing parallel glass surfaces, one of which is piezoelectrically activated and acts as an ultrasonic source. The resulting standing field captures the cells within the antinodes of the field. The trapped cells typically form loose aggregates settling out of the acoustic field. The cells can immediately be disaggregared if required as in the acoustic perfusion process described briefly below. The use of BioSep in an acoustic perfusion process involves continuous addition of fresh medium to the bioreactor, while cells are filtered from the harvest stream by the resonator chamber and returned to the bioreactor. The BioSep can be directly mounted onto the bioreactor head plate. A standard mode of operation employs a harvest pump at the exit port of the resonator chamber, and a recirculation pump for the return of separated cells that settled from the

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Fig. 12.12 Typical configuration of the AppliSens acoustic cell retention system.

acoustic energy field within the resonator chamber. Alternatively the BioSep system can also be set up to allow for semicontinuous operation. In contrast to other cell separation techniques, the BioSep comprises non-contact, non-fouling and non-moving filtration means of separation and concentration allowing for up to thousands of hours of continuous operation. The Electronic Systems Design Group at the University of Southampton (http://eprints.esc.soton.ac.uk) recently designed and demonstrated a silicon microfluidic ultrasonic separator which could separate and concentrate particles from flowing streams. It is suggested that this offered a functional equivalent of a centrifugal separator for microfluidic systems. The devices are highly compatible with established microfabrication techniques, allowing low-cost mass production. Overall, the combination of ultrasonic devices as sample conditioning systems and (bio)sensor detection techniques described previously could allow the development of an integrated in-line sensor system for monitoring food components and contaminants.

12.4.4 Free flow electrophoresis (FFE) FFE is different from the conventional widely used analytical gel-based electrophoresis technique (see review by Patel and Weber, 2000). Thus, in FFE: • electrophoresis is carried out in free solution in the absence of any stationary phase; there is no transport of analytes inside and/or across a solid matrix such as gel (i.e. no screen segmentation)

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233

• the separation medium and the analytes are transported between electrodes, and the direction of the electrical field is perpendicular to the direction of the flow of the separation medium • the separation of the analytes occurs during a single transit through the electrical field (no recycling process). The modes of operation in FFE include zone electrophoresis, isoelectric focusing, isotachophoresis, field step electrophoresis and interval mode electrophoresis. Accordingly, FFE can be used to resolve components with net overall charge (e.g. microorganisms, biopolymers and low M.Wt. ionic species) on a continuous basis. Some of the major technological developments in the micro-engineering field leading to miniaturisation of analytical systems (also referred to as Lab-on-chip technology) include the following. • Microfluidics research has resulted in production of delivery systems (e.g. micro pumps) and associated software to transport samples and reagents accurately in micron-sized channels, where process dynamics are very different from the conventional analytical instrumentation. • Microfabrication processes based on photolithography and hot embossing techniques have allowed cost-effective mass production of often very complex microstructures on a chip that are comparable to the integrated circuits used in the computer industry. It must be emphasised that, by miniaturisation, we are not referring to minor size reduction of the various components of laboratory analytical instrumentation. In effect, developments in the above sector are allowing integration of all the functions necessary for carrying out an analytical process on a micro-scale. In the context of process line sensors, such FFE devices in combination with (bio)sensors could provide yet another means of sample conditioning prior to detection of the desired analyte.

12.4.5 Dielectrophoresis (DEP) DEP has been defined as the motion of a neutral or charged particle (e.g. mammalian or microbial cell) that has undergone polarisation as a result of being placed in a non-uniform electrical field. The non-uniformity of the electrical field results in a non-uniform force distribution on the now polarised particle (known as dipole), causing the particle to move towards the region of highest field intensity. Unlike classical electrophoresis, where movement is largely determined by the overall charge on the particle (or molecule), dielectrophoretic movement is a function of the dielectric properties (conductivity and permittivity) of the particle and the suspending medium. Thus, for DEP to be effective, these parameters must be controlled properly. The DEP uses microstructures for determining the dielectrophoretic properties of cells and the process has been applied widely in biotechnology.

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This includes dielectrophoretic manipulation of cells, such as plant protoplasts, bacterial cells (e.g. E. coli, Lactobacillus brevis and Bacillus subtilis) and yeast cells (e.g. Saccharomyces cerevisiae) from aqueous suspensions. DEP has also been applied to the separation and concentration of microorganisms from relatively complex food matrices to give a clear microbial suspension suitable for analysis by modern techniques (e.g. ATP bioluminescence, impedimetry and flow cytometry) (Pimbley et al., 1998). In this case, a 3-D flow-through dielectrophoretic chamber was used for rapid (30 min) separation of total microbial flora from suspensions of various foods (beef, chicken and skimmed milk powder). The overall technique involved rapid (15 min) desalting of food homogenates (reduction of conductivity from >2000 S cmÿ1 to between 41 and 59 S cmÿ1) followed by DEP (15 min). Further work included DEP application to the separation of spoilage microorganisms (Kluyveromyces lactis and Pseudomonas aeruginosa) from lager beer and spores (Geotrichum candidum, Mucor plumbeus and Penicillium spp) from pasteurised whole milk. DEP has been used in combination with field flow fractionation (DEP-FFF) to separate human breast cancer cells from normal T-lymphocytes. Unlike DEP alone, DEP-FFF has been demonstrated to exhibit a high and electrically controllable discrimination of cell separation. DEP forces produced by microelectrodes at high frequency are used to levitate cells in a thin chamber to equilibrium heights where sedimentation forces balance the vertical DEP forces. A carrier fluid moves through the chamber and establishes a hydrodynamic velocity profile causing cells of different dielectric and density properties to be transported through the chamber at different velocities and thereby separated. Like FFE, DEP-based microstructures have the potential to be used as sample conditioning systems for process line applications. However, since a positive DEP technique is highly dependent on the conductivity of the suspending medium and the particle to be resolved and concentrated, additional procedures involving reduction of sample conductivity must be included prior to DEP and detection of the target particle.

12.4.6 Field flow fractionation (FFF) The FFF technique is a family of chromatographic-like elution techniques, but without the use of solid phases, in which an external field or gradient (e.g. gravitational, centrifugal, electrical, flow and thermal) causes differential retention of biomolecules (e.g. proteins, peptides and polysaccharides) and particles (latex, microbial and parasites) ranging typically from 1 nm to 300 m (Giddings, 1995). In practice, FFF takes place in a thin ribbon-like channel. A field applied perpendicular to the channel axis drives components towards one wall (the accumulation wall) of the channel where each forms a steady-state distribution. In most cases, the particles are driven to within 1–10 m of the accumulation wall. The flow of the sample is laminar and parabolic because of the channel

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dimensions, ranging from 75–250 m. The particles are driven to the exit port where they can be detected and characterised using an array of detection systems, including hyphenated detectors (e.g. electron microscope, scan cytometer, photon correlation spectroscopy, multiangle light scattering, FT-IR, ICP-MS). Four fields have been widely studied. 1.

2.

3.

4.

Flow FFF (FFFF) is one of the most universal separation techniques. In FFFF, a cross flow stream of carrier liquid, in which the force orginates from the friction of the cross flow stream across the components results in the separation of the components. The FFFF is rapid (1 min run time) and can be automated, and has been applied to the fine separation of biomolecules and particles (e.g. single and double stranded DNA, protein dimers) based on differences in the molecule’s diffusion coefficient. Sedimentation FFE (SFFE) is when the force is generated usually by gravity or centrifugation. The sedimentation force acts perpendicular to the flow separation axis and is a prominent method for separation and characterisation of colloidal particles. Temperature gradient or thermal FFF (TFFE) is when the perpendicular force is a result of thermal diffusion. The technique has been used primarily for fractionating polymers of high molecular weight, although recently it has also been applied to particles in both aqueous and non-aqueous media. Electrical FFF (EFFF) is when the force is due to the electrical field and the separation depends on the polymer or particle charge and mass. Unlike capillary electrophoresis, in the EFFF the field is perpendicular to the flow of carrier liquid, the potential differences required to produce selectivity are far less stringent since it is applied across the thin gap in the FFF channel and EFFF can process larger sample volumes and larger particle sizes. Unfortunately, the low currents required to keep the electrode polarisation at a minimum, and the fields maintained at reasonable levels, limit the choice of carrier to solutions of low ionic strengths (e.g. 150 M NaCl).

12.4.7 Improvement of signal-to-noise ratio Food systems by their very nature tend to be ‘noisy’ environments. Two main options exist that can extract the information required, the instrumentation can either data mine the information mathematically, or the food process can be sampled in such a way that isolates only those items of interest. The latter aspect has been covered in detail in the previous section. Data mining is the colloquial term for statistical techniques that can be used to analyse noisy information and obtain real data on submerged information, particularly in techniques generating complex spectral information (e.g. FT-NIR and Raman). It uses multivariate analytical techniques, which are a major part of chemometrics. These techniques are covered in detail elsewhere in the book and so will only be mentioned briefly. To draw full potential from the observed spectra, techniques such as PCA are used (Hasegawa, 2001). The technique

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provides solutions to three major issues: 1 2 3

quantitative spectral calibration chemical discrimination analysis with the use of the spectra spectral separation into pure chemical component spectra.

The latter is extremely useful as it provides information regarding specific constituents of the mix under analysis. It therefore avoids the need for chromatography and can be considered as chromatography of numerical information. PCA has been found to be very useful at drawing out data relating to trace amounts of chemical species in a mixture. Spectra obtained by Fourier Transform methods are particularly suited to analysis by PCA as the information from the spectroscope is already digitised. The operation of PCA on the spectrogram (or other multivariate data samples) represents a large part of the functional intelligence of the instrumentation package. PCA is also used in pattern recognition programs and so could be used alongside imaging techniques of analysis.

12.5

Future trends

To date, to the authors’ knowledge, there is no commercial technology yet available that can be used for specific industrial in-line process monitoring of trace level components and contaminants of interest or concern to the food industry. A range of technologies, in particular the spectroscopic group, is commercially available for certain compositional analysis and these have been covered in detail elsewhere in the book and briefly in this chapter. Examples of some very recent technologies that have become available for in-line process monitoring have also been included. The authors’ view is that in-line sensors are needed by the food processing industries for a variety of applications, ranging from trace level analysis of low and high M.Wt. analytes (e.g. mycotoxins, bacterial toxins and allergens) to particulate contaminants (e.g. microorganisms). However, for sensor technologies to be accepted by the industry, they must comply with certain strict criteria such as overall cost of the sensors, time to result, continual or continuous measurement, low to unskilled operator requirement, self-calibration and applications to both batch or flowing product streams. It is also the authors’ view that ‘bolting’ an analytical system onto a process line is probably not the way forward. The development is likely to comprise integration of several different technologies and multidisciplinary skills to develop a fit-for-purpose application-specific instrument. The proposed combination approaches based on sample conditioning systems, many of which are amenable to low-cost mass manufacturing technologies, are discussed in the chapter and are potentially amenable to process line applications. Thus, the proposed future trends are expected to include:

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• simple sample conditioning techniques that resolve or isolate the component or contaminants of interest from complex food matrices • presentation of the relatively ‘clean’ matrix containing target component to a (fibre-optic) detection system (e.g. spectral detector or biosensor with electrochemical, amperometric, impedimetric or optical detection) • developing the system hardware and software tools for integration into process lines. Finally, there are new fields of expertise continually being researched and developed in other industries that may well feed into future development in food process development, control and monitoring. For example, the microreaction technology opens up new possibilities through development of small, inexpensive and versatile devices that ensure maximum selectivity, minimum waste and investment, and a better control of the process to create a more efficient process (Ehrfeld, 2000). Some of the basic techniques (e.g. microfabrication, etching and embossing) used for the development of microreactors (e.g. mixers, heat exchangers and reactors) are also common to the development of microanalytical systems. Is it possible that in the quest for process line sensors, microseparation and analytical modules can be developed for microreactors of particular value to the food processing industries?

12.6

Sources of further information and advice

In addition to the references given in the text, the following general references and conference proceedings will be of use to the reader if further information is sought. (2001), Nondestructive food evaluation: techniques to analyse properties and quality, New York, Marcel Dekker. HARRIS C M (2003) ‘Raman on the run’, Anal Chem, 1 February, 75A–78A. KRESS-ROGERS E and BRIMELOW C J B (2001) Microwave measurements of product variables. Instrumentation and sensors for food industry, Cambridge, Woodhead Publishing Ltd. MCFARLANE I (1995), Automatic control of food manufacturing processes, Glasgow, Blackie. MOREIRA R G (2001), Automatic control for food processing systems, Gaithersburg, Aspen Publishers. POVEY M and HIGGS D (2001), ‘Ultrasonic spectroscopy – a new tool for the food industry’, Innovations fd technol, 73. SMYTH C, O’DRISCOLL, B, KURYASHOV E and BUCKIN V (2002), ‘High-resolution ultrasonic spectroscopy for dairy analysis’, Eur Fd Scientist, 25–26. Proceedings of the ‘Second World Congress on Industrial Process Tomography’, 29–31 August, Hannover, Germany. GUNASEKARAN S

Internet sites of value www.cpact.com: The centre for process analytics and control technology (CPACT) is a multidisciplinary centre formed thorough the UK Foresight Challenge Initiative.

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CPACT brings together chemical and process engineers, analytical chemists, control systems engineers, chemometricians, signal processing engineers and statisticians, from academia and industry, to research solutions to generic problems in process monitoring and control. www.flair-flow.com: Flair-flow is a network that disseminates food research results in 24 European countries. The Flair-flow 4 synthesis report is entitled ‘Food quality sensors’ and comprises results from about 20 EU-supported scientific projects. www.vcipt.org.uk: Process tomography. www.itoms.com: Industrial process tomography products and applications. www.kcl.ac.uk/neuronet: Network of excellence in neural networks, including international organisations and societies in the field. www.physics.dcu.ie/PhysicsHome/optronics/fos-en.html: website of the fibre optic sensors – European network (FOS_EN). www.frost.com: Market analysis reports on sensors and smart sensors. www.lab-on-a-chip.com: List of major centres of expertise on microarrays, microfabrication technologies, laboratory automation and micrototal analytical systems. Also includes sites showing lab-on-a-chip consortia, and microarray newsgroups and organisations. www.microchemicalsystems.co.uk: A company manufacturing microreaction products and equipment. It also has links to major conferences in the micrototal analytical systems area. www.woice.de/index: Site showing microreaction technology ‘know how’, ‘know who’ and ‘know where’. The microreactors offer a revolutionary alternative to the largescale production facilities in industrial plants.

12.7

References

(1997), ‘Ultrasonic separations in analytical biotechnology’, TIBTECH, 15, 506–511. EHRFELD W (2000), Microreactors: New technology for modern chemistry, Chichester, John Wiley and Sons. GIDDINGS J C (1995), ‘Measuring colloidal and macromolecular properties by FFF’, Anal. Chem., 1 October, 592A–598A. HASEGAWA T (2001) ‘Detection of minute chemical signals by principal component analysis’, Trends in Anal. Chem, 20, 53–64. PATEL P D (2000a), ‘(Bio)sensors for measurement of analytes implicated in food safety: A Review’, Leatherhead Food International Scien and Tech Notes No. 195. PATEL P D (2000b), ‘A review of analytical separation, concentration and segregation techniques in microbiology’, J. Rapid Methods and Automation in Microbiol, 8, 227–248. PATEL P D (2001), ‘Molecular imprint-based technologies in contaminant analysis, in Watson, D. (ed.), Food Chemical Safety, Cambridge, Woodhead Publishing Ltd. PATEL P D (2002), ‘(Bio)sensors for measurement of analytes implicated in food safety: A review’, Trends Anal Chem, 21, 96–115. PATEL P D and WEBER G (2000), ‘Electrophoresis in free fluid: A review of technology and (Agrifood) applications’, Leatherhead Food RA Scien and Tech Notes No. 196. COAKLEY W T

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(1998), Dielectrophoresis, in environmental monitoring, Methods in Biotechnology 12 C, New Jersey, Humana Press. ZAMANI A F, OWEN, R W AND CLARKE, D J (1993) ‘Ultrasonic standing waves: microbiological applications’, in Kroll R G, Gilmour A and Sussman M (eds) New techniques in food and beverage microbiology, Blackwell Scientific Publications, Oxford. PIMBLEY D W, PATEL P D AND ROBERTSON C J

13 Measurement of added water in foodstuffs M. Kent, Consultant, UK

13.1

Introduction

13.1.1 Added water and legislation EC Directives and national regulations1–13 exist to limit the amount of added water and other additives, often by prescribed labelling of product composition and additives, which requires the amount of principal ingredients such as added water to be quantified. Water is often added to foods during processing either inadvertently (e.g. during washing of the raw material) or deliberately (e.g. when adding polyphosphates for various reasons). The deliberate addition of water along with polyphosphates (e.g. sodium tetrapolyphosphate, E450) has been justified on a variety of technological grounds such as reduction of drip-loss from the product, retention of nutrients and possible antioxidant effect.14 The practice of immersing fish in brine (NaCl solutions) to enhance flavour or to impart some preservative qualities to the product can also change the amount of water held in the flesh by various diffusion processes.15 Misuse of all these practices by the occasional unscrupulous processor is designed simply to increase profit margins by selling water instead of the actual product. This section will cover key industry and legislative requirements; limitations in current techniques; and potential errors of measurement. Regulation 16 of the UK Food Labelling Regulations (FLR, as amended)12 requires that when water has been added to a food and is present in amounts greater than five per cent in the finished product, then it must be declared in the ingredients list. This EC Quantitative Ingredients Declaration (QUID) amendment to the Labelling Directive (Regulation 19 of FLR),12 has been in effect since February 2000, and requires an ingredient mentioned in the name of the food to be quantified. For example, ‘pork’ to which water has been added

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would require a QUID declaration of the percentage pork in the ingredient list or in the name of the food. Similar legislation2 lays down limits for the ‘technically unavoidable water content’, which arises during the processing of whole chickens. However, as we shall see, in the case of added water, measurement is very difficult to achieve, especially in the presence of the large amount of water, typically about 70–80 per cent by weight, that exists naturally in many foodstuffs. Distinguishing the ‘natural’ from the added water is part of the problem. If the total water content is measured, its normal range needs to be well known. The water content of scampi (Nephrops norvegicus) can vary seasonally within the range 78–82 per cent so for example, even with a weight gain of 20 per cent through the addition of water it is possible that the scampi could remain within the extremes of the normal range.16 That is to say that an initial water content of 78 per cent becomes just 82 per cent. Thus legislation which specifies that the amount of added water should not exceed a certain level may not be easily enforceable and tolerances may need to be understood. For other products, where added water is a problem, the normal range at any time may be smaller than the example above which was a seasonal physiological effect, but defining the normal water content may be fraught with other difficulties as we shall see.

13.2

Problems in measuring added water

One problem that is extremely difficult to overcome is that all instrumental methods of composition measurement are indirect and thus inferred, none more so than the determination of added water. This means that any instrument can only be as good as, and no better than, the method used to calibrate it. The standard way to determine added water is to measure all the compositional variables (protein, fat, water, carbohydrate, salt and additives) by various means, then to calculate the added water using what is assumed to be the known composition of an unadulterated sample. Thus for meat products W ˆ 1 ÿ …M ‡ A ‡ S†

‰13:1Š

where W is the fraction of added water, M, A, and S are the proportions of meat (including fat), additives, and salt (NaCl), respectively. Additives can be carbohydrates and derivatives, polyphosphate, and nitrate/nitrites, etc., although in the case of added nitrates and nitrites the level of addition will generally not make any significant differences to the percentage of added water. The meat content is calculated from the total nitrogen content, which is assumed to be partly due to protein and partly to added carbohydrate derivatives. The proportion of nitrogen as a fraction of the total carbohydrate is assumed to be two per cent of the carbohydrate mass. Most significantly for the error analysis, a value is assumed for the fat-free percentage of nitrogen in unadulterated meat. This socalled nitrogen factor (NF) can vary with age and weight of the animal and also with the particular joint or cut taken from the carcass. It will also depend on

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whether subcutaneous fat and rind (skin) are included in a particular joint. There will be considerable doubt about the NF for products made of several components. It can vary from a low of 3.4 for neck or collar joints up to 3.9 for loin or back containing rind. A figure of 3.5 has been recommended for general use in the analysis of pork products17 which is also very close to that measured for the defined national (UK) average processing carcass (70 kg weight with 12.3 mm back fat). It is important therefore to understand just how much both the errors of measurement of composition and the error in the assumed NF contribute to uncertainty in the added water determination. The composition of leg meat from the average British processing carcass, comprising lean, intermuscular and subcutaneous fat is 66.8 per cent water, 13.8 per cent fat, 3.0 per cent nitrogen and roughly 1.0 per cent ash, the majority of which is NaCl. Taking this as a basis, and deriving the added water for small changes in each of the important composition terms, it is possible to create a socalled sensitivity diagram. In this the absolute error in the added water calculation is plotted versus the percentage magnitude of the error for each variable (Fig. 13.1). The calculations required to create Fig. 13.1 have also been used to calculate the maximum expected error due to each term based on the typical uncertainties as presented in Table 13.1. For example the protein or nitrogen content is typically determined to within 1.5 per cent of its value. From this diagram it can be seen that this creates an absolute uncertainty of no more than ±0.12 per cent of added water. The nitrogen factor of 3.5 used by the meat industry for calculating the added

Fig. 13.1 Sensitivity diagram showing the major contributions to uncertainty in added water content. A: Nitrogen factor, B: protein, C: fat, D: water. All other contributions are small in comparison to that produced by the error in the nitrogen factor.

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Table 13.1 Maximum error in proximate analyses and their effects on added water calculation

Water Protein Fat Nitrogen factor

Relative uncertainty in variable

Contribution to error in added water (% added water)

±0.84% ±1.5% ±6.0% ÿ3.5%–+11.4%

±0.29 ±0.12 ±0.35 +8.8–3.1

water in pork is subject to a variation of up to 0.045 due to deviation from the 70 kg ‘British national average processing carcass’.17 These deviations could be in actual carcass weight, back fat thickness or even age. In addition different factors should be used for different cuts of pork being 3.5 for leg meat (ham) but 3.66 for loin (back), 3.9 for loin including rind and subcutaneous fat and 3.38 for collar. Further uncertainty arises when mixtures of different cuts are used and when British and foreign pork is used. This is quite critical for meat processors attempting to meet the regulations concerning permitted or advised levels of added water or for regulatory bodies trying to demonstrate abuse. The right extremity of the error curve for NF corresponds to an underestimate of added water content for back bacon (4%) for which NF should be 3.66. A worst case estimate for this error in calculated added water, given the various uncertainties in the composition analysis and in the nitrogen factors, would be in excess of 10 per cent if subcutaneous fat and rind were included (NF=3.9). It is worth looking at some examples of possible error and these are summarised in Table 13.2. We can see that for typical supermarket samples of unsmoked back bacon the wrong NF, i.e. 3.5, can make it appear that the added water is below the limit of 5 per cent. Above this limit the QUID declaration must be made. If the highest possible value of 3.9 for NF were used then the sample would be calculated to contain at least 10 per cent added water. In fact to bring the value of added water just over the QUID limit NF would need to be only 3.63 which is below the recommended value of 3.66 for this product. One final comment on this subject must be to point out that the values quoted in Table 13.2 for unprocessed pork were for a sample to which 10 per cent water had been added. Furthermore, for the UK national average processing carcass there is, of course, no actual added water although the calculation would suggest the opposite. There is certainly more to this subject than meets the eye. Thus there is no hope of improving the accuracy by using the dielectric technique, or any other, if such data is used for calibration. Knowledge is required of exact amounts of added water. Careful blending and weighing can achieve that but then the answers can be at odds with those expected from the above ‘standard’ methodology. As can be seen, it is possible and it frequently occurs that for an unprocessed sample or one with minimal amounts of water added the calculation yields negative amounts of added water. This is of course ignored and the value arbitrarily set to zero.

Table 13.2

Some examples of possible miscalculation of added water

Sample

Moisture (%)

Fat (%)

Ash (%)

Protein (%)

Salt (NaCl) (%)

Added water N=3.5 (%)

Added water NF correct (%)

Unsmoked back bacon Smoked back bacon Unprocessed pork leg UK national average processing carcass

62.7 66.6 65.8 66.8

15.8 14.6 14.0 13.8

4.6 4 2.97 1.0

17.4 16.0 17.4 19.4

2.6 3.1 2.2 <1.0

2.3 9.7 4.32 ÿ3.1

5.7 NF = 3.66 12.9 NF = 3.66 4.32 NF = 3.5 0.0 NF = 3.5

Measurement of added water in foodstuffs

13.3

245

Measuring the dielectric properties of water

In this section areas to be covered include: what dielectric properties are; polar materials; properties of water; dielectric spectra; what dielectrics can tell us about a material; and a brief introduction to some key statistical and mathematical approaches to data analysis.

13.3.1 Dielectric properties The electromagnetic spectrum covers all wavelengths and frequencies with extremes at very low frequency long wavelength communications (VLF) and ˚ ngstroms. As a physical gamma radiation with wavelengths measured in A phenomenon its name is self-descriptive: oscillating coupled electric and magnetic fields travelling through space at 3.108 metres per second. Throughout this spectrum matter can interact with the electromagnetic fields through either the magnetic or the electric field. The latter is what principally interests us here. Magnetic effects are confined to very special classes of materials with magnetic and paramagnetic properties whereas most materials interact with the electric field through what are known as dielectric properties. That interaction takes two main forms: energy storage and dissipative loss. The dielectric properties arise from polarisation effects. In an electric field, charged particles will tend to move along that field. To understand polarisation imagine a material placed between two metal plates with some voltage applied across them. Electrical charges in the material will tend to separate, being attracted to one or the other of these electrodes. The first case of this is called electronic polarisability, which arises when the electrons in orbital shells around atoms are drawn away from their equilibrium position. The equilibrium position is usually where the centres of mass of both electrons and the atomic nucleus coincide. The separation caused by an electric field is virtually instantaneous and results in an induced dipole moment in the atom. A new equilibrium position is reached when the electric field generated by this dipole equals that being applied. The effects of this particular form of polarisation are seen in the optical region of the spectrum where they are responsible for refraction of light. The second case is ionic polarisability and arises when atoms or ions are displaced within a molecule. This displacement is typically associated with changes in bond lengths and angles with resultant dipole moments. Their principal effects are in the infrared region of the spectrum so do not concern us here. The final form of polarisability is called orientational and occurs when molecules have a permanent dipole moment. This is due to the centres of mass of charges not coinciding in the molecule. In respect of the subject of this chapter this is a very important factor since water itself has an unusually large dipole moment given its small molecular size. The centre of charge of the three atomic nuclei in this molecule (two hydrogen and one oxygen, of course) is different from that of the orbiting electrons.

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In an oscillating electromagnetic field all these polarisations will try to follow the direction of the field. The orientational polarisation will be hindered in this by collisions and other interactions with near neighbours. A measure of these interactions can be obtained by considering the effect when a steady electric field, which has been present long enough for all the orientational polarisations to take place, is removed. The orientation of the dipolar molecules is gradually randomised by these collisions and interactions until there is no preferred direction. The time that this takes to occur is a temperature dependent characteristic of a material and is called the relaxation time . Its reciprocal gives a characteristic frequency, the relaxation frequency, which is about 17 GHz (1.7  1010 Hz) for water at room temperature. This is squarely within the so-called microwave region. In the static field case we can assign a property to the material known as the ‘dielectric constant’, s, and this is a measure of how well the material can polarise. If the field is made to vary with time then a different situation develops. A polar dielectric, which in the static field case could have been considered perfect (i.e. dissipated no energy) will now exhibit a form of conduction loss. In an AC circuit a capacitor filled with such a dielectric would effectively be a capacitive reactance with a parallel resistance. For a low frequency sinusoidal variation of the field the dipoles may be able to align and realign exactly in phase with the field, and the capacitor has purely reactive impedance. As the frequency of the oscillation is increased (period decreased) and approaches the dipolar relaxation frequency it is increasingly difficult for the polarisation to become complete. Consequently, the apparent dielectric constant or permittivity begins to fall. At frequencies even greater than the relaxation time only the distortional polarisation contributes to the dielectric properties and the material becomes effectively non-polar. The combined effect is a high dielectric constant, s, at low frequencies and a low dielectric constant, 1 , at high frequencies. This frequency-dependent behaviour is known as dispersion and is characteristic of polar materials. The dispersion of water is shown in Fig. 13.2. For later reference it should also be noted that at very high

Fig. 13.2

Dielectric spectrum of pure water at 20ºC.

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frequencies there is a relationship between the permittivity 1 and the more commonly known optical refractive index  which is written, p ‰13:2Š  ˆ 1 At lower frequencies the refractive index is complex (mathematically having both real and imaginary terms) and as a concept is not generally used outside of physics though we will meet it again shortly. The dielectric nature of polar materials, however, is best understood in terms of a complex permittivity. This is expressed thus:  ˆ 0 ÿ j00

‰13:3Š 0

where the real part or permittivity,  , is the component which represents the ability of the dielectric to store energy, and the imaginary part or loss-factor, 00 , is that part which relates to its ability to dissipate energy. The microwave transmission variables of attenuation and phase (see later) are directly related to the complex permittivity, as are other important properties such as reflection coefficient and the so-called S or scattering parameters. The ratio of 00 =0 is called the loss tangent, written as tan . Power dissipation in a material is proportional to this. The loss-factor 00 behaves in a somewhat different manner to the permittivity, 0 , in that it passes through a peak value as the frequency is swept. This peak occurs at the relaxation or critical frequency, fc given by: 1 ‰13:4Š fc ˆ 2 How 0 and 00 depend on frequency is shown in Fig. 13.2 for water which is a typical polar dielectric. For such a simple dispersion with only a single relaxation time the permittivity and loss factor are defined thus: 0 ˆ 1 ‡ 00 ˆ

…s ÿ 1 † …1 ‡ !2  2 †

…s ÿ 1 † ! …1 ‡ !2  2 †

‰13:5Š ‰13:6Š

where ! is 2f. It is also informative to plot 00 versus 0 . For this kind of relaxation spectrum a semi-circle results with its centre on the 0 axis and intercepting this axis at s at low frequencies and 1 at high frequencies (Fig. 13.3). This is widely known as a Cole-Cole plot and less frequently as an Argand diagram or complex plane plot.

13.3.2 Dielectric dispersion of water in foodstuffs As has already been indicated, water is a very good example of a polar molecule. The spatial configuration of two hydrogen atoms with each oxygen atom is such that the orbiting negatively-charged electrons have a centre of mass slightly displaced from the centre of mass of the positively charged nuclei, with the net result that a permanent electric dipole is formed. In fact the strength of this

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Fig. 13.3

Cole-Cole plot of dielectric spectrum of pure water at 20ºC.

dipole, for the size of the molecule, is exceptional, and the dielectric dispersion is of quite a large magnitude: at room temperature s being about 80 in relative units (relative to the permittivity of empty space) and 1 approximately 4.7. The peak loss factor of ~37 occurs at around 17 GHz at 20ºC and it is all these factors which make water so easily detectable at microwave frequencies. As a comparison, at the same frequencies a material such as vegetable oil has a permittivity of ~2.6, a loss factor of less than 0.2 and no significant dispersion. This comparison, however, should only be taken as a general comment since water in foods at low concentrations can be expected to behave differently to a greater or lesser degree. A point to note is that water is not the only polar molecule of interest to the food industry and that other important food constituents may display dispersion with the loss factor peaking at some particular critical frequency. This raises the question of whether dielectric spectroscopy could be used to identify and measure compounds in materials containing a range of polar compounds. Alcohols, for example, have relaxation frequencies ranging from MHz to GHz. Unfortunately, mixtures of polar molecules do not have well resolved spectra, as say in the infrared spectrum, the dispersion spectrum of each being combined into one synergistic response. Relaxation times are influenced by a number of factors such as viscosity, binding at specific sites on other molecules, temperature or by change of state. Water molecules hindered in their rotation, by solutes or proteins for example, have a lower relaxation frequency than pure water and may even have a distribution of relaxation times. This tends to broaden even further the frequency range of the dispersion. For a Gaussian distribution of relaxation times about some mean value, the semi-circular complex plane plot shown in Fig. 13.3 becomes instead a segment of a circle with the centre of radius lying below the permittivity axis. It is more frequently the case, however, that the distribution is skewed or even discontinuous and the spectrum in such a situation becomes more difficult to interpret.

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Because it is a major food constituent, water strongly influences their dielectric properties but the loss factor also has a strong dependence on the ionic conductivity of the material and hence the concentration of ions of one sort or another. In unmodified foods this is usually the natural NaCl present. The contribution to the loss factor of such ionic conductivity is dependent on the frequency f and is given by  ‰13:7Š 00 ˆ !0 where  is the dc conductivity (siemens m-1), and 0 is the permittivity of free space (farads m-1). It is well known that salt content can be estimated from the dc conductivity of materials. What should now be noted is that conductivity can be obtained from the frequency dependence of the dielectric loss (eq. 13.7), adding a further dimension to the potential of dielectric measurements. Obviously such ions have a much greater influence at lower frequencies than in the microwave range where they are often negligible. This can be seen in Fig. 13.4 where, amongst others, the Cole-Cole plot for raw chicken breast meat is shown. The effects of water can be observed in this graph in the tendency to a semi-circular locus of the high frequency data. It should also be noted in Fig. 13.5 that if pure water was the

Fig. 13.4 Permittivity and loss factor of chicken breast at 3ºC showing the form of the complex dielectric spectrum when plotted in the complex plane (after Kent et al.27). The low frequency data are at the right-hand side starting at 0.2 GHz and the frequency increases from right to left up to 12 GHz. The effects of ionic conductivity can be seen in the loss factor at low frequencies and the presence of water is seen as a tendency towards a semi-circular locus at high frequencies. The relaxation frequency for this dispersion is ~10 GHz. The other two curves are: open circles – water alone added, triangles – water and polyphosphate added (see text).

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Fig. 13.5 The same data for untreated chicken breast (closed circles) as in Fig. 13.4 but plotted as a frequency spectrum.

only polar material present then at low frequencies the permittivity would tend to a horizontal line. This is clearly not the case and the slope of this curve indicates the presence of other dispersions. These may be water with hindered rotation due to its close association with protein molecules. It may be identified possibly with the water that in high moisture foods is unfreezable. The dispersion could also be due to the so-called Maxwell-Wagner effect which arises when there are boundaries between materials of different dielectric properties and conductivities. An accumulation of charge takes place at the boundaries resulting in what is known as interfacial polarisation. The relaxation time for such a phenomenon at a planar interface is given by 1 f2 ‡ 2 f1 0 : ‰13:8Š ˆ f2  4 where 1 and 2 are the permittivities of the two media with volume fractions f1 and f2. In this instance it is assumed that one material has zero conductivity and the other is . At any angular frequency ! the complex permittivity T due to this effect is given by T ÿ 1 1 ‰13:9Š ˆ S ÿ 1 1 ‡ j! S and 1 for this dispersion are given by 2/f2 and 1 2 =…1 f2 ‡ 2 f1 † respectively. Obviously a more complicated situation obtains when both materials have conductivity or when one material is suspended as droplets in the other. The shape and orientation of such inclusions is then very important also. All things considered, the high frequency ‘tail’ of a Maxwell-Wagner dispersion could certainly be responsible for the shape of the observed spectrum

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in Fig. 13.4. Discontinuities in the material can exist at cell walls or at droplet boundaries if the food is emulsified for example. Whatever the reason for the shape observed the effect of adding water is clearly demonstrated (Fig. 13.4 open circles). The semi-circular part of the plot increases in magnitude and at the same time that part due to conductivity falls due to dilution of the ions present. That example is for water taken up by the flesh without any other additions such as might occur during washing. If, on the other hand, polyphosphates are used for whatever reason, the effect is one both of increased water content as evidenced by the change in the water dispersion, and an increase in loss factor at low frequencies. This may be an ionic effect or indeed another mechanism but the difference between the two permittivity spectra (treated and untreated) is constant over the whole low frequency range, only changing in the dispersion itself. This is some indication that only the dielectric incremental effect due to additional water can be seen in this permittivity spectrum.

13.4

Instrumentation for measuring dielectric properties

This section will cover how dielectric properties may be measured, attenuation, phase and reflectance

13.4.1 Microwave attenuation As a first step one should be aware of what is meant by ‘microwave’. The usual loose definition includes all electromagnetic radiation within a broad frequency range from 108 Hz to 1011 Hz (100 MHz to 100 GHz) or in wavelength terms from 3 m down to 3 mm. Historically most commercial instruments have operated in the so-called S and X bands, which cover the approximate frequency ranges 2–4 GHz and 6–12 GHz. In the beginning, just after World War II, it is suspected that for those developing the method the choice was dictated by the availability of surplus military equipment, which operated in X-band. This in turn was probably due to arbitrary decisions regarding waveguide dimensions of 1 inch (25.4 mm) for the critical dimension of the width of waveguide in this frequency band. The nature of electromagnetic propagation in waveguide is such that there is a maximum free-space wavelength that can be transmitted and this is twice the width of the waveguide i.e. 50.8 mm or a frequency of approximately 6 GHz. Above 12 GHz the possible modes of propagation in the waveguide multiply in number so if single mode transmission is desired this limits the upper frequency. From the earliest times of microwave applications they have been used to determine the composition of foods, principally the water content. This has been achieved by the effect described above, that the absorption of power maximises in the tens of GHz region due to the dipolar relaxation of water. As a general rule much of the earlier work utilised a single frequency and measured at most just two variables, usually the phase and attenuation of a wave passed through

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the material. This limited the usefulness of the technique to measurement of one or two material variables. Typically these were the water content and the density of powdered foods. Where other interfering variables were present and density was not a problem then one of these also could be measured. ‘Attenuation’ of microwaves was the method of choice and this is the absorption of microwave power by a material. Defining a plane wave as a wave for which the phase is equal at any point in a plane perpendicular to the direction of propagation then its attenuation and velocity passing through a material give information in a relatively simple form on its complex dielectric properties. The attenuation is a direct consequence of Beer’s law,18 which is expressed as I ÿ I0 eÿx

‰13:10Š

where I0 is the power incident on some plane within the material, I is the power at a plane distance x further into the material and  is a constant known as the attenuation per unit length. In terms of wave amplitudes rather than power, an additional factor of 2 must appear in the exponent. This equation takes no account of the wave nature of the radiation and by considering conditions only within the material it ignores effects of reflective power losses at the material boundaries. The attenuation, , as a function of the dielectric properties is written  q 1=2 2 0 1 ‡ …00 =0 †2 ÿ 1 nepers mÿ1 ‰13:11Š ˆ  2 where  is the free space wavelength in metres. For many low moisture materials tan  << 1 so equation 13.11 reduces to  p0  tan  nepers mÿ1 ‰13:12Š ˆ  At the same time, because of the difference in refractive index of the material compared to free space and therefore as a result of the reduction of velocity of the wave in the material, we can write  0 q 1=2 ! 2  2 00 0 1 ‡ … = † ‡ 1 ÿ1 radians mÿ1 ‰13:13Š ˆ 2  which again when tan  << 1 becomes 2 p0 …  ÿ 1† radians mÿ1 ‰13:14Š ˆ  which is the phase shift that such a wave undergoes passing through the material relative to that it would have had through the same distance of free space. Above a water content of ~30% m/m, the value of  becomes very large even for short path lengths in the material. At 10 GHz for example, 50 dB of power attenuation is readily achieved by a few millimetres of pure water, i.e. a reduction in power by a factor of 105. This makes the presentation of the material to the sensing head extremely difficult since the thickness of such

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samples needs to be restricted to keep the attenuation measurable. Typically 30 dB should be considered maximal. Equations 13.11 to 13.14 refer only to transmission within a material. They do not take into account reflections at boundaries due to changes in impedance at those boundaries, nor the standing wave effects created by those reflections. Equation 13.11, however, was the basis for most microwave transmission applications up until the early 1970s. The unwanted effects of reflected power could be minimised by the expedience of matching the impedance of the microwave applicator or sensor to that of the sample. However, when the permittivity of a material is high the use of simple attenuation and phase is often precluded, because it becomes extremely difficult to achieve this matching. Even if it were achieved for a particular sample composition, any deviation from this would produce sufficiently large changes in the permittivity for the impedance no longer to be matched. What was needed was a method that turned these undesirable effects into something that gave information on the system since they contain as much information about the dielectric properties as the simple transmission equations. What can be measured instead are variables called the scattering or S parameters. It is not necessary to dig deep into the theory of scattering networks to explain these variables. Rather, suffice it to say that any device handling electromagnetic signals can be represented by a matrix of so-called scattering parameters S11, S12, S22, S23, etc., which are complex variables describing the transfer of signal at each input and output port of the device. A dielectric sample in an air-filled transmission line is a two-port system and can be described by four such S-parameters completely describing the reflected and transmitted signals at each of the two interfaces. When the reverse path through the system is physically identical to the forward path then symmetry reduces the four parameters to two, S21 for the transmission and S11 for the reflection. For a sample of permittivity  and thickness d, at a wavelength , S21 may be written as p …1 ÿ ÿ2 † exp …p † p ‰13:15Š S21 ˆ 1 ÿ ÿ2 exp …2p † p p where p ˆ ÿj2d= and ÿ ˆ …1 ÿ †=…1 ‡ † also known as the Fresnel reflection coefficient.

13.4.2 Reflectance measurements As already discussed transmission methods are limited in use by power loss in the material to moisture contents below about 30 per cent. To avoid this problem a thinner sample may be used, but often this is also impractical for mechanical reasons. In addition, the thinner the sample the more precisely must the thickness be known, since the variables are also dependent on this dimension. A better solution is to measure the reflection S parameter S11. In the same nomenclature as above this is given by

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Rapid and on-line instrumentation for food quality assurance p …1 ÿ exp 2p †ÿ p S11 ˆ 1 ÿ ÿ2 exp …2p †

‰13:16Š

Furthermore it is possible, even more convenient, to make the sample large enough that power reflected from the back face is attenuated before reaching the front face. With this absence of interference from multiple reflections S11 becomes effectively the Fresnel reflection coefficient ÿ. This also means that the sample need no longer be contained within a waveguide because we deal only with the transition between two impedances: that of the sensor, an empty transmission line, and that of the sample, which is effectively a semi-infinite body. As can be seen from the definition of ÿ above it is a direct consequence of the difference in dielectric properties between the medium filling the transmission line and the material. The combination of the reflected and incident waves creates a standing wave in the line. The impedance of the unknown dielectric can be found either by measuring the position and magnitude of this standing wave or by measuring the magnitude and phase of the reflected wave. From this impedance the dielectric properties can be computed. Although a waveguide can be used in this mode a potentially more useful form of sensor can be constructed from an open-ended coaxial line.19,20 Such a sensor (see Fig. 13.6(a)) can be used in on-line situations to monitor flowing materials,21,22 for example, slurries and extruded products. Its real potential however lies in its broadband nature. Unlike waveguide, for which transmission cannot occur below a certain cut-off frequency determined by its electrical dimensions, coaxial line can theoretically transmit all wavelengths. It has limitations above a certain high frequency, where its dimensions, must be small to eliminate the propagation of waveguide type modes, but it is generally useful throughout the frequency range that might be considered for composition measurement. Measurements with such a sensor enable spectroscopic investigations, where as much of the dielectric spectrum as is needed can be used. Because the measurement is made on one face of the material and not through it, reflection methods do not have the same limitations as transmission methods in respect of high water contents or high loss samples, although a high degree of sample homogeneity is called for. The reflected power increases as the water content increases though not in a convenient linear manner. The limiting condition of reflection is clearly when all the power is reflected and the reflection coefficient has a magnitude of one. This limit is that of a perfect conductor. As the permittivity of the sample increases it is approached asymptotically. In addition, the limiting phase shift of the reflected wave is , so for high frequencies and high water contents relatively large changes in moisture (permittivity) cause small changes in the magnitude and phase of the reflected wave. At lower frequencies the problem is much less and the changes are easier to measure accurately. In this frequency range, however, the effects of dissolved ions on the loss factor are stronger and if the concentration is too high can cause errors due to problems of power loss and due to the phase shift approaching the limiting value of .

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Fig. 13.6

255

(a) Cross-sectional diagram of coaxial sensor; (b) Equivalent lumped element circuit.

Because the probe is not ideal, various corrections need to be made to the simple theory but the simplest way of looking at the device is to consider that it is a capacitor whose impedance depends on the material with which it is in contact (Fig. 13.6(b)). The impedance at any frequency f of such a probe in contact with a material of complex permittivity  is given by: Z ˆ Z0

…1 ‡ ÿ† …1 ÿ ÿ†

‰13:17Š

where Z0 is the impedance of the transmission line from which the probe is constructed. In terms of the equivalent circuit the admittance (reciprocal of the impedance) is

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Fig. 13.7 Open-ended coaxial sensors for reflectance measurements (courtesy of R Knochel and Christian Albrechts University of Kiel).

Y ˆ j!…Cf ‡ …0 ÿ j00 ††Cs

‰13:18Š

where Cf is the effective capacitance of that part of the internal fringing field within the sensor and Cs is that of the external fringing field the space of which is occupied by the sample. The imperfections of both the sensor and the system cause unwanted reflections within the system and these are eliminated by calibration with known impedances at the sensor position. These impedances can be recognised standards such as short circuit and open circuit, but also dielectric materials of very well characterised properties such as water, alcohols or NaCl solutions. Because determination of the unknown permittivity requires the use of numerical methods, like so many other methods in use today, some computing power is necessary. Some examples of coaxial probes are shown in Fig. 13.7. A major problem with the construction of even a basic reflectometer measurement setup (Fig. 13.8) lies in the necessity for all the components to be broadband. The solutions are attainable but at a price which makes such systems expensive to produce. A prototype was recently constructed as part of an EC-funded project on the measurement of added water (FAIR CT3020). A photograph of the device can be seen in Fig. 13.9.

13.4.4. Guided microwave spectrometry Although the use of waveguide has limitations due to its fairly narrow frequency band of microwave power transmission, a recent development has exploited this fact. This fairly new technique is known as guided microwave spectroscopy (GMS) and as its name suggests it is a broadband approach.23 For convenience a rectangular cross-section waveguide is used, through which both the microwave power and the product to be measured pass in perpendicular directions. Because of its dimensions the waveguide cannot transmit power below a certain cut-off frequency given by c ‰13:19Š fc ˆ p 2a  where c is the speed of light in free space, a is the separation distance between the critical sides of the waveguide and  is the permittivity of the material filling

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Fig. 13.8 Basic reflectometer using a homodyne network analyser (courtesy of F Daschner and Christian Albrechts University of Kiel): 1 microwave source; 2 power splitter; 3 single sideband generator; 4 directional coupler; 5 mixer; 6 matched load; 7 sensor and sample.

Fig 13.9 Composition monitor developed under the EC FAIR programme, project no. CT3020 (courtesy of R Knochel and Christian Albrechts University of Kiel).

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the waveguide. In an empty X-band waveguide as was stated earlier, this cut-offfrequency would be about 6 GHz. Because of the presence of a moist material with a permittivity p of something around 50 to 80 say, then the cut-off frequency is reduced by  to several hundred MHz. By sweeping the frequency from well below cut-off to well above it, a spectrum of the magnitude of the transmission coefficient is obtained. This factor changes abruptly as the cut-off frequency is approached rising over a range of a few hundred MHz by some 60 dB. The position of the spectrum, its shape and amplitude, all depend on the various dielectric parameters, namely conductivity , low frequency permittivity s and relaxation time . In order to extract these data from the measurement transmission spectrum the calculated response of an ideal polar dielectric is fitted, i.e. one with only a single relaxation time. Although there is a mathematical function describing the response of the waveguide cell in terms of the dielectric properties of the material in it, the numerical fitting approach is faster and requires fewer computations. It was also assumed that the variation of the product around the desired composition would be small enough that this simplification would work. In fact an accuracy of better than 0.1 per cent moisture content is claimed. The method has been successfully implemented online for measurement of salt and moisture in dough and batter, composition of meat, poultry, seafood and dairy products and sugar content of sweets. There is no reported use of the method for added water determination, however, but it is mentioned here because of its potential to produce spectral or multivariate data. This, as we shall see, is crucial to the application of dielectric methods to added water determination.

13.5

Applications

In this section it will be shown how the dielectric properties can be used to determine compositional and other variables in foodstuffs.

13.5.1 Measurement of composition Clearly there are changes in the dielectric spectra of foods that reflect fundamental changes in composition. In the past when only one or two variables were to be determined, for example fat and water content, then measurements were made of the real and imaginary parts of the dielectric properties at one frequency. Such an approach leaves the results vulnerable to changes in other components and is not suitable for what is to be described here. Another approach would be to model the food in terms of its constituents and their dielectric properties. While this is an admirably rigorous approach, too little is known of the importance of various components to the dielectric properties and no conceivable model would be accurate enough to describe the system. For example what kind of relaxation spectrum does water have in its hindered states of rotation?

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The apparently qualitative changes in shape can be exploited without a detailed knowledge of the role of each constituent. Chemometric techniques, common in infrared spectroscopy, can be used with these spectra. For example, the multivariate approaches of principal component analysis (PCA), partial least squares (PLS) and artificial neural networks (ANN) have all been applied. PCA can be found in many fields where it is used to reduce large data sets to a few uncorrelated variables of interest. In image analysis, for example, it is known as the Karhunen-Loeve transformation (KLT)24 or Hotelling’s Transform25 and is applied to Geographical Information Systems (GIS), face recognition and many other fields where shape and pattern information is important. In paleontology it is used to enable different species of fossil to be identified from shape descriptors and similarly, even living organisms may be classified. Its value lies in being able to discriminate subtle changes of shape. It can be so used in spectroscopy to determine the underlying variables that cause a change in shape of the spectrum without having a causal model. Its most relevant application to this chapter, however, is in the determination of added water in foods.26,27

13.5.2 Principal component analysis (PCA) and regression (PCR) To carry out a PCA the raw data (say the complex spectral values and any other variable such as temperature) must first be mean centred and standardised (divided by their standard deviations) to produce a set of variables with unit variance. This ensures that no single variable dominates the analysis. PCA then applies a linear transformation to these standardised variables which are by their nature colinear and correlated. The transformation involves both rotation and projection of the variables onto the principal axes of the ellipsoids generated by the data in multivariate space. Figure 13.10 shows this in just the two arbitrary dimensions of the permittivity at two frequencies. The multidimensional hyperellipses must be imagined! This produces a new set of uncorrelated and standardised variables called ‘principal components’ (PCs).28 These components or scores relate to the original variables as follows: Pj ˆ a1j X1 ‡ a2j X2 ‡ . . . aij Xi ‡ . . . aPjj Xj

‰13:20Š

where Pj is the jth PC, the Xi s are the original variables and the coefficients in the eigenvector aij are constants referred to as ‘loadings’. These loadings are calculated in sequence by maximising the variance of each PC (rotation of the axes to the principal axes of the variance). There are several software packages available, which will do all this. The first few PCs often account for most of the variance of the original set of variables. The information on the proportion of the variance associated with each PC is given by eigenvalues associated with the eigenvectors. Having obtained the PCs or scores of the spectra they may then be used to summarise the original data and as an input to further analyses such as multiple regression (PCR). As already stated one purpose of PCA is to reduce a large number of variables to a smaller number containing almost the same amount of

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Fig. 13.10 From dielectric spectra of raw chicken breast with varying amounts of added water (after ref. 27) the permittivity at 12 GHz is plotted against that at 6 GHz. For this pair of results the ellipse axes shown are the principal component vectors and the principal components for the data are the projections onto these lines.

information, but a further important result is that the transformed variables are orthogonal and uncorrelated and can thus be used in regression equations independently. The compositional variables Yk are regressed against the PCs and calibration equations obtained. Yk ˆ b0 ‡ b1 P1 ‡ b2 P2 . . . ‡ bi Pi . . . ‡ bn Pn

‰13:21Š

where the bs are the regression coefficients. In fact only those PCs that contribute the majority of the variance need be used and ideally a step-wise regression should be applied so that variance due to noise does not degrade the result. In this kind of work most of the variance is usually associated with the first few PCs. In general, although increasing the number of PCs in the regression appears to improve the performance there is a danger of over-fitting. It is normal that the number used should be no more than one third of the number of observations.29 The scores for some unknown spectrum can be obtained by multiplying the new data (standardised with the calibration data means and standard deviations) by the PC loadings of the calibration set. These new scores are then used in with the calibration equation to generate predicted values of the appropriate variables. Using this method one can measure certain composition variables with accuracy comparable to that of the proximate analysis used to calibrate the system.30 The variables for which this is true are added water, fat, protein, water and NaCl. As can now be appreciated the emphasis is changed in this approach from trying to identify the relationship between the spectra and each polar constituent

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Fig. 13.11 Three-dimensional plot of principal components of prawn spectra, demonstrating the separation into groups on the basis of just three principal components. The open spheres are untreated samples while the full spheres are those samples with added water.

to that of using the overall change in shape of the spectrum. At this level the PC scores become shape descriptors. Where for any particular object the shape descriptors might be length, breadth, height, here the PCs act as independent variables describing the shape of the spectra. The subtle changes in shape of the spectra as samples are treated with various solutions, are quantified by these PCs and they may even be used to discriminate different treatments, e.g. fresh or frozen, water added or not.31 An example of the discrimination of prawns with added water using just three PCs is shown in Fig. 13.11. PCA can also be used to advantage with the GMS method described above because that is a broadband approach. The spectrum with this method also contains information about the dielectric properties of the material under investigation and their dependence on composition. This information is transformed in a particular way by the transmission properties of the waveguide at and around the cut-off frequency. PCA enables the original sources of variation, i.e. the compositional variables, to be extracted from this convoluted function without rigorous mathematical deconvolution. GMS is probably the only waveguide technique that could be used successfully in these problems.

13.5.3 Partial least squares As an alternative to PCA for spectral decomposition partial least squares (PLS)32 has been applied to dielectric spectroscopic data. Although PLS is similar to principal component regression (PCR) there are major differences, one of which lies in the number of steps involved in producing a calibration equation. PCA uses only the measured variables and their covariance, only the cross-product matrix being considered X 0 X , but the procedure for generating a calibration equation takes two steps. The first step transforms the spectra into a set of loadings and PCs, and the second step uses them in regression equations for

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composition. In contrast PLS uses the fact that there is a correlation between the measured spectral variables and the composition. The decomposition of the spectra combines both the constituent and spectral data using the cross-product matrix Y 0 XX 0 Y . The resulting vectors are thus more directly related to the constituents of interest than in PCA, where they relate only to the dominant spectral variations and require the second step of regression analysis to make some sense in compositional terms. The eigenvectors and scores calculated using PLS are quite different from those of PCR and yield calibration equations directly. Nothing in life is free however, and the trade-off here is that the PLS calculations are much more complicated and computer intensive.33

13.5.4 Artificial neural networks The final form of data analysis that should be mentioned here has proved to be extremely successful in generating calibration functions for compositional analysis,34,35 especially for added water. This is an entirely different approach using artificial neural networks (ANN). The purpose of the ANN is to approximate the unknown (often non-linear) function that describes the relationship between the input and the output variables. In the literature the use of multilayer feed forward (MLFF) networks is suggested for such purposes.36 A primitive linear network is shown in Fig. 13.12.34 Such a network by analogy with the brain is called a neuron. For each composition variable a network of this kind is needed, either in hardware or software. As with the other multivariate processes the data must first be standardised. This standardisation can be done by subtracting the means and dividing either by the standard deviation as before or by the range of the spectral values of each frequency.

Fig. 13.12

Structure of a primitive linear neural network.

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Fig. 13.13 Feed forward network with one hidden layer, eight neurons in the hidden layer with logsig activation functions and linear output neurons.

Every data point is then multiplied by a weight wi and the sum of these products is used as an input to the so-called activation function of the neuron. When the activation function is linear then such a primitive network can emulate PCA and PCR. In this case the product of the matrix which contains the loadings for the calculation of the PCs from the standardised spectra, aij in eq. 13.20, and the matrix of the coefficients of the linear regression equation, bij in eq. 13.21, is identical to the matrix w. With ANNs, however, the weights wi of the connections between the inputs and neurons are determined with an iterative training algorithm. This process minimises the error of the function approximation using the sum of the squared residuals as output. In general greater complexity is required, however, as is shown in Fig. 13.13.34 With this architecture one can choose: • • • •

Number N of input variables Number of hidden layers Number of neurons in the hidden layer The kind of activation function of the neurons of each layer.

The configuration of this network has proved to be very well suited for the application discussed here.34 It has one hidden layer which contains 5–10 neurons the activation functions of which are non-linear (logsig-function) while those of the output layer are linear. The more complex the function to be approximated the greater number of neurons, hidden layers and training data sets required. Unfortunately a great deal of intuition is also required and such networks are designed by trial and error. There are other problems too. Say, for example, that there are 11 input variables and the ANN consists of one hidden layer with eight neurons and one output variable. In that case the performance function is a surface in a multidimensional space with 88+1 dimensions. The complexity of this surface generally increases with the size of the network. Convergence of the iterative

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process at best may be only to a local minimum while in the worst case it might not converge at all. Also because random numbers are used as starting values of the weights the success of a training exercise may depend on those values. A further disadvantage is that ANNs have the tendency to overfit. That means the network imprints only the training data and loses the required general properties. The more complex the network the higher this risk. To avoid this the network output may be observed while a validation set is used as input and stopped if its performance decreases while that of the calibration data output still increases. Generally, as many as possible representative training data are recommended and this usually means many more than are required for PCR or PLSR (partial least squares regression) although there is a distinct improvement in performance. This improvement of course only arises from the use of the non-linear function since, as has been said above, the linear case is equivalent to PCA. The extra effort and complexity required may be justified by this.

13.6

Future trends

13.6.1 State of the art Before we consider what the future trends might be let us examine the state of the art and define its usefulness for the problems discussed in this chapter. Table 13.3 shows that there are very few microwave methods that are suitable for the broadband spectroscopic approach discussed. This is not a serious problem given that there are methods that will work. The problem is that they are not generally commercially available. Problem-orientated prototypes exist as we have seen, but in general if dielectric spectroscopy is to be implemented the would-be user must buy expensive commercially-available instruments such as automatic network analysers (ANA). Such instruments are vastly overdesigned for the job, being sold largely for a wide range of measurements on parameters of microwave components and circuits. They are an essential tool for the microwave engineer so are not food engineer friendly! Calibration of the system itself needs to be carried out with care and precision using known impedances at the sensor interface and must be repeated regularly. The most difficult of these impedances to achieve is undoubtedly the short circuit, which theoretically has a reflection coefficient of ÿ1 but in reality deviates from this at high frequencies. The problem is entirely one of good electrical contact over the whole surface of the probe. However using this sensor and commercially-available laboratory instruments some important results have been obtained in the measurement of added water.26,27,30,31 As an example of the robustness of this methodology consider the following. During 1994–96 a large amount of dielectric spectral data was collected on chicken samples to which water and polyphosphates had been added in varying amounts.26 Using the PCR a calibration equation was devised to predict the amount of added water in chicken breast. Measurements made some four years later27 in different laboratories on different samples

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Table 13.3 Microwave methods and their suitability for broadband multivariate measurements Method

Sensor

Frequency

Suitability

Transmission

Closed waveguide

Narrow-band, fixed frequency; limited by waveguide dimensions e.g. S-band, X-band Broadband Narrow-band limited by antennae dimensions Broadband but much of the information contained within a narrow variable band Narrow-band limited by antennae characteristics Very wide band. High frequency limit due to co-ax diameter Narrow band

Generally not suitable

Stripline Antennae Cut-off waveguide

Reflection

Antennae Coaxial sensor

Resonance

Open: coaxial or waveguide Open: stripline Closed: waveguide

Narrow band Narrow band

Suitable Not suitable Possibly suitable

Not suitable Suitable Not suitable unless combined with resonators at other frequencies Not suitable Not suitable

(obviously) can be used as validation data and the results are shown in Figs 13.14(a) and 13.14(b).

13.6.2 The way ahead Firstly, if the techniques studied are to become commercially available a system designed for use as a tool for the laboratory analyst or for some on-line application in industry must have a more robust calibration system: calibration that is in terms of standard impedances and samples. Apart from the need for a commercially-available dedicated dielectric spectrometer the sample probes themselves need further development to reduce the chances of error caused by entrapment of air between the sensor and the sample, for example. At present there is work being carried out to use other forms of sensor that might alleviate this problem and which will be more suitable for on-line application. Such sensors include open-ended coaxial sensors with modified shape and stripline sensors employing coupling to the dielectric via fringing electromagnetic fields. None of this has been published yet so their application is uncertain. The analysis of the results in terms of the dielectric properties must also be questioned. The only reason for this arises from the need to be able to transfer

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Fig. 13.14 (a) Measurements carried out in 1996 were used to produce a calibration with PCR for the water content of raw chicken. Using this calibration measurements made in later trials are used as validation data; (b) The results of work carried out in 1996 to produce a calibration for the added water content of raw chicken.24 Using this calibration measurements made in later trials are used as validation data.27

calibrations from one instrument to another. If the instrument and sensor are well enough designed this requirement is uneccesary and the data analysis can be carried out on the raw S11 measurement data or any other variables accessible for measurement. It is clear from the nature of the work so far that any device will depend heavily on computing power. Self and continuous calibration are concepts well enough known already and doubtlessly will be incorporated into such systems.

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13.6.3 Recapitulation Finally let us return to the problems outlined in the introduction to this chapter. What exactly is added water? We think we know but the analyst’s choice of parameters such as nitrogen factor can influence the result of any analysis enormously. To reach a consensus very much more examination of this problem needs to be done by industry, regulatory bodies, and of course those whom it affects the most, the consumers.

13.7

Sources of further information and advice

13.7.1

Bibliography of significant publications

The Code of Federal Regulations, Title 21 10113 European Community directives in the field of consumer protection and public health3 Nitrogen factors and calculation of added water17, 30 Multivariate analysis and chemometrics24, 25, 28, 29, 33, 38 Artificial Neural Networks36 Dielectric theory and measurements20, 37

13.7.2

Major trade/professional bodies; research and interest groups, etc.

Leatherhead Food RA, Randalls Road, Leatherhead, Surrey KT22 7RY, UK SIK, The Swedish Institute for Food and Biotechnology, Box 5401, SE-402 Go¨teborg, Sweden Food Standards Agency, Aviation House, 125 Kingsway, London, WC2B 6NH, UK Electrical Engineering Microwave Group, Faculty of Engineering, Christian Albrechts University of Kiel, Kaiserstrasse 2, 24143 Kiel, Germany Meat and Livestock Commission Microwave Consultants Ltd, 17B Woodford Road, London E18 2EL, UK USDA ARS, Russell Research Centre, PO Box 5677, Athens, GA 30604–5677, USA National Physical Laboratory, Teddington, Middx TW11 0LW, UK

13.8 1

2

3

4

References Council Directive 21.12.88: ‘On the approximation of the laws of the Member States concerning food additives authorised for use in foodstuffs intended for human consumption’. Official Journal of the European Communities, 1988 No L 40 27–33. ANON, Commission Regulation 1538/91 ‘Introducing detailed rules for implementing Council Regulation (EEC) 1906/90 on Certain Marketing Standards for Poultry’. Official Journal of the European Communities, 1991 L143. ANON, European Community directives in the field of consumer protection and public health. Volume 1: Foodstuffs and related legislation, Environment, Public Health and Consumer Protection, Special Edition, Office for Official Publications of the EC, Luxembourg, 1992. ANON, ‘Proposal for a Council Directive on food additives other than colours and ANON,

268

5 6 7 8

9 10 11 12 13 14 15 16 17 18 19

20

21

22

23 24 25 26

Rapid and on-line instrumentation for food quality assurance sweeteners’, Official Journal of the European Communities, 1992 No C 206 12– 40. ANON, ‘Commission Regulation 2891/93 amendment to Commission Regulation 1538/91’. Official Journal of the European Communities, 1993 L263. ANON, Council Directive 95/2/EC, ‘Food additives other than colours and sweeteners’, Official Journal of the European Communities, 1995 No L61 1. ANON, ‘Food labelling regulations’, Statutory Instrument 1499 (England, Scotland and Wales), London, HMSO 1996. ANON, Council Directive 97/4/EC of the European Parliament and of the Council of 27/01/97 amending Directive 79/112/EEC, Official Journal of the European Communities, 1997 L.43 21–23. ANON, Commission Regulation (EC) No. 1072/2000 amending Regulation (EEC) No. 1538/91, Official Journal of the European Communities, 2000 L119 21. ANON, ‘Meat Products and Spreadable Fish Products Regulations 1984, as amended’. Statutory Instrument No. 1566, London, HMSO, 1984. ANON, The Food Labelling Regulations 1996, as amended. Statutory Instrument, , No. 1499. London, HMSO, 1996. ANON, The Food Labelling (Amendment) Regulations 1998. Statutory Instrument No. 1398. London, HMSO, 1998. ANON, The Code of Federal Regulations, Title 21 101.4 Revised as of 1 April, 2001, US Government Printing Office, USA 2001. AITKEN A, ‘Changes in water content of fish during processing’ Chemistry and Industry, 1976 18 December: 1048–1051. PETERS R, Diffusion of Water in Frozen Cod, Ph.D. Thesis, University of Aberdeen 1970. KENT M and STROUD G D, ‘Microwave attenuation of frozen Nephrops norvegicus’, J Fd Technol, 1981 16 647–654. ANALYTICAL METHODS COMMITTEE, ‘Nitrogen factors for pork: a reassessment.’ Analyst, 1991 116 761–766. BEER A. Einleitung in die ho ¨ here Optik, 1st edn. Brunswick, 1853. BURDETTE E C, CAIN F and SEALS J, ‘In-vivo probe measurement technique for determining dielectric properties at VHF through microwave frequencies’, IEEE Transactions, MTT, 1980 28 414–427. GRANT J P, CLARKE R N, SYMMS G T and SPYROU N M ‘A critical study of the openended coaxial line sensor technique for RF and microwave complex permittivity measurements’, Journal of Physics E: Scientific Instruments, 1989 22 757–770. CHOUIKHI S M and WILDE P J, ‘Reflection of an open-ended coaxial line and application to moisture content measurement’, Proceedings of the International Measurement Conference on Tests and Transducers, 1986 2 251–264. ¨ CHEL R and MEYER W, ‘Continuous moisture determination in fluids and KNO slurries’, Proceedings of the 1981 IMPI Symposium on Microwave Power, Toronto, 1981 193–195. JEAN B R, WARREN G L and WHITEHEAD F L 1995 US Patent 5455516, 3. PAPOULIS A, Probability, Random Variables, and Stochastic Processes, 3rd edn. McGraw-Hill, New York, 1991. HOTELLING H, ‘Analysis of a complex of statistical variables into principal components’, J. Educ. Psychology, 1933 24 417–441, 498–520. KENT M and ANDERSON D, ‘Dielectric studies of added water in poultry meat and scallops’, Journal of Food Engineering, 1996 28 239–259.

Measurement of added water in foodstuffs 27 28 29

30

31

32

33 34

35

36 37 38

¨ CHEL R, DASCHNER F KENT M, KNO

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and BERGER U-K, ‘Composition of foods using microwave dielectric spectra’, Eur Food Res Technol., 2000 210(5) 359–366. CHATFIELD C and COLLINS A J, Introduction to Multivariate analysis, pp. 57–81 London, Chapman and Hall, 1980. MAFF, ‘Establishment of guidelines for the application of chemometric methods to food authenticity’, Project No. AN0663, Ministry of Agriculture Fisheries and Food, London, UK, 1998. KENT M, PEYMAN A, GABRIEL C and KNIGHT A, ‘Determination of added water in pork products using microwave dielectric spectroscopy’, Food Control 2002 13 143– 149. ¨ CHEL R and DASCHNER F, ‘Determination KENT M, MACKENZIE K M, BERGER U-K, KNO of prior treatment of fish and fish products using microwave dielectric spectra’, Eur Food Res Technol., 2000 210, 427–433. ARCHIBALD D D, TRABELSI S, KRASZEWSKI A W and NELSON S O, ‘Regression analysis of microwave spectra for temperature-compensated and density-independent determination of wheat moisture content’, Applied Spectroscopy, 1998 15(11) 1435–1436. MARTENS H and NÆS T, Multivariate Calibration, pp. 254–258, Chichester, John Wiley & Sons, 1989. ¨ CHEL R and KENT M, ‘Determination of the composition of DASCHNER F, KNO foodstuffs using microwave dielectric spectra and artificial neural networks’. Proceedings of 4th International Conference on ‘ Electromagnetic Wave Interaction with Moist Substances’, (pp. 217–223). Weimar, Germany, 2001. BARTLEY P G, NELSON S O, MCCLENDON R W and TRABELSI S, ‘Determination of moisture content in wheat using an artificial neural network’, 3rd Workshop on Electromagnetic Interaction with Water and Moist Substances, Athens, 1999. D W PATTERSON, Artificial Neural Networks, Theory and Applications, New Jersey, Prentice Hall, 1996. GRANT E H, SHEPPARD R J and SOUTH G P, Dielectric Behaviour of Biological Molecules in Solution, Oxford, Clarendon Press, 1978. WOLD H, ‘Estimation of principal components and related models by iterative least squares’ in Multivariate Analysis, P R Krishnaiah (ed.), New York, Academic Press, 1968.

14 Spectroscopic techniques for analysing raw material quality R. Cubeddu, A. Pifferi, P. Taroni and A. Torricelli, INFMDipartimento di Fisica and Politecnico di Milano, Italy

14.1

Introduction

Time-resolved reflectance spectroscopy (TRS) has been investigated as a novel non-destructive technique for quality evaluation of fruits. In contrast to conventional optical methods, and widely used for non-destructive tests of fruits and agricultural products, TRS yields a complete optical characterisation of the investigated sample through simultaneous estimation of the absorption coefficient and the transport scattering coefficient. This is accomplished by interpreting the attenuation and broadening experienced by a short laser pulse with a proper theoretical model while travelling in a diffusive medium, such as most fruits. Optical properties of fruits constitute a complex system, therefore light (electromagnetic radiation) is affected by many factors in its interaction with fruit tissues. Absorption and scattering are therefore complex effects. However, to a first approximation, the absorption coefficient is primarily dependent on tissue components (water, chlorophyll, sugars), while the transport scattering coefficient is dependent on tissue microscopic structure (cells, fibres). Moreover, key advantages of TRS applied to fruits and vegetables include insensitivity to skin colour and properties and penetration into the pulp of fruits to a depth of more than 2 cm. Sections 14.2–14.6 introduces light propagation in diffusive media and the principles of time-resolved reflectance spectroscopy. A description of instrumentation and data analysis for time-resolved reflectance spectroscopy is useful to understand the novel technique completely. Section 14.7 presents the non-destructive optical characterisation of fruits. Absorption and scattering spectra of different fruits are reported. Tissue components and tissue structure are investigated by interpreting absorption and scattering spectra by Lambert-

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271

Beer and Mie theory, respectively. Section 14.8 is a gallery of preliminary applications of the novel technique. Monitoring of ripening and identification of defects on intact fruits show the potential of time-resolved reflectance. Section 14.9 discusses the relationship between time-resolved reflectance and standard mechanical-chemical tests for fruit quality assessment and the possibility of setting an optical quality index. Section 14.10 gives a survey of research papers, conference proceedings and web sites of interest.

14.2

Advantages of time-resolved optical methods

The internal quality of fruits and vegetables is ordinarily assessed using destructive techniques, based on the evaluation of chemical, physical and mechanical properties, such as acidity or soluble solids (sugars) and firmness, respectively. This necessarily implies that a few samples can be tested and the derived information can then be extended to the whole batch of fruits. Noninvasive methods for the quality assessment could be applied to each single item, even repeatedly if necessary, with evident commercial advantages. Consequently, interest in the development and application of non-destructive techniques for the evaluation of internal quality is growing more and more, not only at a basic research level, but also among people involved in the distribution in the market. Different non-destructive techniques have been proposed to probe a variety of quality-related factors in fruits.1 For example, anthocyanins in strawberries have been detected by photoacoustic techniques.2 The artificial nose, with its potential to detect small quantities of released chemicals, may prove useful for those aspects of quality related to aroma production3 even though few data on such applications are currently available. Ultrasounds cannot penetrate deeply into the pulp of most fruits owing to the porous nature of the tissue, yet some promising results have been obtained using low frequency ultrasounds.4 Nuclear magnetic resonance appears promising in terms of specificity and spatial resolution,5 but is not suitable for in-the-field or mass applications. Other techniques using ultraviolet (UV, 4–400 nm), visible (VIS, 400– 700nm) or near-infrared (NIR, 700–2500 nm) radiation have been devised based on the measurement of the total diffusely reflected signal at different wavelengths. For instance, UV/VIS fluorescence of chlorophyll compounds is used for investigations of photosynthetic activity since chlorophyll content and photosynthetic capacity are often related to maturity or defects.1 In the visible region of the spectrum, colorimetry has been used to determine the colour of the skin of peaches6 and, in the near infrared region, the spectrum of re-emitted light has been studied, mainly to estimate the total sugar content.7 Referring to the optical technique, a key limitation is that the intensity of the diffusely remitted light is strongly dependent on the colour of the skin, thus masking information from the pulp. In particular, the total reflected intensity is determined both by the absorption and the scattering properties, in such a way that it is not

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Fig. 14.1

Absorption spectroscopy in (a) clear and (b) turbid media: photon paths, scattering ( ) and absorbing (●) centres.

feasible to separate the effects of these properties. Absorption and scattering contain distinct information on the medium. Absorption is determined by the pigments and constituents of the pulp that produce characteristic spectral features in the visible and near infrared region of the spectrum. Conversely, scattering is due to the local variation of the dielectric constant inside the medium. Microscopic changes in refractive index caused by membranes, air vacuoles or organelles deviate the photon paths and are ultimately responsible for light diffusion. When considering conventional absorption spectroscopy measurements in a collimated geometry, results may be confounded by the fact that it is impossible to discriminate between absorption and scattering events. The transmitted intensity through a clear medium can be related by the Lambert law to the absorption coefficient a since the distance travelled by light in the medium equals the source-detector distance L (see Fig. 14.1). Conversely, in a diffusive medium an intensity measurement yields the attenuation coefficient t ˆ a ‡ s representing the photon loss due to absorption and to photons scattered into directions different from the one of observation. The effect of scattering can be properly taken into account by direct measurements of photon pathlength. Since photon pathlength is directly related to time-of-flight in the medium, the natural choice is to perform time-resolved measurements.

14.3

Principles of time-resolved reflectance

Consider the injection of a short pulse of monochromatic light within a diffusive medium. By using a simplified description the medium can be regarded as consisting of scattering centres and absorbing centres, and the light pulse can be considered to be a stream of particles, called photons, moving ballistically within the medium. Whenever a photon strikes a scattering centre it changes its trajectory and keeps on propagating in the medium, until it is eventually reemitted across the boundary, or it is definitely captured by an absorbing centre (see Fig. 14.2).

Spectroscopic techniques for analysing raw material quality

Fig. 14.2

273

Photon migration in turbid media: photon paths, scattering ( ) and absorbing (●) centres.

The characteristic parameters of light propagation within the diffusive medium are the scattering length ls and the absorption length la (typically expressed in units of mm or cm), representing the photon mean free path between successive scattering events and absorption events, respectively. Equivalently, and more frequently, the scattering coefficient s ˆ 1=ls (i.e. s ˆ (ls)ÿ1) and the absorption coefficient a ˆ 1/la (i.e. a ˆ (la)ÿ1) (typically expressed in units of mmÿ1 or cmÿ1) can be introduced to indicate the scattering probability per unit length and the absorption probability per unit length, respectively. To account for non-isotropic propagation of photons, the effective scattering coefficient 0s ˆ …1 ÿ g†s is commonly used, where g is the anisotropy factor, that is, the mean cosine of the scattering angle. In a diffusive medium light scattering in the visible and near infrared spectral region is naturally stronger than light absorption, even if the latter can be nonnegligible. This implies that light can be scattered many times before being either absorbed or re-emitted from the medium. The phenomenon is therefore called multiple scattering of light. Multiple scattering of light in a diffusive medium introduces an uncertainty in the pathlength travelled by photons in the medium. Light propagation in turbid medium is therefore addressed by the term photon migration.8 Following the injection of the light pulse into a turbid medium, the temporal distribution of the re-emitted photons at a distance  (see Fig. 14.2) from the injection point will be delayed, broadened and attenuated. A typical timeresolved reflectance curve is shown in Fig. 14.3. To a first approximation, the delay is a consequence of the finite time light takes to travel the distance between source and detector. Broadening is mainly due to the many different paths that photons undergo because of multiple scattering. Finally, attenuation because absorption reduces the probability of detecting a photon, and diffusion

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Fig. 14.3

Experimental TRS curve (diamond), IRF (dashed line) and best fit to diffusion theory (solid line).

into other directions within the medium decreases the number of detected photons in the direction under consideration.

14.4

Instrumentation

14.4.1 Photon migration Photon migration measurements in the time domain rely on the ability to extract the information encoded in the temporal distribution of the re-emitted light, following the injection of a short monochromatic pulse in a diffusive medium. Typical values of the optical parameters in the red and in the near infrared part of the electromagnetic spectrum set the timescale of photon migration events in the range 1–10 ns and fix the ratio of detected to injected power at about ÿ80 dB. The two key points in designing a system for time-resolved measurements are thus temporal resolution and high sensitivity. Temporal resolution is mainly affected by the width of the light pulse and by the response of the detection apparatus. Pulsed lasers, which produce short (10–100 ps) and ultra-short (10– 100 fs) light pulses with a repetition frequency up to 100 MHz, and photon

Spectroscopic techniques for analysing raw material quality

Fig. 14.4

275

Diagrams of the laboratory system (a) and of the compact prototype (b) for TRS measurements.

detection systems with temporal resolution in the range 100–300 ps, are nowadays available. When concerned with sensitivity, the power of the injected light pulse should obviously be fixed at appropriate values, so as to avoid possible damage or injury to the sample. In the case of biological tissues the safety regulations9 set the maximum permissible value to 2m Wmmÿ2 for laser pulses in the wavelength range 600–1000 nm. In the following, two different systems for time-resolved reflectance measurements based on the timecorrelated single-photon counting (TCSPC) technique10 are described (see Fig. 14.4). The first system is a laboratory set-up for broad band absorption and scattering spectroscopy by time-resolved reflectance, whose primary use is for basic studies of tissue components and structures. The second is a compact device working at selected wavelengths, which can be easily moved and therefore used in the field. Results from the two instruments will be presented below.

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14.4.2 Time-resolved spectrometer for absorption and scattering spectroscopy in diffusive media The optimal trade-off between sensitivity and temporal resolution in a TRS system can be achieved using mode-locked lasers as light sources and timecorrelated single-photon counting for detection. The sources available are a dye laser (Mod. CR-599, Coherent, Ca) and a titanium:sapphire laser (Mod. 3900, Spectra-Physics, Ca). Both sources are optically pumped by an argon laser (Mod. Innova, Coherent, Ca) running in mode-locking or continuous wave (CW) regimes, respectively. The dye laser is operated with a DCM (4-(dicyanomethylene)-2-methyl-6-(4-dimethylaminostyryl)-4H-pyran) dye that permits tunability between 610 and 700 nm. Synchronous pumping mode-locking together with a cavity dumper yield pulses shorter then 20 ps (full width at half maximum, FWHM) at a repetition rate of about 8 MHz with an average power of 10mW. The titanium:sapphire laser is tunable between 700 and 1010 nm using two different mirror sets. The laser structure is properly modified to produce a mode-locking regime by means of an acousto-optic modulator, with pulses of about 100 ps (FWHM), a repetition rate of 100 MHz, and an average power of 100–1000 mW over the entire spectral range. The laser light is injected to, and collected from, the sample by means of 1 mm core 1 m long plastic-glass fibres set on the fruit surface at a relative distance of 1.5 cm. An appropriate fibre holder keeps the fibres in contact with the sample, one parallel to the other, which avoids collection of directly reflected light. The distal end of the collecting fibre is placed at the entrance slit of a scanning monochromator (Mod. HR-250, Jobin Yvon, France), coupled to a double micro-channel plate photomultiplier (Mod. R1564U, Hamamatsu, Japan). A small fraction of the main laser beam is split off by means of a glass plate, and detected by a fast PIN (P-type doped, intrinsic, N-type doped silicon) photodiode, which provides a triggering (reference) signal. Also, some laser light is coupled to another optical fibre and fed directly to the photomultiplier to provide an on-line monitoring of the system behaviour. An electronic chain for time-correlated single-photon counting then processes both the photomultiplier signal and the triggering signal. The signals are first delayed by stages, and then preformed by constant fraction discriminators (Mod. 2126, Canberra, Co). The relative delay between the signals is then converted into a voltage signal by a time to amplitude converter (Mod. TC862, Oxford, TN), which is processed by a multichannel analyser (Mod. Varro, Silena, Italy). The temporal width of the instrumental transfer function is <120ps (FWHM) as measured by connecting the injection and collection fibres. The whole system of measurements is driven by a personal computer that automatically controls laser tuning, light attenuation, scanning of the monochromator, data transfer from the multichannel analyser, data visualisation and eventually data storage for further processing.

Spectroscopic techniques for analysing raw material quality

277

14.4.3 Compact prototype for time-resolved reflectance measurements The system employs two pulsed diode lasers (Mod. PDL 800, PicoQuant GmbH, Germany) at 672 nm and 800 nm with a pulse duration of about 100 ps, a repetition rate up to 80 MHz and an average power of 1 mW. The pulsed diode laser is coupled into a multimode graded-index fibre (Mod. MMF-IRVIS-50/ 125, OZ Optics, Canada). The signal is then split into two fibres by a fibre optic splitter (Mod. FUSEDIRVIS 5/95, OZ Optics, Canada). The first fibre receives a small fraction (5%) of the power and is fed directly into the photomultiplier to account for eventual time drifts of the instrumentation and to provide a time reference. The other fibre receives most of the power and delivers light to the sample. The reemitted light is collected from the sample by 1 mm plastic fibres (Mod. EH4001, ESKA) in reflectance geometry. The TRS curves are detected by a metal-channel dynode photomultiplier tube (Mod. RS5600U-L16, Hamamatsu, Japan) and are measured by a timecorrelated single-photon counting PC board (Mod. SPC300, Becker&Hickl GmbH, Germany) with 1 MHz acquisition frequency and 25 ps temporal resolution. Custom made software, written in LabWindows and ANSI C languages, control data acquisition and analysis. The typical instrument response function, obtained facing the injection fibre and the collection fibre, has a FWHM of about 200 ps for both wavelengths.

14.5

Data analysis

The temporal profile of the time-resolved reflectance curve is analysed using a solution of the radiative transport equation under the diffusion approximation for a semi-infinite homogeneous medium11,12   2 1 2 2 R…; t ˆ …4v†ÿ3=2 tÿ5=2 eÿa t eÿ =4Dt z0 eÿz0 =4Dt ÿ …z0 ‡ 2ze †eÿ…z0 ‡2ze † =4Dt 2 [14.1] where R(,t) is the number of photons per unit time (t) and area re-emitted from the tissue at a distance  from the injection point.  is the source-detector distance (or interfibre distance), v ˆ c/n is the speed of light in the medium, n is the refraction index, D ˆ …30s †ÿ1 is the diffusion coefficient, Z0 ˆ …0a †ÿ1 is the isotropisation length, ze is the extrapolated distance which takes into account the refraction index mismatch at the surface. The experimental curve is fitted with a convolution of the theoretical function with the instrumental response function (IRF). The best fit is reached minimising the 2 varying both a , and 0s using a Levenberg-Marquardt iterative procedure. Owing to the lower accuracy of the models in earlier times, the range of the fit includes all the points on the experimental curve with a number of counts higher than 80% of the peak value on the rising edge of the curve and 1% of the peak value on the falling edge. Figure 14.3 shows the best

278

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fit of a typical experimental curve. The instrumental transfer function is also shown for comparison (dashed line). The fitting procedure can automatically analyse a full batch of experimental curves on a standard PC (Athlon AMD, 1GHz) at a speed of 10 curves per second. Synchronisation of the analysis and measurement PCs over the network permits on-line processing of the experimental data, so that the absorption and scattering coefficients are shown on the screen in real time while the measurement is in progress.

14.6

Effect of skin and penetration depth

14.6.1 Skin Measurements were performed on apples (Golden Delicious, Granny Smith and Starking Delicious), peaches, nectarines, kiwifruit and melons. The tests proved that TRS allows the assessment of the internal optical properties and that the optical properties of the skin do not prevent the assessment of information on the bulk, at least for fruits with thin skins. For apples, no significant change in the measured optical properties (both absorption and scattering) is caused by skin removal. This is proved by the experimental finding that in none of the cases considered did skin removal alter the results significantly, despite the different optical properties of the skin in each distinct situation, for example a yellow-skinned apple (Golden Delicious) compared with a red-skinned one (Starking Delicious), as shown in Fig. 14.5. Similar outcomes were obtained for peaches and nectarines (data not shown). The peeling of the skin did not alter markedly the results, confirming that TRS is most sensitive to the internal features. The situation is different for thick-skinned fruits. In particular, for kiwifruit where peeling led to a 20–25% increase in the absorption coefficient over the entire NIR range examined (720–840 nm). However, this effect concerns only the absolute estimate of the optical properties. The spectral line shape is not significantly altered. Consequently, even though the skin influences the results, it does not necessarily make TRS measurements inappropriate for the assessment of internal quality of thick-skinned fruits. For melons (Cantaloupe) measured in the bed region, the skin removal significantly reduces the chlorophyll absorption, while it has no significant effect on the NIR absorption. In both wavelength ranges, a 15–25% decrease is observed in the measured values of the scattering.

14.6.2 Penetration depth In a further experiment, the penetration depth of a TRS measurement was determined. It is well known that the volume probed by a TRS measurement is a ‘banana shaped’ region connecting the injection and collection points.13 It is not easy to define the measurement volume, since the photon paths are more densely

Spectroscopic techniques for analysing raw material quality

279

Fig. 14.5 Influence of skin on TRS measurements: absorption (a) and transport scattering (b) spectra of a Starking Delicious apple before (closed symbols) and after (open symbols) peeling.

280

Rapid and on-line instrumentation for food quality assurance

packed in the banana region but can be distributed in the whole medium. Attempts were made to determine the maximum depth in the pulp that can give some detectable contribution to the TRS curve. A series of measurements were performed on a Starking Delicious apple where slices of pulp were cut from opposite sides of the measurement site. Spectra were taken of the whole apple, and then slices were removed to yield a total thickness of 4.1, 2.7, 2.1 and 1.5 cm. The fitted absorption and scattering spectra are shown in Fig. 14.6. For the absorption measurement, a is unchanged down to a thickness of 2.7 cm. For the 2.1cm thick slice, a starts deviating from the measurement of the whole apple with a discrepancy of 25% at 680 nm, while for a thickness of 1.5 cm the discrepancy increases up to 50%. The highest variations are observed on the tails of the spectrum, where the absorption is lower. The results for the scattering coefficient show a similar behaviour, with almost no changes down to a thickness of 2.7 cm, and discrepancies of 15% and 25% for a 2.1 and 1.5 cm thickness, respectively. Overall, these data show that the TRS measurement is probing a depth of at least 2 cm in the pulp. Of course this is a rough estimate, yet it confirms that the TRS measurement is not confined to the surface of the fruit. Moreover, the penetration depth can be somehow dependent on the optical properties, and deeper penetration is expected in less absorbing and/or scattering fruit.

14.7

Optical properties of fruits and vegetables

14.7.1 Absorption and tissue components Typical absorption spectra of different fruits (apple Starking Delicious, yellow peach, tomato and kiwifruit) are reported in Fig. 14.7(a). The absorption spectrum of the apple is dominated by the water peak, centred around 970 nm, with an absolute value of about 0.4 cmÿ1. Minor absorption features of water are usually detected around 740 and 835 nm, where the absorption coefficient is low (0.05 cmÿ1). A significant absorption peak (0.12–0.18 cmÿ1) at 675 nm, corresponding to chlorophyll-a, is found. Both the line shape and the absolute value of the absorption spectra of peach and tomato are quite similar to those of apples. However, for kiwifruit, as expected from the visual appearance of its flesh, chlorophyll-a absorption is considerable, with a maximum value up to 2 or 3 times the water maximum in the infrared. Information on the water content can be obtained by considering the absolute values of the absorption at 970 nm. In agreement with the different water/fibres ratio in distinct species, a higher absorption was detected in tomatoes (~0.5 cmÿ1), than in peaches and kiwifruits (~0.45 cmÿ1), and in apples (~0.4 cmÿ1). The absorption at 675 nm provides information on the chlorophylla content and preliminary data obtained from apples suggest that this could be a useful parameter to test the ripening stage. A series of measurements performed on the same fruits showed a progressive decrease in red absorption, in agreement with the gradual reduction in the chlorophyll content with post-harvest ripening.14

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281

Fig. 14.6 Absorption (a) and scattering (b) spectra of a Starking Delicious apple. Different curves correspond to measurements on the whole apple, and on slices of the same apple obtained by cutting the fruit on the opposite side of the measurement site.

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Fig. 14.7 (a) Absorption spectra of apple, peach, tomato and kiwifruit. (b) Best fit of chlorophyll-a and water line shape to the absorption spectrum of a Starking apple.

To quantify the percentage volume of water and the chlorophyll-a content in the bulk of the intact fruits, a best fit of the absorption spectrum with the line shape of water15 and of chlorophyll-a16 was performed. To account for the

Spectroscopic techniques for analysing raw material quality Table 14.1

283

Chlorophyll-a and water content in different fruits

Fruit

Chlorophyll-a (M)

Water (%)

Apple (Starking Delicious) Peach Tomato Kiwifruit

0.96 0.49 0.52 6.91

82.6 93.8 95.0 98.8

presence of other chromophores of fruits, such as carotenoids and anthocyanins, which exhibit characteristic peaks at shorter wavelengths than 650 nm, a flat background spectrum of arbitrary amplitude was used as a free parameter in the fit. Figure 14.7(b) shows a typical example of fit for the absorption spectrum of a Starking Delicious apple to the line shape of water and chlorophyll-a. Table 14.1 reports the chlorophyll-a and water content in different fruits. In all cases a 0.02–0.03 cmÿ1 contribution was added by the flat background spectrum.

14.7.2 Scattering and tissue structure The scattering properties for all the species considered showed no particular spectral features. The value of the transport scattering coefficient decreased progressively with increasing wavelength. Typical examples are shown in Fig. 14.8(a) for a Starking Delicious apple, a peach, a tomato and a kiwifruit. The transport scattering spectrum of the kiwifruit was noisier than the spectrum of other fruits, particularly in the 675 nm region where the high absorption of chlorophyll reduced the accuracy of the evaluation of transport scattering by TRS measurements. Even though marked variations in the absolute values were noticed depending on variety and ripeness, kiwifruits and tomatoes are usually characterised by a lower scattering than other species. Further information could be obtained by interpreting the transport scattering spectra with Mie theory. For a homogeneous sphere of radius r, Mie theory predicts the wavelength dependence of the scattering and the relation between scattering and sphere size. Under the hypothesis that the scattering centres are homogeneous spheres behaving individually, the relationship between 0s and wavelength () can be empirically described as follows:17 0s ˆ axb

‰14:2Š

where the size parameter x is defined as x ˆ 2rnm ÿ1 , with the refraction index of the medium nm chosen to be 1.35, and a and b are free parameters. In particular, a is proportional to the density of the scattering centres and b depends on their size. Moreover, b can be empirically expressed as a third order polynomial function of r, therefore the estimate of b can yield the sphere radius r.18 Figure 14.8(b) shows a typical transport scattering spectrum of a Starking

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Fig. 14.8

(a) Scattering spectra of apple, peach, tomato and kiwifruit. (b) Best fit of Mie theory to the scattering spectrum of a Starking apple.

Delicious apple and the best fit to Mie theory. The estimated average size of scattering centres in different fruits is shown in Table 14.2. It was observed that a and b varied in the range 2.9–17.4 cmÿ1 and 0.12–0.95, respectively. This suggests that different fruits have different density and average dimensions of

Spectroscopic techniques for analysing raw material quality Table 14.2

285

Parameters a and b for different fruits

Fruit

a (cmÿ1)

b (cmÿ1)

r (mm)

Apple (Starking Delicious) Peach Tomato Kiwifruit

17.4 14.4 2.9 4.5

0.12 0.20 0.48 0.95

0.759 0.740 0.591 0.266

scattering centres (the range for r is 0.15–0.78 m). It is worth noting that, as the tissues are a complex distribution of cells and fibres, these parameters do not assess the real size of scattering centres in the tissue, rather they are average equivalent parameters, which could eventually be related to physical or chemical fruit characteristics such as firmness or sugar content.

14.8 Applications: analysing fruit maturity and quality defects 14.8.1 Picking date experiment To prove the applicability of the technique in real life applications, the compact prototype for TRS measurements was sent to Horticulture Research International and there tested on a picking date experiment to check the tracking of maturity stages in apples.14 Fruits of the Gala variety were harvested from the same orchard at three different picking dates (pick 1 = 15 September, pick 2 = 25 September and pick 3 = 9 October), stored under controlled atmosphere at 1.5ºC for 7 months, and then measured all together using the prototype. For each fruit, four equally spaced positions on the equatorial plane were measured and averaged. Results are presented in Fig. 14.9, where every fruit is coded by its a and 0s at 672 nm. The measured a decreases passing from pick 1 (black triangle) to pick 2 (grey triangle) and to pick 3 (white triangle), indicating a decrease in chlorophyll (CHL) content. Also the scattering coefficient is somehow related to the picking date with a general decrease for latest harvest. Similar results were found for peaches. The technique is not only able to distinguish between different batches of fruits but can also monitor small variations due to shelf-life storage.

14.8.2 Detection of defects Encouraging results have been obtained by applying TRS to non-invasive detection of defects in fruits. Preliminary measurements show that TRS can discriminate mealiness,19 watercore and bruise in apple, and brown heart in pears.20 Brown heart (BH) is an internal disorder sometimes shown by pears during controlled atmosphere (CA) storage. The symptoms are in no way recognisable from the outside of the fruit and are visible only after cutting

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Fig. 14.9 Plot of the absorption and scattering measurements of 30 apples taken from a Gala cultivar at successive harvest dates: pick 1 (black triangle), pick 2 (grey triangle), pick 3 (white triangle), and measured all together with the prototype after 7 months’ storage under controlled atmosphere.

the fruit. The aim of this work was to test TRS for analysing pears at risk of being affected by BH, in order to check if internal browning can be detected in the intact fruit by non-destructive means. ‘Conference’ pear fruits at low risk (early harvest, low CO2 CA storage) and high risk (late harvest, high CO2 CA storage) for BH were measured with TRS at 690 nm and 720 nm on eight points around the equator. BH was detected in pears by a significant increase of the absorption coefficient a at 720 nm. The absorption coefficient a at 690 nm responded by both increasing in the presence of BH in affected fruits and decreasing with ripening in sound fruits, so it alone cannot have a unique interpretation. The decrease of the absorption coefficient a at 690 nm in sound fruits can be attributed to degradation of chlorophyll, which has an absorption peak at 675 nm. The scattering coefficient 0s at 720 nm was influenced by translucency of soaked looking tissue, as in overripe fruits and in bruised regions. This technique allows a description of the virtual appearance of the internal tissue in the intact fruit to a depth of 2 cm, of the presence of defects and of their position inside the fruit, as it can be visually confirmed only after cutting the fruit. An example is reported in Fig. 14.10, where the plots of the absorption coefficient at 672 nm and of the scattering coefficient at 720 nm are compared with the photograph of a partially BH pear.

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Fig. 14.10 Absorption coefficient at 690 nm (b) and transport scattering coefficient at 720 nm (c) as a function of the position around the equator of a partially BH pear picked at late harvest. Reported measurements were performed at the end of storage (black diamond) and at the end of shelf life (grey diamond). A photograph of the equatorial section of the fruit is shown in (a). Units for absorption and scattering are cmÿ1.

14.9

Future trends

The use of the optical properties of the pulp of fruits and vegetables for the assessment of the internal quality of fruit has still to be investigated. More studies are required to correlate the measured optical properties with other chemical or physical parameters of the fruit such as soluble solids (sugar), acidity or firmness. Since TRS permits the measurement of the absorption spectrum of the pulp independent of the scattering properties, it may be possible to detect absorbing substances such as chlorophylls and anthocyanins in the visible region or sugar and water in the NIR region. This technique might be suitable for following the ripening process pre-harvest, or for monitoring fruit changes during long-term storage. Scattering inside a fruit is mainly due to refractive index mismatches between liquids and membranes. Thus, the mean scattering coefficient could provide information on the internal structure, as suggested by a study on kiwifruits. In our work, changes in the scattering coefficient were related to the stage of maturity and to the ripening process, and could contribute to monitoring them. Clearly, many technical aspects need still to be solved before an industrial application can take place. Most of all, the fruit characterisation in terms of pulp optical properties has to be compared to the presently accepted estimators of fruit quality, that is, sugar content, acidity and firmness. A possible criticism of the usefulness of TRS for applications in agriculture is the cost and complexity of the instrumentation, especially whenever more than one wavelength is needed. However, rapid progress in optoelectronics, particularly in telecommunications, has led to considerable growth in instrumentation for time-resolved measurements, so that the development of a compact and low-cost time-resolved instrument is now feasible. A first

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prototype, working with semiconductor lasers, a compact photomultiplier and all-fibre optics that can be used as a stand alone portable instrument, was built in our laboratory. The compact prototype is characterised by ease of use and portability and a relatively low cost (about 20,000 euro before assembly). Post-harvest selection of fruit at industrial level employs automated machines for grading and sorting of fruits based on external parameters (colour, size) and weight. Typical speed for in-line analysis is 5 fruits per second. The acquisition time of TRS measurements can be as low as 500 ms per point in the wavelength range 700–800 nm on most fruits. In this respect TRS measurements are not far from being applicable in on-line analysis. However, in view of a possible application of the TRS technique at industrial level, it is necessary to address several factors like acquisition time, number of measurement points, use of multichannel acquisition, and contact between fruit and optical probe. Detection of an internal disorder may in fact require mapping of the fruit to localise the defect. Moreover, in performing a non-contact measurement which could speed up the measurement time, care should be taken to reject background light and to enhance the signal. On the other hand, the TRS technique could be useful in the orchards, in the packing house or in the marketing chain as a complementary tool for non-destructive characterisation of fruits.

14.10

Sources of further information and advice

The study of light propagation in diffusive media, or photon migration, is a recent and open field of physics and optics. A limited number of books deal with this issue and most of the support material should be found in the scientific literature, that is in journal and conference proceedings. Most applications fall within the biological, medical and clinical application of lasers and optics, therefore research and interest groups are to be found in these communities.

14.10.1

List of books

(ed) (1989), Photon Migration in Tissues, New York, Plenum Press. van de Hulst H C (1980), Multiple Light Scattering, Volumes 1 & 2, Academic Press, New York. ISHIMARU A (1978), Wave Propagation and Scattering in Random Media, Vol. 1 Single Scattering and Transport Theory, New York, Academic Press. WELCH A J, MATIN J C and VAN GEMERT M J C (eds) (1995), Optical-thermal Response of Laser-irradiated Tissue (Lasers, Photonics and Electro-Optics), New York, Plenum Press. CHANCE B

14.10.2

List of journals

Optical Society of America (OSA): Applied Optics OT & BO division, Optics Letters, Optics Express, Journal of the Optical Society of America A (http://www. opticsinfobase.org/)

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The International Society for Optical Engineering (SPIE): Journal of Biomedical Optics (http://ojps.aip.org/journals/doc/JBOPFO-home/) Institute of Physics (IOP): Physics in Medicine and Biology (http://www.iop.org/ Journals/pb).

14.10.3

List of Conference Proceedings

Trend in Optics and Photonics OSA (http://www.osa.org/pubs/tops/) Proceedings of the SPIE (http://bookstore.spie.org/publications).

14.10.4

List of web sites

www.osa.org www.spie.org

14.11 1 2 3 4 5 6 7 8 9 10 11

12

13 14

References

‘Quality measurement of fruits and vegetables’, Postharvest Biol Technol, 1999 15 207–25. BERGEVIN M, N’SOUKPOE´KOSSI C N, LEBLANC R M and WILLEMOT C, ‘Assessment of strawberry maturity by photoacoustic spectroscopy’, Appl Spectrosc, 1995 49 397–9. BENADY M, SIMON J E, CHARLES D J and MILES G E, ‘Fruit ripeness determination by electronic sensing of aromatic volatiles’, Trans ASAE, 1995 38 (1) 251–5. MIZRACH A, ‘Nondestructive ultrasonic technique for fruit quality determination’, Acta Horticulturae, 2001 553 (2) 465–70. CHEN P, MCCARTHY M J, KAUTEN R and SARIG Y, ‘Maturity evaluation of avocados by NMR methods’, J Agric Eng Res, 1993 55 (3) 177–85. DELWICHE M J, TANG S and RUMSEY J W, ‘Color and optical properties of clingstone peaches related to maturity’, Trans ASAE, 1987 30 (6) 1873–9. GUNASEKARAN S and IRUDAYARAJ J, ‘Optical methods: visible, NIR, and FTIR spectroscopy’, Food Sci Technol, 2001 105 1–38. YODH A and CHANCE B, ‘Spectroscopy and imaging with diffusing light’, Phys Today, 1995 48 34–40, and references therein. Compliance Guide for Laser Products, HHS Publication FDA86-8260, US Department of Health and Human Services, FDA, MD, 1995. O’CONNOR D V and PHILIP D, Time-correlated Single Photon Counting, London, Academic Press, 1984. PATTERSON M S, CHANCE B and WILSON B C, ‘Time-resolved reflectance and transmittance for the noninvasive measurement of tissue optical properties’, Appl Optics, 1989 28 2331–6. HASKELL R C, SVAASAND L O, TSAY T T, FENG T C, MCADAMS M S and TROMBERG B J, ‘Boundary conditions for the diffusion equation in radiative transfer’, J Optical Soc Am A, 1994 11 2727–41. FENG S, ZENG F A and CHANCE B, ‘Photon migration in the presence of a single defect: a perturbation analysis’, Appl Optics, 1995 34 3826–37. ABBOT J A,

CUBEDDU R, D’ANDREA C, PIFFERI A, TARONI P, TORRICELLI A, VALENTINI G, RUIZALTISENT M, VALERO C, ORTIZ C, DOVER C

and

JOHNSON D,

‘Time-resolved

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17

18

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Rapid and on-line instrumentation for food quality assurance reflectance spectroscopy applied to the non-destructive monitoring of the internal optical properties in apples’, Appl Spectrosc, 2001 55 (10) 1368–74. HALE G M and QUERRY M R, ‘Optical constants of water in the 200 nm to 200mm wavelength region’, Appl Optics, 1973 12 555–63. SHIPMAN L L, COTTON T M, NORRIS J R and KATZ J J, ‘An analysis of the visible absorption spectrum of chlorophyll a monomer, dimer and oligomer in solution’, J Am Chem Soc, 1979 98 (25) 8222–30. MOURANT J R, FUSELIER T, BOYER J, JOHNSON T M and BIGIO I J, ‘Predictions and measurements of scattering and absorption over broad wavelength ranges in tissue phantoms’, Appl Optics, 1997 36 949–57. NILSSON M K, STURESSON C, LIU D L and ANDERSSON-ENGELS S, ‘Changes in spectral shape of tissue optical properties in conjunction with laser-induced thermotherapy’, Appl Optics, 1998 37 1256–67. VALERO C, BARREIRO P, ORTIZ C, RUIZ-ALTISENT M, CUBEDDU R, PIFFERI A, TARONI P,

and JOHNSON D, ‘Optical detection of mealiness in apples by laser TDRS’, Acta Horticulturae, 2001 553 (2) 513–18. ZERBINI P, GRASSI M, CUBEDDU R, PIFFERI A and TORRICELLI A, ‘Nondestructive detection of brown heart in pears by time-resolved reflectance spectroscopy’, Postharvest Biol Technol, 2002 25 87–97. TORRICELLI A, VALENTINI G

20

15 Using spectroscopic techniques to monitor food composition P. Grenier and V. Bellon-Maurel, Cemagref, France, R. Wilson, Institute of Food Research, UK and P. Niemela¨, VTT Biotechnology, Finland

15.1

Introduction

The basis of molecular spectroscopy is the absorption of an electromagnetic radiation by molecules. This absorption is possible when the radiation energy matches the energy gap between two molecular quantum levels. Each frequency corresponds to a specific difference in energy between levels. For each frequency, it is possible to quantify the energy absorption by means of Beer Lambert’s law. In the middle infrared (MIR) the energy absorbed corresponds to changes in vibrational energy levels associated with dipoles of major functional groups. Sample presentation is a crucial point in MIR spectroscopy. Attenuated total reflectance (ATR) is one presentation method that permits liquid samples to be analysed at-line. ATR physics is based on infrared radiation entering a prism made from a high refractive index infrared transmitting material in which the light can be reflected totally internally. This internal reflection creates an effect called the evanescent wave which extends beyond the surface of the crystal into the sample that is in contact with the crystal. In regions of the infrared spectrum where the sample absorbs energy, the evanescent wave will interact with the sample and a spectrum can be obtained. ATR produces a very short pathlength for the infrared light in the sample, from several microns to some tens of microns. This makes this technique ideal for highly absorbing materials such as aqueous solutions. Optothermal (OT) spectroscopy is a potential sample presentation technique for use in the near infrared (NIR). Compared to ATR in the MIR region it offers the same advantages of ease of use. The potential for at-line stopped flow use is

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also demonstrated. OT is a possible means of introducing NIR into the dairy and related industries.1 Raman spectroscopy is related to infrared spectroscopy but it is based on inelastic scattering. Scattering is a photon-electron process where an incident photon excites an electron in the sample which releases shortly thereafter a different photon. When the released photon has a different frequency from the captured photon, this is inelastic scattering also called Raman scattering. This is related to how tightly the electrons are bound to the nuclei. It involves vibrational and polarizability aspects. Thanks to new optoelectronics components available on the market, Raman spectroscopy now has a high potential for on-line process control in cases where there is no sample fluorescence. Fluorescence is the luminescence that occurs when an electromagnetic radiation promotes an electron of a bound from a lower energy state into a higher energy state. The electron releases the excess energy in the form of light when it falls back to a lower energy state. The sum of the energies of Raman photons is usually thousands of times less than the sum of the energies of fluorescing photons, so that Raman spectra can be heavily distorted or swamped by the presence of fluorescence. This chapter presents the design process of an instrumentation, respectively in middle infrared, optothermal and Raman spectroscopy, for process control in typical food industry situations, i.e. wheat flour, malt and fermented milk. The research presented in this chapter results from the STAS project, reference FAIR CT 96–1169, entitled ‘Development of Spectroscopic Techniques as Advanced Sensors for the Optimization and Control of Food Processing’. The objective of the project was to develop new tools for improved quality control within the European food industries, and specifically to produce rugged, low-cost prototype infrared, Raman and optothermal devices for use in process environments, and to develop appropriate probes and experimental protocols for a range of sample types and measurements. Partners in the project were Cemagref (coordinator), Institute of Food Research (IFR), VTT Electronics, RHM Technology, Micro-Module, IFBM and Sitia-YOMO.

15.2

Spectroscopic techniques

15.2.1 MIR There is a diversity of technologies that can be used in MIR spectroscopy. The light modulating device is the key to the spectrometric techniques. It can be either an interferential filter, a grating, an interferometer, or an acousto-optic tunable filter (AOTF). An interferential filter is made of parallel partially reflecting strips. The operating principle of this kind of filter relies on the interference between lightwaves reflected in the air cavity between two strips and transmitted after running different optical paths. The higher the number of the cavities, the better the transmission or the rejection of selected wavelengths. Each filter is

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characterized by its central wavelength, full width at half height, transmission rate, rejection rate and the number of cavities. The temperature and the incidence angle perturbate the filter characteristics: the central wavelength varies with temperature in nm/ºC, and if the incident angle is greater than 10º the transmission rate drops. An interferential filter is the most simple and most economical way to isolate a narrow spectral range centred on a given wavelength. In a grating spectrometer, the light is dispersed by the grating which can be rotating or fixed, respectively associated to a single pixel or an array detector. With this technology it is difficult to have both a good resolution and a good sensitivity. The higher the resolution, the narrower the studied spectral range, the less energy arrives to the detector, and the noisier the signal. Fourier transform infrared (FTIR) spectroscopy generally uses a Michelson interferometer. This interferometer divides a beam of radiation into two paths and then recombines the two beams after a path difference has been created by a moving mirror. A condition is thereby created under which interference between the beams can occur. The energy of light detected as a function of time, that is to say as a function of the mirror displacement, is called interferogram. The interferogram is transformed into a frequency spectrum by Fourier transformation. An important characteristic of the Michelson interferometer is that it takes into account all frequencies simultaneously and not individually. AOTF is based on the modification of the refractive index of a crystal under the effect of the propagation of an acoustic wave. The crystal can be made of tellurium dioxide for wavelengths smaller than 4.5 m, or germanium for wavelengths ranging from 2.5 to 11 or 12 m. The acoustic wave is generated by a piezoelectric transducer excited by a radiofrequency wave. FTIR spectroscopy has great potential for quantitative control of quality in the food industry.2–7 It offers a large spectral domain, a high resolution, and good reproducibility. Methods for FTIR analysis are rapid and simple to use for all products thanks to a range of sample presentation techniques including transmission cells and ATR.8–12 This technique is very competitive compared to the other infrared spectroscopic techniques based on filters or gratings. Dispersive grating systems and filter systems have found success in process industries. Using typically two wavelengths per analyte or class of analytes, an analysis can be achieved with less than 20 wavelengths and sometimes only two. In this case, the filter apparatus, even the grating systems, perfectly match the industrial requirements of robustness, simplicity, precision and cost. AOTF competes with alternative IR spectroscopic techniques in the NIR region. Its speed and its large spectral range are comparable to FTIR. But its resolution in the MIR region is a few nanometers and consequently it is worse than with FTIR. More expensive than filter or grating technologies and of lower performance than FTIR, it is not adapted to MIR spectroscopy. For R&D exploratory activities, or for analysis in a quality control laboratory, FTIR is the right choice to make. In industry applications, research using FTIR can lead to the development of a more robust instrumentation. For an

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implementation of instrumentation in harsh industrial conditions, the choice is more open with grating or filter systems or ruggedized FTIR being preferred.

15.2.2 Optothermal Photoacoustic spectroscopy (PAS) measures the energy absorbed by a sample directly, rather than the attenuation of a light beam due to absorption. The photoacoustic effect results from the absorption of radiant energy and its subsequent conversion to acoustic energy via thermal expansion. In general, absorption causes a rise in temperature which can in principle be measured by thermometry. However, this process is slow and inaccurate. A more efficient method is to modulate the incident light intensity and detect the resulting temperature fluctuations. Two methods of achieving this are the employment of a gas-microphone cell, or a piezoelectric element (or thermistor) in close thermal contact with the sample. The generic term for both these techniques is photoacoustic spectroscopy, although the second technique is commonly referred to as optothermal spectroscopy. Although other variants of PAS have been developed, to study, for example, gaseous infrared absorption and thin films (2–D imaging and ‘mirage effect’ systems), the following account will be restricted to only these two variants as it is these which have been applied primarily to food analysis. In traditional photoacoustic spectroscopy (gas-microphone cell) use is made of the fact that an acoustic wave can equally well be described by a pressure, density or temperature oscillation. Thus the temperature change at the sample surface due to radiation absorption in the sample bulk amounts to an oscillating boundary condition which generates an acoustic wave in the air (or other atmosphere) outside the sample. Normally the sample holder is very small compared to an acoustic wavelength and the result is an oscillating pressure fluctuation if the sample cell is ‘closed’, that is, does not leak. Both the signal amplitude and its phase, or alternatively, both the in-phase and the quadrature components of the signal, can be detected by a microphone built into the sample cell. Spectroscopy is achieved by changing the radiation wavelength used to illuminate the sample. In optothermal spectroscopy the sample is placed on a sapphire disc and the heat generated by radiation absorption in the sample (illuminated via the sapphire) is detected as a temperature change in the sapphire. This detection can be achieved through the expansion of the sapphire on heating through appropriately placed piezoelectric ceramics (effectively a microphone coupled directly to the sample) or through detection by an appropriately placed thermistor. The advantage of this method (built-in detection rather than separate detection by a microphone) is that the sample cell is ‘open’ and sample application is simple and quick. One of the main problems with photoacoustic spectroscopy is its intrinsically low signal-to-noise ratio. This is because the efficiency of energy transfer from the light beam to the microphone or transducer is poor. In MIR gas-microphone

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cells this problem is compounded at high frequencies both by the short thermal escape depth and, probably, source intensity fall off. Signal-to-noise ratios are also adversely affected by the photoacoustic band shape. Lineshapes in the gas-microphone cell will in general be broader than in transmission or ATR presentation methods; thus the relative intensity at the peak maximum will be less than in these purely optical experiments. As the absorption gets stronger, the bands will tend to get broader. This is offset to some extent by the increase in total intensity, but this is at the cost of the resolution of the peak maximum.

15.2.3 Raman Raman spectroscopy has traditionally been limited to the study of pure, nonfluorescent samples. The spectrum could be completely masked by fluorescence arising from sample impurities or from the matrix, when visible excitation sources were used in the measurement. Shifting the excitation wavelength into the near-infrared region has reduced this problem considerably and now most ‘real world’ samples can be analysed with present instruments. When compared to the widely applied NIR and FTIR techniques, Raman spectroscopy combines many of their advantages without the limitations traditionally associated with these techniques. Raman produces well-resolved spectra of vibrational fundamentals just as FTIR does. Peak areas are directly proportional to sample concentrations – unlike NIR, which produces poorlyresolved spectra that require complex chemometric analysis and extensive sample training for empirical fitting. In terms of data acquisition and sample preparation Raman, on the contrary, is much more like NIR. It involves visible and NIR radiation which can be transmitted through low-cost optical fibres and no elaborate sample preparation is required, as samples can be detected remotely by back reflection, even through glass windows.13 This is in marked contrast to FTIR, in which the requirement for very short pathlengths and film buildup on probes can cause serious practical limitations. Three major instrumental advances have promoted the ‘renaissance’ of Raman spectroscopy during the last ten years. The first was the demonstration by Hirschfeld and Chase14 that Raman spectra could be obtained with a Fourier transform spectrometer. By equipping these FT-Raman instruments with a Nd:YAG laser, a Rayleigh rejection filter, and a germanium detector, it became possible to obtain reproducible Raman spectra within minutes. The second advance was the commercial release of compact, reliable diode lasers and the final one has been the commercial development of low-noise, multi-channel CCD detectors.15 By coupling an appropriate laser and a CCD detector to a spectrograph, it is now possible to measure Raman spectra in just a few seconds without exciting fluorescence. FT-Raman instruments have been commercially available since the beginning of the 1990s and manufacturers for CCD-Raman spectrometers have appeared on the market during the last few years. Both of these instruments have proved to be

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powerful tools in analytical laboratories and the recent development of sophisticated fibre-optic probes has expanded their applicability range to on-line process measurements as well. CCD-Raman techniques are more suitable to dedicated industrial applications in terms of size, ruggedness and cost.

15.3

Instrument design for on-line applications

15.3.1 Industrial needs The most critical points of the food manufacturing process have to be monitored in order to obtain an end-product which conforms to quality standards. Sensors that could be implemented on-line or at-line, or used off-line right by the side of the process line, are of particular importance since they provide, in real time, the composition and the quality of the production. The following points are important in specifying industrial needs for process control instrumentation. • The process line should be described: implementation in the plant; photographs or informative schematics; number of lines to consider and number of monitoring points in the line. The product to analyse should be described too: texture, heterogeneity, stability, ability to be handled. Should the sampling be on-line, at-line, or off-line? At which frequency? Is contact with the product allowed? • The objective of the measurement has to be specified: one or several molecules? Qualitative or quantitative measurement? At which concentrations? Are the wavelengths already chosen? • In the industrial set-up of the sensor, what are the safety, confidentiality, hygiene, cleaning requirements? What cleaning agents are used? Maintenance is another key point: what are the tasks to be achieved to maintain the system? What qualifications are required of the labour force in charge of maintainance? • The environment of the sensor deployment is crucial. Will it be installed in plant or laboratory? Which conditions of temperature, dust, hygrometry, gas or magnetic perturbations, and vibration will prevail? • In the additional restrictions for sensor design, the computer integration should be considered from the beginning. The cost too: how much would you be ready to pay for a sensor? What is the cost/benefit ratio? Educational aspects should also be examined: how will the operators be trained for using the sensor? 15.3.2 Typical industrial needs in milling, malting and yoghurt production Milling When a 30-ton load of wheat arrives at a mill, how can we be sure that it is of the required quality in no more than ten minutes? A required quality, from a pragmatic point of view, is obtained when a cohesive dough structure which will hold gas, forms a fine strong crumb after baking, etc. can be developed. A required quality,

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from a scientific point of view, can be defined by limiting factors such as glutenin macropolymers, starch lipids, amylase, etc. The best is to collect information about dough properties on as many of these factors as possible in the ten minutes at disposal. FTIR can probe the wide range of chemical species underlying the factors. But reproducibility, sensitivity and data analysis are demanding problems in harsh industrial conditions with dust, vibrations, temperature and humidity variations and air contamination. There is a need for innovative equipment design, sample presentation and analysis methods to succeed. Malting Although a considerable number of analytical tools are available for assessing malt quality, the maltster still lacks a rapid method for monitoring the state of the modification of barley during the malting process. Recently it was shown that NIR with ATR sampling could be a useful tool for malting process control. For the brewers, the malt must be analysed for extract, moisture,  and amylase activities, protein, -glucan, etc. These analyses are carried out by recommended conventional and time-consuming methods. Besides these recommended methods, rapid methods such as FTIR would be helpful for malt quality assessment before shipment to brewers. Both for malting process monitoring and malt quality assessment, there is an industrial requirement for designing an MIR spectroscopy device that would be as informative as an FTIR instrument and as robust as an NIR instrument. Yoghurt production At an industrial scale, it is important that yoghurt quality can be assessed on-line. There are various strategic points where quality must be estimated. One of them is the fermentation tank. Quality assessment is based on the estimation of yoghurt composition, i.e. proteins, fats, total solids, sucrose and percentage of fruit. Standard error should not exceed 10 per cent. Sensor design should aim at preventing chemical and microbiological contamination. The sensor should be easy to maintain and must withstand cleaning with HCl 1% and NaOH 3 per cent solutions. It must function at temperatures of up to 90ºC and in damp conditions. In the pilot plant, there might also be gas contamination, magnetic disturbance, intense vibrations and dust. The sensor must also be easily and quickly calibrated. Presently assessments are carried out off-line using FTIR instruments and biochemical analysis. The adoption of on-line or at-line sensors may reduce staff costs. Most importantly, quality assessment could be carried out more systematically. The production would be monitored more precisely, thus increasing quality and reducing waste. Consequently, the profitability of the process line would be increased.

15.3.3 Design specification The main constraints to be considered for instrumentation design are: noninvasive measurement, precision, robustness, signal acquisition speed,

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maintenance and cost-to-benefit ratio. The keys to success in the instrumental approach are: choose the right components according to the previous list of constraints, make a correct presentation of the sample, stabilize the signal of the apparatus, and choose adapted data processing techniques. The presentation of the sample has been the focus of much research work recently, and today many cells and accessories are available. They need to be chosen according to the composition of the product. It is recommended to try several solutions. For instance, for non-viscous products with a very high moisture content, it is good to compare transmission and ATR. External parameters can influence the signal. Humidity variations can alter signal stability. The temperature variation must also be taken into account. Not only the temperature of the detector needs to be regulated, but also the temperature of the measurement cell. In milk application, the measurement cell is temperature controlled: water circulates around the sample at 40ºC ± 1ºC. As vibrations of the environment deteriorate the signal-to-noise ratio, it is worth mechanically isolating the analyser. Data processing in instrumentation benefits from the increase of computer power and from the progress in mathematics applied to spectroscopy (chemometrics). The partial least square (PLS) method, for instance, associated with reduced calculation times, makes the rapid analysis of the spectral profile of a complex foodstuff for its classification very realistic.

15.4 Design or adaptation of MIR, optothermal and Raman spectrometers 15.4.1 MIR prototype The MIR prototype developed by STAS project is aimed at off-line quality control in an industrial environment. A sample of liquid, paste or powder foodstuff is placed and eventually pressed on top of an ATR crystal. It is designed for fat, protein and sugar content prediction. The ranges of interest are: 1800–1400 cmÿ1 (5.6 to 7 m) for fat and proteins, 1650 cmÿ1for Amide I (C=O +C–N of amide linkage), 1550 cmÿ1 for Amide II (N-H deformation + amide C– N), and 1200–900 cmÿ1 (8.4–11.1 m) for sugars. Target resolution is 8 cmÿ1. Specific features have been emphasized for the prototype mechanical design: heat source sink, opto-electronic thermostability, sealed and robust assembly, air dessication. These features should allow the instrument to operate in rough industrial conditions. The main components are: • a light source Tomatech CS-IR series 20 • a monochromator Jobin-Yvon reference M101–1 with a holographic grating Jobin-Yvon reference 542 00 160 and with 2 slits 2 mm high and 250 and 500 m wide respectively • a Specac ATR crystal in ZnSe with 45º angle and 6 reflections • a 2 mm diameter pyrometric detector Gec-Marconi DTGS.

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15.4.2 Optothermal The principle of measurement is the acquisition of optothermal absorbance signals at a number of different wavelengths selected by the use of interchangeable optical filters. Centre wavelength and bandwidth for main absorbing food constituents are, respectively: 2100 ± 10 nm and 95 ± 15 nm for carbohydrates, 2180 ± 10 nm and 100 ± 100 nm for carbohydrates A commercial Varilab optothermal analyser has been adapted to enable easier and more repeatable filter changing. A new set of filter holders and a mount have been constructed. In addition, readings can either be taken with open-cell or with flow-cell. The sample needs to be pumped into the flow-cell using inlet and outlet pipes with a suggested flow rate of 0.3 l/min. The flow must then be stopped for measurements to be taken. The quantification of major compounds requires a calibration step allowing the relationship between the optothermal signal and the real concentration, determined by a reference method, to be established using a regression analysis technique. This relationship is then used to determine the concentration of interest in unknown samples. It is good practice to monitor the adequacy of a calibration equation by regularly comparing the outcome of the quantification between the optothermal instrument and the reference method.

15.4.3 Raman A CCD-Raman spectrometer has been developed. Although the common FTRaman technique is less sensitive to fluorescent interference, the CCD-Raman technique offers many advantages: small-sized portable instrument, easy adaptation to the sample with the fibre-optic probe and fast acquisition of the spectrum. The main components of the device are a laser diode, a spectrograph, a multielement CCD detector placed in a protective housing, and a fibre-optic probe serving as an interface between the sample and the device. The excitation source is a self-contained laser diode module with a fibre-optic output of 300 mW at 830 nm. The high-power external-cavity laser diode provides the single longitudinal output mode needed in Raman spectroscopy. The frequencystabilized diode laser is also temperature controlled via a two-stage electrical cooler and thermistor. The laser diode and optics are sealed and desiccated to serve as protection against dirt and dust. The spectrograph of the Raman spectrometer was designed and constructed in co-operation between VTT Electronics and Specim Ltd. High resolution (8 cmÿ1) and high throughput were achieved.15 A new fibre-optic probe has also been developed. The probe can be inserted into a process pipe for process monitoring or it is also possible to use it as a hand-held probe head. The probe is suitable for non-contact measurements from samples in bottles and vials through a glass window. The sampling optics and the working distance of the probe are easily changeable for different applications by changing the focusing lens of the probe. In the head of the fibre-optic probe, a

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semiconductor bandgap filter (CdTe) is used to reject the Rayleigh scattering from the sample.

15.5 Applications: analysing the composition of cereal and dairy products 15.5.1 MIR prototype for wheat flour quality control The MIR prototype was tested by RHM Technology under laboratory and industrial conditions for wheat quality control. The conclusions were as follows.16 In the laboratory, the prototype proved to be physically robust and easy to use, and both the prototype and a commercial FTIR instrument (Bio-Rad FTS40) gave similar performance for flour classification. Unfortunately, deployed in an industrial setting the MIR prototype performed poorly, the principal problems being wavelength instability, background variability and poor signal-to-noise ratio. These problems should not be insurmountable. A fundamental concern has to be that the choice of optical system, i.e. moving grating and a DTGS detector, were insufficiently robust for use in a harsh industrial environment. During this industrial experiment, the prototype was not compared to the Bio-Rad FTS-40 commercial FTIR instrument which is not adapted to an industrial environment. 15.5.2 MIR prototype for malt quality control The MIR prototype was tested also by IFBM partner in pilot plant. Two spectral zones were studied with a specific integration time: between 1818 and 1695 cmÿ1 with an integration time of 0.5 second; between 1203 and 952 cmÿ1 with an integration time of 1 second. The samples were ground before spectrum acquisition. Two barley varieties were studied: a winter barley and a spring barley. Results were interpreted using principal component analysis (PCA). PCA involves a mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. The conclusions were as follows.16 A principal component analysis between 1800 and 1700 cmÿ1 was performed. In the representation of the scores on the second principal component against malting steps, the decrease of the scores distinguished between the following steps: end of steeping, first, second, third, fourth and fifth day of germination. The loading associated with the second principal component showed a peak centred on 1742 cmÿ1. This positive peak, characteristic of ester C=O bonds, revealed that the observed score was due to ester linkage disappearance. It could be concluded that the MIR prototype could be used to follow the disappearance of ester linkages during malting.

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A principal component analysis between 1188 and 952 cmÿ1 was performed. In the representation of the scores on the first principal component against malting steps, the increase of the scores distinguished between the following steps: end of steeping, first to fifth day of germination. The loading associated to the first principal component showed a large peak between 1160 and 1030 cm-1.. This positive peak, characteristic of carbohydrates, revealed that the score increase observed was due to a carbohydrate concentration increase. It could be concluded that the MIR prototype allowed the control of the carbohydrate modification during malting. The MIR prototype could therefore be a tool for the malting industry, with some re-engineering.

15.5.3 Optothermal prototype for fermented milk quality control A flow-cell was tested in a pilot plant with series of mixtures (0.1–4 g/100 g), full fat (either un-homogenized or homogenized samples) and skimmed milk. For each at-line measurement, the flow-cell was flushed with sample, the flow was stopped, and once the signal was stable, measurements could be made. Using this procedure, the results of the calibration with mixtures of full-fat and skimmed milk were very similar to those obtained off-line without flow-cell. Reference values were obtained with an Anadis FTIR for fat, protein, sucrose and total solids, and also with traditional biochemical methods: • fat: FIL-IDL 152A: 1997 • protein: FIL-IDF 20B: 1993 • sucrose: NH2 column HPLC method. Data analysis was a simple linear regression for each of the components of interest (fat, protein and sucrose). As fat, protein and sucrose contents are not affected by fermentation, their concentration was only measured with the reference methods before fermentation. The distribution of the residuals suggested that although the fat content, for instance, was not changing, the optothermal signal changed during fermentation. It was presumably affected to some extent by the texture. Therefore separate analyses were carried out with either only the nonfermented, or only the fermented samples. In these conditions, interesting results were obtained for fat content. But this technique does not seem to be appropriate for protein or sucrose content determination in milk.16

15.5.4 Raman prototype for monitoring yoghurt production The Raman prototype was tested for milk and yoghurt quality control in the pilot plant of Sitia YOMO. The prototype proved to be very effective for on-line process control. One fermentation was selected for illustrating its potential. Online spectrum acquisitions were carried out at a one-hour period, from inoculum time until eight hours of fermentation. Ten spectra were acquired each time for repeatability study. Each Raman spectrum ranged from 96 cmÿ1 to 2000 cmÿ1, which covered 1084 wavelengths. Repeatability was good. A PLS model was

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calibrated for pH prediction by cross-validation with blocks of ten individuals. The coefficient of correlation of pH prediction with this PLS model was 0.94. The standard error of cross validation was 0.18, which represents eight per cent of the range of variation during the fermentation process. These results suggest that on-line Raman spectroscopy is very valuable in particular in the case of milk fermentation control, with pH as the correlated marker. The prototype was also tested at-line with concentrated whole milk with or without ingredient (no colour), and concentrated milk with cream. Measurements were achieved in triplicate. Second derivatives were calculated on 151 points: the Raman signals of the 1084 spectrum frequencies were turned into 334 signal second derivatives. When the spectrum signals were processed as raw, repeatability was fairly good. When transformed by the 151-point second derivative calculations, repeatability was excellent. With this transformation, fats were predicted from Raman spectra with a correlation coefficient of 0.32 and an error of about 0.31%w/w. Proteins were predicted with a correlation coefficient of 0.98 and an error of about 0.1 I%w/w. Sucrose was predicted with a correlation coefficient of 0.39 and an error of about 0.71%w/w. These predictions could still be improved with more samples and further chemometrics. They encourage future development of this technology in the dairy industry.

15.6

Future trends

15.6.1 MIR spectroscopy The need for robustness in harsh industrial conditions for MIR spectroscopy makes it opportune to investigate systems without any moving parts. The 1998 Jobin-Yvon monochromator was a real progress in spectroscopy instrumentation with a high-precision stepwise engine moving the grating. Emphasis has been placed so far on moving gratings because commercial detector performance was poor when using arrays instead of single pixels. This is changing fast. Microbolometers coming from military and aerospace areas are now commercially available at a reasonable cost. It is worth testing new designs with a spectrograph instead of a monochromator, using a matrix array of microbolometers. FTIR manufacturers are trying hard to make some of their products more rugged. The mobile mirror of the Michelson interferometer is a true handicap with respect to robustness, but its problems might be overcome some day. Thus it could be said at the present time that the future of industrial MIR spectroscopy seems to be in between FTIR ruggedization and microbolometer matrix development. MIR spectroscopy has the advantage of dealing with fundamental vibrations of molecules. This gives access to molecule fingerprints. It also has the drawback of poor signal-to-to-noise ratios, except for FTIR thanks to the use of an interferometer, and of Fourier transform which makes better use of the energy of the source. NIR spectroscopy has the advantage of good signal-to-noise ratios, but it has the drawback of dealing with combinations and harmonics of

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fundamental vibrations, and is therefore less informative. Considering the recent progress in chemometrics, the choice between NIR and MIR spectroscopy is to be discussed in each case. Whatever the design, ATR is an interesting solution for at-line liquid analysis by infrared spectroscopy. If optical fibre technology advances enough in the MIR spectral domain, it will become possible to develop probes for on-line process control.

15.6.2 Optothermal spectroscopy Optothermal spectroscopy, as a part of photoacoustic spectroscopy, shares the future of NIR spectroscopy. It can be considered as a specific sample presentation technique combined to a thermal detection of infrared absorption. The instrumentation design can be as simple as in the case of filter NIR spectroscopy. Consequently, when thermal detection is feasible, it can present advantages of robustness, reliability and cost over the other NIR spectroscopy techniques. Optothermal spectroscopy can ideally be implemented at-line. Attention should be paid to the contact between the sample and the sensor from the hygienic point of view.

15.6.3 Raman spectroscopy New optoelectronic components now readily available are boosting the potential for industrial applications of Raman spectroscopy. The use of optical fibre technology with a probe contributes to spectral quality and cost reduction by setting a distance between the product and the equipment. It permits the optimization of the focus of the exciting light and the collection of the scattered light. The progress in the performance of interferential filters, notch filters and dichroic mirrors contributes also to the spectral quality. The effectiveness of the notch filters allows us to use only one grating in the monochromator. Without them it would be necessary to use a triple monochromator to reject the Rayleigh line, which would increase the size and the cost of the equipment. Progress in laser technology and in CCD detectors are also of significant importance in spectral quality. When the sample to be analysed does not contain any fluorescent analyte, and in cases where Raman detection feasibility has been previously established in the laboratory, Raman spectroscopy can compete with MIR spectroscopy in indusrial implementation as far as performance and cost are concerned. In addition, it presents a clear advantage for on-line sensing, not requiring any contact with the product to be analysed (no microbiological hazard). It is even possible to monitor several fermentors with a single device by using several probes.

15.7

Sources of further information and advice

The partners of the European project STAS represent a valuable source of knowledge in the area of spectroscopic techniques used as advanced

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sensors. Each of these partners belongs to scientific and technological networks. For the application of spectroscopy techniques to food quality control, an excellent reference in the European Union is: the Institute of Food Research (IFR), Norwich Laboratory, Norwich Research Park, at Colney, Norwich NR4 7UA, UK. Tel 44 1 603 25 52 10, fax 44 1 603 50 77 23, email: reg.wilson@ bbsrc.ac.uk. Companies or associations involved in the application of spectroscopy techniques to food quality control are also very valuable contacts: RHM Technology, The Lord Rank Centre, Lincoln Road, High Wycombe, Bucks, HP12 3QR, UK, tel 44 1 494 42 82 92, fax 44 1 494 42 81 14, e-mail: Simon_Branch/[email protected]; IFBM, 7 rue du bois de la Champelle, BP 267, 54512 Vandoeuvre Cedex, France, tel 33 3 83 448 800, fax 33 3 83 441 290, email: [email protected]; Sitia-YOMO, Via Quaranta 42, 20139 Milano, Italia, tel 39 290 014 212, fax 39 290 905 3336, email: [email protected]. When interested in innovative design of spectroscopy equipment, especially in Raman technology, you can contact: VTT Electronics, Kaytova¨yla¨, 1, PO Box 1100, FIN-90571, Oulu, Finland. Tel 358 8 551 2288, fax 358 8 551 2320, email: [email protected]. For specific design of infrared spectroscopy equipment for food and agriculture, you can contact: Cemagref, Centre of Montpellier, Information and Technology Research, BP 5095, 34033 Montpellier Cedex 1, France. Tel 33 4 67 04 63 00, fax 33 4 67 04 63 06, email: [email protected]. This team emphasizes robustness equipment through physics and chemometrics.

15.8 1

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References ‘A preliminary study of optothermal spectroscopy: a potential near infrared sample presentation method for the analysis of milk’, J. Near Infrared Spectrosc., 2000, 8, 267–272. GRIFFITHS P. R., DE HASETH J. A., ‘Fourier Transform Infrared Spectrometry’, NewYork, John Wiley & Sons, 1986. KEMSLEY E. K., WILSON R. H., BELTON P. S., ‘Potential of Fourier transform spectroscopy and fiber optics for process control’, J Agric Food Chem, 1992, 40, 435–438. KEMSLEY E. K., ZHUO L., HAMMOURI M. K., WILSON R. H., ‘Quantitative analysis of sugar solutions using infrared spectroscopy’, Food Chemistry, 1992, 44, 299–304. VAN DE VOORT F. R., SEDMAN J., EMO G., ISMAIL A. A., ‘A rapid FTIR quality control method for fat and moisture determination in butter’, Food Research International, 1992, 25, 193–198. DEFENEZ M., WILSON R. H., ‘Mid-Infrared spectroscopy and chemiometrics for determining the type of fruit used in jam’, J Sci Food Agric, 1995, 67, 461–467. BRANCH S., Dough Analysis by FTIR Spectroscopy, STAS specific report, High Wycombe, RHM Technology, 1998. WILSON R. H., MADSEN P. K., DEFERNEZ M., TAPP H. S.,

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‘Quantitative analysis of sugars solutions using a novel fiber-optic sapphire ATR accessory’, Applied spectroscopy, 1993, 47, 10, 1651–1654. BELLON-MAUREL V., VALLAT C., GOFFINET D, ‘Quantitative Analysis of individual sugars during starch hydrolysis by FTIR/ATR spectrometry. Part I: Multivariate calibration study, repeatability and reproducibility’, Applied Spectroscopy, 1995, 49, 5, 556–562. BELLON-MAUREL V., VALLAT C., GOFFINET D., ‘Quantitative Analysis of individual sugars during starch hydrolysis by FTIR/ATR spectrometry. Part II: Influence of external factors and wavelengths parameters’, Applied Spectroscopy, 1995, 49, 5, 563–568. WILSON R. H., TAPP H. S., ‘Mid-infrared spectroscopy for food analysis : recent new applications and relevant developments in sample presentation methods’, Trends in Analytical Chemistry, 1999, 18, 2, 85–93. ALLOSIO-OUARNIER N., ROBERT P., BOIVIN P., BERTRAND D., ‘Utilisation de la spectroscopie moyen infrarouge en re´flexion totale atte´nue´e pour suivre le proce´de´ de maltage’, 27th European Brewery Convention, Cannes, 1999, proceedings published by Elsevier Scientific Publishing Company, 485–492. LEWIS I. R., GRIFFITHS P. R., ‘Raman Spectrometry with Fiber-Optic Sampling’, Applied Spectroscopy, 1996, 50, 10, 12A–30A. HIRSCHFELD T., CHASE B., ‘FT-Raman spectroscopy development and justification’, Applied Spectroscopy, 1986, 40, 133–137. ¨ P., SUHONEN J., ‘Rugged fiber-optic Raman probe for process monitoring NIEMELA applications’, Applied Spectroscopy, 2001, 55, 10, 1337–1340. KEMSLEY E. K., WILSON R. H., POULTER G., DAY L. L.,

GRENIER P., BELLON-MAUREL V., WILSON R. H., NIEMELA P., BRANCH S., BENOIˆT A., OUARNIER N., TAGLIABUE C.,

‘Final report of STAS project’, Montpellier, Cemagref,

2000.

15.9

Acknowledgements

The European Commission (EC) is acknowledged for the financing of the STAS project, reference FAIR CT 96–1169, entitled ‘Development of Spectroscopic Techniques as Advanced Sensors for the Optimization and Control of Food Processing’. Special thanks also to the EC Officers Dr Laurent Bochereau and Dr Achim Boenke who invested in implementing the project and in guidance respectively. Also acknowledged is the contribution to the STAS project of the company Micro-Module (Technopole Brest Iroise, 38 rue Jim Sevellec, 29200 Brest, France) and especially of former director Andre´ Benoıˆt.

16 Confocal scanning laser microscopy (CSLM) for monitoring food composition R. H. Tromp, Y. Nicolas, F. van de Velde and M. Paques, Wageningen Centre for Food Sciences, The Netherlands

16.1

Introduction

To control the performance and functionality of processed food products, insight in the spatial arrangement of the (micro)structure and the interactions between the structural elements is crucial. To achieve this insight, confocal scanning laser microscopy (CSLM), in conjunction with other techniques, is a very powerful tool. Most food products contain a selection of the following ingredients: fat, protein, polysaccharides, water and air. The physical and chemical properties of the ingredients are a function of the processing and manufacturing conditions. Heat treatment is frequently applied for texture formation and can result in (partial) ‘denaturation’ of protein molecules. The resulting conformational changes of the protein molecules often go together with changes in their physical and chemical properties. For example, these changes give rise to aggregation and gelling behaviour, or the tendency to accumulate at interfaces. As a consequence, new structural elements are formed (e.g. droplets, air cells, particles, crystals and strands) which are organised at various length scales in a new microstructure. These ‘building stones’ of the texture and their interactions are responsible for system properties such as: shelf-life, spreadability and mouth feel attributes such as creaminess. To be of use, knowledge of structural changes on a microscopic level does not have to be quantitative. Often, it is a helpful guide for the analyst or experimenter who tries to explain, predict and control macroscopic functionality. CSLM has been used regularly in medicine and biology since 1985. In material science and food science it has been used from the late 1980s (Pawley, 1995). Its use in food science was explored by the Unilever group (Heertje et al., 1987; Blonk and van Aalst, 1993; Blonk et al., 1995). These early successful

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experiments to explore the capabilities of CSLM covered fat spreads, mayonnaise, cheese and rising dough. The desirability to perform measurements under dynamic conditions was, among others, an important motivation for these experiments. Variables addressed with respect to their effect on dynamic processes were: temperature, pH, concentration and pressure. Later they presented a new CSLM-based approach to study the displacement of emulsifiers from interfaces, as a better alternative for more laborious methods existing at that time (Heertje et al., 1990; Heertje et al., 1996). Fluorescence recovery after photo-bleaching (FRAP) was described as an important new tool to study the mobilities of molecular species in liquid systems to understand dynamic phenomena (Blonk and van Aalst, 1993). The displacement of fluorescein isothiocyanate-labelled sodium caseinate by monoacylglycerols and proteins was studied. Quantitative information on the competitive adsorption as a function of the type of emulsifier and the concentration was obtained. These early results showed the possibility to study the dynamic processes in the stabilisation of emulsions. Later work also showed the power of the technique for investigation of a whole range of other systems, e.g. phase separating biopolymer mixtures (Blonk et al., 1995), fat spreads (low-fat, bi-continuous, water-continuous, double emulsions), butter, margarine, emulsifiers, cheese and bread (including observations during proofing). The message was easy to understand, and many colleagues followed. Today CSLM is widely used in research, innovation, development and quality control environments. Most examples of successful application of CSLM are concerned with cases in which ingredients are phase separated or incompletely mixed. Such phase separated systems include aggregation of denatured protein, mixed gels of proteins and polysaccharides, foams or aerated systems stabilised by fat globules or surface active protein, emulsions and systems containing starch granules as a thickening or gelling agent (Moss, 1976; Lore´n et al., 1999; Herbert et al., 1999; Tromp et al., 2001; Du¨rrenberger et al., 2001; van de Velde et al., 2002; Tester and Karkalas, 2002). Another important application of CSLM in food science is concerned with imaging cells of yeast and bacteria in fermented products (Auty et al., 1999; Gardiner et al., 2000; Auty et al., 2001), and the viability assessment of microorganisms by viability staining. As a characterisation technique, light microscopy is versatile, easy to apply and sensitive to details that matter on the human sensory level, i.e. down to micron-sized inhomogeneities. Among light microscopic techniques, CSLM has some distinct advantages. Firstly, the resolution in depth is a fraction of a micron. So the image only contains information from the focal plane, in contrast to wide field microscopy where ‘focal’ and ‘non-focal’ information is superpositioned at the cost of image quality and reducing spatial resolution. This ‘focal image’ can be considered as an optical section. The optical section can be moved through the specimen and the stack of images acquired represents a volume sample of the specimen providing insight in the 3D spatial arrangements. This information is obtained in a non-invasive way. Secondly, the necessary use of intense laser light brings about the use of fluorescent labels.

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Different labels may be associated (covalently or otherwise) with different phases and ingredients. This provides the contrast mechanism needed for imaging. Simultaneous visualisation of multiple phases is feasible. Thirdly, generally speaking the penetration depth, which is achieved by the laser into the specimen exceeds the size of most structural elements. This allows observations at depths well away from the outer surface to study bulk structural properties. Depending on scattering and refractive index of the sample penetration depths up to the working distance of the objective lens used (of the order of one hundred microns) can be obtained. An early review of CSLM use in food systems can be found in Brooker (1995). In this chapter, the background of the technique will be discussed. After that, the focus will be on sample preparation. The aim of sample preparation is visualisation of different ingredients at the same time. It involves the introduction of fluorescent dyes. In general, it is simple to stain regions rich in protein or fat, and starch granules. Components more hydrophilic than fat and protein, such as polysaccharides have to be labelled covalently by chemical reaction between component and staining agent molecules. Covalent labelling also allows specific identification of these labelled molecules in the image which is a powerful tool but restricted to systems made at lab-scale. Examples will be shown of characterisation by visualisation on a microscopic scale of the effect of processing on model systems of food ingredients and real food systems. Some examples present actual rapid characterisation methods, others can at present only be achieved after some sample preparation. The examples demonstrate some of the many instances in which CSLM is able to characterise interactions between food ingredients and changes on processing. Finally, some current and future developments are described.

16.2

The principles of CSLM

The essential difference between classical (wide field) light microscopy and confocal microscopy is the fact that for the latter all information originates from the focal plane. In the case of classical microscopy, there is always some light coming from material above and below the focal plane. This light carries nonfocused information and lowers the quality of the image including spatial resolution. For confocal microscopy, a light source of the intensity of a laser is virtually necessary, because selecting only focused light means a much lower amount of light to be detected for the image as compared to wide field microscopy. The principle of CSLM is shown in Fig. 16.1. The laser beam enters through the illumination pinhole. After the pinhole, the divergent beam passes through a dichroic mirror (a mirror that reflects one wavelength, and transmits another, in this case the wavelength of the incoming laser beam) and is focused by the objective lens. The light excites fluorescent probes, which subsequently start to emit light at some specific, longer wavelength relative to that of the incoming

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Fig. 16.1

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The working principle of a confocal microscope.

beam. This emitted light is divergent and is refocused by the same objective lens onto the dichroic mirror, which is in the reflective mode for the emitted light of longer wavelength. The reflected light passes through the detection pinhole, which transmits only the part of the emitted light, which is focused exactly into it. Light which is focused exactly into the pinhole originates from a single spot in the sample. Light from other planes is barred by the detection pinhole from reaching the detector. By moving the mirror, the spot from which the emitted light is selected by the detection pinhole scans the sample. The main manufacturers for traditional CSLM instruments in the field are, among others, Leica, BioRad and Zeiss. Some manufacturers (Zeiss) combine fluorescence correlation spectroscopy (FCS) with CSLM imaging. FCS is a spectroscopic technique for the study of molecular interactions in solution. FCS monitors the random motion of fluorescently labelled molecules inside a defined volume element irradiated by a focused laser beam. These fluctuations provide information on the rate of diffusion or diffusion time of a particle and this, in turn, is directly dependent on the particle’s mass. FCS is an ideal approach for the study of thermodynamic and kinetic features of molecular interactions in solution. Those that have the experimental need for monitoring the evolution of dynamic processes have to optimise in acquisition rate. The ability to image dynamic processes is in essence the ability of sufficiently fast acquisition of a sequence of images in time (see also Section 16.5). A particularly attractive configuration for real-time imaging is the tandem scanning microscope (TSM) (Petra´n et al., 1968; Boyde et al., 1990). In these instruments the illumination and detection pinhole arrays are diametrically opposed on a spinning Nipkow disc. A subsequent development permitted the same pinhole array to be used for both illumination and detection (Xiao et al., 1998). A recent development to this ‘single sided’ TSM has been the introduction, by the Yokogawa Company (Japan), of a lenslet-array to permit

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more efficient use of the illumination light. The manufacturer’s literature claims light transmission efficiencies of the disc of up to 60 per cent when used for fluorescence imaging with coherent illumination sources. When using traditional single photon confocal microscopy a fraction of the photons emitted in the focal volume are scattered into trajectories that do not pass through the detector aperture, and a fraction of the out-of-focus photons are scattered into the aperture resulting in a blurred image. Multiple-photon excitation (MPE) fluorescence microscopy with wide field collection can minimise both of these problems. First, a brief explanation is given. The sample is illuminated at a wavelength around twice the wavelength of the absorption peak of the fluorophore being used. For example, in the case of fluorescein, which has an absorption peak around 500 nm, 1000 nm excitation could be used. However, if a high peakpower pulsed laser is used (so that the mean power levels are moderate and do not damage the specimen), two-photon events will occur at the point of focus. At this point the photon density is sufficiently high that two photons can be absorbed by the fluorophore essentially simultaneously. This is equivalent to a single photon with an energy equal to the sum of the two that are absorbed. In this way, fluorophore excitation will only occur at the point of focus thereby eliminating excitation of out-of-focus fluorophore and achieving optical sectioning. Since out-of-focus fluorescence is essentially non-existent in MPE and all photons emitted are useful for image formation, the collection optics can be extremely simple and efficient. More detailed technical information on confocal microscopy can be found in Pawley (1995).

16.3

Sample preparation

16.3.1 Introduction The major part of the sample preparation for CSLM involves the application of one or more staining agents for distinguishing one or more ingredients. In contrast with wide field microscopy, the thickness or transparency of the sample is of little importance, as irradiation and detection take place on the same side of the sample. There are two ways to apply staining: non-covalently and covalently. Non-covalent labelling is done by directly adding a solution of fluorescent label to the sample. Covalent labelling involves the use of food ingredients that have undergone a chemical reaction with a reactive fluorescent label. De Belder and Granath (1973) describe a route for the synthesis of fluorescein labelled dextran, which has turned out to be widely applicable to polysaccharides. Today’s CSLM equipment offers the possibility to use up to three photomultipliers, each operating at a different wavelength interval to detect the emitted light from different fluorescent probes. In combination with a set of three incident laser lines at different wavelengths, this allows the simultaneous excitation and detection of different fluorescent probes. Imaging

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of a sample results in three separated images, each representing one wavelength interval that corresponds with one fluorescent dye. These individual images are combined in an overlay image in which separate components are represented in different colours. The main constraint of this technique is the availability of fluorescent probes that have well separated absorption maxima and emission spectra, as well as different affinities for different compounds/ingredients in the sample under study. The use of multiple labelling techniques, either covalently or non-covalently, allows the simultaneous visualisation of several individual components in one food product. A combination of FITC and Nile Red is eminently suited for the visualisation of both the protein phase and the fat phase in food products, such as custards, (whipped) cream and mayonnaise (Blonk and van Aalst, 1993). The use of non-covalent staining techniques for the visualisation of different ingredients has been described for the labelling of proteins, lipids and whey in dairy products (Herbert et al., 1999). A combination of covalent and noncovalent labelling has been used for the visualisation of multiple ingredients in food model products (Moss, 1976). Here, it is assumed that the sample does not exhibit autofluorescence. However, in the case of the availability of a UV laser, autofluorescence may be observed and act as a source of contrast. A compound or a group bound to an ingredient could be excited by UV radiation and emit visible light. In that case no label has to be added. In practice, the amino acid tryptophan is the most important source of autofluorescence.

16.3.2 Non-covalent labelling Two ways of applying non-covalent labelling can be distinguished: application before mixing and processing, and application afterwards. In the first method, staining agents are added to one or more of the ingredients to be mixed and processed, in the latter method solution of staining agent is brought into contact with the (final) product. The structure of the product should of course not be changed by the staining process. The product should therefore either be a (soft) solid, not broken down by contact with the staining solution, or the product should itself be a solution. The first method (application of staining prior to mixing) is least suitable for rapid characterisation, but it is the best for getting unbiased structural information. It is often not applicable in food grade processing equipment as the fluorescent probes are in general not food grade chemicals. It does not, however, suffer from the drawback of applying the staining afterwards, i.e. the fact that ingredients are stained by the agent according to affinity and accessibility. This means that in time, the colour contrast between different ingredients may change, until the staining agents have reached their equilibrium distribution. This may take, depending on temperature and composition of the sample, several hours to a day, and should be taken into account when setting up the characterisation procedure.

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For instance, in preparing cheese for CSLM visualisation, a drop of staining solution is put on top of a slice of 2 mm in thickness. The most homogeneous distribution of staining agent is reached at the bottom side of the slice. Therefore, this side is examined by CSLM. Typically, it takes one night at 4ºC for the staining to reach a stable distribution across the thickness of the slice. Of course, cheese is a relatively dense, low-water system and therefore a difficult case. Many foodstuffs are less dense and higher in water content, so penetration of staining agent will take place more quickly. In general, gelled solutions of protein (yoghurt, egg white) are stained within a few minutes.

16.3.3 Covalent labelling The covalent labelling technique involves the covalent linking of a fluorescent probe to the desired biopolymer prior to sample or product preparation. Covalent labelling is the only method available today to visualise polysaccharide in food products and food product models. Covalent labelling of proteins is applied to visualise one specific protein in a complex mixture or to overcome the uncertainties that arise from the use of free fluorescent dyes. Protein molecules contain a sufficient amount of highly reactive amino groups on their surface, which facilitates the coupling reaction with the reactive probes. In general, the coupling reaction is performed under slightly alkaline aqueous conditions (Haugland, 1996). A wide variety of probes with different fluorescent properties containing protein reactive groups, such as isothiocyanate, carboxylic acid succinimidyl ester, or sulfonyl halides, are commercially available. Carbohydrates are less reactive and a limited number of reactive probes (isothiocyanates derivatives) can be used only under harsh conditions (de Belder and Granath, 1973; Tromp et al., 2001).

16.3.4 Staining agents In the case of non-covalent affinity-based staining, different fluorescent probes are on the market. In principle two groups are identified: staining agents with affinity for hydrophilic domains and staining agents that show the tendency to accumulate in hydrophobic domains. Depending on the properties of each individual staining agent and staining solution (pH, ion strength, degree of hydrophilicity), and the hydrophilic/hydrophobic nature of the different phases in the sample the staining agents show a differential distribution over the sample’s microstructure. Examples of staining agents for the staining of the aqueous protein rich phases are rhodamine B, fluorescein or its isothiocyanate derivative FITC, ANS (8-anilino-1-naphthalene sulphonic acid) and acid fuchsin (see Table 16.1). For example, the probe rhodamine B accumulates in the protein rich domains of a phase separated mixture of gelatine and dextran (Tromp et al., 2001). Other fluorescent probes are selective for hydrophobic, e.g. oil, and/or fat rich phases, e.g. Nile Red, Nile Blue A and BODIPYTM 665/676. For the third group of food ingredients, sugars and polysaccharides, selective

CSLM for monitoring food composition Table 16.1

313

Properties of different fluorescent probes

Fluorescent probe

Used fora

Excitationb Emissionc (nm)

Acid Fuchsin 8-Anilino-l-naphthalene sulphonic acid (ANS) BODIPYTM 665/676 Fluorescein Fluorescein isothiocyanate (FITC) Nile Blue A Nile Red Oregon GreenTM 488 succinimidyl ester Rhodamine B Rhodamine B isothiocyanate Safranin O Texas RedTM succinimidyl ester

P Green P UV F Red P Blue CC, CP, P, S Blue F Blue F Green CP Blue P Green CC, CP Green P, S Blue CP Green

575–640 450–490 > 670 518 518 628 524 625 625 540 615

a CC: covalent labelling of carbohydrates, CP: covalent labelling of proteins, F: non-covalent staining of fat/oil phases, P: non-covalent staining of proteins S: non-covalent staining of starch granules/remnants. b UV: 364 nm (UV-laser), Blue: 488 nm (Ar/Kr-laser), Green: 543 nm (He/Ne-laser) or 568 nm (Ar/ Kr-laser), Red: 633 nm (He/Ne-laser) or 647 nm (Ar/Kr-laser). c Either the emission ranges or the emission maxima are given.

fluorescent probes are not commercially available. Therefore, the visualisation of polysaccharides in biopolymer mixtures is only possible using covalent labelling prior to sample preparation. An exception to this described rule of thumb for polysaccharide labelling is starch. Starch granules and granule remnants are stained with commercially available dyes, e.g. rhodamine B, FITC and safranin O (Du¨rrenberger et al., 2001; van de Velde et al., 2002). Protein selective probes, especially rhodamine B and safranin O are, in the absence of proteins, useful for the staining of starch granules. However, by increasing protein concentration in the matrix the probe molecules accumulate in the protein phase (van de Velde et al., 2002).

16.4

Applications: food composition

16.4.1 Introduction Particle shape, particle size distribution and interactions between particles are the three major areas in which CSLM can be used for rapid characterisation of foodstuffs. Particle interactions can be probed to the extent in which these interactions are reflected in the way particles are distributed in space. The strength of interactions can only be measured by applying some source of controlled deformation, e.g. optical tweezers or simple shear.

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16.4.2 Starch granules Starch granules form accessible samples for demonstrating rapid characterisation by CSLM: apart from their size and size distribution, which is available from wide field microscopy as well, their shape can be monitored in three dimensions before and after processing. Figure 16.2 shows six types of starch granules. Each type has its distinct features with respect to shape and size (distribution). Potato starch (Fig. 16.2A) consists mostly of large oval granules with a granule size ranging from 10 to 75 m. The granules of wheat starch (Fig. 16.2D) are a little smaller, 5 to 45 m and have round shape. The granules of the other starches used in this study (Fig. 16.2B. Corn, C. tapioca, E. mung bean, and F. sweet potato) are much smaller with a granule size ranging from 5 to 30 m. Corn starch granules have a typically truncated shape. Tapioca starch consists of a mixture of truncated and round granules, the larger granules being round and the smaller ones being the truncated ones. Mung bean starch granules have mostly an oval shape. The granules of sweet potato have a round or truncated shape and are comparable with those of tapioca, as they are both root starches. The size and shape of starch granules from different sources are in agreement with the characteristic summarised in textbooks and review articles dealing with starch (Moss, 1976; Tester and Karkalas, 2002). The CSLM imaging technique is suitable for detecting surface irregularities on the

Fig. 16.2 Three-dimensional images of starch granules from A. potato, B. corn, C. tapioca, D. wheat, E. mung bean, F. sweet potato (image size: 160  160  36 m). Colouring agent: rhodamine solution. With permission adopted from ‘Visualisation of starch granule morphologies using confocal scanning laser microscopy (CLSM)’ by F. van de Velde, J. van Riel, R.H. Tromp, J. Sci. Food Agric. ß 2002 Society of Chemical Industry, first published by John Wiley & Sons Ltd.

CSLM for monitoring food composition

Fig. 16.3

315

Size distribution of potato starch and tapioca starch.

granules shown in Fig. 16.2, e.g. nodes on the potato starch granules (left top corner) and corn starch granules (middle) and also holes in the surface of the corn starch granules (right top corner). A quantitative interpretation of CSLM photographs is obtained by image analysis. In Fig. 16.3, the size distributions of potato and tapioca starch, calculated from two-dimensional images, are shown. Heating starch granules dispersed in water leads first to swelling and eventually to disruption of the granules. This process is called gelatinisation, because by subsequent cooling one obtains a gel. Native starch is most efficient in forming a gel. In some cases, e.g. in custards, full gelatinisation is not desirable. Instead, the starch granules should act as a thickener. Treatment of starch by a crosslinking agent is a way of modification, which hinders full gelatinisation. CSLM monitoring of the heating of native and crosslinked starch granules shows the difference in structure after gelatinisation (Fig. 16.4). Native starch granules practically disappear on heating. A gel is formed in which only ghosts of granule fragments are discernible. Crosslinked starch granules only swell, releasing some non-crosslinked material from their interior. The result after cooling is a paste.

16.4.3 Protein gels Protein gels are in general formed when soluble globular proteins are made insoluble by denaturation or by removing the conditions for charge or steric stabilisation. Examples are yoghurt production (insolubilisation of casein micelles by acidification), the boiling of an egg and the whipping of cream (denaturation by shear forces). The texture of the result of protein gelation is

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Fig. 16.4

Potato starch granules (modified by crosslinking) before and after heating at 80ºC. Colouring agent: rhodamine solution.

dependent on processing conditions such as rate of denaturation, concentration and shear forces. An example of the effect of shear forces on gel texture is given in Fig. 16.5. Here, a solution of whey protein is slowly acidified from pH 7 to pH 5. The isoelectric point (i.e. the pH value at which the protein molecules get an overall charge of zero) is near pH 5.2. This acidification has been done under stagnant and simple sheared (70 sÿ1) conditions. In stagnant conditions, one obtains a weak, turbid gel. The microscopic texture has an open, regular appearance. On small length scales (< 1 micron), a fractal structure can be shown to exist. Shear flow during acidification causes the gel to fracture as soon as it forms. The fragments, which consist of insoluble material, become more compact through syneresis and rotational forces. The result is an irregular structure and no macroscopic gel formation. The two situations shown in Fig.

Fig. 16.5 Gelation by acidification of whey protein particles. Left: acidification in absence of shear; right: acidification under a shear strain of 70 sÿ1. Colouring agent: rhodamine.

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16.5 resemble closely set and stirred yoghurt. CSLM as a rapid characterisation technique is used in monitoring yoghurt stability.

16.4.4 The kneading of dough The kneading of dough is a process which is difficult to characterise in physical terms. Experience and tradition often determine the duration and method of kneading. The purpose of kneading is to mix starch and gluten with water and to break down the gluten polymers by reduction. A key parameter in dough kneading is the amount of mechanical energy per unit time, which is adsorbed by the dough. If the value of this parameter is too high, the gluten network will melt and the dough becomes sticky. CSLM could in principle be used to characterise the different stages of kneading. This would result in an objective assessment of the result of a certain amount of kneading in terms of mixing on a microscopic length scale. In Fig. 16.6, an example is given of bread dough after rising, before and after kneading. Before rising, the flour/water mixture was kneaded for ten minutes. In Fig. 16.6, starch granules (grey), gluten network (white) gas cells (dark) are clearly distinguishable. During proving (rising) gas (CO2) cells appear. It turns out that gas cells tend to be surrounded by starch rich regions, rather than by gluten. Kneading after rising, however, changes the situation quite dramatically. Now, the gas cells are preferentially lined by gluten (visible as a thin white line, easier to see when colours are available). This transition from starch to gluten coating of gas cells is easily explained by the fact that yeast needs starch to develop CO2. However, the surface tension between gluten and gas will be lower than that between starch and gas. When given the chance, gluten rather than starch will prefer to share an interface with gas cells.

Fig. 16.6 The effect of kneading dough. Left: after short kneading and subsequent rising, right: the system on the left after five minutes of kneading. Stained by adding FITC and rhodamine solutions prior to kneading.

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Fig. 16.7 The dough (350 g wheat flour, 250 g butter and 250 g sugar) of the crust of apple pie, before (left) and after (right) baking. Staining agents: FITC and Nile Red, applied after kneading (left) and after heating (right).

16.4.5 The effect of baking Figure 16.7 shows another type of dough, mimicking that of the crust of apple pie. Before baking, one sees a nearly fat (butter) continuous system (grey), interrupted by large air bubbles (black). Inside the fat regions, starch granules are observed (oval shape). After baking the butter phase has melted and broken up in isolated patches. The starch granules have been largely gelatinised.

16.4.6 Interactions between ingredients CSLM is particularly suited for showing which ingredient goes where in a food system with different ingredients. For example, images (not shown) of whipped cream demonstrate that air bubbles are coated by fat globules, while a protein (casein) matrix fills the space in between. Here, photographs are shown (Fig. 16.8) of two models systems, both containing 1% (w/w) crosslinked starch granules and 0.02% (w/w) carrageenan. Both systems were heated after mixing for a few minutes at 80ºC. The carrageenan was covalently labelled with a fluorescent probe (FITC). Therefore, all contrast in the photographs is due to carrageenan. Two different hybrid carrageenans were used: 70%kappa/30%iota (A) and 50%kappa/50%iota (B). The chains of these hybrid carrageenans contain both kappa and iota characteristics. The photographs show that the two types of carrageenans have quite different interactions with the swollen starch granules. Type A is mainly found on the surface of the granules, whereas type B fills the space in between the granules. This difference in behaviour is reflected in the rheological properties. The values for G0 (storage modulus) (10 Hz) at 4.5% starch/0.1% carrageenan were found to be 70 Pa for A and 25 Pa for B. In the mixture with type A, there is a strong interaction between carrageenan and starch granules, providing the granules with the role of an active filler. On the other hand, in

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Fig. 16.8 1% (w/w) crosslinked starch granules heated in a 0.02% (w/w) carrageenan solution. Two hybrid carrageenans were used: 70%kappa/30%iota (A) and 50%kappa/ 50%iota (B). Carrageenan was covalently labelled with FITC, therefore carrageenan causes all contrast in these photographs.

the system with carrageenan B, there is little interaction between the two components, causing the starch granules to be passive fillers.

16.5

Future trends

The major trends in developing new capabilities for microscopic imaging techniques concern specific identification of molecules, relating quantified image properties (3D image analyses) with other system parameters and realtime observation of dynamic processes. The development of morphological, multi-scale, time-resolved and multi-phase image analysis for the typical structure characteristics like size, shape, spatial distribution, topology, connectivity and orientation still have a way to go. With respect to specific identification spectroscopic tools are required. An important development in this area is Raman-CSLM. The image frame is built from full spectra obtained at each image point. By selection of parts of the spectrum that are characteristic for the molecules (ingredients) of interest, images are made based on the distribution of the selected molecule: ‘ingredient maps’. It provides insight into the nature of contribution of an ingredient to the microstructure of a sample. Limiting drawbacks of the technique are the duration of analysis (up to 24 hours) and the consequent stability of the specimen needed. Real-time imaging of dynamic processes is not yet possible. Imaging techniques are most suited for characterisation of the spatial organisation: ‘structure’. Individual structural elements can be identified, localised, distribution of one type of structural elements determined relative to the distribution of other, and quantified. Most imaging techniques do not allow in-situ monitoring of structural evolution in time, which has a strained relation with the growing need to study the origin of product performances in

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(micro)structure as function of manufacturing and oral processing conditions. Some non-invasive techniques do allow the observation of dynamic processes, e.g. real-time CSLM, MRI, X-ray tomography, environmental scanning electron microscopy (ESEM), acoustic microscopy and scanning probe microscopy. The ability to image dynamic processes is in essence the ability to acquire sufficiently swiftly a sequence of images in time. Physics says that increasing time resolution is always at the cost of spatial resolution. When the acquisition time is reduced, the S/N-ratio becomes worse and vice versa. The relationship of these parameters has to be optimised for each separate problem. The detection of a certain structural element dictates the need for a minimal spatial resolution. In addition, when this structural element represents a particular step in the evolution of a changing structure as part of a dynamic process, its duration of existence should fall within the temporal resolution of the technique. When the duration of existence of this structural element is shorter than the minimal acquisition time needed for the required spatial resolution, imaging will not be possible. In the field of polymer science specialised sample holders were developed that allow interfacing with microscopes, and nowadays also commercial cells are available to observe material under deformation (Linkam). Taylor (1934) introduced the concept of zero velocity plane, a ‘four roll’ apparatus and a parallel band apparatus by counter displacement of ‘roll’ or ‘belt’ in liquid. This allows stable imaging conditions of particle deformation without hinder of flow. Other developments have been achieved with different geometries such as disks (Grizzuti and Bifulco, 1997) and concentric cylinders (Haas et al., 1998). The same principle was applied in food systems for a four-roll mill configuration (Hamberg et al., 2001) or during extension in horizontal direction (Plucknett et al., 2001) by a mini extension-compression apparatus (Minimat 2000, Rheometric Scientific). Recently three new configurations have been developed that combine and allow simultaneously confocal imaging and deformation cells providing ‘zerovelocity’ planes or a stagnation point to allow stable imaging conditions (Nicolas, 2003a [submitted]). The method demands two counter rotating parts of the sample cell, the zero velocity plane arising somewhere in between. In the zero velocity plane, there is no flow relative to the microscope coordinates, but there is a shear stress. The first oscillatory shear configuration involves linear shear of parallel plates driven by piezoelectric devices (Nicolas, 2003b [submitted]). This configuration also includes detection of multiple light scattering (diffusing wave spectroscopy) to monitor the particle mobility. The second setup (continuous shear configuration) contains a cell composed of the cone and plate moving in counter rotation. Both respective rotational speeds can be changed simultaneously (but maintaining constant shear rate) to change the angle of the zero-velocity plane in order to keep a particle in the observation window (Paques, 2003a [submitted]; Paques, 2003b). The latter configuration interfaces the inverted confocal microscope and an Instron apparatus for extension and compression by vertical displacement (Nicolas, 2003c [submitted]). This configuration allows acquisition of rheological and imaging

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information during bi-axial extension of samples. The deformation applied is according the ‘imperfect squeezed flow’ for semi-liquid products published earlier (Suwonsichon and Peleg, 1999). Fast rate image acquisition (up to 1000 frames per second) that also allows maintenance of high spatial resolution would bring work in this area a large step further. All of the above greatly facilitate the investigation of structure-function relationships to allow intelligent product structure design giving rise to the desired product behaviour.

16.6

References (1999), ‘Viability Assessment of Probiotic Bacteria in Milk using LIVE/DEAD Staining and Confocal Laser Microscopy’, Scanning 21, 117.

AUTY M A E, GARDINER G, MCBREARTY S, STANTON C, ROSS R P

AUTY M A E, GARDINER G E, MCBREARTY S J, O’SULLIVAN E O, MULVIHILL D M, COLLINS J K,

(2001), ‘Direct In Situ Viability Assessment of Bacteria in Probiotic Dairy Products Using Viability Staining in Conjunction with Confocal Scanning Laser Microscopy’, Appl. Envir. Microbiol. 67: 420–425. BLONK J C G, VAN AALST H (1993), ‘Confocal scanning light microscopy in food research’, Food Research International 26, 297–311. BLONK J C G, VAN EENDENBURG J, KONING M M G, WEISENBORN P C M, WINKEL C (1995), ‘A new CSLM-based method for determination of the phase behaviour of aqueous mixtures of biopolymers’, Carbohydrate Polymers 28, 287–295. BOYDE A, JONES S J, TAYLOR, M L WOLFE, L A, WATSON T F (1990), ‘Fluorescence in the tandem scanning microscope’, J. Microsc. 157, 39 49. BROOKER B E (1995), ‘Imaging food systems by confocal laser scanning microscopy’, in Dickinson E, New physico-chemical techniques for the characterisation of complex food systems, London, Chapman & Hall, 53–67. DE BELDER A N, GRANATH K, (1973), ‘Preparation and properties of fluorescein-labelled dextran’, Carbohydrate Res. 30, 375–378. ¨ RRENBERGER M B, HANDSCHIN S, CONDE-PETIT B, ESCHER F (2001), ‘Visualization of food DU structure by Confocal Laser Scanning Microspcopy (CLSM)’, Lebensm.-Wiss. Technol. 34, 11–17. FITZGERALD G F, STANTON C, ROSS R P

GARDINER G E, O’SULLIVAN E, KELLY J, AUTY M A E, FITZGERALD G F, COLLINS J K, ROSS R P,

(2000), ‘Comparative Survival Rates of Human-Derived Probiotic Lactobacillus paracasei and L. salivarius Strains during Heat Treatment and Spray Drying’, Appl. Envir. Microbiol. 66, 2605–2612. GRIZZUTI N, BIFULCO O (1997), ‘Effects of coalescence and breakup on the steady state morphology of an immiscible polymer blend in shear flow’, Rheol. Acta, 36, 406– 415. HAAS K H, VAN DEN ENDE D, BLOM C, ALTENA E G, BEUKEMA G J, MELLEMA J (1998), ‘A counter-rotating Couette apparatus to study deformation of a sub-millimeter sized particle in shear flow’, Rev. Sci. Inst., 69, 1391–1397. HAMBERG L, WALKENSTROM P, STADING M, HERMANSSON A-M (2001), ‘Aggregation, viscosity measurements and direct observation of protein coated latex particles under shear’ Food Hydrocolloids, 15 139–151. STANTON C

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(1996), Handbook of fluorescent probes and research chemicals, Eugene, OR, USA, Molecular Probes Inc. HEERTJE I, VAN DER VLIST P, BLONK J C G, HENDRICKX H A C M, BRAKENHOFF G J A (1987), ‘New CSLM-based method for determination of the phase behaviour of aqueous mixtures of biopolymers’, Food Microstructure 6, 115–120. HEERTJE I., NEDERLOF J, HENDRICKX H A C M, LUCASSEN-REYNDERS E H (1990), ‘The observation of the displacement of emulsifiers by confocal scanning laser microscopy’, Food. Struct. 9, 305–316. HEERTJE I, VAN AALST H, BLONK J C G, DON A, LUCASSEN-REYNDERS E H (1996), ‘Observations on emulsifiers at the interface between oil and water by confocal scanning light microscopy’, Lebensm.-Wiss. U.-Technol. 29, 217–226. HERBERT S, BOUCHET B, RIAUBLANC A, DUFOUR E, GALLANT D J (1999), ‘Multiple fluorescence labelling of proteins, lipids and whey in dairy products using confocal microscopy’, Lait 79, 567–575. LORE´N N, LANGTON M, HERMANSSON A-M (1999), ‘Confocal laser scanning microscopy and image analysis of kinetically trapped phase-separated gelatin/maltodextrin gels’, Food Hydrocolloids 13, 185–198. MOSS G E (1976), ‘The microscopy of starch’, in Radley J A, Examination and analysis of starch and starch products, London, Applied Science Publishers, 1–32. HAUGLAND R P

NICOLAS Y, PAQUES M, VAN DEN ENDE D, DHONT J, VAN POLANEN R, KNAEBEL A, STEYER A,

(2003a), ‘MicroRheology: observation of food material during deformation’. Submitted to Food Hydrocolloids.

MUNCH J P, BLIJDENSTEIN T, VAN AKEN G

NICOLAS Y, PAQUES M, KNAEBEL A, MUNCH J P, STEYER A, VAN BLIJDENSTEIN T, VAN AKEN G.

(2003b) ‘Observation by confocal microscope and diffusing wave spectroscopy of network breakdown under oscillation’, submitted to Rev. Sci. Instr. NICOLAS Y, PAQUES M (2003c), ‘A new tool to understand the behaviour of food microstructure under oral deformation conditions’, submitted to J. Sci. Food Agric. PAQUES M, NICOLAS Y, TROMP R H, DHONT J, VAN ENDE D (2003a), ‘Particle deformed under shear by using counter rotation and confocal microscope’, submitted to Rev. Sci. Instr. PAQUES M, NICOLAS Y, VAN BLAADEREN A, IMHOF A (2003b), ‘Method and device for imaging the dynamic behaviour of microstructures under the influence of deformation’ Filed Patent 01204378.2. PAWLEY J B (1995), Handbook of Biological Confocal Microscopy, New York, Plenum Press. PETRAN M, HADRAVSKY M, EGGER M D, GALAMBOS R (1968), ‘Tandem scanning reflected light microscope’, J. Opt Soc. Am. 58, 661–664. PLUCKNETT K P, POMFRET S J, NORMAND V, FERDINANDO D, VEERMAN C, FRITH W J, NORTON I T

(2001), ‘Dynamic experimentation on the confocal laser scanning microscope: application to soft-solid, composite food materials,’ J Microscopy – Oxford, 201, Part 2, 279–290. SUWONSICHON T, PELEG M (1999), ‘Rheological characterisation of almost intact and stirred yogurt by imperfect squeezing flow viscometry’, J Sci Food Agric 79, 911– 921. TAYLOR G I (1934), ‘The Formation of Emulsions in definable fields of flow’, Proc. R. Soc. London, 29 501–523. TESTER R F, KARKALAS J (2002), ‘Starch’, in Steinbu ¨chel A, DeBaets S, VanDamme E J (eds), Biopolymers Vol. 6: Polysaccharides II: Polysaccharides from Eukaryotes, Weinheim, Wiley-VCH, 381–438.

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(2001), ‘Confocal scanning light microscopy (CSLM) on mixtures of gelatine and polysaccharides’, Food Res. Int. 34, 931–938. VAN DE VELDE F, WEINBRECK F, EDELMAN M W, VAN DER LINDEN E, TROMP R H (2001, submitted), ‘Visualisation of biopolymers mixtures using Confocal Scanning Laser Microscopy (CSLM) and covalent labelling techniques’. VAN DE VELDE F, VAN RIEL J, TROMP R H (2002), ‘Visualisation of starch granule morphologies using Confocal Scanning Laser Microscopy (CSLM)’. J. Sci. Food Agric. 82, 1528–1536. XIAO G Q, CORLE T R, KINO G S (1998), ‘Real time confocal scanning optical microscope’ Appl. Phys. Lett. 53, 716–718. TROMP R H, VAN DE VELDE F, VAN RIEL J, PAQUES M

17 Using electronic noses to assess food quality H. Zhang, University of Florida, USA

17.1

Introduction

The quality of a food depends on its color, taste, odor, texture, nutrition and microbial content. Odor is the most complicated and difficult attribute to measure. Inspired by the way the human olfactory system works, the electronic nose is an instrument designed to analyze odors. Commercial electronic noses became available at the beginning of the 1990s. Since then, the electronic nose has generated a widespread interest, and numerous studies have shown its use as a quality control or process-monitoring tool. Compared with traditional odor analysis methods (sensory panels and gas chromatography), the advantages of the electronic nose are high sensitivity, easy sample preparation, fast detection, lower cost, non-destructive operation, and objectivity. There are three general considerations when applying electronic nose technology to evaluate the quality of a food: • first, can the electronic nose be used in the system of interest for measurement of quality? • second, what type of electronic nose should be selected? • finally, what is a suitable data analysis method/procedure? The next sections review the theory, use, available hardware and data analysis methods regarding electronic noses. Section 17.6 (applications) reviews a number of practical examples. A short commentary on likely future trends and a short description of further information sources are provided at the end of this chapter.

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17.2

325

The theory of electronic noses

A well-accepted definition of the electronic nose is ‘an instrument which comprises an array of electronic chemical sensors with partial specificity and an appropriate pattern recognition system capable of recognizing simple or complex odors’ (Gardner and Bartlett, 1994). Generally, an electronic nose is composed of: • • • •

a a a a

sampling system sensor array data acquisition system pattern recognition algorithm.

Mimicking the human nose, the operation of electronic nose begins with ‘sniffing’: collecting and conveying the volatile components of the sample to the sensor array. Sensor ‘states’ are altered through chemical or physical interaction between volatile components and sensors, resulting in electronic signals. These are captured by a data acquisition system, and a ‘cleaning’ procedure is then applied to restore previous conditions in both sensors and sampling system. The electronic signals are further analyzed by pattern recognition algorithms or other data analysis techniques. Electronic noses are different from traditional gas sensors in two aspects: partially selective sensors that are all broadly tuned and multidimensional data that should be analyzed by a multivariate technique. Electronic noses can be used to evaluate food quality when the food has volatile compounds that change with quality changes, either qualitatively or quantitatively. The difference of volatile compounds in headspace should also be large enough to be detected by electronic noses. When the difference is too small to detect at room temperature, heating the sample can release more volatile components into the headspace and may help the analysis. Electronic noses differ from human noses in both their sensors’ types/numbers and signal processing methods, resulting in unmatched detection thresholds and odor discrimination capabilities (Doleman and Lewis, 2001). As a result, what an electronic nose smells is not the same as what a human nose smells (Burl et al., 2001). Therefore, the electronic nose is not a primary analytical technique. An electronic nose has to be trained to fulfill its capability of odor identification. The data for training can be collected in two ways. One is by correlating an electronic nose’s responses with other primary analytical methods, such as sensory evaluation, chromatography and wet chemistry analyses. The other is by collecting an electronic nose’s response toward the ‘known’ samples. The ‘known’ samples can either be prepared or be obtained from a supplier.

17.3

Comparing sensor types of electronic nose

The aroma of a food is a complex mixture of volatile compounds. Often the difference between a ‘good’ or ‘bad’ food odor is in the relative amounts of the

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volatile compounds. It is impossible to have sensors specific to each single compound to measure the difference. The broadly tuned sensors of electronic noses are designed to solve this problem. Except the non-specific requirement, ideally each sensor’s response should be orthogonal to each other. The less the correlation between different sensors, the more information about sample proprieties is provided by the sensor array’s responses. To evaluate a food whose quality deteriorates fast, the sensor’s response and recovery time is also important. Other requirements of the electronic nose sensors include reproducibility to a given odor and insensibility to changes in temperature, humidity and flow rate. At present, three types of gas sensors – metal-oxide, polymer, and surface acoustic wave gas sensors are widely used in commercial instruments (Snopok and Kruglenko, 2002). The significant advantage of metal-oxide sensors (MOS) is their low sensitivity toward humidity and low amount of drift over time. Comparing with MOS, the polymer sensors have the advantages of: • responding to a broad range of organic vapors • operating at room temperature • rapid response and recovery time. Surface acoustic wave gas sensors are of two main types: the bulk acoustic wave device (BAW), also referred to as the quartz crystal microbalance (QCM) and the surface acoustic wave device (SAW). QCM has a linear response to concentration, compared with the power law of metal-oxide and Langmuir response for conducting polymers. The disadvantage of BAW and SAW is their high sensitivity to disturbances such as temperature and humidity fluctuations (Gardner and Bartlett, 1999). There are new types of chemical sensors under development for electronic noses. One example is the so-called Smell-SeeingTM (Rakow and Suslick, 2000) sensor, which detects odors using the colorimetric response from a library of immobilized vapor-sensing dyes. These sensors are insensitive to humidity and provide visual identification of odors. Yet, for the purpose of objective data analysis, the color graphs generated from these sensors may need complex preprocessing to generate suitable multidimensional data. It is arguable whether a single sensing device that produces an array of measurements of odors can be regarded as an electronic nose sensor array (Mielle et al., 2000). There are two ‘single sensing devices’ that had been put under the umbrella of electronic nose sensor arrays: mass spectrometry based sensors (MS-sensor) and gas chromatograph based SAW sensors (GC/SAW). In the MS-sensor system, volatile components are introduced into mass spectrometry without separation and then selected fragment ions are treated as sensor array (Dittmann et al., 2000). As a mature technology, mass spectrometry provides the benefit of reproducibility and standard calibration method. Yet the cost of the instrument may compromise its benefit. For the GC/SAW system, volatile components are separated by a fast GC column and detected by SAW sensor; the responses of the single sensor at different times are viewed as a

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sensor array. The problem with this sensor is that the single sensor used may not respond to some of the important volatiles. When selecting sensors for applications, not only the type of the sensors but also the number of the sensors should be considered. Given that each sensor’s response is orthogonal to each other, the more sensors selected for the electronic nose sensor array, the more information is provided by each measurement of the electronic nose. However, more variables in the multidimensional data will significantly increase the complexity of data analysis. It should be noted that the human nose, which can identify numerous different odors, contains 5 million olfactory receptors, of which there are 1,000 different types. It is almost impossible to have a universal sensor array capable of analysis of all odors associated with foods. For a specific food system, the sensor array may be tailored to measure its related odors. The selection of sensors may base on sensor responses to the food system, which will be discussed in Section 17.5 (data analysis methods).

17.4

Current commercial instruments and selection criteria

Dozens of companies are now designing and selling electronic noses. Table 17.1 lists the major companies and their products for food quality evaluation. The aspects that should be considered when selecting an electronic nose instrument include: • its sampling system • sensor type/numbers • data acquisition system. Table 17.1 Company Name

Main commercial available electronic noses for food quality evaluation Product Name

Sensor types/numbers

Website

WMA Airsensor PEN, i-PEN, MOS, 10 sensors PEN-EDU

http://www.airsense.com/uk/main.htm

Alpha MOS

Fox

http://www.alpha-mos.com/index.htm

Chemsensing Inc.

Chemsensing Smell-SeeingTM Sensor, 36 sensors

Kronos

MOS, Polymer or QCM; 6-24 sensors Mass Spectrometry

http://www.chemsensing.com/index.html

Cyrano Sciences Cyranose

Polymer; 32 sensors

Estcal HKR Sensorsysteme

zNose QMB 6 MS-SensorÕ SensiTOFÕ

GC/SAW technology http://www.estcal.com/ QCM, 6 sensors http://www.hkr-sensor.de/start.htm Mass Spectrometry Mass Spectrometry

http://cyranosciences.com/

Lennartz Electronic

MOSES II

QCM, 8 sensors MOS, 8 sensors

Smart Nose

SMart NoseÕ Mass Spectrometry

http://www.lennartz-electronic.de/ indexmain.html http://www.smartnose.com/welcome.html

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Among the three aspects, sensor type and numbers have been discussed in Section 17.3. The rest will be discussed below.

17.4.1 Sampling system The sampling system is designed to provide a stable and reproducible sensor reading environment so that all factors capable of influencing sensor responses are kept under control (Falcitelli et al., 2002). Generally the sampling system of electronic noses has two separate chambers: a sample chamber and a sensor chamber. The temperature and/or humidity of both chambers are controlled. The static or dynamic headspace of samples are conveyed to sensor chambers through gas flow. Inert gas is then applied to both chambers to clean any possible odor memory of previous sample or possible contamination. Most of the hand-held electronic noses, such as the product from Cyrano Sciences, do not have a sample chamber. It is necessary in that case to design your own sample chamber to ensure repeatable measurements. Non-absorbent and inert materials should be selected to build both chambers to prevent leftover odors from previous sample or other contaminations (Falcitelli et al., 2002). The optimal volume of the sample chamber varies for different applications. For example, for ground spices with uniform composition with the available amount for sampling is limited, a big chamber may reduce the concentrations of volatile components in the headspace and result in a lower signal/noise ratio of sensor responses. On the other hand, if the ripeness of apple is under investigation, a small sample chamber may not be large enough to hold the sample. The fluid dynamics of sampling systems should also be properly designed to avoid any stagnant regions. If the electronic nose was designed for different applications, the cleaning procedure of using inert gas to remove odor memory should have flexibility. For foods with strong volatile contents, a longer cleaning time or fast cleaning gas flow rate is expected. For some other applications, such as monitoring the quality of foods that deteriorate fast, too long a cleaning time may make some readings not feasible.

17.4.2 Data acquisition system The way to acquire the time-dependent analog sensor signal is an important design parameter for an electronic nose. Steady-state or static, rather than transient or dynamic are the descriptors commonly used to define the sensors’ responses. From a user’s point of view, it would be ideal if the data acquisition system of electronic nose would record the sensors’ responses over time and make it available for users. There is more information carried in the time-series data than in the ‘static’ data, as illustrated in Fig. 17.1. Among the responses of an electronic nose’s sensor toward different spices, the discrimination between turmeric and basil and between turmeric and pepper were difficult when only the steady-state information (sensor’s responses at 240 sec) were used. However, the slope of sensors’ responses on their way toward steady-state were significantly

Using electronic noses to assess food quality

Fig. 17.1

329

Responses of an electrical nose’s sensor toward three different spices.

different. The recorded time-series data or parameters extracted from time-series data may provide better discrimination ability through proper data analysis procedures. Even when static data is preferred for applications due to its simplicity, the time-series data of sensor responses are still valuable. It is necessary to optimize the best response time for some commercial electronic noses whose sensor response will never reach steady state. Access to the sensors’ responses over time will facilitate the optimization with less experiments and higher accuracy. Most commercial electronic noses provide their pattern recognition software package (Snopok and Kruglenko, 2002). However, outside software, such as SASÕ, STATISTICAÕ, S-PlusÕ, SPSSÕ, MATLABÕ or other Neural Network packages are also popular in electronic nose data analysis.

17.5

Data analysis methods

The underlying relationship between the responses of electronic nose and the characteristics of the sample is determined by two basic approaches – statistical multivariate analysis or artificial neural networks. Currently, the data analysis methods for electronic noses are solely used to classify or recognize patterns. The commonly used statistical multivariate methods include principal component analysis (PCA), discriminant function analysis (DFA), Cluster analysis (CA), partial least squares (PLS) and canonical correlation analysis (CCA). For electronic nose data analysis, the most commonly used neural

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Fig. 17.2

Classification scheme of the commonly employed electronic nose data analysis methods.

network structure is multi-layer perceptron (MLP) trained by back-propagation (BP). Other neural network structures that have been applied to electronic nose data include neuro-fuzzy system (Ping and Jun, 1996), self organizing maps (SOM) (Marco et al., 1998), MLP with genetic algorithms (GA) (Kermani et al., 1999), adaptive resonance theory (ART) (Distante et al., 2000) and radial basis function (RBF) (Evans et al., 2000). Most statistical multivariate methods are based on a linear approach while neural networks are non-linear. In cases where the data correlations are nonlinear, neural networks may perform better than conventional multivariate methods. The number of replicates necessary depends not only on the model structure, but also the number of the parameters used in the model. Compared with linear multivariate methods, neural networks generally need more parameters to model the data. Therefore, neural networks tend to need more replicates to be obtained in electronic nose experiments. According to the goal of the study, the data analysis methods applied for electronic noses can be divided into two categories: exploratory methods and predictive methods, as shown in Fig. 17.2. Exploratory methods are used to detect whether there are systematic patterns of the investigated data set, while predictive methods are designed to find the prediction model to describe the data structures when there is priori knowledge about their existence. Among the commonly used e-nose data analysis methods, principal component analysis, cluster analysis and unsupervised neural networks, such as self organizing maps belong to the category of the exploratory methods. Discriminant function analysis, partial least square, canonical correlation analysis and all supervised neural networks, including multilayer perceptron, are predictive methods. Canonical correlation analysis, a technique for analyzing the relationship

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between two sets of variables, can be used both in exploring and predicting the relation between electronic nose measurements and sample characteristics. As discussed in Section 17.3, the sensor array may be optimized for a specific food system and the selection of sensors should be based on sensors’ responses toward the specific food system. The rules of variable selection in some traditional multivariate methods can be applied to the selection of sensors since each sensor’s response can be viewed as a variable. The rules of variable selection can be based on either exploratory or predictive methods. Both discriminant analysis, a representative predictive method, and principal component analysis, a representative exploratory method, have well-defined rules for variable selection (Khattree and Naik, 2000). Boilot et al. (2003) also reported using genetic algorithms to search for optimal sensor combinations for fruits. As one of the most widely applied multivariate techniques, principal component analysis can also be used as a data compressing tool for pre-processing of data in neural networks (Principe et al., 1999). By reduction of the information’s dimensionality, principal component analysis reduces the number of inputs for neural networks, resulting in a lower number of parameters necessary in neural networks. Like any other predictive structure, the generalization ability of a supervised neural network will be enhanced with a decreasing number of parameters. Hence, pre-processing of data by principal component analysis improves the generalization ability of neural networks. This method has been applied to classify different brands of coffee (Pardo et al., 2000).

17.6

Applications

Substantial work has been done to apply electronic nose technology for food quality evaluation.

17.6.1 Fruit ripeness determination For fruits, one of the most important parameters that influence quality is their ripeness stage. In many fruits, ripening is associated with significant changes of ethylene concentration as well as other volatile compounds emitted. By detecting the change of volatiles, electronic nose technology is promising to predict ripeness of individual fruits during harvesting as well as in the sorting and grading process (Benady et al., 1995). Compared with other existing ripeness determination methods, electronic nose technology excels in two aspects: non-destructive and low cost (Llobet et al., 1999). The first application of electronic nose technology in fruit ripeness determination was to classify muskmelons with four ripeness stages (Benady et al., 1995). The instrument used was a single sensor ‘electronic sniffer’ and the samples were classified based on Bayesian maximum likelihood criteria. The same procedure has been applied for blueberry ripeness determination (Simon et al., 1996). More sophisticated electronic nose devices (sensor numbers range

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from 4 to 12) and data analysis procedures (PCA, DFA, neural networks such as MLP, SOM, ART and neuro-fuzzy) were applied to ripeness determination of bananas (Llobet et al., 1999), tomatoes (Maul et al., 1999), peaches, pears and apples (Brezmes et al., 2000). All works mentioned above reached a high classification rate of fruit ripeness stages, ranging from 80 to 100%.

17.6.2 Fermentation process monitoring Organoleptic properties of fermented foods undergo continuous changes during the fermentation process, resulting in new or improved flavors, aromas and textures in the food products (Deshpande and Salunkhe, 2000). The quality of fermented foods highly depends on their aromas and the fermentation process is sensitive to many operational parameters. In the aroma-producing fermentation, the application of electronic nose technology to monitor aroma profiles allows direct evaluation of the product’s aroma quality and makes the detection and correction of deviations possible at an early stage (Pinheiro et al., 2002). Feasibility studies have shown the ability of electronic nose technology to monitor the fermentation of beer (Pearce et al., 1993), sausage (Eklo¨v et al., 1998), baker’s yeast (Bachinger et al., 1998) and wine (Pinheiro et al., 2002). The results reported in the wine application were quite interesting: without the sample pre-concentration through a membrane process called pervaporation, the electronic nose used (A32S AromaScan) can only perceive the evolution of ethanol during fermentation. However, after integrating pervaporation as a selective enrichment step, the electronic nose can detect the difference of aroma contents, even in the presence of ethanol. One of the current weakness of electronic noses is that some systems are highly sensitive to humidity, ethanol and other high polar compounds (Harper, 2001). Since a lot of fermented foods, such as beer, wine, soy sauce, etc., have high contents of polar compounds, considerations should be made in applying electronic noses to these fermentation process.

17.6.3 Spoilage detection Food spoilage is the process by which the original nutritional value, texture and flavor of the food are damaged. This eventually renders a product unacceptable for human consumption. For fresh food the quality change can be microbial or chemical based, or a combination of both. Both microbial and chemical activities will result in off-flavor formation. Classical microbial contamination detection methods for perishable foods is of limited value in early stages since these foods are cold or eaten before the results of microbiological tests are available (Huis in’t Veld, 1996). By detecting the changes of volatiles during spoilage, an electronic nose is a good candidate for early detection of food spoilage. The electronic nose technology has been successfully applied to detect deterioration in fish (Schweizer-Berberich et al., 1994), vacuum-packaged beef

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(Blixt and Borch, 1999), frying fat (Muhl et al., 2001), wheat (Evans et al., 2000), fresh cut vegetables (Riva et al., 2001), dairy product (Korel and Balaban, 2002) and tofu (Park et al., 2002).

17.6.4 Correlation with sensory evaluation Consumers are the ultimate arbiters of food quality. Sensory evaluation using consumer or trained human panelists to assess the quality of food provides original information about food quality. However, sensory evaluation has the shortcomings such as time-consuming and expensive. As indicated in the introduction, aroma is the largest contributor to the diversity of food quality. Sensible to a broad range of aromas, electronic noses have great potential to be correlated with sensory evaluation scores and then to be a substitute of sensory evaluation after proper training with samples labeled by sensory testing. Many studies showed that electronic noses related well to sensory evaluation results. Annor-Frempong et al. (1998) reported that the electronic nose’s responses toward the intensities of ‘boar taint’ related to those of the sensory panel with a canonical correlation of 0.78. Shen et al., (2001) found that for the vegetable oils (canola, corn and soybean), the correlation coefficients between sensory evaluation and electronic nose sensors’ responses were within a range of 0.76 to 0.99. Garcia-Gonzalez and Aparicio’s work showed that the fusty attribute of olive oils could be explained well by the principle components of an electronic nose’s sensor response, with multiple-R equal to 0.98 (Garcia-Gonzalez and Aparcio, 2002). In some studies, the electronic nose data alone did not show excellent correlation with sensory evaluation results. However, by coping with other objective sensory measurement methods, such as machine vision and electronic tongues, the electronic sensory system correlated well with the scores from human sensory panelists (Korel et al., 2001; Bleibaum et al., 2002).

17.6.5 Detection of packaging odors Packaging materials used for food include paper, fiberboard, glass, tinplate, aluminum and various types of plastics. Among them, plastics, paper and fiberboard may transmit odors to food. Printing inks, which are on most food packages, may also cause unpleasant odors. To ensure the quality of food, it is important that the whole package is free of undesirable odors. Many volatile compounds have been shown to contribute to odor taints (Tice, 1996). Electronic nose technology as a possible tool for packaging odor detection is quick and easy to use compared with the traditional method – gas chromatography. Electronic nose technology has been successful in detecting packaging odor taints from paper (Holmberg et al., 1995), PET (Poling et al., 1997), and printing inks on assorted plastic films (Deventer and Mallikarjunan, 2002). However, Heinio¨ and Ahvenainen reported that the packaging aroma itself was not a reliable indication of the taints perceived in the packaged food (Heinio¨ and Ahvenainene, 2002).

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17.6.6 Aroma profile control Raw materials of plant origin vary with climate, geography, species, and growth stage at harvest (Shu and Lawrance, 1996). To ensure consistent quality of finished food products, it is ideal that the raw materials have similar aroma profiles. Electronic noses have been reported to be feasible in quality control of raw materials such as coffee (Gretsh et al., 1998), herbs (Hirschfelder et al., 2000) and olive oils (Stella et al., 2000).

17.7

Future trends

Electronic nose technology is still in its development phase, both with respect to odor sensing systems (hardware) and data analysis methods (software). Active future research is expected in the following areas: • the development of sensor technology to enhance sensors’ selectivity, sensitivity and reproducibility • the improvement of sampling system for optimal stability and reproducibility • the adaptation of new data analysis techniques from the areas of multivariate statistics and artificial neural networks. The major weakness currently associated with electronic nose technology is lack of a universal and acceptable calibration method, which puts the repeatability of an electronic nose instrument in question in case of sensor drift or poisoning. Lack of sufficient calibration methods also make it difficult to transfer data among different electronic noses. An effective standard calibration method must be developed in the future through the collaboration of electrical engineers, statisticians and food scientists. With a better calibration system, researchers can pool data together to build a shared database (or library) to facilitate their studies, such as those already developed in the area of mass spectrometry. Currently qualitative studies are still the main focus of electronic nose technology. Nevertheless, it will be valuable for the purpose of food quality evaluation to know not only the qualitative aspect of samples but also their quantitative attributes. In the future, there must be new research efforts directed at quantitative determination of the composition and properties of food products using electronic nose technology. Future research efforts may also be geared towards the development of sensor sets that are specifically tuned toward a special application since the broad selectivity sensor arrays normally implemented in commercial instruments may prove to be not useful for some applications. The criteria of an ‘optimal sensor set’ for a certain food system can be developed through a good understanding of the chemical basis of the food volatile system. For example, if the quality of a food has a high correlation with its content of alcohols, the ‘optimal sensor set’ should provide sufficient selectivity and sensitivity to these alcohols. With new advances in both odor sensing systems and data analysis methods, the electronic nose technology is likely to find even wider applications. One

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possible application is in the area of flavor release in which the time-intensity profiles of flavors are studied (Yeretzian et al., 2000). Such an application will require the dynamic data capture ability of electronic noses as well as data analysis procedures that can handle time-series data. Electronic nose technology provides the potential to monitor fast on-line changes in volatile compositions. In the near future, technological advancements such as faster responding sensors, automated sampling systems and unsupervised data analysis will make it possible for electronic noses to serve as tools for on-line quality evaluation that was traditionally done off-line in a laboratory setting.

17.8

Sources of further information and advice

http://www.nose-network.org This is the website of the NOSE network that is intended to improve the effectiveness of European R&D in electronic nose technologies and is a forum for information exchange between users, researchers, developers and producers of devices and systems. The review part of the website (http://www.nose-network.org/review) lists some active research groups and major commercially available instruments all over the world in the electronic nose area. Corresponding links to individual research groups and commercial instrument manufacturers are also provided. Haykin S (1999), Neural Networks – A comprehensive foundation, 2nd edn, Upper Saddle River, New Jersey, Prentice-Hall. This book discusses the basics in artificial neural networks and available topologies.

17.9

References

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and FOLLAND RS (2003), ‘Electronic noses intercomparison, data fusion and sensor selection in discrimination of standard fruit solutions’, Sensors and Actuators, B: Chemical, 88, 80–88. BREZMES J, LLOBERT E, VILANOVA X, SAIZ G and CORREIG X (2000), ‘Fruit ripeness monitoring using an electronic nose’ Sensors and Actuators, B: Chemical, 69, 223– 229. BURL MC, DOLEMAN BJ, SCHAFFER A and LEWIS NS (2001), ‘Assessing the ability to predict human percepts of odor quality from the detector responses of a conducting polymer composite-based electronic nose’ Sensors and Actuators B: Chemical, 72, 149–159. DESHPANDE SS and SALUNKHE DK (2000), ‘Grain legumes, seeds and nuts: rationale for fermentation’ In Fermented grain legumes, seeds and nuts: a global perspective, Rome, Food and Agriculture Organization of the United Nations. DEVENTER DV and MALLIKARJUNAN P (2002), ‘Optimizing an electronic nose for analysis of volatiles from printing inks on assorted plastic films’ Innovative Food Science and Emerging Technologies, 3(1), 91–99. DISTANTE C, SICILIANO P and VASANELLI L (2000), ‘Odor discrimination using adaptive resonance theory’ Sensors and Actuators B: Chemical, 69, 248–252. DITTMANN B, ZIMMERMANN B, ENGELEN C, JANY G and NITZ S (2000), ‘Use of the MS-sensor to discriminate between different dosages of garlic flavoring in tomato sauce’ J. Agric. Food Chem, 48, 2887–2892. DOLEMAN BJ and LEWIS NS (2001) ‘Comparison of odor detection thresholds and odor discriminablities of a conducting polymer composite electronic nose versus mammalian olfaction’ Sensors and Actuators B: Chemical, 72, 41–50. ¨ V T, JOHANSSON G, WINQUIST F and LUNDSTORM I (1998), ‘Monitoring sausage EKLO fermentation using an electronic nose’ Journal of the Science of Food and Agriculture, 76, 525–532. EVANS P, PERSAUD K, MCNEISH A, SNEATH R, HOBSON N and MAGAN N (2000), ‘Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data’ Sensors and Actuators B: Chemical, 69, 348–358. FALCITELLI M, BENASSI A, FRANCESCO FD, DOMENICI C, MARANO L and PIOGGIA G (2002), ‘Fluid dynamic simulation of a measurement chamber for electronic noses’ Sensors and Actuators B: Chemical, 85, 166–174. GARCIA-GONZALEZ DL and APARICIO R (2002), ‘Detection of defective virgin olive oils by metal-oxide sensors’ European Food Research and Technology, 215(2), 118–123. GARDNER JW and BARTLETT PN (1994), ‘A brief history of electronic noses’ Sensors and Actuators B: Chemical, 18–19, 211–220. GARDNER JW and BARTLETT PN (1999), Electronic Noses: Principles and Applications, Oxford, Oxford University Press. GRETSH C, TOURY A, ESTEBARANZ R and LIARDON R (1998), ‘Sensitivity of metal oxide sensors towards coffee aroma’ Seminars in Food Analysis, 3(1), 37–42. HARPER WJ (2001), ‘The strengths and weaknesses of the electronic nose’, In Rouseff RL and Cadwallader KR, Headspace Analysis of Foods and Flavors: Theory and Practice, New York, Kluwer Academic, 59–71. ¨ RL and AHVENAINEN R (2002), ‘Monitoring of taints related to printed solid boards HEINIO with an electronic nose’ Food Additives and Contaminations, 19(supplement), 209–220. ¨ RSTER, A, KU ¨ HNE, S, LANGBEHN, J, JUNGHANNS, W, PANK, F and HIRSCHFELDER, M, FO HANRIEDER, D (2000), Using multivariate statistics to predict sensory quality of BOILOT P, HINES EL, GONGORA MA

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PARDO M, NIEDERJAUFNER G, BEBUSSI G, COMINI E, FAGLIA G, SBERVEGLIERI G, HOLMBERG M

and LUNDSTORM I (2000), ‘Data preprocessing enhances the classification of different brands of Espresso coffee with an electronic nose’ Sensors and Actuators B: Chemical, 69, 397–403. PARK EY, NOH BS and KO S (2002), ‘Prediction of shelf life for soybean curd by electronic nose and artificial neural network system’ Food Science and Biotechnology, 11(3), 245–251. PEARCE TC, GARDNER JW, FRIEL S, BARTLETT PN and BLAIR N (1993), ‘Electronic nose for monitoring the flavor of beers’ The Analyst, 118, 371–377. PING W and JUN X (1996), ‘A novel recognition method for electronic nose using artificial neural network and fuzzy recognition’ Sensors and Actuators B: Chemical, 37, 169–174. ¨ FER T and CRESPO JG (2002), ‘Monitoring the aroma PINHEIRO C, RODRIGUES CM, SCHA production during wine-must fermentation with an electronic nose’ Biotechnology and Bioengineering, 77(6), 632–640. POLING J, LUCAS Q and WEBER K (1997), ‘Quality control of packaging with the electronic nose Fox-4000’ Journal of Automatic Chemistry, 19(4), 115–116. RIVA M, BENEDETTI S and MANNINO S (2001), ‘Shelf life of fresh cut vegetables as

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measured by an electronic nose: preliminary study’, Italian Journal of Food Science, 13(2), 201–212. PRINCIPE JC, EULIANO NR and LEFEBVRE WC. (1999), Neural and Adaptive Systems: Fundamentals through Simulations, New York , John Wiley & Sons. RAKOW NA and SUSLICK KS (2000) ‘A colorimetric sensor array for odour visualization’ Nature, 406, 710–714. ¨ PEL W (1994), ‘Characterisation of food SCHWEIZER-BERBERICH M, VAHINGER S and GO freshness with sensor arrays’ Sensors and Actuators, B: Chemical, 18/19, 282–290. SHEN N, MOIZUDDIN S, WILSON L, DUVICK S, WHITE P and POLLAK L (2001), ‘Relationship of electronic nose analyses and sensory evaluation of vegetable oils during storage’, Journal of the American Oil Chemists’ Society, 78(9), 937–940. SHU C and LAWRENCE BM (1996), ‘Reasons for the variation in composition of some commercial essential oils’ In Risch SJ and Ho C, Spices: flavor chemistry and antioxidant properties, Washington, DC, American Chemical Society, 138–159. SIMON JE, HETZRONI A, BORDELON B, MILES GE and CHARLES DJ (1996), ‘Electronic sensing of aromatic volatiles for quality sorting of blueberries’ J. Food Sci., 61, 967–970. SNOPOK BA and KRUGLENKO IV (2002), ‘Multisensor systems for chemical analysis: stateof-the-art in Electronic Nose technology and new trends in machine olfaction’ Thin Solid Films, 418, 21–41. STELLA R, BARISCI JN, SERRA G, WALLACE GG and DE ROSSI D (2000), ‘Characterization of olive oil by an electronic nose based on conducting polymer sensors’ Sensors and Actuators, B: Chemical, 63, 1–9. TICE P (1996), ‘Packaging material as a source of taints’ In Saxby MJ, Food Taints and Off-Flavors, 2nd edition, London, Blackie Academic and Professions, 226–263. YERETZIAN C, JORDAN A, BREVARD H and LINDINGER W (2000), ‘Time-Resolved Headspace Analysis by Proton-Transfer-Reaction Mass-Spectrometry’, In Roberts DD and Taylor AJ, Flavor Release, Washington, DC, American Chemical Society, 58–72.

18 Rapid olfaction arrays for determining fish quality ´ lafsdo´ttir, Icelandic Fisheries Laboratories G. O

18.1

Introduction

The rapid detection of food quality has been one of the main application areas for the newly developed electronic nose technique. In the early 1990s commercial instruments were launched on the market for this purpose, but some drawbacks to their immediate use in the industry became apparent. The main reasons were that the instruments were not fully developed, and problems occurred because of their sensitivity to humidity and environmental conditions, which led to misinterpretation of their performance. Active research in this area has since focused on characterising better the overall technique. Improvements of the selectivity, sensitivity and reproducibility of the gas sensors have been the key issues. Identification of other important factors not directly related to the development of the sensor technologies have been brought into focus. This includes sampling techniques and data analysis which are essential parts of the overall technique (Haugen and Kvaal, 1998; Haugen, 2001; Mielle and Marquis, 1999; Gardner and Bartlett, 1999). Application areas for electronic noses include quality and safety monitoring of food related to the detection of volatile compounds associated with oxidative changes or the growth of microorganisms (bacteria, moulds, fungi) leading to off-flavour development. This chapter summarises developments regarding the use of the electronic nose technique for quality and safety monitoring of fish. Criteria to determine the freshness quality of fish and the industrial need to measure fish freshness will be discussed. An overview of sensor technologies used in electronic noses will be given and the importance of sampling and data analysis for the measurement of fish volatiles will be highlighted. Examples will be given of applications to detect spoilage of fish using an electronic nose developed in our laboratory that is based on electrochemical sensors.

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Rapid and on-line instrumentation for food quality assurance

Spoilage odours and product quality: the case of fish

The odour of fresh fish is one of the most important quality parameters to determine if the fish is acceptable for consumption. Complicated processes including physical, chemical, biochemical and microbiological changes occurring post mortem in fish are responsible for the loss of freshness and the onset of spoilage. Various factors such as species, handling and different storage conditions influence the spoilage pattern. The characteristic odour of fish changes because of degradation of the tissue, and the development of the characteristic spoilage odours of fish have been associated with varying levels of different volatile compounds present in the headspace of fish during storage (Lindsay et al., 1986; Josephson et al., 1986). Species-related fresh fish odours are contributed by long chain alcohols and carbonyl compounds (i.e 1, 5octadien-3-ol, 2, 6–nonadienal) that are oxidatively derived from long-chain polyunsaturated fatty acids such as eicosapentaenoic acid 20:5!3 (Josephson et al., 1984). Spoilage odours develop as a result of mirobial and oxidative degradation of the fish components, and compounds such as trimethylamine (Oehlenschla¨ger, 1992), short-chain alcohols, (Kelleher and Zall, 1983; Ahmed and Matches, 1983), carbonyls, esters, hydrogen sulfide, methylmercaptan, dimethyl disulfide and dimethyl trisulfide are produced (Herbert et al., 1975; Kamiya and Ose, 1984). Oxidation of fatty acids contributes to the spoilage odours of fish with the formation of aldehydes like hexanal, 2, 7–heptadienal and 2, 4, 7–decadienal (McGill et al., 1974). These are a few examples of important compounds that can be used to monitor freshness and spoilage of fish. The microbially formed degradation compounds are present in high concentrations (ppm) in the headspace of fish while the compounds contributing to fresh fish odour and oxidation odours are present in much lower ´ lafsdo´ttir and Fleurence, 1998). concentrations (ppb) (O Low flavour thresholds of key odorants enable them to have an impact on the odour, but their concentrations are often below the detection limit of the gas sensors. Therefore, it cannot be assumed that the gas sensors are measuring the overall odour but rather detecting both odorous and non-odorous components that are in the highest concentration in the headspace. Selected volatile compounds or a combination of them, that represent the different changes occurring during spoilage, have been suggested as indicators of freshness and spoilage in fish (Lindsay et al., 1986). Volatiles associated with safety of fish product like histamine causing scombroid poisoning is the leading cause of finfish-borne illness. Histamine has traditionally been used as an indicator of safety. Selective detection of bioamines has been suggested using an amperometric detection of histamine with a methylamine dehydrogenase polypyrrole-based sensor (Zeng et al., 2000). Rapid measurement of the headspace of fish with an electronic nose has a potential to detect the freshness or quality stage of the fish, if the sensor array can detect the respective indicator compounds that truly represent the condition of the fish.

Rapid olfaction arrays for determining fish quality

18.3

341

Electronic noses: principles and applications

The key principle involved in the electronic nose concept is the transfer of the total headspace of a sample to a sensor array that detects the presence of volatile compounds in the headspace, and a pattern of signals is provided that characterises the sample (Gardner and Bartlett, 1999). The use of pattern recognition techniques is required to interpret the resulting signals that are dependent on the sensors’ selectivities and sensitivities and the characteristics of the volatile compounds in the headspace. The qualitative discrimination power of the electronic nose technique is similar to the subjective discrimination of odours by the human nose. The common approach in the multipurpose commercial electronic noses is to characterise odours by generating patterns with a sensor array using numerous sensors with partly overlapping selectivities. Another approach is the use of highly selective sensors for specific indicator compounds. For both approaches basic understanding of the composition and chemistry of the volatile compounds being measured is a key factor to ensure meaningful evaluation of the sensor responses. Many studies have stressed the importance of comparing the electronic nose techniques to traditional analysis of volatile compounds by gas chromatography and obtain information about the most abundant volatiles in the headspace. This knowledge can be used further to select sensors in an array that are sensitive to some key indicator compounds in the headspace. For quantitative analysis a few sensors from the array can give adequate information, given that the selectivity of the sensors cover the different classes of compounds relevant for the particular application. Numerous electronic nose instruments based on different types of sensors, sampling systems and data analysis procedures have been developed. The leading companies manufacturing electronic nose instruments are from Europe, USA and Japan. Instruments frequently used for food application in the scientific literature are from Alpha M.O.S. (France), Cyrano Science (USA), Neotronic Scientific, Inc. (USA), Lennartz Electronic, Germany, Univ. Rome Tor Vergata, Italy, Nordic Sensor Technologies and S-SENCE, Sweden.

18.3.1 Different sensor technologies Gardner and Bartlett (1999) gave a comprehensive overview of the electronic nose technique in a recent book, including sensor technology, data analysis and selected applications. A brief summary will be given here to introduce the different sensor technologies used in electronic noses. • Metal oxide semiconductors (MOS): A commercially available device called the Tagushi sensor is based on tin oxide (SnO2) sensors which are doped with precious metals to alter the response characteristics of the semiconductor. The gas analytes interact with surface absorbed oxygen and the conductivity of the tin oxide film changes. Typical operating temperatures are high (around 300–400ºC) to ensure that the surface reactions are rapid and to decrease the chance that chemisorbed water will interfere.

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• Metal oxide semiconducting field effect transistors (MOSFETs) are related to the MOS sensors but the output signal derives from the change in potential when the gas molecules react at the catalytic surface. The operating temperature is 100–200ºC. • Quartz crystal microbalance (QCM) devices measure the mass of molecules adsorbed on the sensor surface. The active element is piezoelectric crystal with a fundamental resonant frequency which decreases when mass is added to it. Bulk acoustic wave (BAW) sensors and thickness shear mode resonators are common types of these devices. They are operated at room temperature and have high stability over time. • Surface acoustic wave (SAW) sensors have interdigitated electrodes on a piezoelectric substrate and a thin coating of a selective absorbing material on the surface. Different coatings include polymers, lipids, Langmuir–Blodgett films and self-assembled monolayers. A radio frequency voltage is applied which changes when molecules are absorbed on the surface. SAW sensors have higher sensitivities and faster response times than QCM devices and can be mass-produced at low cost. They have good reproducibility, but their main limitation, as for many types of sensors, is their sensitivity to humidity. • Conduction polymer (CP) chemiresistors are based on measurements of the resistance of a thin polymer film most often made from polypyrrole which has a conjugated -electrode system. The sensors are made by electropolymerizing a thin polymer film across a narrow electrode gap. The devices can be operated at room temperature and the sorption of gas molecules changes the conductivity of the polymer. • Optical sensors are based on a light source that excites, for example, the gas analyte, and the signal measured is the resulting absorbance, reflectance, fluorescence or chemiluminescence • Electrochemical sensors contain electrodes and an electrolyte. The responses generated are dependent on the electrochemical characteristics of the molecules that will be oxidised or reduced at the working electrode and the opposite reaction will take place at the counter electrode. The output signal is the measured voltage between the electrodes generated by the reactions. Their advantages include long-term stability, insensitivities to humidity and linear dependence on gas concentrations. An electronic nose FreshSense developed in our lab in collaboration with the company Bodvaki (Iceland) is based on commercial electrochemical gas sensors (CO, SO2, H2S and NH3). In this chapter examples of applications of this instrument to detect spoilage of fish will be given to demonstrate the various parameters related to sampling, temperature control, choice of reference methods and data analysis.

18.3.2 Sampling systems Sampling conditions Sampling in electronic nose systems involves the transfer of headspace from the sample to the sensor array. In general it depends on the volatility of the

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343

components to be analysed and what sampling methods are most suitable. Closed sampling compartments are commonly used to allow the headspace to equilibrate and become enriched with the sample’s volatiles. Temperature directly influences the composition of volatiles in the headspace above the food sample and their concentration is also dependent on the sample size, in particular the exposed surface area of the sample and the ratio of sample to headspace in the sampling container. Transfer of headspace Static headspace sampling methods are simple and low-cost. The benefits of using simple sampling systems and static headspace analysis is the short time and simple equipment involved in sampling. In addition the volatiles are collected in concentrations which represent their actual vapour pressure in the sample which allows more meaningful comparison to sensory methods. Mielle and Marquis (1999) compared five different transfer methods irrespective of the sensor techniques and concluded that the transfer techniques considerably influenced the system performance. Use of a carrier gas to transfer volatiles is common in electronic nose systems. The introduction of a carrier gas to transfer the headspace will result in lower concentration of volatiles reaching the sensors and less sensitive detection. Flushing of sensors with inert gas is often necessary to clean the sensors and speed up their recovery after exposure to samples. Concentration of volatiles Solid-phase microextration (SPME) and other more complicated preconcentration sampling techniques commonly used in gas chromatography analysis have been used to increase the concentration of components that are present in low levels to allow detection by electronic noses. Marsili (2001) used an alternative electronic nose technique by using SPME, mass spectrometry (MS) and multivariate anlysis (MVA) for assessing oxidation off-flavours in foods. By selecting peaks that contributed directly to the oxidation flavour and ignoring unimportant background chemicals it was possible to improve PCA groupings. They claimed that this technique was more robust than the new chemical sensor technologies currently used in e-nose instruments because of the proved track record of MS detectors. Influence of temperature For improved performance of current e-nose systems it would be useful to take into account the equilibration kinetics of the headspace (Mielle and Marquis, 1999). The most important factor is the control of the sample temperature. Many of the commercial instruments today are equipped with automatic sampling systems and temperature controls. The influence of temperature on the repeatability of measurements and discrimination efficiency of SnO2 sensors has been studied by Roussel et al. (1999). They quantified the influence of the temperature of the headspace, the measurement cell and the way of injecting samples and concluded that for each

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application of aroma classification it is necessary to perform an optimisation of the experimental conditions. For the experimental conditions studied, ‘static’ injection proved to be more discriminant and repeatable than the ‘dynamic’ ones for wine off-flavour volatiles. The lower temperature of the headspace studied (35ºC) proved to be more discriminant for the headspace generated than the higher temperature (60ºC) although the volatiles were more concentrated at higher temperature. The temperature of the sample will thus influence the composition of the volatile compounds in the headspace. To increase the amount of volatiles in the headspace higher temperatures can be used, but at the same time the warming up of samples is often the time-limiting factor in the analysis. At low temperatures, only very volatile compounds are present and slight changes in sample temperature will influence the vapour pressure of the volatiles and the headspace composition. Figure 18.1 illustrates the influence of sample temperatures on the response of the NH3 sensor in a storage study of haddock in ice in our laboratory. The study included preliminary experiments to observe the effect of different temperature on the response of the sensors when measuring fish samples. Samples (heads and fillets) from a storage experiment of whole haddock stored in ice were measured. Haddock heads were used to represent the whole fish. Spoilage signs of whole fish will first appear on the gills and the skin, while the invasion of the microbial flora into the fillets is much slower. Therefore, the responses of the sensors to the heads are much higher than to the fillets. The temperature of the samples was monitored but not controlled during the measurements at room temperature. The temperature of the samples was 8– 10ºC before the first measurement, increasing to 10–12ºC before the second

Fig. 18.1 An example of temperature influences on the repeatability of e-nose measurements showing the response of the NH3 sensor to haddock fillets and heads during repeated measurements of the same sample at temperatures, daily sampling.

Rapid olfaction arrays for determining fish quality

345

measurement, and reaching 14–15ºC before the third measurement started. In Fig. 18.1 the increasing responses of the sensors with temperature illustrate clearly the importance of controlling the sample temperature during measurement. This influence of sample temperature has implications for the development of hand-held devices. Sampling systems with temperature control will improve the repeatability of measurements. Tempering of the samples at room temperature before measuring may also give more reproducible results, but this is not practical for non-destructive sampling.

18.3.3 Data analysis Jurs et al. (2000) give an excellent overview of methods for analysis of data from electronic noses including multivariate methods and neural networks. The first step is the assembling of the data set of the sensor array responses for the analyte of interest. The second step is to describe the use of data preprocessing and normalisation to provide more useful input for the mathematical tools like principal components analysis or neural networks selected for the data analysis. The third step is the selection of the features to be used for the pattern recognition. This is often the steady-state response from each sensor as the input, but no general guidelines exist and it is necessary to explore different strategies for each application and sensor array. The fourth step is performing the data analysis and the techniques used can be catogorised as statistical techniques or neural network-based approaches. These various approaches can be used for classification while others can be used for quantification. The fifth step is the validation of the models preferably using data not used in creating the models. The methods most often used for electronic noses are linear discriminant analysis (LDA), principal component analysis (PCA), principal component regression (PCR), partial least squares (PLS), cluster analysis and artificial neural networks (NN). • LDA has been used successfully to classify samples based on formulating boundaries between components of different classes and thus separate the samples into for example, ‘good’ and ‘bad’ lots. • PCA is an efficient approach to reduce the dimensionality of a data set. Often two or three principal components provide an adequate representation of the data for a graphical output. Visual examination of the data can thus provide useful information about both samples and sensors. Loading plots can determine which sensors are providing similar information and which are providing unique information and can thus form a basis for selecting the useful sensors in an array. PC plots describe data variation and may or may not provide class separation. PCA is sometimes used to obtain quantitative information but normalisation of the sample responses may remove the concentration effects from the responses. • PCR provides a link between the response information and the concentration information and is especially useful for sensors with linear responses. The

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calibration information can be used to find approximation to the regression coefficents and can be used to obtain quantitative estimates for anlytes in the calibration samples. • PLS gives similar information as PCA and PCR and is frequently used for quantitative prediction of analytes based on sensor responses. • Cluster analysis requires a large number of training data and is used as an exploratory or a pattern recognition technique often associated with evaluating the performance of a sensor array to correctly cluster various analytes. • Artificial neural nework techniques are popular for electronic nose data processing because of their ability to analyse complex and non-linear data. Various methods exist including feed forward neural networks and self organising maps that apply various algorithms to adjust the weights and biases of the computational processes involved in the training to achieve pattern recognition.

18.3.4 Application of e-noses for quality evaluation of food Electronic noses have been suggested for various applications related to quality evaluation of different foods like monitoring freshness and the onset of spoilage or bioprocesses of food. Many of these applications are based on detecting volatile compounds produced because of the growth of fungi, moulds or microbes or changes occurring in food because of oxidation (Schnu¨rer et al., 1999; Olsson et al., 2002; Keshri et al., 2002; Pradyumna et al., 1998; McEntegart et al, 2000; Boothe and Arnold, 2002) In recent years attempts to use electronic nose technology to track the spoilage processes occurring in fish have been reported in numerous papers. Most of these are feasibility studies, showing the ability of the electronic nose to discriminate between different spoilage levels or storage time of samples. Instruments based on different sensor technologies have been used as shown in Table 18.1, which summarises the applications, types of gas sensors, instruments and the reference methods used. Currently the sensors that are used in electronic noses are non-selective towards individual compounds, but some of them show selectivities towards certain classes of compounds, and research efforts have focused on developing membranes for selective detection of important quality-indicating compounds. Deng et al. (1996) developed sensitive sensors aimed specifically at the detection of selected compounds contributing to freshness odours of fish. Development of sensors for the selective detection of microbial metabolites like TMA has been the goal of numerous researchers (Storey et al., 1984; Egashira et al., 1990; de Saja et al., 1999; Zhao et al., 2001). In a study in our laboratory using electrochemical sensors to monitor the onset of spoilage of capelin during storage, one sensor in the array, the NH3 sensor, gave the best ´ lafsdo´ttir et al., 2000). Ohashi et al., results to predict TVN value of capelin (O (1991) evaluated the performance of a semiconductive trimethylamine gas sensor (In2O3-MgO) to detect the freshness of cod, sardine and yellow tail stored

Table 18.1

Application of electronic noses based on various sensor technologies to detect changes during storage and processing of seafood

Type of fish/application

Sensors

Instrument

Validation/Reference

References

Haddock/freshness – storage time Cod/freshness – storage time

metaloxide sensors QMB electrochemical sensors QMB electrochemical sensors electrochemical sensors

FreshSense LibraNose FreshSense LibraNose FreshSense FreshSense

Sensory analysis TVB-N Sensory analysis

´ lafsson et al. (1992) O Di Natale et al. (2000) ´ lafsdo´ttir et al. (2002) O Di Natale et al. (1996) ´ lafsdo´ttir et al. (1997c, 2000) O ´ lafsdo´ttir et al. (1997b) O

Cod-fillets/freshness – storage time Capelin/freshness storage time Herring/freshness – storage time Cod roe/ripening stage Redfish storage time Herring fillets

MOSFET and MOS (Taguchi)

Tuna Tuna Trout/freshness – storage time

conducting polymers (CP) conducting polymers (CP) amperometric sensors/ heating filaments conducting polymers (CP)

AromaScan e-Nose 4000 prototype

TVB-N/sensory TVB-N/sensory sensory TMA/microbial count/ sensory lipid oxidation products antioxidants/sensory analyses microbial count/sensory microbial count/sensory no reference

e-Nose 4000

sensory

conducting polymers (CP)

AromaScan

microbial count/sensory

conducting polymers (CP) Semiconductor gas sensor (TiO2 – In2O3 – MgO

e-Nose 4000 prototype

NH3 – electrode/sensory K value, TVB-N

Salmon fillets Salmon fillets/freshness – storage time Shrimp/freshness – storage time Cod, sardine, yellow tail/TMA/ DMA/NH3 detection

´ lafsdo´ttir et al. (2002) O Haugen and Undeland (2003) Du et al. (2001) Newman et al. (1999) Schweizer-Berberich et al. (1994) Luzuriaga and Balaban (1999b) Du et al. (2002) Luzuriaga and Balaban (1999a) Egashira et al. (1994), Ohashi et al. (1991)

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at 10ºC and 27ºC. The same group reported further on these semiconductor based gas sensors (In2O3-MgO, TiO2) for the selective detection of TMA, DMA and ammonia to monitor fish freshness (Egashira et al., 1994). They concluded that even a single sensor was quite useful for freshness evaluation.

18.4

Validation of the performance of the electronic nose

Measurements of standard compounds can be used to evaluate the precision, both repeatability and long term reproducibility of the instruments. Direct comparison of the performance of the different e-nose systems is difficult because the results are strongly dependent on the choice of the sensor array and the sampling conditions used. Therefore, it is essential that the experimental conditions are carefully detailed so that the different systems can be compared to give advice and recommendations to future users. The selection of appropriate reference methods is important to validate the performance of electronic noses for different applications.

18.4.1 Measurements of standards – repeatability and reproducibility Standards are essential to evaluate the performance of the electronic nose. Repeatability represents the short-term precision and is measured with the same sample the same day, while reproducibility gives the long-term precision and is determined by measuring different samples on different days. Earlier studies have shown that the repeatability (%CV) for the standards selected (ethanol, acetaldehyde, TMA and dimetyldisulfide) is around 10 per cent. Results of reproducibility measurements using the CO sensor responses to different concentrations of ethanol over one year period showed that the %CV is less than ´ lafsdo´ttir et al., 2002). 30 per cent (O The inability to provide absolute calibration and reproducible result for gas sensors is often accounted for because of drift in sensor responses with time. This is of concern when renewing sensors in an array or when compiling data from different instruments with the same sensor array. Attempts have been made to compensate for sensor drifts using mathematical methods (Balaban et al., 2000; Tomic et al., 2002).

18.4.2 Selection of reference methods for freshness quality of fish The application of electronic noses to monitor the spoilage process as a function of storage days is a classical way to estimate the ability of the electronic nose to discriminate samples of different spoilage level. Different handling and storage conditions, in particular the temperature, will influence the spoilage pattern of fish and consequently information about storage days may give ambiguous information about the freshness stage of the fish (Di Natale et al., 2001). A more useful approach is to compare the electronic nose responses with a measurement

Rapid olfaction arrays for determining fish quality

349

that gives information about the microbial and oxidative processes influencing the freshness quality like sensory analysis, chemical measurements of volatile compounds like TVN or TMA and microbial analysis.

18.4.3 Identification of key indicator compounds in fish stored in ice When selecting sensors in an array for quality monitoring of food, it is important to have an understanding of the sensitivities of the sensors towards the key indicator compounds in the sample giving information about the quality. Information on the identities and quantities of volatile compounds present in the headspace during storage of fish is essential to select the key indicator compounds for freshness quality that the electronic nose can detect. Development of a standard procedure based on a test mixture of qualityindicating compounds would be useful to evaluate the performance of the different electronic nose devices. The main classes of compounds that are present in high concentrations in the headspace during storage of fish are short chain alcohols, aldehydes, ketones, esters, sulphur compounds and amines (Lindsay et al., 1986). Analysis of selected standards, which are representative of these main classes have shown that the electrochemical gas sensors (CO, SO2 and NH3) in the electronic nose ‘FreshSense’ show different sensitivities towards compounds from these classes ´ lafsdo´ttir et al., 1998). To obtain more detailed information about identities (O and the level of the most abundant volatile compounds present in the headspace during storage of fish, measurements were done in our laboratory using static headspace sampling and collection of volatiles using a TENAX preconcentration technique and analysis by GC. Table 18.2 shows the main classes and identities of compounds present in the highest concentration in the headspace. Quantitites were estimated based on peak areas. A TENAX tube and an air pump were used to collect the headspace volatiles of haddock fillets stored for 3, 7, 10 and 14 days. The composition of compounds analysed in ice-stored fish is very similar to the volatile profile of smoked salmon reported by Joffroud et al. (2001). They found that 2, 3–butandione, 3–hydroxy-2–butanone, 2-methyl-1–butanol- and 3methyl-1–butanol were the most abundant volatiles in the headspace and these could be used as indicators of spoilage for smoked fish. It is of interest to compare the composition of the headspace of different fish products during storage to guide the development of an electronic nose for quality monitoring of fish products. A similar set of sensors can be used for fish species that are stored and processed in a different way. Based on earlier measurements of pure chemical standards the FreshSense sensors are known to be sensitive to the classes of compounds detected by the ´ lafsdo´ttir et al., 2002) and thus the identity of the volatiles TENAX method (O that the individual sensors of the electronic nose are detecting can be estimated. Figure 18.2 shows a graph giving the sum of the concentration of compounds representing each class shown in Table 18.2. The ketones are not detected by the sensors and have therefore not been included in the graph. Figure 18.3 shows the

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Rapid and on-line instrumentation for food quality assurance

Table 18.2 Headspace volatiles of haddock fillets during storage collected by an air pump on a Tenax trap followed by thermal desorption, separation and detection by GC-MS RI DB-5msa 3 days 7 days 10 days 14 days Alcohols ethanol 2-methyl-1-propanol 1-penten-3-ol 3-methyl-1-butanol 2-methyl-1-butanol 2,3-butandiol

< 173 227 263 312 314 357

Aldehydes acetaldehyde 3-methyl-butanal hexanal heptanal nonanal decanal

< 173 245 376 494 703 803

Esters ethyl acetate propanoicacid-2-methyl,ethylester acetic acid, 2-methylpropyl ester butanoic acid, ethyl ester 2-butenoic acid, ethyl ester butanoic acid, 2-methyl, ethylester butanoic acid, 3-methyl, ethylester hexanoic acid, ethyl ester

209 333 348 381 428 433 439 595

Ketones 2,3-butandione 3-pentanone 3-hydroxy-2-butanone

207 273 282

Sulphur compounds dimethyl sulphide dimethyl disulphide dimethyl trisulphide

182 319 562

Amines TMA

174

a

++ +

++

+

++ + ++

+++ +++ ++ +

+ +

++

++ +

+ + ++ +

++ +

++ +

++

++

++

+++ ++ + +++ + ++ ++ ++

++

++ + +++

+ +++

++

++

++

++ ++ +

++

++

+++

+++

+

+++

Calculated ethyl ester retention index on DB-5ms capillary column.

results of the electronic nose measurements of the same samples with the FreshSense instrument. Figures 18.2 and 18.3 give a comparison of the analysis of volatiles by GC and sensor responses of the electronic nose FreshSense. A similar trend of the sensor responses and the concentration of volatiles analysed in the headspace of the haddock fillets during storage is evident. Figure 18.3 shows that the responses of the CO sensor detecting alcohols, aldehydes and

Rapid olfaction arrays for determining fish quality

351

Fig. 18.2 Sum of the peak areas of compounds representing the three different classes of compounds detected by GC in the headspace of haddock fillets during storage in ice.

Fig. 18.3

Responses of the CO, SO2 and NH3 sensors towards haddock fillets during storage in ice.

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Rapid and on-line instrumentation for food quality assurance

esters is the highest and increases early in the spoilage process while the other sensors´ responses increase later in the spoilage process. The NH3 sensor detects amines, mainly TMA and ammonia and the SO2 sensor detects volatile sulphides. The TENAX technique does not detect the very volatile small molecules like ammonia, hydrogen sulphide, methyl mercaptan and ethanol that are also known to be present in abundance in the headspace of spoiled fish. The electrochemical sensors can, however, detect these compounds and therefore the slopes and shapes of the curves are slightly different. The CO sensor has a linear response with storage time. The spoilage odour development based on the odour characteristics of the compounds analysed and their increasing concentration with storage time can be rationalised based on sensory analysis. The odour of the fresh fillet is very little or neutral and low responses of the sensors are observed on day 3. The first spoilage odours of the fillets are sweet-like odours that are contributed by the alcohols that give sweet, solvent-like odours in combination with the aldehydes giving sweet, oxidised-like odours (day 7). The amines contribute to salted fish or stock fish odour and in combination with the sulphur compounds, cheesy and foul odours develop and the fillets are stale as seen on day 10. Finally, the esters analysed in high levels on day 14 have characteristic sweet, fruity odours. When these sweet odours are mixed with the foul smell of the sulphur compounds and ammonia-like stockfish character of the amines, the odour of the fillet becomes TMA/ammonia-like and sour/putrid-like, signalling the overt spoilage. In the fish industry smell is one of the most important quality attributes for raw fillets. Standards can be selected based on GC analysis to evaluate the performance of different electronic nose instruments to monitor freshness quality of fish.

18.5

Developing rapid and on-line applications

Sensory assessment has always played a key role in quality and freshness evaluation in the fish industry. Sensory attributes influencing the freshness and quality of fish related to appearance, texture, smell, colour, defects and handling are all considered very important in quality control. Chemical measurements of total volatile bases and total microbial counts are also used in the industry to verify the freshness of fish according to regulations. Alternative techniques have been developed to monitor changes occurring post mortem in fish. None of these methods have been widely implemented in the industry although research has shown that some of these techniques have shown very promising results for estimating freshness. Detection of ATP metabolites, physical measurements evaluating changes in texture, microstructure, electrical properties and colour are well established and newer technologies based on image analysis and spectroscopic methods have shown promising results (O´lafsdo´ttir et al., 1997a). A survey aimed at obtaining the view of the European fish sector on the importance of various quality attributes of fish and methods of measuring them has been reported (Jørgensen et al., 2003). The results show general agreement regarding the need for

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353

rapid instrumentation methods in the processing industry, in fish auctions and in the trade of fish to measure the overall concepts freshness and quality of fish. Although odour is one of the key determinants of the quality, other factors also influence the overall quality, and instruments based on a single technique to measure individual properties were not considered as important as the overall concept freshness (Jørgensen et al., 2003). Evaluation of the odour only may not be sufficient to assess the complex post mortem processes occurring in fish during storage. A combination of a few instrumental techniques to form a multisensor for the detection of fish freshness has been suggested in a recent European project FAIR CT98–4076 (MUSTEC). The techniques used were based on visible light spectroscopy, electrical properties, image analysis, colour, electronic noses and texture. Combining the outputs of the instrumental techniques and calibrating them with sensory scores of quality index method (QIM) (Bremner et al., 1987) for attributes like appearance, smell and texture, gives an artificial quality index (AQI) that can be as accurate and precise as the QIM sensory score (Di Natale, 2003).

18.5.1 Developing rapid and on-line/at-line and hand-held applications Lack of reproducibility of electronic noses has been regarded as one of the most crucial problems preventing their usage for on-line quality monitoring. Electronic noses based on various sensor technologies have been suggested for quality monitoring of fish. None of the electronic nose instruments which are available commercially have been implemented in the fish industry. However, many studies have shown promising results and various electronic nose instruments have a potential to be useful for quality evaluation as mentioned before. Most applications have recommended the use of a closed sampling system which requires that the analysis has to be performed at-line rather than on-line. The main limitations to the development of hand-held devices is the caution that is needed in sampling relating to temperature influences because of the volatile nature of the analytes and environmental disturbances. Changes of the environmental parameters result in variations of both quantity and quality of the headspace. These give rise to an additional signal source that can sometimes completely hide the resolution of the electronic nose. The effect of environmental influences can be minimised in the data analysis. Di Natale et al. (2002) used an independent component (ICA) method to segregate environmental disturbances from the meaningful part of the data with good results. Humidity of samples and the sensitivity of some sensors to water vapour give rise to signals which may not be of interest to characterise the samples. An alternative way of excluding components of no interest in an electronic nose analysis was suggested by Muenchmeyer et al. (2000) who used a trap and a thermal desorption unit in combination with a portable electronic nose (WMA Airsense). They were able to lower the detection limit and improve the selectivity of the device by excluding disturbing components because of their

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Rapid and on-line instrumentation for food quality assurance

low affininty for the trap and selectively concentrating components with high affinity for the trap and thereby increasing the sensitivity of the system. Hofmann et al. (1997) used high resolution gas chromatography and FID sensor and compared the signals to a multisensor array composed of metal oxide and surface acoustic wave sensors. They selected key odorants in butter and showed that the multisensor array could detect and discriminate the key odorants as well or better than the FID. However, they pointed out that when monitoring real foodstuffs the influence of the remaining vapour ingredients might interfere with the multisensor array. This is a limiting factor when analysing the total headspace and the components that are in the highest concentration will mask other components present in lower concentrations. McEntegart et al. (2000) showed that an array of eight QMB sensors, eight MOS sensors and four electrochemical sensors could be used to discriminate cultures of two types of coliform bacteria (Escherichia coli and Enterobacter aerogenes) and suggested that the electronic nose could potentially detect microbial contamination of foods. They were able to improve the sensitivity of the system by using a gas dryer to reduce water vapour in the system and thus compensate for the sensitivities of the QMC and MOS sensor towards water. The user-friendly hand-held approaches at low cost are in contrast to a sophisticated sampling system. Lengthy sampling procedures involved in the concentration of components present in low levels in foodstuffs are not considered practical for rapid measurement techniques. Automated sampling and temperature control are critical to ensure reproducible results of electronic nose measurements. Hand-held devices using selective sensors and applicationspecific sampling may become useful as a quality monitoring tool in the fish industry.

18.6

Future trends

18.6.1 Technology development The benefits of the electronic nose technique lie in the speed of analysis because of limited sample preparation and the fast data generation and data interpretation. The electronic nose technique provides a rapid detection of the volatiles in the headspace and the resulting pattern of the sensor responses gives qualitative or quantitative information about the headspace composition. Both sophisticated laboratory instruments and application-specific simple instruments with selective sensors will find applications in the future for quality and safety monitoring in the food industry. The fast development of sensor technologies and data processing techniques able to treat large data sets of volatile profile will improve the possibility of specific applications for quality monitoring in the food industry. Models using multivariate analysis or neural networks for various applications can be developed by training the e-nose with representative samples and thus providing a basis from which to determine the fitness of tested samples to the model.

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18.6.2 Application of e-noses for quality evaluation in the industry Currently there is an increasing demand for traceability of food products in relation to the quality and safety of these products for the consumers. In this respect, screening techniques for tracing food contamination with fungi, moulds, pathogens, biotoxins and viruses are needed for safety purposes. Techniques to verify the quality of food are essential to gain consumer trust and will facilitate the marketability of food products. The possibility to use electronic noses to detect volatiles associated with quality and safety has been demonstrated. However, the reluctance of the industry to apply new techniques may be associated with the long time tradition to use sensory analysis for quality, that gives the best description of consumer perception. Another explanation may be that the industry is required to use traditional chemical or microbial methods that are quoted in regulations, to measure components related to the safety of food. Therefore, it is essential that new techniques are superior to the older techniques and also standardised and calibrated against the traditional methods so that regulatory bodies and commercial partners will understand their output. For perishable food products like fish the possibility to measure objectively their freshness or quality is of importance for the industry both in process management and in the trade of fish. Remote buying via the internet has become more common and an objective method to evaluate the quality would facilitate e-commerce. Sensory evaluation of raw fillets is difficult and therefore it is likely that the fish industry would welcome a reliable and easy to use device for that evaluation. Additionally, the evaluation of the freshness of various processed fish products is also of interest for the marketability of these products. The primary advantage of e-noses as a quality assurance tool for the food industry is speed of analysis in terms of data generation and data interpretation. The recent developments of the liquid counterpart of electronic noses, namely array of sensors working in solution (the so-called electronic tongue) may be used in combination with the electronic nose for better characteristation of the flavour of food (Winquist et al., 1999). Similarily, the simultaneous use of instrumental devices as counterparts of other sensory attributes like texture and appearance contributing to overall quality, will give a more precise estimation of the quality of food.

18.7

Sources of further information and advice

Recent reviews on the electronic nose technique are recommended for further reading: Jurs et al. (2002), Garcia-Gonza´lez and Aparicio (2002), Harper (2001), Haugen (2001), Gardner and Bartlett (1999), Schaller et al. (1998). Information on the website of The Nose II 2nd Network on Artificial Olfafactory Sensing aims to stimulate information exchange between scientists, manufacturers and end-users in Europe in order to develop synergy in the sensor community, improve the efficiency of R&D, encourage interdisciplinary research, and promote application of new ideas (http://www.nose-network.org/)

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18.8

References

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and BALABAN M O (1999a). Evaluation of Odor Decomposition in Raw and Cooked Shrimp: Correlation of Electronic Nose Readings, Odor Sensory Evalauation and Ammonia Levels. In: Hurst W J (ed.), Electronic nose and sensor array based system, Design and Applications. Proceedings of the 5th International Symposium on Olfaction and the Electronic Nose. Technomic Publication Company, Inc., USA, 177–184. LUZURIAGA D A and BALABAN M O (1999b). ´Electronic nose odor evaluation of salmon fillets stored at different temperature’ In: Hurst W J (ed.) Electronic nose and sensor array based system, Design and Applications. Proceedings of the 5th International Symposium on Olfaction and the Electronic Nose. Technomic Publication Company, Inc., USA, 162–169. MARSILI, R T (2001). SPMS-MS-MVA as a rapid technique for assessing oxidation offflavors in foods. In: Rouseff and Cadwallader (eds), Headspace Analysis of Food and Flavors: Theory and Practice. Kluwer Academic/Plenum Publishers, New York, 89–100. MCENTEGART C M, PENROSE W R, STRATHMANN S and STETTER J R (2000). ‘Detection and discrimination of coliform bacteria with gas sensor arrays’, Sensors and Actuators B 70, 170–176. MCGILL, A S, HARDY T, BURT R J and GUNSTONE F D (1974). ‘Hept-cis-4–enal and its contribution to the off-flavour of cold-stored cod’, J. Sci. Food Agric, 25, 1477– 1481. MIELLE P and MARQUIS F. (1999). ‘An alternative way to improve the sensitivity of electronic olfactometers’, Sensors and Actuators B-Chem. 58(1–3), 526–535. MUENCHMEYER W, WALTE A and MATZ G. (2000). Improving electronic noses using a trap and thermal desorption unit. Sensors and Actuators B 69, 379–383. NEWMAN D J, LUZURIAGA, D A and BALABAN M O. (1999). Odor and Microbiological Evaluation of Raw Tuna: Correlation of Sensory and Electronic Nose Data. In: Hurst W J (ed.), Electronic nose and sensor array based system, Design and Applications. Proceedings of the 5th International Symposium on Olfaction and the Electronic Nose. Technomic Publication Company, Inc, USA, 170–176. ¨ GER J. (1992). Evaluation of some well established and some underrated OEHLENSCHLA indices for the determination of freshness and/or spoilage of ice stored wet fish. In: Huss H H, Jakobsen M, Liston J (eds), Quality Assurance in the Fish Industry, Elsevier Science Publisher, Amsterdam, 339–350. OHASHI E, TAKAO Y, FUJITA T, SHIMIZU Y and EGASHIRA M (1991). ‘Semiconductive trimethylamine gas sensor for detecting fish freshness’, J. Food Sci. 56, 5, 1275– 1278. ´ LAFSDO ´ TTIR G and FLEURENCE J (1998). Evaluation of fish freshness using volatile O compounds – Classification of volatile compounds in fish. In: Methods to Determine the Freshness of Fish in Research and Industry, Proceedings of the Final meeting of the Concerted Action ‘Evaluation of Fish Freshness’ AIR3 CT94 2283. Nantes, 12–14 Nov, 1997. International Institute of Refrigeration, 55–69. LUZURIAGA D A

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and NILSEN H. (1997a). ‘Methods to evaluate fish freshness in research and industry’, Trends Food Sci. Technol, 8, 258–265. ´ LAFSDO ´ TTIR G, MARTINSDO ´ TTIR E and JO ´ NSSON E H (1997b). ‘Rapid gas sensor O measurements to predict the freshness of capelin (Mallotus villosus)’. J. Agric. Food Chem. 45, 7, 2654–2659. ´ LAFSDO ´ TTIR G, MARTINSDO ´ TTIR E and JO ´ NSSON E H (1997c). Gas sensor and GC O I, MACKIE I M, HENEHAN G, NIELSEN J

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19 On-line analysis and control of product quality G. Montague, E. Martin and J. Morris, University of Newcastle, UK

19.1

Introduction

This chapter describes two industrial case studies of the application of food process modelling, inferential estimation and control. The first case study describes the development of inferential models for the provision of real-time, on-line estimates of the quality of a breakfast cereal for production line operators, which represented the first food processing application of such technologies. Five quality variables were selected and on-line measurements reflective of the key process conditions were identified. Following process data logging, a number of linear and non-linear data-based modelling methods were applied to identify relationships between the on-line measurements and the product quality. Off-line verification of the models indicated that the prediction accuracy achieved was sufficient to offer the opportunity for quality control improvements. The models were subsequently implemented on-line to provide the process operators with frequent estimates of product quality. Performance assessment has indicated a reduction in the variability of all five quality parameters. In addition to details of the modelling, the decisions relating to the development strategy and justification for implementation are considered. The second case study describes the results of a process control improvement study carried out on a food processing line making frozen french-fries. A strategy for process control improvement is proposed and its success demonstrated. Initial knowledge elicitation from process experts, coupled with data analysis, revealed the existing operational strategy. The information gathered highlighted limitations of the current strategy, indicated how it could be improved upon and allowed a benefits analysis to be undertaken to justify the work from a financial viewpoint. Results from the implementation of the control

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enhancements demonstrate the success of the strategy and indicate the predicted improvements were achieved.

19.2

Process models

Over the last decade there has been increasing awareness of the benefits that computerised data logging systems can bring in terms of the operation of food processes. For instance, it is now common for variables such as temperatures and flow rates to be logged and stored on a minute basis. In its raw state this monitored plant data may be of limited use as its relationship with product quality is generally not immediately apparent. Unfortunately it is the product quality that is critical in a competitive market and such information is usually only gathered infrequently through off-line laboratory analysis. However, if it were possible to extract from the database of on-line variables information that related the on-line measurements to the quality variations, then it could be utilised more effectively for improving product consistency. Essentially this could be achieved by combining data from a variety of sources (data fusion) to generate a model to provide advice to the operator. A model (in the most general sense) is one of the most concise representations of the process database. A range of techniques that operate in a ‘data-rich’ environment are already available to compress process information and generate a ‘model’. However, many of the methodologies are limited in their ability to handle ‘real’ situations. Characteristics typical of food processes such as non-linearity, process complexity and lack of process understanding can severely limit the performance of the modelling procedure. Two possible approaches for model development are ‘black box’ modelling (model with a structure that is not physically or chemically related to the process) or mechanistic modelling (where the model structure depicts physical attributes). Both approaches have their particular advantages and disadvantages. The physically-related structure of a mechanistic model is useful in that, together with the model parameters, it provides a means of assessing the ‘internal characteristics’ which determine the specific process behaviour. In developing the model it is essential not only to understand the global characteristics of the system, but also the more subtle details of process behaviour. If this approach is followed then the resulting model can be extremely useful for supervision purposes. However, the penalty paid is that model development can be a major undertaking and as a result models of this form are costly to build. An alternative route is to utilise the ‘black box’ modelling technique. Here, the model attempts to relate process data to quality data under the assumption that a generally structured relationship exists to describe the process intricacies. Deep process knowledge (in terms of physically understandable characteristics) is not required. However, the generally structured nature of the model typically precludes gaining any internal physical understanding. Since only global process understanding of system behaviour is required, data-based model development

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time is more rapid and less costly than first principles modelling. However, considerable attention must be paid to data collection and data pre-processing. Cost is not the only consideration when deciding on which approach to follow; accuracy and robustness of prediction is equally important. The generally structured approach to modelling usually relies on techniques that make simplifications in terms of model structure. For example, the model utilised is often linear. Thus modelling accuracy is compromised for the sake of rapid model formulation. Once a process model has been formulated, it can be used within a variety of supervisory procedures to improve system operability. Of particular value is information regarding the behaviour of the process outside the region of normal plant operation. Process operators are likely to have limited knowledge of such conditions yet it is exactly these situations in which corrective action is required. One of the most fundamental requirements is to obtain information on the performance of the process quickly so that deviations from normal behaviour can be detected and acceptable operation restored rapidly. In this case the model is used to produce reliable estimates of ‘key’ quality variables. If an accurate model exists then this may theoretically be used in place of, or complementary to, existing on-line instrumentation. This methodology is known as inferential estimation. It is advantageous from the control viewpoint as the feedback signals will not be subject to measurement delays or limited by off-line sampling frequencies. Linear inferential estimation methodologies exist and some progress has been made in terms of their application to a variety of process systems (e.g. Montague et al., 1992; Lant et al., 1993). However, the performance of current inferential estimation methods is potentially limited by linearity assumptions. Consequently, significant improvements in control performance may arise by utilising more representative non-linear generic models.

19.3 Case study 1: quality assessment in breakfast cereal production This case study describes the development of inferential models for quality prediction in a food processing plant operating in continuous mode to produce breakfast cereal. The importance of careful model structure selection is considered and the control improvements gained by deploying the model are detailed. A basic schematic of the process is shown in Fig. 19.1. Cereal is processed using several unit operations to arrive at a packaged product. Five quality measurements (Q1–Q5) are made on the finished product to ensure customer satisfaction. It is not possible to make these measurements on-line, so periodic off-line sampling is carried out. Twenty-eight process measurements (P1–P28) are measured on-line including feed rates, residence times and temperatures. The sampling rate for the on-line measurements was configurable with the option to obtain data as frequently as every ten seconds.

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Fig. 19.1

Schematic of the production line of the food process studied.

This high frequency sampling was used to obtain an indication of short term process noise but in general data sampling was carried out on a minutely basis. For confidentiality reasons it is not possible to physically identify the P and Q variables. The first stage in the development and implementation of any monitoring and control technique is to perform a benefits analysis to highlight areas where potential savings are possible and identify the likely technological requirements. Analysis of historical process data revealed the potential for improvements in quality control. Table 19.1 demonstrates that while the quality variables were seldom outside the acceptability limits (where product would be scrapped), they lay outside the target range on several occasions. These limits were specified for plant operating policy purposes. It should be noted that not all quality variables have a target or acceptable range. Such simple measures provide one means of justification for improvement but potential advances may also be gleaned from simple trend plots. For Table 19.1

Plant performance prior to application of inferential models

Quality property

% outside acceptable range

% outside target range

Mean

Target

Q1 Q2 Q3 Q4 Q5

0.7 2.3

22

0.2 3.0

35 50

153 244 237 6.4 82

150 240 230 5.6 86

On-line analysis and control of product quality

Fig. 19.2

365

Q4 behaviour showing bias in the trend (December 1993 to March 1997).

instance, Fig. 19.2 shows that the operating mean for variable Q4 was biased from the desired value to avoid constraint violation. This is a result of the operators’ natural caution as they were aware that operation towards the lower end of the specification limit was problematic. If operation at the target value was the objective then the high degree of variation in Q4 would lead to frequent violation of the lower specification limit. However, when operating above the target limit, upper specification violations could be avoided more easily by taking appropriate action. An analysis of this information indicated that there was scope for improvement that could result in significant financial gains. Analysis of operating policy pointed to the fact that measurement limitations were a major contributory factor to process deviations. Comparing the rate of possible process changes with the frequency of sampling indicated that deviations could persist for some time before they were picked up through laboratory analysis. Such issues are discussed in the following section on data gathering. Given the process quality measurement limitations, inferential techniques were proposed to cope with off-line sampling constraints. Furthermore, the large volume of online information from a well-instrumented line suggested that a data-based inferential model was an attractive option.

19.3.1 Data gathering The first stage in constructing a data-based model involves gaining a qualitative process understanding and using this information to specify provisionally the data requirements for model building. Operational staff familiar with process operation provided the prime inputs to this. Over the course of the project several campaigns of data collection were undertaken. The first data logging exercise, during which both on-line measurements and samples were taken for off-line analysis, were obtained at as high a frequency as feasible. This allowed the determination of the most appropriate rate for data gathering in the future

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(balancing the data requirements with the practicalities of process sampling). Analysis revealed that the cereal took one hour to process from entering the line to leaving as a packaged product. The dynamic changes in the process were significantly faster than this and thus if a dynamic model was to be considered a sample rate of several minutes would be required. While this was perfectly feasible for on-line data, off-line samples could not be taken at such a frequency. As a consequence, it was clear that data-based models would be restricted to steady-state descriptions. The logging policy adopted was to gather on-line data at a fast rate to detect any short-term changes but only take off-line samples at a rate that the laboratory could cope with. Since the off-line samples were typically collected over the period of one week, the fastest practical rate of sampling was every 15 minutes. Concentrated periods of data gathering took place over the three-year project. Typically data was gathered in weekly periods over different seasons of the year. Thus if seasonal variations occurred in the cereal quality, it would have been captured. Since a data-based modelling approach was utilised, it was essential that process variables were subject to change to enable cause and effect information to be gathered. In many data-based model developments this requires plant testing to be undertaken. Unfortunately, the requirement to maintain production throughout restricted the opportunity for introducing known disturbances. As a consequence changes in plant throughput and natural disturbances were relied on to provide a reasonably rich data set. However, over the logging period significant changes in process variables were observed particularly as plant throughput was increased. From each set of logged information, the steady-state process data was selected for model construction. A set of relatively straightforward rules based on the on-line process variables remaining at ‘steady state’ (i.e. within the noise bands) were used to sub-sample the data to construct model building information. This required tying off-line analysis to the on-line information that had been time shifted to account for the process transport delay. The time delays were determined by consideration of the flow rates through the line. A key question early in the development of the inferential models was how much data would be required for model building purposes. This was very pertinent since data logging periods were costly in terms of staff time. This was very difficult to answer as many issues are involved, such as data excitation, limits of model applicability, data accuracy and model type. To obtain some insight (but in this case in hindsight), Fig. 19.3 shows the degree of variation of quality parameter Q4 measured over different time scales. It can be seen that little additional variation is present over three years compared with two weeks. The implication of this is that observing the process over a period of weeks (undertaking a rigorous sampling campaign) should provide as much information for model building purposes as would be obtained over a period of several years. Although not meticulous, for instance the breadth of input variation was not considered, this does indicate qualitatively that the data requirements were not excessive.

On-line analysis and control of product quality

Fig. 19.3

367

Pooled variation in Q4 against logging period.

It is generally the case that logged process data requires a significant degree of pre-processing before it can be used for model construction. Noise removal and coping with data outliers is a common problem. The noise on the raw process measurements was comparatively low level and the reliability of the logging system was high resulting in few outliers. Whilst welcome, this situation is rare and more effort is usually required in terms of data rectification.

19.4

Building models of breakfast cereal production

Following the pre-treatment of the data to obtain steady-state process information, a number of well-defined modelling stages were executed. The four major issues addressed were model structure specification, model input specification, model parameterisation and model capability. To capture the relationship between on-line process variable measurements and quality variables a number of structural forms of models were compared. The methodologies considered were linear regression methods (multiple linear regression, principal component regression and projection to latent structures) and a non-linear modelling approach (feedforward artificial neural networks).

19.4.1 Multiple linear regression (MLR) The simplest approach to building a relationship between variables measured on-line and a quality variable is to carry out multiple linear regression (MLR) using the least squares method. While the method is straightforward, the results can be affected by a number of data issues. In most practical situations, there is typically some degree of correlation between the process variables. This is known as multicollinearity and can result in the matrix inverse, required when performing the least squares method, to be close to singular (Willan and Watts,

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1978). In practical terms this means the model coefficients are imprecise, the model is sensitive to noise and inaccurate predictions result. To overcome this problem, it is necessary to address the problem of multicollinearity. This can be achieved by process variable elimination but this approach can be difficult if a large number of process variables form the basis of the analysis. An alternative solution is to transform the process variables to new uncorrelated variables via principal component analysis (PCA).

19.4.2 Principal component regression (PCR) Principal component regression (PCR) is a two-step procedure. The first stage involves the transformation of the process variables by PCA; this is then followed by a multiple linear regression between the principal component scores and the quality variables. Principal component analysis (PCA) is a projectionbased technique that summarises the underlying sources of variability in a data set comprising a single data matrix through the definition of an orthogonal set of latent variables (principal components). The first principal component, a linear combination of the original variables, defines the direction of greatest variability in the data set and hence the largest amount of variability in the data, the second principal component explains the second largest amount of variation, and so on until the final principal component which explains the least variation. Through the application of PCA, process variables (Xi ; i ˆ 1; . . . m) are transformed to new variables (ti ) as follows: ti ˆ p1i  X1 ‡ p2i  X2 ‡ . . . ‡ pmi  Xm where m is the number of samples and p represents the loading matrix that defines the direction of maximum variance in the data and t are known as the principal components. The individual entries of the principal component vector, ti , are termed the principal component scores and are the new ordinates in the principal component space. In addition the principal components are mutually orthogonal and this has benefits in terms of PCR as discussed later. Further details of the method can be found in Geladi and Kowalski (1986). There are several benefits obtained by applying PCA transformation. Although as many principal components as there are number of process variables are generated, given that a degree of multicollinearity exists, there will be some redundancy in the data. Hence it is possible to describe the significant variation in the process by fewer principal components. This serves to reduce the dimensionality of the problem. Furthermore the lower order principal components will typically describe noise in the process data. Removing them from the transformed data results in process noise reduction. There are a number of approaches for deciding on the number of principal components to use. In this work the k-fold cross validation technique of Wold (1978) is adopted. Discussions of this and other approaches can be found in Albert (1999). The overriding reason for applying the PCA transformation relates to the second stage of the PCR procedure. A regression is carried out on the principal

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component scores rather than the original data. As a result of the orthogonality properties of the principal components, the matrix inverse in MLR is no longer badly conditioned, in fact the inverse is carried out on a diagonal matrix. Hence the model parameters are robust and better prediction accuracy is obtained.

19.4.3 Projection to latent structures (PLS) Projection to latent structures (PLS) is a linear multivariate modelling procedure for solving regression problems with highly collinear process variables. The algorithm operates in a similar manner to principal component analysis by projecting the input and output data down onto a new space defined in terms of latent variables. The relationships between these new variables is then modelled by univariate linear regression. As the algorithm calculates latent variables sequentially and univariate regression is performed between each set of latent variables, problems associated with modelling correlated data are removed. Furthermore, as with PCA, the dimensionality of the problem can be reduced and fewer latent variables than process variables can be selected. The overall objective of PLS is to maximise the covariance between the input and the output space. A kfold cross-validation technique is used to determine the appropriate number of latent vectors. The most commonly applied PLS algorithm is NIPALS described by Geladi and Kowalski (1986). The fundamental difference between PCA and PLS is that PCA transforms the process inputs irrespective of the outputs, whereas PLS attempts to explain variation in the inputs which is most predictive of the outputs. An overview of PLS can be found in Kourti and MacGregor (1995).

19.4.4 Feedforward artificial neural networks (FANNs) While MLR, PCR and PLS in their standard form are ‘linear in the parameters’, the final approach adopted was fundamentally non-linear. Feedforward artificial neural networks (FANNs) have become a popular modelling approach and several authors have demonstrated their application potential and powerful approximation capability (e.g. Morris et al., 1994). A FANN is simply a network of non-linear processing functions that act on weighted combinations of process variables. The neurons are arranged in layers. Information is introduced into the network and flows through to the output. The weightings between the neurons modify the signal strength with biases at the neurons acting to improve the approximation capability. The weightings and biases within the network are determined through the application of an optimisation routine to determine the values that are most predictive of the process output behaviour. In this case the Levenberg–Marquardt approach (Scales, 1985) was used to determine the network weightings. Further details of FANNs and their application can be found in Albert (1999). In the case of FANNs, in addition to deciding on the model inputs and outputs, it is necessary to specify the topology. In this application only single hidden layer networks were considered as previous studies had proved that this

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was sufficient (Cybenko, 1989). It is then necessary to specify the number of neurons in the hidden layer. The problem is fundamentally the same as determining the number of principal components or latent variables and the same procedures can be applied. Albert (1999) compares the approaches and a cross-validation technique was again adopted. A further consideration with FANNs is that the network parameters are initially randomised and the solution is dependent on the initial conditions. Multiple training runs were therefore undertaken to obtain the most descriptive model in terms of prediction accuracy on data not directly involved in model building. To build accurate and robust models of the quality parameters, it was necessary to choose a sub-set of on-line process variables that influenced the quality parameters of interest. The elimination of irrelevant and highly correlated information is desirable as their inclusion could potentially degrade model performance. Data analysis procedures providing this ability were investigated. Firstly, back elimination was applied to an MLR model, that is irrelevant variables were identified by applying a t-test to determine whether a regression coefficient generated with an MLR model was effectively zero. The procedure is iterative eliminating the least significant variable from the regression as long as the ratio of its regression coefficient to the standard deviation of the regression coefficient was greater than two. The regression model was then reconstructed with reduced inputs and the procedure repeated until only significant variables remained. The results from undertaking this procedure were not convincing as through process insight there was a clear indication that variables known to be influential on the process were being removed. These problems were a consequence of applying MLR to correlated information as discussed previously. The next step was to investigate whether using PCA or PLS at the regression stage would result in improvements in the model. The results were more encouraging in this case from a practical point of view. Removal of insignificant process variables based on their regression coefficients (on original rather than principal component/latent vector data) did not degrade model performance but significant improvements were not observed. This can be observed in Table 19.2 where the root mean squared (RMS) error is reported for test data set using a PLS-based model. Table 19.2

Model performance on the full and reduced validation sets Full validation data set

Q1 Q2 Q3 Q4 Q5

Reduced validation data set

Number of variables

RMS error

Number of variables

RMS error

21 21 21 21 21

0.580 0.258 9.07 3.17 5.83

12 9 6 9 10

0.505 0.252 9.25 2.99 5.23

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Generally minor improvements in RMS error can be observed but the benefit is limited. This is partly to be expected as the PCA/PLS methods are more resilient to the problems posed by irrelevant/correlated information. In this case the benefit from variable removal comes about through the reduced model dimension (approximately 50 per cent of the variables have been removed) and the ease of interpretability of the resulting model. In several cases decisions on variable removal were clear cut but in others a more difficult choice had to be made. The difficulties arose from achieving a balance between including variables with low information content and the deleterious effect on predictions from the noise corrupting the information. Furthermore, this decision could not be made in isolation from the modelling approach as the abilities of the modelling method to cope with correlated inputs/ noise varies from technique to technique. To determine the structure of the data-based models and their parameters it was necessary to take the logged process data and partition it into three separate sets: • Parameterisation set to determine the model coefficients. The modelling techniques of MLR, PCR, PLS and FANNs were applied to this data set to determine the parameters of the models. • Validation set to specify the model structure. The number of principal components, latent vectors and network hidden nodes were determined from cross-validation using this data set during training. When using the k-fold cross-validation method the parameterisation and validation sets were combined and partitioned several times to give different parameterisation/ validation sets. • Testing set used to determine model performance. This set does not feature in model construction and provides an indicator of likely on-line performance. In each data set it is necessary to have data that is sufficiently exciting to pick up the cause–effect relationship between inputs and outputs. Furthermore, if a linear model does not prove to be an acceptable approximation, to generate a non-linear model it is essential for process input/output data to span the range of intended model applicability. In this study, the data was partitioned by visual inspection and with consideration of the range of a variable to obtain data sets satisfying the model parameterisation requirements discussed above. With the data set partitioned, models were then constructed and accuracy comparisons in terms of RMS error were made on the testing data set. Table 19.3 below shows the results obtained for the best linear method (PLS) against the non-linear FANN approach. The accuracy of the models validated on data collected in 1994 and 1996 provided an indication of their long-term performance. A number of conclusions can be drawn from these results. Firstly, models generated using PLS perform well and maintain their accuracy between the two data sets. The same cannot be said for the FANN models that tended to give a good fit to the training data but did not generalise as well over the two-year period. It is clear that there is little

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Table 19.3

Model accuracy (RMS error) prior to implementation PLS

Q1 Q2 Q3 Q4 Q5

FANN

1994

1996

1994

1996

0.675 0.276 8.903 4.31 6.36

0.486 0.228 9.23 2.02 5.29

1.23 0.318 8.84 1.97 8.33

0.739 0.204 9.04 1.74 3.32

benefit to be obtained by adopting the non-linear model structure of the FANN since the PLS model generally appears to perform more reliably. A comparative model assessment was undertaken. In this analysis it was necessary to make an assessment of the amount of variability captured by the model compared to the potential variability able to be modelled. It was essential to quantify short-term variation that was impossible to model. This was determined by taking a number of samples from the production line over a short time scale (several seconds) and analytically quantifying the five quality variables. The ideal model should possess an error of prediction comparable with the short term noise levels. Table 19.4 compares the short-term noise on the quality variables with the model error. The overall variation experienced is also reported. It can be seen from this table that the model error is indeed comparable with the short-term noise. This is not the only issue. If the overall variation is comparable to the short-term noise then it would not have been feasible to generate a useful predictive model. This table indicates that this may indeed be the case with Q3 but is not so for the other quality variables. Secondly, given that a reasonable model could be obtained, was it sufficiently accurate to provide an opportunity for improved control? This involved a comparison of model accuracy with acceptable levels of variation. This issue is discussed in the following section.

Table 19.4

Comparison of model errors with process variability

Quality property

 overall

 model error

 noise

Q1 Q2 Q3 Q4 Q5

15.9 2.77 6.22 0.43 13.8

6 1.58 4.17 0.22 7.34

4.6 1.37 3.91 0.16 6.63

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19.5

373

On-line implementation and performance

Once the inferential models were constructed and the best performing model (whether it be the linear PLS on non-linear FANN) for each quality variable selected, it was necessary to make a judgement on whether the models were sufficiently accurate to offer process control opportunities. If this was felt to be the case, then additional hardware and software would be required to implement the models on-line. The justification for the capital outlay to plant management therefore required careful thought. The policy adopted was to consider the model accuracy and relate this to the band of operation. The following rules were used: For a model to be acceptable: 6  RMS model error < Target range Mean model error  0 The justification behind this rule set was that the model error must be significantly smaller than the target range to be a useful indicator. If this was not the case then there would be no confidence that model predictions inside the target range corresponded to actual operation in the range. Furthermore, the operators had to act on the information, hence their response times and the dynamics of the process must also be taken into consideration. This could only be judged after implementation. So the simplified rule set above was used, bearing in mind that this was the minimum requirement and the model accuracy would ideally need to be better than this. The results in Table 19.5 were obtained. From Table 19.5 several conclusions were drawn. Firstly, the model predictions did not show significant bias with the mean model errors being close to zero. In addition, for Q4, the quality of prediction satisfied the established criteria for acceptance discussed above and for Q1 fell on the limit. The model for Q5 failed to provide the required level of predictive capability. For Q2 and Q3, where a target range was not specified, the model accuracy was better than the typical deviations experienced in the variables, suggesting that useful information was available. Thus although not spectacular, indications that benefits could be gained (particularly in Q4) were accepted by the plant management and the hardware and software modifications carried out.

Table 19.5 Quality property Q1 Q2 Q3 Q4 Q5

Model performance against acceptability criteria 6 Overall 96 17 37 2.6 83

Target range 35 2.5 22

6 Error for model 36 9 25 1.3 44

Mean model error ÿ2 ÿ0.3 ÿ1 0.1 ÿ8

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The models were implemented in a PC which accessed process data in realtime from the ‘Factory Link’ SCADA system via the LAN. A standalone PC was chosen as it did not interfere with the robustness and performance of the existing system. A Microsoft Visualbasic program providing the capability, if necessary, to run multiple models for comparison purposes was written and accessed process data over a DDE link.

19.5.1 On-line performance Following model implementation and verification that the software was working as expected, accuracy checks on the performance of the models and the variation in the quality parameters were made. Initially a six-week trial period was planned and if successful, longer term usage would be considered. As an example of the results, Fig. 19.4 shows that for variable Q4 the model maintained accuracy over a six-week period of operation. Similar results were obtained for the other quality variables. More important than the maintenance of model accuracy was the behaviour of the quality parameters over the longer term. Quality data from a trial period was compared with data before the implementation of the models and the results are presented in Table 19.6. Improvements in all the quality parameters were found. This could potentially be attributed to several causes: • The inferential models were providing useful information that the operators were acting upon to reduce process variance. • When trials were underway to improve the control system, greater attention than normal was given to plant behaviour. Thus a reduction in variation would be observed which would not be sustained over the longer term. • The period of the test was one of natural low variability and did not represent the long-term characteristics of the process. If either of the latter two cases was responsible for the improvements in plant behaviour then long-term assessment of the process would indicate a return to behaviour comparable to that experienced before the implementation of the inferential models. This has not proved to be the case with long-term behaviour proving the worth of the inferential information provided by the models.

Fig. 19.4

Accuracy of Q4 predictions over trial period.

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Table 19.6 Standard deviation of quality parameters before and after the implementation of the inferential models Quality property

Variation before model

Variation after model

Q1 Q2 Q3 Q4 Q5

16.4 6.8 6.2 0.45 14.0

13.6 1.7 5.9 0.42 8.7

19.5.2 Conclusions for case study 1 The case study set out to demonstrate that, through the application of inferential estimation, reductions in food process variability can be achieved. Results from on-line trials have demonstrated that this was indeed the case. However, the lessons learnt from the application are valuable when technology transfer to other food processing lines is considered. Following the successful implementation of the estimator algorithms on the breakfast cereal line, subsequent studies have considered their application to a very different food process. Early results indicate that the estimator algorithms discussed in this paper and lessons learnt in the breakfast cereal study have generic applicability. Important lessons from the breakfast cereal study include: • Model construction is a comparatively small element in terms of implementation of the inferential estimator. The gathering and pre-processing of the data is a more time-consuming component of model development. • Appreciation of the process characteristics is key to success. Although the models utilised do not rely on mechanistic process understanding, without this detailed knowledge of the process, data validation and model construction becomes an unnecessarily long and inefficient task and success is compromised. • A thorough analysis of the data and subsequent model capability is essential to build a sound case for opportunity estimation. A reliance on unsubstantiated beliefs will not win the support of plant management. Furthermore, the ability to verify improvements is essential for long-term utilisation of the estimation methods. • The efficient construction of ‘black-box’ process models requires experience in the use of the technology and a theoretical understanding of the method. Failure to appreciate this can result in models operating well below their capabilities. • The best model structure to use is not necessarily the most complex. Indeed, the most prudent philosophy to adopt is to start with the simplest linear structure and only consider more complex approaches when performance is less than satisfactory. A difficult balance exists here in that in some cases

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increasing model complexity improves predictive capability but at the expense of robustness. This was not found to be the case in this application as the PLS approach out-performed the non-linear methods. • When on-line implementation is considered, winning over the operators’ confidence is essential. Only introducing the estimate information to the operators following commissioning and tailoring the interface to suit their needs served to ensure commitment for long-term use. However, given all these, the ultimate key to success is having an industrial champion of the technology who takes an active role in the project and is committed to the long-term maintenance of the inferential estimation package. The research staff from the manufacturer played such a role and had the vision to see that benefits would be gained.

19.6 Case study 2: improving process control quality improvement in french-fry manufacture For many control engineers who work on the majority of process systems, feedback control and more advanced techniques such as feedforward or cascade control are standard. Sophisticated control systems take the form of model-based predictive control, LQG optimal control and similar methods. There have been applications of such complex algorithms in the food industry (e.g. Haley and Mulvaney, 2000; van Straten and van Boxtel, 1996) but they are rare. Haley and Mulvaney (1995) discuss the state of the art of advanced control techniques in the food industry while Ilyukhin et al. (2001) survey the wider automation practices. Clearly the food industry makes considerable use of sophisticated instrumentation, monitoring and control systems but the application scope tends to be limited. McGrath et al. (1998) discuss the problems faced by the food industry and how instrumentation and control technology is advancing towards their solution. There are several types of loops where control can be effective, for example temperature regulatory systems in ovens (e.g. Ryckaert et al. 1999) but in general such local loops only serve to maintain the environment to which the product is exposed. Certainly not all process loops are blessed with a state-of-the-art control system. In the food processing industry, for instance, when it comes to product quality, control has meant for many years off-line sampling and manual correction. In the vast majority of instances process control of product quality is limited primarily due to the difficulties involved in measurement. Measurements such as taste, texture and cooked product colour are difficult to make and even in some cases problematic to quantify. Product sampling and analysis in the quality control laboratory have provided the information for process control. Inevitably manual sampling means infrequent analysis and all the control problems that brings. At its most simple, a product that is sampled infrequently to assess quality could be out of specification between samples. The more infrequent the sampling, the more out of specification product produced.

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As with any complex processing line there are many disturbances that can cause product quality to deviate, but in food processing one of the critical variations is due to the raw materials. If knowledge of raw material variation is available then theoretically feedforward control could eliminate its effects. This project set out to examine whether predictive process control techniques, championed in the chemical industry, could be easily applied to a food processing line. However, seldom is predictive control sufficient by itself and thus an overall scheme with a predictive element as a critical component was envisaged at the outset. Achieving process control improvements is not simply a matter of identifying weaknesses of the existing scheme and coming up with a better strategy. Obviously that is a key part, but persuading management to invest in control improvement, managing operational change and long-term maintenance of the system are all critical aspects and potential pitfalls. This case study outlines an application where control improvements have been sought for a french-fry production line. Research into improved control of french-fry production lines has not been widely reported. Recent work at the National Technical University of Athens (Krokida et al. 2000; 2001a; 2001b) is a notable exception. The mechanistic modelling of moisture and colour variation was the focus of their work rather than the methodology for control. This chapter addresses specifically the control issues that arise. The production of frozen french-fries involves processing in a number of unit operations. Figure 19.5 shows the main units in the production line. Note that in this figure and in further discussions the timings given are used to clarify the discussion rather than being precise for the process under consideration. Lorries transport potatoes from various growers to the factory in loads of 20– 30 tonnes. Each load is a single variety and from a single supplier. When they arrive at the factory they are subjected to a number of quality control tests and if they pass these, they are unloaded into a storage bin. The typical characteristics considered are shown in Table 19.7. When required for production, the potatoes from the bin are fed via conveyor belt to the production line. Firstly the potatoes are peeled and then fed to a cutter to produce chips of the required size and characteristic. The size is varied in response to customer requirements. A sophisticated vision analysis system then removes from the line any of the cut potatoes that contain defects. Following this the cut potatoes are partly cooked in a blancher and they then pass into the dryer. The dryer acts to regulate moisture to give the final product its correct texture. After drying, the chips are partially fried. The product is then frozen and packed ready for distribution to the customer. Quality control tests are carried out on the final packed product to confirm that it meets customers’ specifications. Typical measurements made are shown in Table 19.7. Note that in the discussion below quality specifications discussed are tighter than the customers specifications’ in order to ensure greater adherence to the customers’ requirements. There are two sources of information on the production: the quality control laboratory and the computer supervisory control system. The source of

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Fig. 19.5

Overview of the french-fry production line.

information available is indicated in Table 19.7. The paramount production objective is to manufacture french-fries to quality criteria specified by the various customers. To do so requires frequent changes to the processing equipment to routinely make product whose quality falls within target range. Changes are required because: Table 19.7

Typical process information availability

Quality control laboratory

Computer supervisory system

Information

Frequency

Information

Frequency

Raw material assessment Solid content Sugar concentrations Defects Potato size distribution

Per load

Process line settings Temperatures Flows Residence times Production rate

Per minute

Final product assessment Moisture content Fat content Colour of fry Texture Chip size distribution

Per hour

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379

• Customers have their own quality ranges. For example one customer may want chips manufactured to a different moisture content than another. Furthermore, a customer might sell an economy brand as well as the ‘best grade’ product. • Variations in chip size require changes to be made to processing equipment. As an example, steak fries are considerably different in size to ‘shoe string’ fries. This obviously requires changes to the cutting blades but also it affects the surface area to volume ratio and thus the processing characteristics. • Different ultimate cooking techniques require variation in product characteristics. For instance microwave chips may require a lower moisture content than oven chips. • Raw potato characteristics will change from load to load and as different varieties are used. There will also be seasonal patterns in potato characteristics that will cause variation in french-fry quality. These fundamental differences, together with other more subtle variations such as production environment, mean that modifications to the line must be carried out.

19.6.1 Current control policy The first stage in the study involved determining what information existed about process variations and the current plant control policy to deal with these. At the outset it was clear that there was a considerable degree of manual intervention in plant operation. Whilst control loops regulated variables such as blancher, dryer and fryer temperatures, the set points of these controllers were specified by the plant supervisors based on their process expertise. The first step was to check that these controllers were behaving acceptably. If local loops were not functioning correctly then controller set point specification would be pointless. Observations of loop behaviour confirmed that all local control loops were functioning correctly. Following this it was necessary to get an appreciation of how and why the operators modified the controller set points to regulate product quality. This information gathering involved a series of knowledge elicitation sessions from the plant technical manager and shift supervisors. A new knowledge acquisition technique (KAT), developed by CK Design, has been proved to be an efficient knowledge elicitation tool and to result in a complete, correct and consistent knowledge base (Duke, 1992). This technique is based on Karl Popper’s view of epistemology (theory of knowledge). The knowledge elicitation proceeds through successive overturning of the states of belief of the expert about the core belief state. The line of questioning is carried out until the expert believes there is no further condition to overturn the belief under the preceding conditions. The knowledge base is structured in the form of exception graphs that capture the expert’s decision process. Using the KAT method, working from the core belief that the product quality was under control, exceptions were sought and actions in event of these exceptions were obtained.

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It is usually the case that no one person possesses all the knowledge pertaining to the problem domain. It is therefore necessary in the initial project stages to identify all those that may contribute to the knowledge base. A degree of overlap of knowledge between ‘experts’ is desirable as inconsistencies can be highlighted. In this project several process supervisors and quality control laboratory staff were interviewed, along with the past and present production manager. A set of several exception graphs from the various experts resulted. The next stage was to combine them into a single exception graph. This requires the project ‘owner’ to adjudicate if conflicts arise. If the degree of inconsistency between ‘expert’ views is significant then little can be gained from the knowledge elicitation other than indicating that the whole process operational strategy requires reconsideration. This was not the case in this study, with only minor inconsistency, primarily in the severity of action operators took in response to process problems. As a result the current control strategy was determined in the form of an exception graph. The exact details of the current control strategy are confidential as are the precise details of the CK Design technique, but the information shown in Fig. 19.6 is typical of the rules obtained and level of detail produced. Here it can be seen that State 1 indicates that the chip quality is acceptable unless State 2 or State 3 is true. To indicate the type of structure and rules that arise consider the left-hand side of the tree and the situation when a measurement is received to indicate that State 2 is true (i.e. the moisture is high). Action 1 associated with State 1 is taken. This confirms that State 1 is, in fact, true. As moisture is a measured value and subject to error from a variety of

Fig. 19.6

Example of control strategy information.

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sources this reconfirmation is necessary. If State 1 is still true after reconfirmation then State 4 is next considered. If the raw material moisture is falling significantly then it will soon result in product moisture fall so no action is required. Otherwise Action 2 should be taken. In this scenario Action 2 is likely to involve a reduction in product drying.

19.6.2 The process control problem From Table 19.7 it can be seen that there are several chip characteristics that are important for the finished product. Whilst in the long term each one could be tackled, focus upon a key parameter that could immediately bring financial benefit to the company was seen as the first priority. Moisture was identified as particularly important as product was sold by weight and moisture targets set by customers are quite tight. At this stage the managing director of the company not unreasonably asked how much would improving moisture control save him in order to ascertain whether it was a worthwhile undertaking. Answering such a question requires the use of cost/benefit analysis techniques. The fundamental question to answer is how much is a control scheme going to save but this must be answered before it is implemented. To attempt to resolve this ‘Catch 22’ question use was made of techniques proposed by Anderson (1996) and Anderson and Brisk (1992) and proved in other industrial sectors (for example Lant and Steffens, 1998). Considering Fig. 19.7(a), here a product quality range specified by the manufacturer lies between ÿ4 and 2 and the figure shows the distribution of chips within that range. Quality control is good enough to achieve operation within the target range but the mean value of product quality lies at approximately ÿ1. Improved control translates to reduced product variance therefore the situation shown in Fig. 19.7(b) could arise. By decreasing the product quality variance it is still possible to stay within the range of acceptable product but with a mean value of product quality of 0. In practice in this case, this could lead to the mean value of product moisture increasing while still satisfying the customers’ quality control demands. From this situation, a simple financial calculation can be undertaken to reveal what a move of 1 in product quality mean is worth.

Fig. 19.7

Process variability: (a) before improved control; (b) after improved control.

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However, a significant assumption is required to arrive at the distributions shown in Fig. 19.7. The current operational records will provide the information to generate Fig. 19.7(a). The fundamental assumption proposed by Anderson (1996) is that by implementing sophisticated control procedures on a process plant the variance of the product quality will at least halve. Indeed, on plants where significant manual intervention is currently the norm, this is quite pessimistic. Thus from Fig. 19.7(a), it is possible to halve the distribution variance, determine the likely new product distribution and thus calculate where the new mean operating point is to ensure that quality control still remains within the target range, as shown in Fig. 19.7(b). Clearly the figures relating to the application are financially sensitive and can not be revealed here. However, the procedure outlined above was followed and the potential savings indicated were significant and justified the continuation of the study.

19.6.3 The new process control policy Although product moisture is influenced by several operations on the line, the main influence and therefore the control variables are within the dryer. The operation of the dryer is not an insignificant task. Analysis of the existing control policy for moisture control revealed two important issues: • the severity of control changes to the same deviation varied from operator to operator • the operators acted to correct process deviations using a feedback strategy acting on information from the quality control laboratory. While the first issue could be easily rectified, the second highlighted a fundamental control problem. To appreciate the control problem faced, it is necessary to understand the timing involved in process changes. Figure 19.8 shows the approximate process delays that exist in the processing line. Two important conclusions can be drawn from the information presented in Fig. 19.8. Firstly, feedback control is not a particularly effective means of controlling the process. Delays in the overall loop of 35 minutes at best are significant. This would occur if a sample was taken from the line immediately a change reaches the sampling point. In the worst case since samples to measure product moisture are only taken every hour then the delay could amount to 95 minutes. When the line is producing many tonnes of product, this could amount to significant off-specification product. Of equal concern is that with significant disturbances coming from raw material variation, a change in product moisture takes at least 55 minutes to be observed. Corrective action could then be taken but by this time a new load of potatoes will be being fed to the line since it takes around 60 minutes to process a load. Such corrective action would therefore be completely inappropriate. Thus it is clear that this scheme is fundamentally flawed.

On-line analysis and control of product quality

Fig. 19.8

383

Approximate process delays in the control scheme.

In analysing the existing control scheme it is apparent that the problems are a result of process and measurement delays and the sampling rate of the quality variables. Even if the sampling rate could be increased significantly, which given human resource requirements would be difficult, the fundamental problem remains one of process delay. Overcoming the problem of delay requires a predictive control philosophy. If the answer to two fundamental questions could be obtained then control performance could be improved upon considerably. The two questions are: 1. 2.

If a change is made to the dryer how will the product quality respond? If the raw potato quality is known can its effect on product quality be predicted? If so how much and when should the dryer be changed to compensate for it?

If answers to both these questions could be obtained, an improved control scheme could be developed for the following reasons. Answer to Question 1 If the product is off target or a change to the operating target is required, information on how to change the dryer to get the product approximately within range will avoid a major reliance on delayed feedback. Although predictive information will never be perfect, the predictive action will move the product quality close to the desired value and feedback could provide fine modifications to the operation. This will avoid typically well over an hour’s worth of production potentially out of specification.

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Rapid and on-line instrumentation for food quality assurance

Answer to Question 2 If it can be anticipated how a raw material change will influence product quality, corrective action can be taken in a feedforward control sense to nullify any changes in raw material. It is realised that perfect process information will not be available but even approximate process information can serve to provide effective feedforward control, with feedback control again providing fine modifications.

19.6.4 The process control strategy The modified control strategy is shown in Fig. 19.9. The strategy can be found in standard control texts (e.g. Ogata, 2002; Stephanopoulos, 1984). A simple linear steady-state approach is adopted rather than a more complex strategy such as proposed by Baffi et al. (2002). Two key control strategy parameters had to be specified in order for the scheme to function acceptably. Firstly the feedforward controller gain was determined from analysis of data produced from some simple plant tests. Observations of independent variations of dryer temperature and raw material moisture on product moisture provided the necessary information to determine the feedforward controller gain. Secondly inversion of the information on dryer temperature/product moisture provided the predictive information to determine by how much to increase temperature to correct product moisture deviation.

19.7

On-line implementation and performance

Trials of the new control scheme took place over a number of days of operation. From a practical perspective it is important to note that no new instrumentation

Fig. 19.9

The modified control strategy for product moisture.

On-line analysis and control of product quality

385

was required and few, if any, extra laboratory analyses undertaken. The essential aspect of the new control philosophy was to use the available information but respond at appropriate times using knowledge of the likely outcomes of process changes. A demonstration of the procedures can be seen in Figs 19.10 to 19.13. Before details of the timing aspects are discussed, the general concepts are demonstrated. Figure 19.10 shows the effect of changes in potato solids on the product moisture. Note that for reasons of industrial confidentiality, the ‘y-axis’ scales have been modified. The top-left graph in Fig. 19.10 shows the product moisture and the target limits as dashed lines. It can be seen that initially the moisture is in the target band. Here the error bars on moisture refer to measurement accuracy alone. An increase in potato solids occurs as observed in the top-right graph. No significant action was taken with the dryer temperature (as shown by the continuous line in the bottom graph). Here the previous feedback control philosophy is adopted and as a result a significant deviation in product moisture occurs taking it outside the target range. The dashed temperature line is that suggested by the new control scheme and in this case not used. A fall in potato solids at around 0.4 days brings the product moisture back within specification. At 0.55 days potato solids again increases but now preventative action is taken and the temperature of the dryer is decreased. The graph indicates that the operator took slightly more severe action than the new control scheme suggested. Only a very minor change in product moisture can be observed. Again when the potato solids fall and temperature is increased (following the new feedforward control scheme) product moisture remains predominantly unaffected. These results clearly demonstrate that feedforward control can compensate for potato solids disturbances effectively. The example above demonstrates disturbance rejection but the moisture is not always within the target range. Take for example the situation shown in Fig. 19.11. This was one of the early tests carried out and some of the early control scheme modifications can be seen. It can be seen that chip moisture is initially outside the target range. Control with the new scheme commenced at around 0.47 days (11am – the second sample on the graph). A fall in potato solids and a reduction in dryer temperature brought the moisture up towards the target range. The predictive aspects of the control scheme are demonstrated at around 0.525 days (12.30pm). The moisture was under the target range and therefore needed to be increased. A change in potato solids suggested that product moisture would increase by around 2 so no dryer temperature change was implemented. In actual fact the moisture increased by 3 and overshot the target range. Thus the assumption that a 2 decrease in solids leads to a 2 increase in product moisture is not correct and the gain should be increased. Using this new gain, when the potato solids increases by 2.5 at around 0.57 days, if no change to the dryer temperature is made, a drop of almost 4 could be expected in the chip moisture. This would take it well below the target range. Using the feedforward control rule, dryer temperature is decreased to result in the product moisture falling within the target range.

386

Rapid and on-line instrumentation for food quality assurance

Fig. 19.10 Example of potato solids disturbance rejection.

On-line analysis and control of product quality

Fig. 19.11 Control of product moisture when out of target range.

387

388

Rapid and on-line instrumentation for food quality assurance

Fig. 19.12 Timing of process changes.

On-line analysis and control of product quality

389

The timing of changes for feedforward and predictive changes is critical. The following example, illustrated in Fig. 19.12, demonstrates the timing aspects and how predictive changes are made. • At 11.30am (0.47 days) production switches to a new product and the moisture target changes (denoted by the dashed lines). • At the same time the line feed switches to a new load of potatoes. The solids fall from 7.1 to 3.7 (i.e. by 3.4). • Although within the target range, the moisture is towards the lower end of the target. • If no temperature change is made the feedforward control rules tell us that the moisture will rise by around 3.4. This would overshoot the upper target limit giving around 21.3. • Aiming for a moisture of 20 (allowing a margin for error) suggests a 1.3 reduction of moisture. At 4ºC for every 0.5 moisture change this would require a 12ºC increase. A 15ºC increase was applied 40 minutes after load switched (i.e.12.10pm). This was to allow for the delay between storage bins and dryer. • 40 minutes later a moisture sample is taken and returns a result of 20.24%, towards the higher end of the target range. • Following the change in target range described above, a very small change (0.2) in potato solids occurs at 0.53 days. This should have minimal impact on product moisture but a significant increase is observed taking it outside the target range. This change was observed although no other production line changes were made. For this reason the assay was considered suspect. Subsequent assays reveal that indeed the assay was dubious and operation close to the top of the target range was maintained. The results above were obtained in a series of process tests undertaken by the development team in collaboration with the process operational staff. During such tests, closer attention than normal is paid to the process plant operation. The worry is therefore that although plant improvements are indicated, in the longer term when normal day-to-day operation resumes, without a specific focus on the new policy, little additional benefit will be found. Long-term performance compared with process behaviour before the introduction of the scheme is the best way to judge whether this is indeed the case. This information is shown in Fig. 19.13. Figure 19.13(a) shows the performance of the production line before the implementation of the control scheme. Laboratory samples measuring moisture content are shown along with the tight bounds within which it is desirable to operate. It can be seen that deviations outside the bounds were frequent (56% of samples fall outside the bounds). Figure 19.13(b) shows the behaviour of the process following the introduction of the control scheme. Much tighter regulation of the moisture content is apparent (10% of the samples fall outside the bounds). Slight oscillatory behaviour is observed within the bounds of operation. One of the reasons for this is that potato loads are not selected at random to go through the production line. The operators make an effort to put

390

Rapid and on-line instrumentation for food quality assurance

Fig. 19.13 (a) Performance before control scheme implementation; (b) performance after control scheme implementation.

loads of a similar moisture to the previous load through the line, hence introducing the observed perturbations. In interpreting these figures it must be remembered that the operational bounds are tighter than the customers’ requirements but nevertheless, for the reasons discussed previously, it is important to reduce variation as much as possible. Returning to the cost/benefit analysis carried out before the implementation, it is interesting to observe the process variation has been almost exactly halved which is in line with the prevailing wisdom on improved control benefits.

19.7.1 Conclusions for case study 2 This case study sets out to demonstrate that variations in product quality in a food processing line could be reduced by the application of advanced control

On-line analysis and control of product quality

391

methods. A number of challenges were faced, the first of which was simply to ascertain what the existing control scheme was in order to assess the potential for improvement. Since the line was predominantly under operator-based feedback process control this was not straightforward. KAT knowledge elicitation proved effective at obtaining an initial idea of the strategy. It highlighted where problems existed but it did not provide a total solution. It was evident in later trials that whilst we identified differences in operators’ actions, it had not overcome the problem of people in some cases responding with what they thought they were supposed to do rather than what they actually should have done. Thus following initial KAT analysis, process observations were needed to obtain a more realistic picture. Once the failings of the current control scheme were identified, cost/benefit analysis revealed very clearly that improvements were possible and the likely savings would more than justify the investment. The control strategy itself was fairly straightforward to devise from a theoretical viewpoint, with simple process trials revealing approximate process gains which were sufficient for control design purposes. Implementation on the production line to prove that the methods worked was remarkably trouble free. In the longer term, whilst the new control strategy is simple to implement, it does rely on manual changes to be made at roughly the correct time. This is a fundamental problem as staff in a small company tend to have many calls upon their time and this will be seen as one more. However, failing to respond to raw material changes has serious financial consequences on the production line. A general awareness of the scale of the potential loss may serve as an encouragement to adopt the new strategy. As the strategy is adopted as the standard operating policy for the line, so experience in its use will see variety, product and time of year being taken into account in terms of feedforward gain modification. Results to date have indicated that the very promising behaviour observed during plant trials has continued. As confidence grows in the strategy, the operators will feel more assured that they can operate closer to the operational constraints and so the financial benefits will begin to accrue. Finally, the control scheme developments described within this chapter have been a joint effort between a university and a company. The skills of both were required. If in the future production line changes are made, the control scheme may need to be re-assessed. It is likely that this would only involve changing the timing or magnitude of changes but still process tests and/or data interpretation would be required. In many small companies the skills to implement such schemes in-house are not available. This is not necessarily a problem, with the critical capability being to recognise that there is a control problem in the first place.

19.8

Future trends

Batch processes are of considerable importance in many parts of the food industry. Batch processing is synonymous with flexible and responsive

392

Rapid and on-line instrumentation for food quality assurance

manufacturing and has the advantage over continuous production in that the production system can be more easily modified to take account of changes in product formulations resulting from new product introductions and product changes to meet customer requirements. With increasing competition, it is of critical importance to ensure that each batch run produces product that is of consistent high quality, maximum yield and in the shortest possible batch time. This requires that process performance monitoring technologies, based on multivariate statistical process control (MSPC) methodologies (Martin and Morris, 1996; Lane et al., 2001; Martin and Morris, 2002), become an integral part of process operations. The aim of process performance monitoring is to achieve early warning of process malfunctions and abnormalities and faults as well as changes in process operation that may result in reduced product yield or lower quality production. The early identification of such abnormalities may enable batch mid-course corrections to be made or in the worst case scenario, the batch being terminated early. An early warning of a process change may allow, in some applications, corrective action to recover the batch or alternatively ensure that subsequent batches are not out of specification. Finally, a major objective in process monitoring is the determination and prediction of product quality, the prediction of reaction end points, detection of impurities, etc. To meet this objective there has been increased use of spectroscopic techniques such as, NIR, MIR, Raman, fluorescence, acoustic emission, etc. There is now a real need to develop tools that enable the integration of spectral and process data and integrate them into on-line real-time process performance monitoring operational support systems.

19.9

Sources of further information and advice

For additional information on process control, inferential estimation, optimisation, statistical process control, multivariate statistical process control and performance monitoring, contact www.cpact.com.

19.10

Acknowledgements

The authors would like to acknowledge the financial support of the UK Department of Trade and Industry, Department of Environment, Food and Agriculture DEFRA (formerly Ministry of Agriculture, Food and Fisheries), Weetabix, St Ivel, Nestle UK and Cadbury for Case Study 1 and the Teaching Company Scheme industrial partner for financial support for Case Study 2. Particular thanks also go to Dr Christina Goodacre of DEFRA for the considerable support and assistance that she provided. The contributions from Adrian Conlin, Mark Willis and Jarka Glassey are also gratefully acknowledged. Finally the authors would like to acknowledge Elsevier for their permission to reprint the material originally published in the Journal of Food Engineering,

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Vol. 57, No. 4, 2003, pp. 357–365, Montague et al.: ‘French-fry Quality Improvement using Advanced Control Techniques’ and Albert et al.: Vol. 50, No. 43, 2001, pp. 157–166, ‘Inferential Quality Assessment in Breakfast Cereal Production’.

19.11

References

(1999). Modelling techniques for improved supervision of food processes – a case study, Ph.D. thesis, University of Newcastle, UK. ANDERSON J.S. (1996). ‘Control for Profit’, Trans. of Inst. M.C., 18, 1. ANDERSON J.S. and BRISK M.L. (1992). ‘Estimating the economic benefits of advanced process control’, I.Chem.E. Symposium Advances in Process Control III, York, 23– 24 September. BAFFI G., MORRIS A.J. and MARTIN E.B. (2002). ‘Non-linear model based predictive control through dynamic non-linear partial least squares’, Chemical Engineering Research and Design, 80, 75–86. CYBENKO, G. (1989). ‘Approximations by superpositions of a sigmoidal function’, Math. Cont. Signal & Systems, 2, 303–314. DUKE, P. (1992). ‘KAT – A Knowledge Acquisition Technique, Methodology manual’. CK Design Handbook. GELADI, P. and KOWALSKI, B.R. (1986). ‘Partial least squares regression: A tutorial’, Analytica Chimica Acta, 185, 1–17. HALEY T.A. and MULVANEY S.J. (1995). ‘Advanced process control techniques for the food industry’, Trends in Food Science and Technology, 6, 4, 103–110. HALEY T.A. and MULVANEY S.J. (2000). ‘On-line system identification and control design of an extrusion cooking process: Part II. Model predictive and inferential control design’, Food Control, 121–129. ILYUKHIN S.V., HALEY T.M. and SINGH R.K. (2001). ‘A survey of automation practices in the food industry’, Food Control, 12, 285–296. KOURTI, T. and MACGREGOR, J.F. (1995). ‘Process analysis, monitoring and diagnosis, using multivariate projection methods’, Chemometrics and Intelligent Laboratory Systems, 28, 3–21. KROKIDA M.K., OREOPOULOU V. and MAROULIS Z.B. (2000). ‘Water loss and oil uptake as a function of frying time’, Journal of Food Engineering, 44, 39–46. KROKIDA M.K., OREOPOULOU V., MAROULIS Z.B. and MARINOS-KOURIS D. (2001a). ‘Colour changes during deep fat frying’, Journal of Food Engineering, 48, 219–225. KROKIDA M.K., OREOPOULOU V., MAROULIS Z.B. and MARINOS-KOURIS D. (2001b). ‘Effect of pre-drying on quality of french fries’, Journal of Food Engineering, 49, pp 347– 354. LANE S., MARTIN E.B., KOOIJMANS R. and MORRIS A.J. (2001). ‘Performance Monitoring of a Multi-Product Semi-Batch Process’. Journal of Process Control, 11(1), 1–11. LANT P. and STEFFENS M. (1998). ‘Benchmarking for process control: ‘‘should I invest in improved process control?’’’, Water Science and Technology, 37, 49–54. LANT P.A., THAM M.T. and MONTAGUE G.A. (1993). ‘On the Applicability of Adaptive Bioprocess State Estimators’, Biotech. Bioeng, 42, 1311–1321. MARTIN E.B. and MORRIS A.J. (1996). ‘An Overview of Multivariate Statistical Process Control in Continuous and Batch Process Performance Monitoring’, Trans. Inst. ALBERT S.

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M.C, 18(1), 51–60. and MORRIS A.J. (2002). ‘Monitoring Process Manufacturing Performance’, IEEE Control Systems Magazine, 22, 5, 26–39. MCGRATH M.J., O’CONNOR J.F. and CUMMINS S. (1998). ‘Implementing a process control strategy for the food processing industry’, Journal of Food Engineering, 35, 313– 321. MONTAGUE G.A., MORRIS A.J. and THAM M.T. (1992). ‘Enhancing bioprocess operability with generic software sensors’, Journal of Biotechnology, 25, 183–201. MORRIS A.J., MONTAGUE G.A. and WILLIS M.J. (1994). ‘Artificial neural networks (Studies in Process Modelling and Control)’, Trans. Institute of Chemical Engineers, 72(A), 3–19. RYCKAERT V.G., CLAES J.E. and VAN IMPE J.F. (1999). ‘Model based temperature control in ovens’, Journal of Food Engineering, 39, 47–58. SCALES, L.E. (1985). Introduction to non-linear optimization, Macmillan Publishers Ltd. STEPHANOPOULOS G. (1984). Chemical process control: an introduction to theory and practice, Prentice Hall. VAN STRATEN G. and VAN BOXTEL A.J.B. (1996). ‘Progress in process operation by goal orientated advanced control’, A. Rev. Control, 20, 209–223. WILLAN A.W. and WATTS D.G. (1978). ‘Meaningful multicollinearity measures’, Technometrics, 20, 407–412. WOLD S. (1978). ‘Cross validatory estimation of the number of components in factor and principal component models’, Technometrics, 20, 397–405. MARTIN E.B.

Index

absorption coefficient 273, 286–7 absorption spectroscopy 271–2 fruits 280–3 acceptable daily intake (ADI) 92–3 AccuProbe 144–5 acephate 56 acetylcholine esterase (AChE) 66–7 inhibition 49–50, 57, 61–2 acoustic/piezoelectric sensors 24–5, 149, 150, 192 acoustic resonance densitometry 141 acoustic wave guide (AWG) 24 acousto-optic tunable filter (AOTF) 293 action limits (ALs) 96 added water 240–69 applications of dielectric property measurement 258–64 dielectric properties 245–51 future trends 264–7 instrumentation for measuring dielectric properties 251–8 and legislation 240–1 problems in measuring 241–4 additives and micronutrients 185–204 biosensors 192–3, 196 chromatographic techniques 186–9 enzymatic methods 195 FIA 194 future trends 196–7 ICP-AES 196 immunoassay 192

indirect methods 189–91 ISE 193 PCR 191 range of rapid methods 186 spectrophotometry 195–6 adenosine 5’ triphosphate (ATP) testing 140 adsorption 62–3 affinity constant 16 affinity receptor molecules, new 31–3 affinity sensors 15 alcohols 349–52 aldehydes 349–52 algal blooms 116, 125 -D-galactosidase 144, 152–3 AMBRI SENSIDX System 24 AMEL 130, 131 amine coupling 80 amines 349–52 amino-methoxyflavone 47 amnesic shellfish poisoning (ASP) 50–1, 116, 118 see also domoic acid amperometric sensors 23, 24 microbial contamination 147–8 pesticides 66–8 amperometry 146–7 amylases 152 antibodies availability 28 new receptor systems 31–3

396

Index

antibody-antigen reaction 15–16 labels 17–18 see also immunoassays antibody engineering 17 antigens 15–16, 60 antimicrobial drug residues see veterinary drug residues antisera 17 API tests 143–4 apples 278, 279, 280–5, 285, 286 AppliSens BioSep 231–2 aptamers 33 aroma profile control 334 artificial neural networks (ANNs) 262–4, 330, 346, 369–70, 371–2 artificial receptors 31–3 ascorbate oxidase 61 aspartame 195 at-line measurement 216 electronic noses 353–4 atomic absorption spectrophotometry (AAS) 196 attenuated total reflectance (ATR) 191, 291 attenuation, microwave 252–3 attenuation coefficient 272 autofluorescence 311 AUTOLAB 130, 131 automatic network analysers (ANA) 264 bacterial injury 162 Bactometer 145, 146, 170 Bactoscan Automated Microbiology System 139 baking 318 BAS in vivo UF probe 229–30 batch processing 391–2 Beer’s law 252 Belgium dioxin incident 46–7 veterinary drug residues 93 benzimidazole drugs 47 benzo(a)pyrene 48 -D-galactosidase 152, 168–9 -D-glucoaminidase 152 -D-glucosidase 152–3, 168–9 -D-glucuronidase 168–9 -lactam antibiotics 82–3 BIAcore sensors 22, 27, 79–82, 150, 193, 225 bile, porcine 82, 85 bioassays 40–54 acetylcholine esterase inhibitors 49–50 dioxins 41–8

future trends 51 oestrogen 49 shellfish poisons 50–1 see also immunoassays biochemical methods for moulds 150–3 biological coupling 64 biological layer/molecules 62–4 biological methods for pesticides 59–62 bioluminescence 140, 154 bioMerieux API tests 143–4 biomimics 6, 31–3 BIOQUANT 195 BIOS-1 biosensor 225 biosensors 6, 62–72, 215–16 additives and micronutrients 192–3, 196 developing low-cost biosensors 69–70 electrochemical see electrochemical sensors future trends 72 microbial contamination of water 170, 174, 177 modes of action 64, 65 optical see optical sensors pesticide residues 70–2 principles 62–9 for process-line applications 224–7 veterinary drug residues 79–88 see also immunosensors BioSep 231–2 Biotrace Uni-Lite instruments 140 black box modelling 362–3 bovine serum albumin (BSA) 17 breakfast cereal production 363–76 data gathering 365–7 model implementation and performance 373–6 process modelling 367–72 brevitoxins 50–1 brown heart (BH) 285–7 bulk acoustic wave (BAW) sensors 326, 342 butyrylcholine-esterase 61 caffeine 191 CALM 169 CaMV35S promoter 211–12 canonical correlation analysis 331 capelin 346 carbon dioxide 70–1 capillary electrophoresis (CE) 188–9, 196–7 carbaryl insecticide 29 carnitine 195

Index carrageenan 318–19 carriers 17 catalase 144 catalytic sensors 15 CCD-Raman spectroscopy 295–6, 299 CellFacts I 140–1 cellular components detection 143–5 Centrifuge Stratos 231 cereals 70–2, 150–2, 300–1 cheese 312 CHEMFETs 23 chemical contaminants 6–7 chemical (covalent) coupling 63–4, 80 ChemScan RDI instrument 172 chicken 249–50, 260, 264–5, 266 chlorinated hydrocarbons 55 chlorophyll-a 280–3 chlorpyrifos 56, 58 chlorpyrifos-methyl 58 choline 195 chromatography 26 additives and micronutrients 186–9 veterinary drug residues 95–6 see also under individual techniques Ciguatera toxins 50 citrus pulp incident 45–6 CK Design KAT 379–81, 391 Clean-Trace instruments 140 Clearview 142 cluster analysis 346 coaxial sensors 254–6 cobalt phthalocyanine (CoPC) 66–8 Cole-Cole plots 247, 248, 249 Colifast Analyser 169, 177 coliform group 165–6 Colilert colorimetric test 144 Colilert Quanty-Tray 168–9 Colilert 3000 177 colony count technique 164–5, 177 combination approaches 227–36, 236–7 combinatorial library technique 32–3 competitive immunoassay format 18, 19, 60, 117–18 competitive mode of action 64, 65 complex food matrices 227–36 complex permittivity 247, 250 composition CSLM for monitoring 313–19 dielectric approach to measuring 258–9 spectroscopic techniques for cereal and dairy applications 300–2 concentration of volatiles 343 conductimetry 191

397

conduction polymer (CP) sensors 326, 342 conductivity 145, 249 confirmation methods 95–6 rapid on-line confirmation of veterinary residues 98–112 confocal scanning laser microscopy (CSLM) 306–23 applications 313–19 future trends 319–21 principles 308–10 sample preparation 310–13 consumers 218 contaminant detection 3–13 chemical contaminants 6–7 comparison of techniques 11 costs of contamination 5 foreign bodies 7–10 process issues 5 types of contaminant 3–5 see also bioassays; immunoassays; and under individual types of contaminant continuous centrifuge 231 continuous shear configuration 320 corn starch 314–15 corticosteroids 47 cost-benefit analysis 381–2 cotton 209 Coulter counters 140–1, 154 coupling chemistries 63–4, 80 covalent coupling 63–4, 80 covalent labelling 310, 312 Cryptosporidium 174–6 culture methods 162–5, 177 cyclic voltammetry (CV) 147 data analysis electronic noses 329–31, 345–6 spectroscopic techniques 277–8, 298 data gathering electronic noses 328–9 process modelling 365–7 data mining 235–6 defects, in fruits 285–7 deformation cells 320–1 delayed feedback 382–3 detection limit 96, 97 dexamethasone 47 dialysis probes 228–30 diarrhetic shellfish poisoning (DSP) 50–1, 116, 122 see also okadoic acid dichlorodiphenyltrichloroethane (DDT) 55

398

Index

dichlorvos 58, 68 dichroic mirror 308–9 dielectric constant 246–7 dielectric properties of water 245–67 applications 258–64 dispersion in foodstuffs 247–51 future trends 264–7 instruments for measuring 251–8 dielectrophoresis (DEP) 233–4 DEP-FFF 234 differential pH technique 195 differential pulse voltammetry (DPV) 118, 129–31 dioxins 29, 41–8 dipping system 70 direct competitive ELISA 117–18 direct detection immunoassays 18, 170–2 direct epifluorescence filter method (DEFT) 139–40 direct immunosensors 21 direct mode of action 64, 65 dispersion, dielectric 246, 247–51 displacement immunoassays 18 disposable biosensors 69 rapid detection of toxins 118, 129–31 disturbance rejection 385, 386 diuron 29 DNA 206–7 see also genetically modified organisms DNA amplification 208 DNA microchip arrays 177–8 domoic acid (DA) 50–1, 118–22 dough effect of baking 318 kneading 317 DR-CALUX assay 41–8 Belgian dioxin incident 46–7 citrus pulp incident 45–6 development 41–2 future developments 48 specificity 47–8 use for other samples 47 validation for milk fat 42–5 dry weight method 137 E-screen 49 ED-XRF 189–90 electrical FFF (EFFF) 235 electrochemical methods 145–7 electrochemical sensors 22–4, 192, 342 pesticides 64, 66–9 seafood toxins 118, 120–2, 123–5, 128–9, 129–31

see also under individual types electromagnetic spectrum 245 electron microscopy 142 electronic noses 6–7, 10–12, 324–38 applications for quality 331–4, 346–8, 355 commercial instruments 327–9 data acquisition 328–9 data analysis 329–31, 345–6 developing rapid and on-line applications 352–4 fish quality 339–60 future trends 334–5, 354–5 microbial contamination 153–4, 154 principles 325, 341 process monitoring 224 sampling systems 328, 342–5 sensor technologies 341–2 technology development 354 types of 325–7 validation of performance 348–52 electronic polarisability 245 Electronic Systems Design Group, University of Southampton 232 electronic tongue 355 electrophoresis 94 electrostatic bonds 62–3 endocrine disruption 49 Enterobacteriaceae 165–6 enterococci 165–6 Enterococcus 166 Enterolert Quanty-Tray 169 environment 218 Environmental Protection Agency (EPA) 20 enzyme-linked immunosorbent assay (ELISA) 15, 77, 94–5, 117–18 additives and micronutrients 192 microbial contamination 143 toxins 118–29 enzymes changes and deterioration due to moulds 152–3 enzymatic methods for additives and micronutrients 195 metabolising and microbial contamination 144, 154 pesticide detection 61–2 specific enzyme activity methods for microbial contamination of water 168–9, 177 epifluorescent microscopy 139–40, 154, 171, 177 epitopes 16

Index ER-CALUX assay 49 ergosterol 150–2 EROD-assay 41 Escherichia coli (E. coli) 147–8, 166 esters 349–52 estrogen assays 49 Europe 205, 212–13 European Union (EU/EC) added water monitor prototype 256, 257 GMO labelling 206 Quantitative Ingredients Declaration (QUID) 240–1 veterinary drug residues 75, 78, 92, 93 Foodsense project 84–6 evanescent wave 65–6, 291 EW immunosensor 22 exception graphs 380–1 explanatory data analysis methods 330–1 fat-soluble vitamins 187 feedback process control 216–17, 382–3, 384 feedforward artificial neural networks (FANNs) 369–70, 371–2 feedforward control 384–91 fermentation 332 fermented milk 301 fibre optic (FO) sensors 227 in-line process sensors 222–4 Raman spectroscopy 299–300, 303 field flow fractionation (FFF) 234–5 fish oils 44–5 fish quality 339–60 spoilage odours 340 validation of performance of electronic nose 348–52 key indicator compounds 349–52 reference methods 348–9 FlashTrak 144–5 flow cytometry 141, 171–2, 177 flow FFF (FFFF) 235 flow injection analysis (FIA) 25–6, 68, 147–8 additives and micronutrients 194 FIA-FTIR 191 fluidics system 70 flunixin 100, 107, 109 fluorescein 310 fluoroscein isothiocyanate 139–40 fluorescence 292 fluorescence correlation spectroscopy (FCS) 309 fluorescence recovery after photo-

399

bleaching (FRAP) 307 fluorescent labels 30, 66, 307–8 fluoroimmunosensor 27–8 fluoroquinolones 86 food additives see additives and micronutrients food control systems 87 food industry 87–8 electronic noses and fish quality 355 needs and instrument design 296–7 and process control 218 Food Labelling Regulations (FLR) (UK) 240 food matrices, complex 227–36 food scares 14, 91 Foodsense project 84–6, 86–7 foreign bodies 3–4 detection 7–10 four plate test (FPT) 94 Fourier Transform infrared (FTIR) spectroscopy 191, 293–4, 302 FT-Raman spectroscopy 295–6 free flow electrophoresis (FFE) 232–3 French-fry manufacture 376–91 cost-benefit analysis 381–2 current process control policy 379–81 on-line implementation and performance of new scheme 384–91 new process control policy 382–4 process control strategy 384 freshness 352–3 Fresnel reflection coefficient 254 FreshSense 342, 349–52 fruit 271 electronic noses and ripeness 331–2 pesticide residues 70–2 TRS 278–88 absorption and tissue components 280–3 defects 285–7 effects of skin 278, 279 maturity 285, 286 penetration depth 278–80, 281 scattering and tissue structure 283–5 fuel cell technology 145–6 fumonisin 152 functional mode of action 64, 65 galactosidase 144, 152–3, 168–9 gas chromatography (GC) 26, 59, 95, 189 GC-MS 59, 95, 189 SAW sensors (GC-SAW) 326–7 gas liquid chromatography (GLC) 59

400

Index

gas sensors 326–7, 341–2 see also electronic noses gelatinisation 315 gels, protein 315–17 GEN-PROBE hybridisation protection assay (HPA) technique 144–5 GeneChip technology 145 genetically-modified organisms (GMOs) 205–14 future trends 212–13 identification in practice 211–12 labelling 206 PCR techniques 208–11 principles of analysis 206–7 sample preparation 207 gentamicin 81 Giardia 174–6 global competition 218 glucocorticoids 109 glucuronidase 144, 168–9 gluten 317 good laboratory practices (GLPs) 210–11 good manufacturing practice (GMP) 12 grain 70–2, 150–2, 300–1 Gram stain microscopy 139 grating spectrometer 293 guar gum 191 guided microwave spectrometry (GMS) 256–8, 261 haddock 344–5, 349–52 hand-held devices 7, 353–4 haptens 16–17 harvest date 285, 286 Hazard Analysis Critical Control Point (HACCP) 12, 136 heterofunctional cross-linking agents 63–4 heterogeneous immunoassay 19 heterotrophic plate count (HPC) 165 high performance liquid chromatography (HPLC) 59, 186–8 high performance thin layer chromatography (HPTLC) 58–9 histamine 340 homofunctional cross-linking agents 63–4 homogeneous immunoassay 18 humidity control 219 hybridisation techniques 144–5, 172 hybridoma technology 17 HYCON Contact Slides 138 HYCON Dip Slides 138 hydrophobic bonds 62–3

IAsys system 22, 150 IBIS biosensor 225 IC50 (inhibitory concentration – 50%) 61 identification points (IPs) 96–7 image analysis 259 in-line process monitoring 219–20, 221 image analysis software 10 imaging techniques 319–21 immunoassay test strips 20 immunoassays (immunochemical assays) 14–39 additives and micronutrients 192 ELISA see enzyme-linked immunosorbent assay food contaminant analysis 20–1 future trends 30–3 immunosensors see immunosensors microbial contamination 142–3 water 170–2, 177 pesticides 60–1 principles and applications 15–20 veterinary residues 76, 77, 94–5 see also bioassays immuno-magnetic separation 19, 143, 177 immunosensor arrays 20 immunosensors (immunochemical sensors) 21–5, 34, 117–18 electrochemical see electrochemical sensors future trends 30–3 microbial contamination 147–50 on-line in food processing 25–9 optical see optical sensors piezoelectric/acoustic 24–5, 149, 150 see also biosensors impedance 8–9, 11, 255–6 ‘intelligent pipe’ 220–2 microbial contamination 145, 146, 154, 170, 177 in-line measurement 216 in-line sensors 215–39 complex food matrices 227–36 current commercial systems 219–27 drivers for process line sensors 218–19 future trends 236–7 operational characteristics 217–18 principles of 216–19 indicator compounds 349–52 indicator organisms 165–7 indirect competitive ELISA 118 indirect immunoassay 18 indirect immunosensors 21–2

Index inductively coupled plasma atomic emission spectrometry (ICP-AES) 196 inelastic scattering 292 see also Raman spectroscopy inferential estimation 363 see also process control infusion-MS 99–113 ingredients, interactions between 318–19 inhibition assay format 79 injection sites 98–113 injectable (standard) solutions 99 instrumental response function 274, 277–8 insulin-line growth factor-1 (IGF-1) 29 ‘intelligent pipe’ 220–2 interferential filter 292–3 interferometer 293 iodine 195 ion-channel switches (ICSs) 23–4 ion chromatography 188, 189 ion mobility spectrometers 7 ion selective electrodes (ISE) 193 ionic conductivity 249 ionic polarisability 245 iron 195–6 ISFETs 23 isoproturon 29 Jorin ViPA 219–20, 221 Kaiku ’intelligent pipe’ 220–2 ketones 349–52 keyhole limpet hemacyanin (KLH) 17 kiwifruit 278, 280–5 kneading of dough 317 knowledge acquisition technique (KAT) 379–81, 391 labelling in CSLM 310–13 GMOs 206 in immunoassays 17–18, 60 Lambert law 272 lasers 274–5, 276, 277 see also confocal scanning laser microscopy lateral flow devices 60–1 legislation/regulation 218 added water 240–1 GMOs 205–6 veterinary drug residues 92–3 light addressable potentiometric sensors (LAPS) 23, 148–9

401

light-based systems 8, 11 linear discriminant analysis (LDA) 345 liquid chromatography (LC) 26, 95 LC-MS 95–6, 97, 188, 196 veterinary drug residues 99–113 LC-MS/MS 188, 196 liquid enrichment methods 163 locust bean gum (LBG) 191 loss factor 247, 248, 249–50 low-cost biosensors 69–70 luciferase 42 machine vision systems 8, 10 magnetic biosensors 72 magnetic resonance imaging (MRI) 10, 11 magnetic separation 19, 143, 177 maize 209 malathion 56 malicious contamination 4 malt 300–1 Malthus system 145, 170 malting process control 297 mass spectrometry (MS) 26, 59, 326 GC-MS 59, 95, 189 infusion-MS 99–113 LC-MS 95–6, 97, 99–113, 188, 196 LC-MS/MS 188, 196 MS-MS 95–6 matrices 224 complex food matrices 227–36 maturity of fruit 285, 286 maximum residue limits (MRLs) 14 pesticides 57, 58 veterinary drug residues 78, 91, 93, 96 Maxwell-Wagner effect 250–1 meat 81–2 added water in meat products 241–4 chicken 249–50, 260, 264–5, 266 meat homogenate 227, 228 mechanistic modelling 362 Medeci analyser 146–7 membrane filtration (MF) 138, 164–5, 168, 177 metal detection 7 metal oxide semiconducting field effect transistors (MOSFETs) 342 metal oxide semiconductors (MOS) 326, 341 methyl parathion 68–9 Michelson interferometer 293 microarrays (microspot arrays) 20, 30–1 microbial contamination 136–60 biochemical methods and moulds 150–3

402

Index

bioluminescence 140, 154 cellular components detection 143–5 commercial instruments 154–5 conventional detection methods 136–9 electrochemical methods 145–7 electron microscopy 142 electronic noses 153–4, 154 epifluorescence technique 139–40, 154, 171, 177 flow cytometry 141, 171–2, 177 immunoassay techniques 142–3, 170–2, 177 immunosensors 147–50 particle counting 140–1, 154 water see water microbial inhibitor tests 76–7, 94 microbolometers 302 microcystin-LR 23, 24, 32, 33 Microcyte 171–2 microfabrication 129, 233 microfluidics 70, 233 MicroFoss 139 micronutrients see additives and micronutrients microreaction technology 237 microscopy 139 see also under individual techniques microspot arrays 20, 30–1 microwave methods 9, 11 added water 251–8, 264, 265 attenuation 252–3 GMS 256–8, 261 reflectance 253–6, 257, 265 middle infrared (MIR) spectroscopy 291, 292–4, 298, 300–1, 302–3 Mie theory 283–4 milk fermented milk quality control 301 validation of DR-CALUX for milk fat 42–5 veterinary drug residues 81–2, 85–6, 87 milling 296–7 miniaturisation 129, 233 enzymatic MPN methods 169 FIA systems 197 mini-validation 98 MIST (Maritime In Vitro Shellfish Test) 51 moisture content process control for French-fry manufacture 379, 381–91 water content in fruits 280–3 molecular techniques 172–3

molecularly-imprinted polymers (MIPs) 32, 33 monoclonal antibodies 17 most probable number (MPN) technique 163–4, 168–9, 177 motility test 137–8 moulds biochemical methods 150–3 electronic noses 153, 154 multi-analyte biosensors 72 multiangle light scattering (MALS) 175–6 multiangle, multiwavelength particle characterisation 175 multicollinearity 367–8 multiple linear regression (MLR) 367–8, 370 multiple-photon excitation (MPE) fluorescence microscopy 310 multiple scattering of light 273 multivariate methods 235–6, 259–64, 329–31, 345–6 mung bean starch 314 mussels 120–2, 123–5, 127–8 MUSTEC project 353 mycotoxins 151–2 N-acetyl- -D-glucosaminidase 152–3 naphtoflavone 47 near infrared (NIR) spectroscopy 191 near-line measurement 216 Netherlands 47 neural networks, artificial 262–4, 330, 346, 369–70, 371–2 neuroblastoma cell test 50–1 neurotoxin shellfish poisoning (NSP) 50–1, 116 new receptor systems 31–3 nitrogen factor (NF) 241–4 noncompetitive (sandwich) immunoassays 18, 19 non-covalent labelling 310, 311–12 nonsteroidal anti-inflammatory drugs (NSAIDs) 109 notch filters 303 nuclear magnetic resonance (NMR) 59 nucleic acid ligands 33 nucleic acids 62, 144–5 ochratoxin A 28 oestrogen assays 49 okadaic acid (OA) 29, 50, 122–5 on-line measurement 216 on-line water monitoring 173–6 on-line techniques 3–13

Index comparison of techniques 11 detection of chemical contaminants 6–7 detection of foreign bodies 7–10 process issues 5 optical sensor set 334 optical sensors 22, 27, 64, 65–6, 149–50, 342 additives and micronutrients 192, 193 veterinary drug residues 79–88 see also surface plasmon resonance (SPR) sensors optothermal (OT) spectroscopy 291–2, 294–5, 299, 301, 303 organophosphates (OPs) see pesticides organophosphorus hydrolase (OPH) 61, 68–9 orientational polarisability 245–6 oscillatory shear configuration 320 oxidase 144 oxytetracycline 100, 110–11, 112 p-nitrophenol 68 p-nonyl-phenol 49 packaging odours 333 paramagnetic beads 19, 143, 177 paralytic shellfish poisoning (PSP) 50–1, 116, 125 see also saxitoxin paraoxon 67, 68–9 partial least squares (PLS) 261–2, 298, 346 particle counting 140–1, 154 particle interactions 313 peaches 280–5 pears 285–7 penetration depth CSLM 308 TRS 278–80, 281 penicillin-binding protein sensor arrays 82–3 Penzym test 82–3 peptide receptors 33 permittivity 246–51 pervaporation 332 pesticides 55–74 applications of biosensors 70–2 biological methods 59–62 future trends 72 low-cost biosensors 69–70 physicochemical methods 58–9 principles of biosensors 62–9 phase contrast X-ray imaging 8 phase separated systems 307

403

photoacoustic spectroscopy (PAS) 294 see also optothermal (OT) spectroscopy photon migration 272–3, 274–5 physicochemical methods for pesticides 58–9 phytosol solvents 71 piezoelectric/acoustic sensors 24–5, 149, 150, 192 pig production 81–2, 85 plate culture 164, 177 polarisation 245–6 polyaromatic hydrocarbons 47, 48 polyclonal antibodies (PAbs) 17 polydimethylsiloxane 196 polymer sensors 326, 342 polymerase chain reaction (PCR) 144 additives and micronutrients 191 GMOs 208–11 real-time PCR 209–11 microbial contamination of water 172–3, 177 polyphosphates 251 polysaccharides 312, 313 portable instrumentation process monitoring 222–4 toxin detection 129–31 TRS 275, 277, 287–8 potato starch 314–15, 316 potatoes 377, 379 potentiometric sensors 23–4, 148–9 pour-plate method 164 prawns 261 precision 211 predictive data analysis methods 330–1 predictive process control 377, 382–91 prednisolone 100, 108, 109 principal component analysis (PCA) 235–6, 300–1, 331, 345, 368, 370–1 added water 259–61 principal component regression (PCR) 259–61, 345–6, 368–9 process control 361–94 breakfast cereal production 363–76 French-fry manufacture 376–91 future trends 391–2 process models 362–3 process delays 382–3 process line sensors see in-line sensors process models 362–3 see also process control process performance monitoring 392 projection to latent structures (PLS) 369, 370–1, 371–2

404

Index

protein gels 315–17 protein microarrays 30, 31 proteins 62, 212, 306, 312 protozoan parasites 174–6 pyrethrum 55 qualitative methods 96 quantification 97, 98 limit 96 quantitative methods 96 quartz crystal microbalance (QCM) 24, 326, 342 R39 enzyme 82–3 RABIT 170 RADAR project 33 radiative transport equation 277 radioimmunoassay (RIA) 77, 94–5 Raman spectroscopy 292, 295–6, 299–300, 301–2, 303 Raman-CSLM 319 rapeseed 209 RapidChek 142 Raptor 22 raw materials process control and 377, 379 TRS and raw meat quality 270–90 real-time PCR 209–11 receptor tests 76, 77, 94, 95 receptors, new 31–3 recombinant antibodies 17 recovery limit 96 ‘red tide’ 116 redox mediators 146–7 reference methods additives and micronutrients 185 electronic noses and fish quality 348–9 reflection microwave methods 253–6, 257, 265 regulation see legislation/regulation relaxation frequency 246, 247 relaxation time 246, 248, 250 repeatability 348 reproducibility 211, 348 resonance microwave methods 265 resveratrol 47 retention time 97 reusable biosensors 69 REVEAL 142 River ANALyser (RIANA) System 25–6 sample chamber 328 sample conditioning systems 227–36, 236–7

sample preparation CSLM 310–13 GMOs 207 sample presentation 69–70 sampling systems for electronic noses 328, 342–5 sandwich immunoassays 18, 19 saxitoxin 50–1, 125–9 scattering 292 TRS 271–2, 273, 283–5 scattering coefficient 273, 286–7 scattering (S) parameters 253, 253–4 screen-printed carbon electrodes (SPCE) biosensors 66–8 screen-printed electrode (SPE) immunosensors 23, 118, 120, 123–5, 128–9 screening tests 76–8, 93–5 sedimentation FFF (SFFF) 235 sensitivity 167, 210, 224, 275 sensitivity diagram 242 sensors, defining 215 sensory evaluation 333, 355 separation techniques 58–9 sequential injection analysis (SIA) 194 shear 316–17 shellfish oils 44–5 shellfish toxins 40, 50–1, 116–35 developing on-line applications 129–31 domoic acid 50–1, 118–22 okadaic acid 29, 50, 122–5 saxitoxin 50–1, 125–9 signal-to-noise ratio 235–6 single-shot biosensors 69 skin, fruit 278, 279 Smell-Seeing sensor 326 solid-phase microextraction (SPME) 343 soybean 208, 209, 210 specificity 210 DR-CALUX 47–8 spectrophotometric ELISA 118, 119–20, 120–2, 126–8 spectrophotometry additives and micronutrients 195–6 microbial contamination 138–9 spectroscopic techniques food composition monitoring 291–305 applications 300–2 design of spectrometers 298–300 future trends 302–3 instrument design for on-line applications 296–8 MIR 291, 292–4, 298, 300–1, 302–3

Index optothermal 291–2, 294–5, 299, 301, 303 Raman 292, 295–6, 299–300, 301–2, 303 process line 219, 222–4 raw material quality see time-resolved reflectance spectroscopy spices 328–9 spoilage detection with electronic nose 332–3 odours and product quality 340 spread-plate method 164 Spreeta miniature biosensor 225 square wave voltammetry (SWV) 147 staining agents 310, 312–13 standard injection protocol (SIP) 96–7 staphylococcal enterotoxin B 29 starch 313, 317 granules 314–15, 318 STAS project 292 static headspace sampling 343 statistical multivariate tests 235–6, 259– 64, 329–31, 345–6 stirred solution amperometry 67–8 Streptococcus 166 structure imaging techniques 319–21 tissue structure of fruit 283–5 sulphadiazine 82 sulphadimethoxine 100–6 sulphadoxine 101–2, 103 sulphamethazine 29, 80, 81, 82 sulphonamides 81–2, 85–6, 100–1 sulphur compounds 349–52 supercritical fluid extraction (SFE) 70–1 surface acoustic wave (SAW) sensors 24, 326, 342 GC-SAW 326–7 surface plasmon resonance (SPR) sensors 22, 27, 65–6 microbial contamination 149–50, 154 veterinary drug residues 79–88 sweet potato starch 314 Sycopel microdialysis biosensor 229–30 Taguchi sensor 341 tandem scanning microscope (TSM) 309–10 tapioca starch 314–15 temperature 219 electronic nose and 343–5 temperature gradient FFF (TFFF) 235 temporal resolution 274–5 TENAX technique 349–52

405

tetracyclines 112 thermal FFF (TFFF) 235 thermal sensors 192 thick-skinned fruits 278 thin layer chromatography (TLC) 58–9, 188 Threshold Immunoassay System 149 time for testing and obtaining results 136, 137 timing of process changes 388, 389 time-correlated single-photon counting (TCSPC) technique 275, 276 time-resolved reflectance spectroscopy (TRS) 270–90 advantages of time-resolved optical methods 271–2 data analysis 277–8 effect of skin 278, 279 fruit maturity and defects 285–7 future trends 287–8 instrumentation 274–7 compact prototype 275, 277, 287–8 optical properties of fruits 280–5 penetration depth 278–80, 281 principles 272–4 time-series data 328–9 tissue composition for fruits 280–3 structure for fruits 283–5 titanium dioxide 196 tomatoes 280–5 toxic equivalency factors (TEFs) 41, 42, 43 toxins see shellfish toxins transducers, types of 64, 192 transfer of headspace 343 transgenes 206–7 transmission microwave methods 251–3, 265 trimethoprim 101 tryptophan 311 tunable diode laser absorption spectroscopy (TDLAS) 7 turbidity 138–9 see also spectrophotometry 2,4–D 23, 29 ultrafiltration (UF) 228–30 ultrasonic imaging 9–10, 11 ultrasonic standing waves 231–2 United States (US) EPA 20 GMOs 205–6, 212

406

Index

validation of detection methods for veterinary drug residues 96–8, 113 performance of electronic nose 348–52 variable removal 370–1 variable selection rules 331 vegetables 70–2 veterinary drug residues 75–115 biosensor applications in food 83–6 confirmation methods 95–6 developing biosensors 79–81 future trends 86–8, 112–13 legislation 92–3 rapid on-line confirmation 98–112 screening methods 76–8, 93–5 using biosensors to detect 81–3 validating detection methods 96–8 viable but non-culturable (VBNC) cells 162 viable count method 137–8 vision systems 8, 11 visual process analyser (ViPA) 219–20, 221 vitamins 187–8, 191, 192 volatile compounds (volatiles) 325–6 concentration 343 detection techniques 6–7, 10–12 electronic noses see electronic noses spoilage of fish 340 key indicator compounds 349–52

voltammetry 147 DPV 118, 129–31 water added see added water content see moisture content microbial contamination 161–82 current techniques and their limitations 162–3 future trends 176–8 indicator organisms 165–7 on-line monitoring 173–6 rapid detection methods 167–73 water-soluble vitamins 187–8 waveguide approaches 24, 251–4 GMS 256–8, 261 WD-XRF 190 wheat flour quality control 300 wheat starch 314 whey protein 316–17 wine 28 withdrawal period 93 X-ray fluorescence (XRF) 189–90 X-ray systems for foreign bodies 7–8, 11 XenoSense Ltd 86 yeast-based assays 49 yoghurt production 297, 301–2 zero velocity plane 320–1