New Topics in Food Engineering

FOOD SCIENCE AND TECHNOLOGY

NEW TOPICS IN FOOD ENGINEERING

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FOOD SCIENCE AND TECHNOLOGY

NEW TOPICS IN FOOD ENGINEERING

MARIANN A. COMEAU EDITOR

Nova Science Publishers, Inc. New York

Copyright © 2011 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book. LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA New topics in food engineering / editor: Mariann A. Comeau. p. cm. -- (Food science and technology) Includes index. ISBN 978-1-61209-896-8 (eBook) 1. Food industry and trade. I. Comeau, Mariann A. II. Series: Food science and technology series (Nova Science Publishers) TP370.N44 2011 664--dc22 2011002382

Published by Nova Science Publishers, Inc. © New York

CONTENTS   Preface Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

vii  Tempering, Polymorphism and Fat Crystallization During Industrial Chocolate Manufacture: Regimes, Behaviours and their Effects on Finished Chocolate Quality Emmanuel Ohene Afoakwa and Alistair Paterson  Non-linear Modeling of Quality of Cooked Ground Beef Patties with Visible-NIR Spectroscopy Sreekala G. Bajwa and Jason K. Apple  Molecular Size Distribution in Long-aged Food Beverages and Alcoholic Drinks: A Preliminary Inquiry towards Understanding Physical-and Sensory-Related Properties Pasquale Massimiliano Falcone and Paolo Giudici  Hyperspectral Waveband Selection for Detection of Almonds with Internal Damage Songyot Nakariyakul  High Frequency Ultrasonic Techniques Dedicated to Food Physical Properties Assessment D. Laux, J. Y. Ferrandis, V. Cereser Camara, H. Blasco and M. Valente  Trends in High Pressure Processing of Foods: Food Quality and Bioactive Components Shirani Gamlath and Lara Wakeling  Thermodynamic and Kinetic Criteria to Study the Stability of Dried Foods Cesar I. Beristain, Eduardo J. Vernon-Carter and Ebner Azuara  Development of Vacuum Spray Drying System for Probiotics Powder Yutaka Kitamura and Yukari Yanase 

13 

35 

57 

81 

99 

121 

151 

171 

vi Chapter 9

Contents Application of Vacuum Impregnation and Edible Films to Improve The Quality of Raisin-Cereal Systems Pau Talens and María José Fabra 

233 

Chapter 10

Food Packaging: Innovative Concept and Necessities Kelen Cristina Dos Reis 

249 

Chapter 11

Predictive Modelling of Thermal Properties of Foods James K. Carson 

261 

Chapter 12

Applications of Membrane Contactors in the Food Industry Catherine Charcosset 

279 

Chapter 13

Possibilities for Removal of Glucose From Various Foodstuffs and Food Bioprocesses K. Bélafi-Bakó 

Chapter 14 Index

Instant Rice Physicochemical Properties and Eating Quality Prisana Suwannaporn 

289  301  313 

PREFACE In the development of food engineering, one of the many challenges is to employ modern tools and knowledge to develop new products and processes. Simultaneously, improving quality, safety, and security remain critical issues in food engineering. Additionally, process control and automation regularly appear among the top priorities identified in food engineering. This book presents topical research in the study of food engineering, including: ozone technology in the food industry; current trends in drying and dehydration of foods; strategies for extending the shelf-life of foods using antimicrobial edible films; developments in high-pressure food processing; as well as tempering and polymorphism during chocolate manufacture. Chapter 1 - Tempering, a technique of shearing chocolate mass at controlled temperatures is used to promote cocoa butter crystallization in a thermodynamically stable polymorphic form. During chocolate manufacture, the process is used to obtain the stable form V (or ß2) of cocoa butter having a melting temperature of 32-34 °C, which gives the desired glossy appearance, good snap, contraction and enhanced shelf life characteristics. However, the tempering sequences, their behaviour during pre-crystallization, the consequential regimes attained and their effects on product quality characteristics are not very well understood. Variations in temper regimes attained during pre-crystallization of chocolates influence their crystallinity, polymorphic status and other physical quality characteristics. Over-tempering causes increases in product hardness, stickiness with reduced gloss and darkening of product surfaces. Under-tempering induces fat bloom in products with consequential quality defects in structure, texture, melting properties and appearance (colour and surface gloss). Thus, the different temper regimes attained during pre-crystallization result in wide variations in product quality attributes with varied influences on quality. In a modern competitive confectionery market, understanding the variables leading to chocolate pre-crystallization during tempering and effects of the regimes attained on the quality of the finished products are vital to assurances in quality and shelf characteristics. Chapter 2 - Chemometric models based on partial least square regression (PLSR) have been successfully used to estimate nutrient content of different raw meat products from spectroscopic measurements. Preliminary studies to establish a chemometric model for estimating nutrient concentration of cooked ground beef patties from spectroscopic data indicated that the linear PLSR models are not adequate to represent fat and calories. Therefore, this study was conducted to examine two non-linear modeling methods using PLSR and artificial neural networks (ANN). In this study, spectral absorbance in the visible and near infrared (VNIR) region along with data from proximate analysis was utilized to

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develop and validate the two non-linear models for predicting fat, calories, cholesterol and moisture content of cooked ground beef patties. When compared to a linear chemometric model based on PLSR, both non-linear models performed significantly better. The ANN model exhibited the best performance which was indicated by a validation R2 value of 0.93 and residual predictive deviation (RPD) of 3.3 and 3.4 for fat and calories respectively. Both non-linear models resulted in RPD ≥ 3 under validation, indicating that they are acceptable. However, the model performance was only fair for cholesterol and moisture content. Chapter 3 - The present study supports the idea that physical- and sensory-related properties of long-aged beverages and alcoholic drinks containing reducing sugars would be described not by an unique value rather as a distribution of values due to the time-dependent increase of molecular heterogeneity in molecular sizes and structure. A wide range of beverages and alcoholic drinks obtained after different aging periods at room temperature were fractioned by Size-Exclusion Chromatography (SEC), then the elution profiles were analyzed by using a chemical-groups sensitive detector, i.e. an ultraviolet-visible (UV-VIS), coupled to a mass-sensitive detector. i.e. a differential refractive device (DRI). The analysis of the probability density function as well as of the cumulative density function allowed comparing the distribution properties over a wide range among the investigated samples. This is because, unlike small molecules, such liquid matrices undergo accumulation of high molecular size biopolymers (melanoidins) throughout the aging period. In general, results proved that all the investigated matrices would be defined as heterogeneous mixtures of chromophore-labeled copolymers, uncolored and brown, highly polydispersed with respect to their molecular size (ranging between 0.2kDa to over 2000kDa) and their chemical structure. In particular, the molecular size distribution of the end-products was attributed to the raw materials used for their production; while, the relative content of the biopolymers is strictly related to the extent of the thermal treatment applied along to the making process (when it is applied) as well as to the length of the storage time at room temperature. Chapter 4 - Detection of concealed damage in almonds is an important production inspection application. Internally damaged almonds are not easily distinguished from normal ones by their external appearances, and, when cooked, they taste bitter. Prior study showed that using the whole spectrum of hyperspectral data from 700-1400 nm could distinguish internally damaged nuts from normal ones at an error rates as low as 12.4%. However, the hyperspectral system is rather slow and cannot achieve an inspection rate of 40 nuts/s required by almond processing plants. Thus, a feature selection algorithm is needed to choose only a small subset of useful wavebands from hyperspectral data for use in a real-time multispectral camera. In this study, author introduce two novel feature selection techniques; one method is developed to select an optimal subset of individual wavebands, while the other aims to find good sets of band ratios. Author thoroughly discuss the advantages and disadvantages of both algorithms. Experimental results demonstrate that the author proposed methods give higher classification rates than other state-of-the-art algorithms. Chapter 5 - Usually, viscoleastic properties of materials (complex shear moduli and viscosity) are evaluated with rheometers which can give G’, G’’ for instance, on wide bandwidths thanks to the Time Temperature Superposition principle. It is clear that the knowledge of such properties is very interesting on a fundamental point of view because information on material microstructure can be deduced from master curves. On a more practical point of view, it can be used to improve fabrication processes, to perform quality controls, especially in food industry and engineering. This powerful method can fail to give

Preface

ix

large bandwidth information in several cases: phase transition of the material with temperature, volume to be analyzed too small, bad morphology of the sample. In order to overcome such a difficulty, it is possible to use ultrasonic waves. This paper, dedicated to high frequency methods (from a few kHz to many MHz) presents a review of existing methods and improvements developed in the author labs. Several applications in the domain of food engineering will be given in order to prove the huge interest of high frequency approaches which are sometimes neglected compared to classical low frequency rheology. Chapter 6 - High pressure processing (HPP) is a non-thermal food processing technology that offers great potential for the processing of a wide range of food products. Application of HPP can inactivate micro-organisms, affect food-related enzymes and modify structures with minimal changes to nutritional and sensory quality aspects of foods. The effects of high pressure on the inactivation of micro-organisms in food have been thoroughly reviewed. Recent research on HPP has mainly focused on fruits and vegetables with an emphasis on food quality and bioactive components. This chapter highlights the current trends in HPP research and provides a summary of the available findings on the effect of HPP on chemical, nutritional and bioactive components and health related properties of a wider range of commodities. Strategies to maintain the quality attributes and health related components in HPP foods and identification of the gaps for future research in HPP are also discussed. Chapter 7 - In order to assure the quality of dry foods it is important to maintain a strict control over the moisture content and temperature conditions during storage. Quality loss in dry foods can be due to enzymatic and non-enzymatic browning, lipid oxidation, loss of nutrients, loss of flow and microbial contamination, among other factors properties. Determining the optimum moisture content and temperature conditions that minimize the detrimental processes of foods is a difficult task that requires a profound understanding of the interactions of water with other food ingredients. Despite the use of increasingly sophisticated analytical techniques that seek to shed information regarding water-food interactions and water-water interactions, the water sorption mechanism in foods is not still understood wholly. The majority of foods can be considered as complex colloidal systems in which amorphous and crystalline regions occur, with water acting as a plasticizer. Water is a solvent that may take part in detrimental reactions, but in most cases, acts as a medium that provides mobility to reactants allowing them to come into close contact and react. Thus, it is convenient to control the participation of water within foods. Up to date there is still not a 100% reliable method for predicting and controlling food stability. Both, the water activity and the glass transition, are parameters that have been accepted as of food stability criteria worldwide. However, several scientific studies have demonstrated that both parameters exhibit great limitations, and that it is necessary to approach this problem from a fresh point of view. Regarding this, two factors are worthwhile considering: (1) equilibrium or thermodynamic considerations, and (2) rate or kinetic processes. A reaction may not take place if the thermodynamic parameters are unfavorable. Thus, management of the thermodynamic parameters that describe the state of a food is a good starting point for achieving a better understanding of the stability of stored food. On the other hand, even when a reaction is thermodynamically feasible, it cannot occur if the process kinetics does not occur at a feasible rate. In this chapter the thermodynamic and rate processes are proposed as criteria for establishing the stability of stored dry foods. It will also be shown that these two factors are closely interrelated, as the equilibrium state of a system depends in great measure

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of the kinetics followed to reach it. Furthermore, it will be established that it is possible to improve food stability by controlling its microstructure. Chapter 8 - In recent years, health consciousness has grown in relation to health hazards associated with lifestyle and living environment, and various types of functional foods are attracting attention (Matsuda, 2002; Bureau of Citizens, Culture and Sports, Tokyo Metropolitan Government, 2005; Sashihara et al., 2005). In addition, the functionality of food is currently an important added value for the food industry, and the development and applications of functional foods are being actively advanced. Among various functional foods, probiotics is one of the materials that have attracted considerable attention (Sashihara et al., 2005). Probiotics are living microbes that exert healthful effects on the living body, lactic acid bacteria being a typical example. The wide-ranging health effects of various types of bacteria have been reported, as shown in Table 1.1. Further, epidemiological studies on probiotics for disease prevention and scientific study and commercial development involving useful lactic acid bacteria are being pursued (Watanabe et al., 2005). Probiotic foods are able to ameliorate the effects of environmental factors, and are useful in disease prevention; establishing the safety of probiotics is easy as compared to doing so for medical chemicals, because the bacterial cells of probiotics have long been used for storing and improving the flavor of food. Therefore, various probiotic foods are being researched, developed, and produced. These will be indispensable functional ingredients in the future food industry in Japan, where health consciousness is increasing. Chapter 9 - The shelf life of multicomponent food systems depends, among others things, on how fast the water transfer between components takes place. This moisture migration can result in undesirable physical and chemical changes in the system, affecting its quality and shelf life. Several factors influence the amount and rate of moisture migration in multicomponent foods. However, water activity equilibrium and rate of diffusion are the two main factors. To control this migration, several principles can be utilized. A raisin-cereal mixture is one of the multicomponent food systems whose quality and shelf life is affected by moisture migration between components. In this system, water is transported from the raisin to the cereals, resulting in quality deterioration to both components. One way to reduce the moisture migration in this system is to reduce the water activity of raisins. The problem is that the raisin texture, one of most important factors governing their quality, becomes unacceptably hard when their water activity decreases below 0.40. Another possibility for controlling moisture migration is to add an edible barrier between components. In this study, in order to try to reduce the water activity of raisins, while maintaining their softness, raisins were infused with a glycerol-water solution (5:1) applied by vacuum impregnation. Mechanical properties, weight changes and water activity were evaluated before and after infusion to study the effect of the glycerol on raisins. On the other hand, in order to try to reduce the moisture transfer between components, the raisins were coated by directly applying starch and beeswax to them or by using different film forming emulsions made with whey protein, beeswax and glycerol at different protein:lipid:plasticizer ratios. The results of these application procedures have shown that the use of combined solute infusion and vacuum impregnation methods are an effective way of reducing the water activity properties of the raisins as well as serving as an alternative formula towards maintaining raisins at their best possible conditions for keeping a homogenous raisin-cereal system. After 1 hour of treatment with glycerol, testing revealed that the water activity of the raisins decreased, thus decreasing the gradient of water activity between both components.

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Furthermore, testing has indicated that this treatment does not affect the texture of raisins. The hardness of the raisin skin was not influenced by the presence of glycerol, allowing for a produced softness inside the raisins. As regards to the testing of various coating effects, the direct application of beeswax seemed to be the best protection from water loss. Among the film forming emulsions based on whey protein, the higher plasticizer content lead to a decrease in the water loss protection; although, these differences were not significant. In spite of the fact that the tested films have different water vapour permeabilities when they are treated as independent structures, it seems that the interactions that transpired, between these films and the surface of the product, eliminated the above mentioned differences. Chapter 10 - The type of packaging used has an important role in determining the shelf life of a food and the main purpose of food packaging is to protect the food from microbial and chemical contamination, oxygen, water vapour and light. Innovative food packaging concepts has been introduced as a response to the continuous changes in current consumer demands and market trends. Chapter 11 - Thermal properties of foods are vital inputs for many food process models. With the recent advances in mathematical modelling and significant reduction in the cost of computational power, uncertainties in model inputs are more and more becoming the limiting factor in model accuracy rather than the model formulation or solution process. In this chapter methods and models are presented for predicting thermal properties based solely on data for the composition of the food in terms of its basic components (liquid water, ice, protein, fat, carbohydrate, ash and air). This type of model provides genuine predictions of thermal properties since no thermal property measurements are required, as is necessary with some effective property models that may be found in the literature. Foods are divided into different classes, depending on the difficulties they pose for thermal properties prediction. Simple guidelines for thermal property prediction are presented along with worked examples to serve as illustrations. Chapter 12 - Membrane contactors represent an emerging technology in which the membrane is used as a tool for inter phase mass transfer operations [Sirkar et al. 1999, Drioli et al. 2003]. The membrane does not act as a selective barrier, but the separation is based on the phase equilibrium. This review specifically addresses to two membrane contactor processes: membrane distillation and membrane emulsification and their applications in the food industry. Chapter 13 - In many areas of food industry removal of glucose is considered as an important step in the processing line. In some raw materials – like eggs – glucose concentration should be reduced to avoid undesired co-reaction during drying process. In the beverage industry glucose level of certain fruit juices needs to be controlled/lowered either to produce low-caloric beverages or to get low-alcohol wine after fermentation (e.g. grape must). In addition glucose removal is a significant step in some enzymatic processes like polysaccharide hydrolysis or fructo-oligosaccharide synthesis, where glucose is an inhibitory by-product. Separation methods applicable for glucose removal are discussed and compared in this chapter. Chapter 14 - Instant, or quick-cooking, rice is becoming more popular nowadays. However, it still poses problems with respect to rehydration time and quality. The effects of processing factors which are: moisture content, pressure and drying temperature has a significant effect on its physicochemical properties and eating quality. The hardness and chewiness of rice decreased as moisture content and pressure increased. Higher drying

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temperatures caused increases in hardness and chewiness. Only pressure and moisture content affected density, rehydration ratio, and increase in the volume of instant rice, which was due to the porosity of the kernels. Rehydration ratio had a negative correlation with density (r= 0.886) but a positive correlation with volume increase (r = 0.637). Pressure was the main factor influencing the pasting properties of instant rice. All pasting properties of instant rice were far lower than those of milled rice, but instant rice had higher cold paste viscosity, which is typical of pregelatinized flour. This indicated rapid water absorption and shorter cooking time. Instant rice processing also caused development of amylose-lipid complexes observed as the V-type pattern in an X-ray diffractometer

In: New Topics in Food Engineering Editor: Mariann A. Comeau

ISBN: 978-1-61209-599-8 © 2011 Nova Science Publishers, Inc.

Chapter 1

TEMPERING, POLYMORPHISM AND FAT CRYSTALLIZATION DURING INDUSTRIAL CHOCOLATE MANUFACTURE: REGIMES, BEHAVIOURS AND THEIR EFFECTS ON FINISHED CHOCOLATE QUALITY Emmanuel Ohene Afoakwa1* and Alistair Paterson2 1

Department of Nutrition and Food Science, University of Ghana P. O. Box LG 134, Legon – Accra, Ghana 2 Centre for Food Quality, Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Royal College Building, 204 George Street, Glasgow G1 1XW, U. K.

ABSTRACT Tempering, a technique of shearing chocolate mass at controlled temperatures is used to promote cocoa butter crystallization in a thermodynamically stable polymorphic form. During chocolate manufacture, the process is used to obtain the stable form V (or ß2) of cocoa butter having a melting temperature of 32-34 °C, which gives the desired glossy appearance, good snap, contraction and enhanced shelf life characteristics. However, the tempering sequences, their behaviour during pre-crystallization, the consequential regimes attained and their effects on product quality characteristics are not very well understood. Variations in temper regimes attained during pre-crystallization of chocolates influence their crystallinity, polymorphic status and other physical quality characteristics. Over-tempering causes increases in product hardness, stickiness with reduced gloss and darkening of product surfaces. Under-tempering induces fat bloom in products with consequential quality defects in structure, texture, melting properties and appearance (colour and surface gloss). Thus, the different temper regimes attained during * Corresponding author: [email protected] / [email protected] Tel: +233 (0) 244685893

14

Emmanuel Ohene Afoakwa and Alistair Paterson pre-crystallization result in wide variations in product quality attributes with varied influences on quality. In a modern competitive confectionery market, understanding the variables leading to chocolate pre-crystallization during tempering and effects of the regimes attained on the quality of the finished products are vital to assurances in quality and shelf characteristics.

Keyword: Chocolate, cocoa, cocoa butter, tempering, crystallization, polymorphism, melting, appearance, microstructure.

INTRODUCTION Chocolate manufacturing involves several complex physical and chemical processes requiring numerous technological operations and the addition of different ingredients, to achieve products of suitable physical and chemical attributes and an attractive appearance and taste. Most of the parameters involved influence the rheological characteristics, flavour development and sensory perception [1, 2]. During chocolate processing, a process known as tempering is used to promote cocoa butter crystallization in a thermodynamically stable polymorphic form. Temperature adjustment is utilized to promote formation of seed crystals in the correct polymorphic forms to effect good product snap, contraction, gloss and shelf life characteristics. Tempering involves pre-crystallization of a small proportion of triacylglycerols (TAGs), with crystals forming nuclei (1 – 3% total) for remaining lipid to set in the correct form, resulting from a nucleation process which is highly dependent on the process parameters used. The final crystal form depends critically on the shear-temperature-time process which the material has undergone. The tempered chocolate is then deposited in moulds and cooled so that subsequent crystal growth occurs upon the existing seed crystals. Tempering has four key steps: melting the chocolate mass to completion (at 50°C), cooling to point of crystallization (at 32°C), crystallization (at 27°C), and conversion of any unstable crystals (at 29-31°C) (Figure 1), and it is a function of recipe, equipment, particle size and the final purpose [2-8]. Poorly tempered chocolates result in unstable crystal growth and poor setting characteristics, making products more susceptible to fat bloom, a physical imperfection that often manifests itself as a white or greyish-white layer on the surface of the chocolate product during storage. Afoakwa et al. [9] noted that fat bloom occurs when a lower and unstable crystal form IV changes into a higher and more stable form VI. The most important physical and functional characteristics (i.e. texture, snap and gloss) of chocolates are dictated by the crystal network formed by its constituent lipid during crystallization [5, 10-12]. In industrial chocolate manufacture, tempering is vital, influencing quality characteristics such as colour, hardness, handling, finish and shelf-life characteristics [5, 12, 13-18]. Fat crystallization is a complex process influenced by processing conditions that determines chocolate microstructure and physical properties and thus crucially important to the final quality of finished chocolates. The control of crystallization is critical for texture, melting properties and other quality characteristics [11, 12, 16, 19-21]. Several authors have studied the melting profiles of chocolates using pulsed nuclear magnetic resonance (pNMR) and differential scanning calorimetry (DSC) [6, 8, 22-24].

Tempering, Polymorphism and Fat Crystallization …

15

50 °C Heat

Cool 32 °C 30 °C

Chocolate

Cool

Reheat 27 °C

Solid chocolate

All fats melted

Correct number of stable

Unstable crystals melted

Figure 1. Tempering sequence during lipid crystallization in chocolates [1].

POLYMORPHISM AND FAT CRYSTALLIZATION IN CHOCOLATES Polymorphism - the existence of two or more distinct crystalline forms of the same substance - is a critical concept in the study of fat crystal structure. In fat systems, TAG molecules are able to pack in different crystalline arrangements or polymorphs and exhibit significant melting temperatures [25]. Cocoa butter can crystallize in a number of different forms, as a function of triglyceride composition, with fatty acid composition influencing how the liquid fat solidifies. Six polymorphic forms (I – VI), have been identified, the principal being α, β and β' (Figure 2); following the two main identified nomenclature schemes [26, 27]. Form V, a β polymorph, is the most desirable form in well-tempered chocolate, giving a glossy appearance, good snap, contraction and resistance to bloom [1]. In cocoa butter, Forms VI is the most stable form and difficult to generate although formed on lengthy storage of tempered chocolate accompanied by fat bloom. In addition Form VI has a high melting temperature (36°C), and crystals that are large and gritty on the tongue. The unstable Form I has a melting point of 17°C and is rapidly converted into Form II that transforms more slowly into III and IV. Polymorphic triglyceride forms differ in distance between fatty acid chains, angle of tilt relative to plane of chain end methyl group and manner in which triglycerides pack in crystallization [3]. Polymorphic form is determined by processing conditions. Fatty acids crystallize in a double- or triple-chain form depending on triglyceride composition and positional distribution. All the polymorphic forms could be formed directly from melted TAGs, or via melt-mediated or solid-state monotropic phase transformations [28].

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Figure 2. Temperature regimes and degree of stability of six polymorphic forms of cocoa butter [3].

Phase transitions in cocoa butter polymorphs from less to more stable forms are irreversible and dependent on temperature and time. Polymorphism in relation to solid continuous phases of cocoa butter has a large impact on chocolate quality, dictating their structural properties [29]. Structural factors such as microstructural elements and microstructure characteristics can provide quantitative information about the mechanical properties of the network, and therefore information about the sensory hardness of the network [30]. Polymorphic changes can be observed as overall contraction of chocolate, appearance, or undesirable fat bloom formation dependent on relative stabilities of crystal forms and temperature. As information on cocoa butter isothermal phase behaviour during chocolate manufacture is important for optimizing production processes that maintain product quality, this chapter would provide vital information on current industrial tempering processes, polymorphic behaviours and pre-crystallization regimes during chocolate tempering, and their effects on the microstructure and melting properties of fat systems during industrial chocolate manufacture. Additionally, it would provide valuable quality control indicators to ensure the structure and other quality attributes of fat networks being produced on a production line during chocolate manufacture are consistent and would yield the desired mechanical and sensory qualities during post-processing handling, supply chain management and consumption. Understanding these processes is important for process design and assurances in quality of products.

Tempering, Polymorphism and Fat Crystallization …

17

TEMPERING PROCESSES DURING INDUSTRIAL CHOCOLATE MANUFACTURE Chocolate was hand-tempered before the advent of tempering machines, a strategy still occasionally used by Chocolatiers who produce relatively small quantities of hand-made confections. Currently, tempering machines are used and consist of multistage heat exchangers through which chocolate passes at widely differing rates making it difficult to identify optimum conditions. Time-temperature combinations are of paramount importance in process design and in continuous tempering; molten chocolate is usually held at 45°C then gently cooled to initiate crystal growth. Tempermeters are then used to measure the cooling curves on a chart recorder and the degree of temper determined by the shape of the curve based on operators experience. More recently Tricor Systems in the USA have produced a tempermeter that uses a built-in algorithm to calculate the degree of temper in chocolate temper units (CTUs) and slope (temper index); these can then be used numerically to define the state of temper. In recent times, a number of innovative technologies have been initiated in the confectionery industry to enhance process efficiency and product improvement. Two Swiss technologists Windhab (ETH Zurich) and Mehrle (Buhler AG, Uzwil), working with the Buhler "Masterseeder", found that increasing shear during seed tempering can be beneficial as the kinetics of fat crystal nucleation and polymorphic transformations (α → β2 → β'1) are greatly accelerated. The outcome is enhanced overall product quality with reductions in fat bloom. Chocolate can also be tempered by the use of high pressure with molten chocolate compressed to 150 bar. This increases chocolate melting point and causes it to solidify into solid crystals of all polymorphic forms. When pressure is released, lower polymorphic forms melt leaving behind tempered chocolate. Subsequent batches can be seeded with stable fat crystals. Cocoa butter equivalents (CBEs) and replacers (CBRs) may also find application in the chocolate industry. While cocoa butter equivalents are compatible with cocoa butter, cocoa butter replacers (CBRs), which do not require tempering, can only be used if almost all the cocoa butter is replaced. These CBRs melt in the same temperature range as cocoa butter, but crystallise only in the β' form [3, 33, 34]. As well, effects of shear on chocolate or cocoa butter tempering in a number of different flow geometries have been reported. Important studies of scraped surface heat exchangers with cocoa butter and chocolate [35], Couette geometries with milk chocolate [36] and cocoa butter [37], cone and plate systems with cocoa butter [37, 38], parallel plate viscometers with milk chocolate [39], and a helical ribbon device with cocoa butter [15], have been reported with significant application in modern chocolate confectionery industry. Tricor has recently introduced its latest Model 225 Chocolate Temper Meter: a single, compact, low-cost, easy-to-operate unit that can be used in the laboratory or on the production line. It allows operators to fill a sample cup with chocolate, place it in the unit and print or display the temper results within minutes, thereby allowing corrective action to be taken before production yield and quality or shelf life are affected. A specially designed thermoelectric cooling system combined with a design that controls sample size, probe depth and probe insertion temperature is said to eliminate measurement errors. In another development, the chocolate equipment specialist, Sollich, recently unveiled its latest TurboTemper Champ TCN technology, which is designed to improve the tempering process

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in all known chocolate and fat masses. It is fitted with a Tempergraph that measures and saves the tempering conditions throughout production for continuous monitoring of the temper quality. Sollich can supply the system with an integrated decrystallisation function or with a Turbo Temper AIRO for aeration of chocolate masses for fat-based fillings.

FAT CRYSTALLIZATION BEHAVIOURS DURING TEMPERING Four different regimes have been reported by several authors to exist during chocolate tempering, comprising un-tempering, under-tempering, optimal tempering and overtempering. Each of these regimes show different pre-crystallization behaviours, attain varied polymorphic status with some associated transformations and varied consequential effects on the structure and quality of finished chocolates during processing, post-processing handling and supply chain management [2, 5, 12, 40-44].

Optimal Tempering During the formation of optimal temper, the temperature of the chocolate being crystallized drops rapidly during cooling until it reaches thermodynamic equilibrium. At this point, the crystallization heat released is balanced by an equal amount of cooling energy rending a rather flat time-temperature curve (with a zero slope). The equilibration temperature attained promotes formation of stable fat crystals, which subsequently undergo further growth and maturity during cooling and storage to effect shelf stability of the product. The temperature of the chocolate then dropped further when the liquid cocoa butter is transformed into solid crystals resulting in solidification of the products (Figure 3). Fat crystallization process can also be followed by means of viscosity changes as function of time [31]. Before crystallization starts, the melt shows Newtonian behaviour but with the formation and growth of crystals, the viscosity increases almost linearly with the amount of crystals in the suspension until it reaches a thermodynamic equilibrium [45]. This technique has also been used to follow the isothermal crystallization of refined palm oil, chocolate and palm stearin in sesame oil [15, 46, 47]. Properly tempered chocolate leads to the formation of Form V (β2), the most desirable polymorphic form. Well tempered chocolate has the following properties; good shape, colour, gloss, good contraction from the mould, good weight control, good stability (harder and more heat resistant with fewer finger marks during packaging) and longer shelf-life. The tempering regime for milk chocolate slightly differs from that for dark due to the influence of milk fat molecules on crystal lattice formation. Milk chocolate contains a proportion of butter fat that causes eutectic effect, which prevents bloom formation, results in a low melting point, softening of texture and lowering of temperature to obtain crystal seed for the tempering process (around 29.4°C compared to 34.5°C for plain chocolate).

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Figure 3. Pre-crystallization curves of the different temper regimes in chocolate manufacture [5].

Under-Tempering and Untempering Under-tempering (insufficient tempering) is caused by the relatively higher temperatures released between multi-stage heat exchangers during tempering. The process causes development of more crystallization heat within the product during solidification, effecting quick cooling, as more liquid fat is transformed quickly into solid form. The distinct increase in temperature observed at the beginning of the crystallization, declined again after reaching a maximum point where most of the stable crystals formed were re-melted prior to cooling, resulting in the formation of very few stable fat crystals (Figure 3). Previous studies revealed that un-tempering – an insufficient temper regime, produces no stable fat crystals as the heat

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exchange system generated higher crystallization heat during cooling, resulting in consistent cooling of the completely melted product with no inflexion point for stable fat crystal formation [5]. The crystallization processes in both un-tempered and under-tempered chocolates lead to the formation of unstable Form IV (β'1) polymorph, which quickly transforms into Form V within a few hours and with further transformations into a more stable Form VI (β1) polymorph during storage. The phenomenon leads to fat bloom, appearance of whitish or dull-white particulate spots on the surface of chocolate, rendering products unacceptable for human consumption [1, 5]. Fat bloom arises from changes in the fat structure in chocolate and is caused by a variety of factors, including poor tempering of the chocolate, incorrect cooling methods and the presence of soft fats in the centres of chocolates. Warm storage conditions, and the addition of fats that are incompatible with cocoa butter, can also cause fat bloom. It frequently results in significant product losses for confectionary manufacturers as, although it does not affect the taste, the tell-tale sign of the bloom – a white frosting – is unacceptable to consumers. Additionally, it has been reported that untempering and under-tempering regimes exhibit different crystallization behaviours but results in similar unstable fat crystal nucleation and growth, with similar associated storage polymorphic transformations and defects in textural properties and appearance of products [5].

Over-Tempering Over-tempering occurs when relatively lower temperatures are exchanged between the multi-stage heat exchangers of the tempering equipment. This causes significant part of the liquid fat to solidify within the continuous phase of the chocolate, transforming the product into solid form when less liquid fat was available for pumping it through the multiple coolant regions of the temperer. The process effects very slow cooling as very little crystallization heat is released during the process, rendering a rather flat and slow cooling curve causing the chocolate to solidify very quickly (Figure 3). In over-tempering, the crystallization heat released is balanced by an equal amount of cooling energy causing nucleation of stable fat crystals (β2) to effect shelf stability of the product. However, the period of equilibrium is very short relative to that of optimal tempering, and this is suspected to affect the crystal size, mass (number), strengths and adequacy of the fat crystals formed and thus possible defects in structure and product shelf stability [41, 43]. As a substantial part of the phase transition (from liquid to solid) took place before the chocolate reached the mould, less contraction occurred in the mould, leading to demoulding problems with defects in final product texture and appearance (gloss and colour) and consequential effects on shelf life of products [5, 42].

TEMPERING EFFECTS ON FINISHED CHOCOLATE QUALITY Effects on Mechanical and Textural Properties The temper regime attained during pre-crystallization of chocolate has a dramatic influence on the mechanical properties (hardness and stickiness) of finished products. Previous reported findings showed varying degrees of hardness and stickiness of products

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with different temper regimes [5]. Under-tempered products had the greatest hardness (texture), attributable to the re-crystallization process undergone by the fat in the chocolates resulting in intense hardening of products. This trend in hardness was followed by overtempered samples with the optimal-tempered products possessing relatively lesser hardness levels. These suggest that over-tempering of chocolates leads to increased hardness in chocolates as compared to their respective optimally-tempered products. However, the degree to which these changes occur depend on the particle size distribution of the chocolate. Particle sizes have been reported to be important determinants of hardness of fat crystal networks in many confectionery products [10, 11, 16, 48]. Earlier studies showed inverse relationships of hardness in tempered dark chocolates with particle sizes at varying fat and lecithin levels [49], attributed to the relative strengths of their particle-to-particle interactions [11, 50]. Consistent reductions in hardness (texture) of milk chocolates with increasing particle sizes has also been reported [51]. Other important parameter defining the mechanical properties of chocolates is the level of stickiness. Stickiness in confectionery gives information about their deformability related to oral sensory characters, an index that defines the rate of melting of products during oral processing – lower stickiness levels is an indication of fast melting character whereas higher levels suggests prolonged melting [52]. Previous report explained that temper regime attained during pre-crystallization of chocolates has remarkable effects on their stickiness levels, with an inverse relationship with increasing particle sizes [5]. Over-tempered products had the greatest stickiness levels, followed by the optimally-tempered products with the undertempered samples having the least. The behaviour of fats during crystallization heavily influences the microstructure and physical properties of products such as margarine and chocolates, and ultimately affect the final product structure, texture and quality [20]. Attainment of optimal-temper during pre-crystallization of chocolate is therefore vital to controlling the mechanical properties (hardness and stickiness) during processing and postprocessing quality, the knowledge of which are important for quality control and in new product development.

Effect on Appearance (Colour, Gloss and Product Image) Appearance involves all visual phenomena characterizing objects, including gloss, colour, shape, roughness, surface texture, shininess, haze and translucency [53]. Work done on chocolates revealed that temper regime affects to varying levels all colour and gloss measurements [5]. Under-tempering has been reported to attain relatively higher L*-values than both the optimally-tempered and over-tempered samples within 14 days after processing, as a result of blooming effect on products. As well, under-tempering causes great reductions in C* and h° at all PS. Components of colour such as L*, C* and h° respectively represent food diffuse reflectance of light, degree of saturation and hue luminance, which are dependent on particulate distribution, absorptivity and scattering factors or coefficients. In a densely packed medium, scattering factor is inversely related to particle diameter [54, 55]. Chocolates with varying temper regimes differ in structure and particulate arrangements influencing light scattering coefficients and thus appearance [5]. Over-tempered chocolates were found to possess relatively lower L* values at all PS as compared to their corresponding optimallytempered products. These suggest that over-tempering reduces the degree of lightness in

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chocolates, effecting product darkening and thus affecting quality of finished chocolates. However, no noticeable effect on C* and h° were observed among the optimally- and overtempered products. Under-tempered (bloomed) chocolates tend to scatter more light, appear lighter and less saturated than over-tempered and optimally-tempered products. The blooming process results in higher scattering coefficients, with subsequent paleness (whitening) - higher L* values. The whitish haze in bloomed chocolate is caused by the dispersion of light of fat crystals [41]. Colour of foods may be affected by various optical phenomena among them are scattering and surface morphology, therefore an accurate understanding of the influence of appearance on measured colour is essential. Gloss relates to capacity of a surface to reflect directed light at a specular reflectance angle with respect to normal surface plane [56]. Differences in temper regime have been reported to influence gloss measurements in chocolates to varying levels. Blooming of undertempered samples result in drastic reductions in gloss values than their respective optimallytempered and over-tempered samples. In our previous work, under-tempered chocolates were found to possess the lowest gloss values, followed by the over-tempered and then optimallytempered products [5]. Under-tempering was shown to exhibit the greatest influence on appearance and gloss of products but differences between optimally- and over-tempered products were significant with the over-tempered showing relatively slightly reduced gloss, an indication that tempering is a key determinant of chocolate gloss. In under-tempered chocolates light scattering is affected by reductions in surface regularity. Gloss stability of edible coating formulations of chocolates have been studied [57-59]. Gloss is an important quality attribute in chocolate and tempering a key processing step to control it [60]. The relationship between colour and gloss of chocolates are of current interest to many manufacturers. Digital images of dark chocolates (18 µm PS) assembled to show surface appearances of the optimally-, under- and over-tempered products before and after the 14 days conditioning (Figure 4) showed similar initially smooth and glossy surface appearances soon after tempering but after 14 days, clear differences were apparent. The optimally- and overtempered chocolates maintained their characteristic glossy appearance and dark brown colour but the under-tempered samples bloomed, with appearance of surface whitish spots, rendering them dull and hazy in colour (Figure 4). Similar increases in whiteness in under-tempered and untempered (bloomed) chocolates have been reported [17, 42, 43]. This phenomenon has been attributed to re-crystallisation of fats from a less stable Form IV to a more stable Form VI polymorph, with changes in light dispersion on small surface fat crystals (> 5µm), consequently impacting on both appearance and textural attributes [12, 41]. Fat bloom development, mechanisms and effects on chocolate appearance, quality and marketability has been extensively studied [7, 9, 17, 19, 22, 23, 42, 43, 61]. Given that chocolate products are meant to respond to consumers acquired expectations, their appearance is one of the most important commercial attributes. Attention to tempering is therefore necessary for consistency in chocolate appearance during product development and quality control.

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Figure 4. Photographic images of (a) fresh and (b) matured (conditioned) optimally-tempered, undertempered and over-tempered dark chocolates (18 µm PS). [5].

Effects on Melting Properties Thermal properties of cocoa butter and chocolates have been studied using pulsed nuclear magnetic resonance (pNMR) and differential scanning calorimetry (DSC) and together they

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provide information on the phase behaviour of the inherent fats during both mechanical and oral processing [3, 6, 8, 22, 24]. In a publication, it was found that typical DSC thermograms of chocolates manufactured from the optimally-tempered, over-tempered and under-tempered regimes exhibited similar distinct single endothermic transitions between 15 and 55 °C with peak onset (Tonset,), corresponding to the temperature at which a specific crystal form starts to melt; peak maximum (Tpeak), that at which melting rate is greatest; end of melting (Tend), completion of liquefaction and enthalpy of melting (ΔHfat), the energy required to effect complete melting [12]. All these information relate to the crystal type, and predicts the polymorphic status of the product. Peak height, position and resolution are dependent on sample composition and crystalline state distribution [63]. Differences in temper regime result in varying degrees of crystallinity and melting properties using DSC (Figure 5). The observed differences in the peaks were explained to suggest that variations in crystallisation behaviour in chocolates exist during tempering and influence the degree of crystallinity and crystal size distribution (CSD) of their derived products. Under-tempered (bloomed) chocolates showed the greatest peak width, followed by the over-tempered samples having slightly wider CSD than the optimally-tempered products with resultant variation in their melting profiles (Figure 5). The distribution of crystal sizes in foods play key roles in final product quality, defined by the total and specific characteristics of their crystalline material. Number of crystals and range of sizes, shapes, and polymorphic stability, as well as arrangements in network structures dictates mechanical and rheological properties [2, 19]. Knowledge and control of CSD can be important for optimizing processing conditions.

Figure 5. Typical DSC thermograms of fat melting profile showing optimally-tempered, over-tempered and under-tempered (bloomed) dark chocolates [12].

Under-tempered chocolate were reported to complete melting at higher temperatures than optimally- and over-tempered products. The changing melting end (Tend) values the products showed that the crystallites in optimally and over-tempered chocolates were in ßV polymorph

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while that of under-tempered, ßVI. Similar melting temperatures and polymorphic status have been reported [41, 42]. However, these polymorphic statuses are best identified by powder Xray diffraction patterns and could be a subject for further investigation. The mechanism of identification and principle of the technique has been explained.20 Similarly, under-tempered (bloomed) chocolate had higher melting duration index than their corresponding optimal and over-tempered products, suggesting that the under-tempered chocolate might require longer time to melt than the optimally and over-tempered products. Likewise, over-tempered samples were noted to require longer melting durations than the optimally-tempered, with the prediction that the differences found would have likely impact on their behaviour during consumption, attributable to the relative strengths of their mechanical properties (hardness and stickiness), a significant finding for process quality control. Thermal behaviours and ratio of sugar/fat melting enthalpies in chocolates differing in temper regime were studied using DSC to provide information on differences in fat and sugar structure. The DSC thermograms (Figure 6) showed differences in fat melting profile, resulting from the widened peak width in the under-tempered (bloomed) sample; but no differences were noted in the sugar melting profiles, explaining the structural (polymorphic) transformations in the fat component in the under-tempered product. The report indicated that the DSC data on fat and sugar melting properties (Tonset, Tend, Tpeak, ΔHfat, ΔHsugar and ΔHsugar/ΔHfat) related to temper regime were similar to the trends for fat (Figure 4) - fat melting profiles suggested the ßV polymorph in both optimally- and over-tempered chocolates with Tend of 32.3 °C and 32.9 °C respectively, and a more stable ßVI polymorph in under-tempered sample with Tend of 35.8°C, with significant influences on Tonset, Tpeak, ΔHfat in chocolates.

Figure 6. Typical DSC thermograms showing (A) fat and (B) sugar melting profiles of optimallytempered, over-tempered and under-tempered (bloomed) dark chocolates at 18 μm PS [12].

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Contrary, results of the sugar melting properties showed only marginal differences in all the melting properties (Tonset, Tend, Tpeak, ΔHsugar) with the different temper regimes, suggesting that no structural change in sugar were found in products from the three temper regimes [12]. Similarly, the ratios of sugar to fat melting enthalpies in products from optimal, over- and under-tempered samples were 1.25, 1.24 and 1.17 respectively with no significant difference among them. These explain that the lower ΔHsugar/ΔHfat ratio noted in the undertempered sample resulted from the higher ΔHfat as a result of recrystallization of fat [19, 42]. These findings support our earlier report that fat and sugar components are present in similar quantities in both bloomed and optimally-tempered dark chocolates [12], but contrast with the report that the melting peak of fat in untempered (bloomed) chocolate was almost nonexistence with ΔHfat being ten-fold smaller than that obtained for optimally-tempered chocolate [43], concluding that the whitish spots in bloomed chocolates were mainly composed of sugar crystals and cocoa powder and nearly devoid of fat. The presence of fat components in bloomed dark chocolate has also been reported [64], suggesting the mechanisms of bloom development in chocolate involves phase separation associated with the growth of xenomorphic fat crystals.

Effects on Microstructure Microstructural examination using stereoscopic binocular microscopy after 14 days of conditioning showed clear variations in both surface and internal peripheries of chocolates from varying temper regimes (Figure 7) [12]. Over-tempered products had relatively darker surfaces and internal appearances than optimally-tempered confirming the reported relative differences in L*. Under-tempered products showed both bloomed surface and internal periphery with large whitish, and distinct smaller brown spots (Figure 7). The observed whitish appearance on surfaces and internal peripheries appear to be mixtures of fat and sugar crystals, and the small brown spots, cocoa solids. These whitish spots were primarily sugar crystals and cocoa powder and nearly devoid of fat [42, 43]. These differences in interpretation are recommended as the subject for further investigations. Microstructural examination using scanning electron microscopy after 14 days of conditioning showed clear variations in crystalline network structure, inter-particle interactions and spatial distributions of network mass among optimally-, over- and under-tempered samples, becoming well defined (Figure 8). Microscopy of the optimally-tempered chocolate showed an even spatial distribution of small number of dense crystalline network with well defined inter-particle connections among the crystals suggesting stable β-polymorph (Figure 8). Similarly, micrographs of the over-tempered chocolate showed a spatial distribution of a dense mass of smaller crystals (relative to those of the optimally-tempered) within a network structure of both well- and ill-defined particle-to-particles crystal connections suggesting their βpolymorph stability [12]. These larger numbers of small crystalline networks noted in the over-tempered samples is suspected to result from early nucleation and growth of seed crystals due to the slow cooling (Figure 8), leading to the formation of sub-micron primary crystallites from the melt, with the resulting fat crystal network stabilized by van der Waals forces, possibly with steric and Coulombic forces [10, 65, 66].

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Figure 7. Micrographs of surface (a) and internal (b) structures respectively of (1) optimally-tempered, (2) under-tempered and (3) over-tempered dark chocolate (18 µm PS) [12].

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(A)

(B)

(C) Figure 8. Scanning electron micrographs showing crystalline network microstructures at magnifications of x 1,500 of (a) tempered, b) over-tempered and (c) under-tempered (bloomed) chocolates at 18 μm PS. C shows some of the well-defined crystal structures; iC shows some of the ill-defined crystal structures; i shows some of the inter-crystal connections. The arrows indicate some of the pores, cracks and crevices; B shows some of solid bridges; L shows some of the large (crystal) lumps on the crystal structure [12].

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Under-tempered (bloomed) chocolates showed dissolution, re-arrangement and recrystallisation of the numerous small crystals noted in the over- and optimally-tempered products to a smaller number of larger (lumps) fat crystals (Ostwald ripening), and polymorphic transformation, nucleation and growth of new large crystals in a more stable polymorphic form. These observations induced formation of solid bridges with weak and less inter-crystal connections made up of pores, pits and crevices within the crystalline network structures (Figure 8). This phenomenon is attributed to the thermodynamic differences in equilibrium between large and small crystals within a network structure leading to recrystallization of unstable fat polymorphs [41]. In another study, surface imperfections pores, pits, in filled chocolates were reported on the microstructure of bloomed chocolate [67]. Similar findings were reported while studying fat bloom formation and development during storage of under-tempered dark chocolates [9]. Both reports explained that morphological changes on the surface of the chocolate were dominated by the growth of needle-like crystals and spherulites on the chocolate with large crystals ~ 100 µm in length, and concluded that from a microstructural perspective, both diffusion and capillarity appear to be involved in fat bloom formation and development, though temperature, particle size distribution of the product and the presence of a filling fat strongly dictate the rate and type of mechanism that dominate the process. Thus, differences in crystallization behaviour during tempering leads to formation of different microstructural organizations of crystal network structure with associated physical changes in chocolates. Characterizing the nature of crystals in confectionery is an important step in quantifying the physical and sensory properties, as the resulting three-dimensional fat crystal network along with the phase behaviour and structural arrangements impact on mechanical, rheological, and melting properties and shelf life [11, 16, 19]. Parameters such as cooling rate and thermal history (i.e., crystallization temperature and tempering) influence kinetics and ultimate physical properties of the crystallized fat systems during processing.

CONCLUSIONS AND RECOMMENDATIONS FOR PRODUCT QUALITY IMPROVEMENTS Fat crystallization behaviour during tempering of chocolates plays a key role in defining their ultimate structure, texture, appearance and melting properties. Variations in temper regimes attained during pre-crystallization of products influence their crystallinity, polymorphic status and other physical quality characteristics. Over-tempering causes increases in product hardness, stickiness with reduced gloss and darkening of product surfaces. Under-tempering induces fat bloom in products with consequential quality defects in texture and appearance (colour and surface gloss). Structure of under-tempered products causes dissolution of a large number of small crystals through re-arrangement and recrystallization, into a small number of larger (lumps) fat crystals (Ostwald ripening). In this process there is polymorphic transformation, nucleation and growth of new large crystals in a more stable polymorphic form with formation of solid bridges with weak and fewer intercrystal connections within the chocolate matrix. Thus, attainment of optimal temper regime during pre-crystallization of chocolate is necessary for the achievement of premium quality products and avoidance of defects in structure, texture, appearance and melting character.

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With the competitiveness in modern confectionery market, manufacturers want to eradicate or minimise the occurrence of fat bloom during chocolate manufacture, and have been involved in rigorous research to resolve this problem. Fat bloom caused by temperature damage is more or less under control, where manufacturers know that their chocolate needs to be stored at a controlled temperature. However, fat bloom or fat migration due to inappropriate tempering (untempering and under-tempering) is still a global problem and causes some product losses during storage, largely linked to the quality of the distribution chain. Even though taste is not affected by this bloom as it is in the case of sugar bloom (bloom resulting from moisture defects during storage of products under high humidity conditions), the ‘mouth-feel' of the product changes, melting differently in the mouth due to the higher melting point. Most manufacturers currently use traditional methods to combat fat bloom. For example, one key technique legally acceptable worldwide is the addition of full cream fat powder to milk chocolate to delay the migration of fat bloom. In dark chocolate – which only contains cocoa butter, 3-4% butter oil can lengthen the shelf-life and the mechanism involved has been explained.2,40,44 Fat bloom is a common problem in the confectionery industry. However, the reported research findings confirm that when chocolate is properly/optimally tempered as described, fat bloom could be completely avoided, when stored in controlled ambient temperature (18-25 °C) and humidity (50-55%) conditions. These findings have significant commercial implications as they are essential to optimizing process development during chocolate tempering, and could lead to the control and/or improvements in product quality during their supply chain and thus cost savings for the global chocolate confectionery industry.

ACKNOWLEDGMENT The authors wish to thank the Government of Ghana and Nestlé Product Technology Centre (York, UK) for the Research Support that led to most of the findings in this chapter. We also want to thank Mark Fowler, Joselio Vieira, Steve Beckett, John Rasburn and Angel Manez (Nestlé PTC, York) for expert technical discussions.

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[43] Lonchampt P and Hartel RW, Surface bloom on improperly tempered chocolate. Euro J Lipid Sci Tech 108:159–168 (2006). [44] Beckett ST, The Science of Chocolate. Second Edition. London: Royal Society of Chemistry, (2008). [45] Breitschuh B and Windhab EJ, Parameters influencing cocrystallization and polymorphism in milk fat. J Amer Oil Chem Soc 75:897–904. [46] Loisel C, Lecq G, Keller G and Ollivon M, Dynamic crystallization of dark chocolate as affected by temperature and lipid additives. J Food Sci 63:73–79 (1998). [47] Chen CW, Lai OM, Ghazali HM and Chong CL, Isothermal crystallization kinetics of refined palm oil. J Amer Oil Chem Soc 79:403–410. [48] Marangoni AG and Narine SS, Identifying key structural indicators of mechanical strength in networks of fat crystals. Food Res Inter 35:957–969. [49] Afoakwa EO, Paterson A, Fowler M and Vieira J, Particle size distribution and compositional effects on textural properties and appearance of dark chocolates. J Food Eng 87:181 – 190 (2008). [50] Afoakwa EO, Paterson A, Fowler M and Vieira J, Microstructural and mechanical properties relating to particle size distribution and composition in dark chocolate. Inter J Food Sci Tech 44, 111–119 (2009). [51] Do T-AL, Hargreaves JM, Wolf B, Hort J and Mitchell JR, Impact of particle size distribution on rheological and textural properties of chocolate models with reduced fat content. J Food Sci 72:E541 – E552 (2007). [52] Narine SS and Marangoni AG, Elastic modulus as an indicator of macroscopic hardness of fat crystal networks. LWT – Food Sci Tech, 81:117–121 (2001). [53] Briones V and Aguilera JM, Image analysis of changes in surface color of chocolate. Food Res Inter 38:87–94 (2005). [54] Hutchings JB, Food Colour and Appearance. Blackie A & P., Glasgow, UK (1994). [55] Saguy IS and Graf E, Particle size effects on the diffuse reflectance of a sucrosecaramel mixture. J Food Sci 56:1117-1120 (1991). [56] ASTM, Standard test method for specular gloss. Designation D 523. In 1995 Annual Book of ASTM Standards. Volume 6.01: Paint-tests for chemical, physical and optical properties; appearance; durability of non-metallic materials. Philadelphia: American Society for Testing and Materials (1995). [57] Trezza TA and Krochta JM, The gloss of edible coatings as affected by surfactants, lipids, relative humidity and time. J Food Sci 65:658–662 (2000). [58] Talbot G, Chocolate temper. In Industrial Chocolate Manufacture and Use (pp. 218230) Beckett, S. T. (Ed.), Oxford, Blackwell Science, 3rd edition (1999). [59] Briones V, Aguilera JM and Brown C, Effect of surface topography on color and gloss of chocolate samples. J Food Eng 77:776–783 (2006). [60] Seguine E, Tempering – the inside story. Manuf Confectioner 71:118-125 (1991). [61] Bricknell J and Hartel RW, Relation of fat bloom in chocolate to polymorphic transition of cocoa butter. J Amer Oil Chem Soc 75:1609–1615 (1998). [62] Timms RE, Phase behaviour of fats and their mixtures. Progress in Lipid Res 23:1–38 (1984).

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[63] McFarlane I, Instrumentation. In S. T. Beckett (Ed.), Industrial Chocolate Manufacture and Use (pp. 347–376). New York: Chapman & Hall (1999). [64] Kinta Y and Hatta T, Composition and structure of fat bloom in untempered chocolate. J Food Sci 70:S450–452 (2005). [65] deMan JM, Relationship among chemical, physical, and textural properties of fats. In N. Widlak (Ed.), Physical properties of fats, oils and emulsions (pp. 79–95). Champaign, IL, USA: AOCS Press (1999). [66] Tang D and Marangoni AG, Modified fractal model and rheological properties of colloidal networks. J Coll Interface Sci. 318:202 – 209 (2008). [67] Rousseau D and Smith P, Microstructure of fat bloom development in plain and filled chocolate confections. Soft Matter 4:1706 – 1712 (2008).

In: New Topics in Food Engineering Editor: Mariann A. Comeau

ISBN: 978-1-61209-599-8 © 2011 Nova Science Publishers, Inc.

Chapter 2

NON-LINEAR MODELING OF QUALITY OF COOKED GROUND BEEF PATTIES WITH VISIBLE-NIR SPECTROSCOPY Sreekala G. Bajwa1* and Jason K. Apple2 1

Department of Biological & Agricultural Engineering, University of Arkansas, 203 Engineering Hall, Fayetteville, AR 72701, USA 2 Department of Animal Science, University of Arkansas, B103C Agricultural, Food & Life Sciences Building, Fayetteville, AR 72701, USA

ABSTRACT Chemometric models based on partial least square regression (PLSR) have been successfully used to estimate nutrient content of different raw meat products from spectroscopic measurements. Preliminary studies to establish a chemometric model for estimating nutrient concentration of cooked ground beef patties from spectroscopic data indicated that the linear PLSR models are not adequate to represent fat and calories. Therefore, this study was conducted to examine two non-linear modeling methods using PLSR and artificial neural networks (ANN). In this study, spectral absorbance in the visible and near infrared (VNIR) region along with data from proximate analysis was utilized to develop and validate the two non-linear models for predicting fat, calories, cholesterol and moisture content of cooked ground beef patties. When compared to a linear chemometric model based on PLSR, both non-linear models performed significantly better. The ANN model exhibited the best performance which was indicated by a validation R2 value of 0.93 and residual predictive deviation (RPD) of 3.3 and 3.4 for fat and calories respectively. Both non-linear models resulted in RPD ≥ 3 under validation, indicating that they are acceptable. However, the model performance was only fair for cholesterol and moisture content.

*

E-mail address: [email protected]

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Sreekala G. Bajwa and Jason K. Apple

INTRODUCTION In developed countries, the consumer's willingness to pay a premium price for meat products that maintain a consistent quality standard has resulted in a demand for fast and efficient methods for estimating meat quality. In the US, the increasing consumer demand for food products in the ready-to-eat form has resulted in many processed food products that may be even partially cooked. There is a need for understanding the nutrient value of these various meat products to enable customers to make an informed purchase. The persistent problem of obesity has created great awareness among people about the nutrient value of the food they consume. Also, certain medical conditions may require people to choose food with a certain nutrient profile or caloric value. The new health care bill passed by the US government this year will require all restaurants to include calorie counts of all food items on the menus, menu boards and drive-throughs. Currently, the accuracy of such estimations may be questionable. Therefore, there is a growing need to develop methods and/or devices that can measure or estimate the caloric value and other quality parameters of cooked food products in a fast and accurate manner. Such methods can have applications in food processing lines for controlling the quality of food products, in making decisions regarding the end use of meat, and to understand the nutrient profile and energy content of cooked food products in various food outlets. Standard methods for assessing the nutritional profile of food materials are based on guidelines established by the Association of Official Analytical Chemists (AOAC)[1], and use laboratory-based chemical analyses. Since chemical analyses are often time consuming and expensive, many scientists have focused on rapid assessment of nutrients such as fat and protein in raw food, particularly using near infrared reflectance spectroscopy (NIRS) and chemometric modeling methods [2-33]. Optical spectral sensing, particularly NIRS, has proven to be a reliable tool for assessing quality of raw meat products. The advantages of indirect methods such as spectral sensing include speed, non-destructive nature of the tests, reliability and ability to be incorporated into food processing lines for real-time process control. Some of the studies on spectral sensing of food quality have also led to the establishment of online monitoring systems for quality control in food processing lines [23,34-35]. A review of the literature on the application of NIRS to estimate quality of meat and meat products is provided in Table 1 and also by [36]. Past research on this topic focused on estimating protein, intra-muscular fat (IMF), moisture content (MC), dry matter (DM), gross energy (GE) or calories, specific fatty acids or fatty acid groups (FAG), myoglobin (Mb) and minerals in meat products using chemometric models based on partial least square regression (PLSR) or modified PLSR (mPLSR) (Table 1). The meat products addressed in these studies included beef, pork, chicken, lamb and fish in various forms including steak, minced and homogenized, sausages and surimi. It is evident from these past studies that the NIRS works remarkably well for raw meat products in minced form, particularly for estimating proteins, IMF, MC and DM, but not that well for intact muscles and cooked meat products [2-33].

Table 1. Summary of research reported in the last 10 years on chemometric modeling of nutrient value of food products from spectral measurements, in reverse chronological order. The model performance indicated is for independent prediction or cross-validation or calibration in that order of preference, based on availability Study author [2] Bajwa et al., 2009

[3] Ripoll et al., 2008 [4] GaitanJurado et al., 2008 [5] Andrés et al., 2007 [6] Khodabux et al., 2007 [7] OrtizSomovilla et al., 2007 [8] Pla et al., 2007

Meat product Beefground patties (raw) Beef-ground & cooked patties Beef

Spectral sensor (wavelength) ASDLabSpec Pro spectroradiometer (400-1075 nm)

Model PLSR

Nutrients studied IMF, calories, cholesterol, MC

Model performance R2 = 0.94 (IMF), 0.94 (calories), 0.94 (MC), 0.80 (cholesterol)

ASDLabSpec Pro spectroradiometer (400-1075 nm) FOSS NIR System 6500 (408-2492.8 nm)

PLSR PLSR, mPLSR

IMF, calories, cholesterol, MC Protein, IMF, MC, Mb

R2 = 0.85 (IMF), 0.86 (calories), 0.72 (MC), 0.79 (cholesterol) R2 = 0.16(protein), 0.76 (IMF), 0.72 (MC). 0.91(Mb)

Pork sausage

Perten 7000 Diode Array NIR /VIS sensor (400-1700 nm)

mPLSR

Protein, IMF, MC

R2 = 0.95-0.96 (protein), 0.981(IMF), 0.88-0.99(MC)

Lamb

FOSS NIR System 6500 (408-2498 nm)

PLSR

IMF, MC

R2 = 0.41 (IMF), 0.69 (MC)

Fish - Tuna

ASD LabSpec Pro NIR Sensor(350-2500 nm)

PLSR

Protein, IMF, MC

R2 = 0.99 (protein), 0.95 (total IMF). 0.96 (free fat), 0.98 (MC)

Pork sausage minced

Perten 7000 Diode Array NIR/VIS Sensor (400-1700 nm)

mPLSR

Protein, IMF, MC

R2 = 0.93 (protein), 0.98 (IMF) 0.98 (MC),

Rabbitground

FOSS NIR System 5000 (1100-2498 nm)

PLSR

Fatty acids

R2 = 0.5 – 0.9 (each ), 0.83-0.92 (grouped)

Table 1. (Continued) Study author [9] Viljoen et al., 2007 [10] Barlocco et al., 2006 [11] Locsmánd i et al., 2006 [12] Prieto et al., 2006 [13] Savenije et al., 2006 [14] Uddin et al., 2006 [15] Berzaghi et al., 2005 [16] HovingBolink et al., 2005

Meat product Mutton

Pork LT intact & minced Goose liver -ground

Spectral sensor (wavelength) InfraAlyser 500 Bran + Luebbe GmbH Sensor (1100-2500 nm) FOSS NIR System 6500 (400-2500 nm)

FOSS NIR System 6500 (11002500 nm)

Model PLSR

Nutrients studied Protein, IMF, DM, ash

Model performance R2 = 1.00 (protein), 1.00 (IMF), 0.96 (DM), 0.97 (ash)

PLSR

IMF, MC

R2(intact/minced) = 0.30/0.87 (IMF), 0.66/0.90 (MC)

PLSR

Protein, IMF, DM, FAG

R2 = 0.63 (protein), 0.81 (IMF), 0.72 (DM), 0.79-0.81 (FAG)

R2 = 0.87 (protein), 0.92 (IMF), 0.93 (GE), 0.87 (DM), 0.17 (ash), 0.44 Mb R2 = 0.40-0.58 (IMF), 0.31-0.35 (DL), 0.40-0.71 (pH)

Beefground LT muscle Porklongissimus muscle

InfraAnalyzer 500 Spectrophotometer (1100-2500 nm)

PLSR

FOSS NIR System 6500 (400-2500 nm)

mPLSR

Protein, IMF, GE, DM, ash, Mb IMF, DL, pH

Fish Surimi

FOSS NIR System 6500 (400-1100 nm)

PLSR

Protein MC,

R2 = 0.98 (MC & protein)

Poultry breast meat

Foss NIR System 5000 (11002498 nm)

PLS-based discriminan t analysis

Protein, IMF, DM, cholesterol, FAG

R2 = 0.91 (protein), 0.99 (IMF), 0.91 (DM), 0.34 (cholesterol), 0.940.98 (FAG)

Pork muscles

Zeiss MCS 511/522 diode array VIS/NIR system (380-1700 nm)

Linear regression

IMF

R2 = 0.35 (IMF)

[17] Kadim et al., 2005 [18] McDevitt et al., 2005 [19] Prevolnik et al., 2005 [20] Viljoen et al., 2005 [21] Xiccato et al., 2004 [22] Alomar et al., 2003 [23] Anderson & Walker, 2003 [24] Geesink et al., 2003

Poultry

Foss NIR System 5000 (800-2800 nm)

PLSR

Protein, IMF, DM, Ca and P

R2 = 0.96(protein), 0.99 (IMF), 0.82(DM), 0.90(Ca), 0.91(P)

Poultry dried & ground

Foss NIR System 6500 (400-2498 nm)

mPLSR

Protein, IMF, ash

R2 = 0.86 (protein), 0.93 (IMF), 0.71 (ash)

mPLSR

IMF

R2 = 0.84-0.99

PLSR

Protein, IMF, DM, ash

R2 = 0.94(protein), 0.98 (IMF), 0.71(DM), 0.50(ash)

Pork & beef muscle

FOSS NIR System 6500 (4002500 nm)

Ostrichminced, freeze dried Fish – minced sea bass Beef – minced & homogeniz ed Beef minced

FOSS NIR System (11002500 nm)

Pork

InfraAlyser 500 Bran + Luebbe GmbH (1100-2500 nm) FOSS NIR System 6500 (400-2500 nm)

PLSR

Protein, IMF, MC, GE

R2 = 0.30-0.68 (protein), 0.47-0.97 (water), 0.48-0.97 (IMF), 0.28-0.96 (GE) R2 = 0.82 (protein), 0.82 (IMF), 0.77 (DM), 0.66 (ash), 0.18 (collagen)

PLSdiscrim. analysis

Protein, DM, IMF, ash, collagen

Perten DA-7000 Diode Array NIR /VIS spectrometer (400-1700 nm)

PLSR

IMF

R2 = 0.83-0.93 (IMF)

Spectrum One NTS FTIR spectro-photometer (10002500 nm)

Stepwise MLR & PLSR

DL, color, pH, shear strength

R2 = 0.51 (DL)

Table 1. (Continued) Study author [25] Togerson et al., 2003 [26] Windham et al., 2003 [27] Chan et al., 2002 [28] Cozzolino & Murray, 2002

[29] Cozzolino et al., 2002 [30] Abeni &Bergogl io, 2001

Meat product Beefground, semifrozen Poultrymuscle

Pork

Spectral sensor (wavelength) InfraAnlyzer 500 Bran + Luebbe GmbH (1100-2500 nm)

Model PLSR

FOSS Model 1265 Meat Analyzer (850-1050 nm)

PLSR

Perten DA-7000 Diode Array NIR /VIS Sensor (400-1700 nm) FOSS NIR System 6500 (400-2500 nm)

Nutrients studied IMF, MC, protein

IMF

Model performance R2 = 0.97 (IMF), 0.96 (MC), 0.80 (protein)

R2 = 0.95-0.99 (IMF)

PLSR

Protein, IMF, MC

R2 = 0.70 (protein), 0.61 (IMF), 0.69 (MC)

mPLSR

Protein, IMF, MC

R2(intact/minced) = 0.26/ 0.73(protein), 0.79/0.89 (IMF), 0.01 /0.87(MC) R2(intact/mined) = 0.49/0.79 (protein), 0.18/0.71 (IMF), 0.36/0.72(MC) R2(intact/mined) = 0.71/0.97 (protein), 0.29/0.87-0.92 (IMF), 0.38/0.96 (MC) R2 = 0.54-0.71 (protein), 0.87-0.92 (IMF), 0.09-0.41 (MC)

Beef LD: intact & minced Lamb: intact & minced

FOSS NIR System 6500 (4002500 nm)

mPLSR

Protein, IMF, MC

Poultry: intact & minced

FOSS NIR System 6500 (4002500 nm)

mPLSR

Protein, IMF, MC

Beef LD muscle minced

FOSS NIR System 6500 (400-2500 nm)

mPLSR

Protein, IMF, MC

Poultry breast minced

FOSS NIR System 4500 (1308-2388 nm)

mPLSR

Protein, IMF, MC, ash

R2 = 0.97 (IMF)

[31] Brondum et al., 2000 [32] Cossolino et al., 2000 [33] Rodbotten et al., 2000

Pork -loin muscle

OceanOptic FOP (500-980 nm), FOSS NIR System 6500 (802-2500 nm)

PLSR

IMF, MC (with different instruments)

R2 = 0.02-0.59 (IMF), 0.03-0.45 (MC)

Lambminced

FOSS NIR System 6500 (400-2500 nm)

PLSR

Protein, IMF, MC

R2 = 0.20-0.84 (protein), 0.26-0.88 (IMF), 0.29-0.96 (MC)

Beef -LD muscle

Infraanlyzer 500 Bran + Luebbe GmbH (1100-2500 nm)

PLSR

IMF

R2 = 0.61-0.72

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Sreekala G. Bajwa and Jason K. Apple

There is limited research conducted on the application of NIRS for estimating quality of cooked meat products. The cooking process denatures the Mb and hemoproteins, resulting in a reduction in the corresponding absorbance peaks [2]. Also, the color of the meat products, which is indicative of the nutritional constituents in the raw form, changes into different shades of brown and light pink after cooking. Yet, the need for characterizing the nutrient value of cooked meat is only increasing due to the increased demand for “heat-and-eat” or “ready-to-eat” meat products that are accurately labeled. Also, the few studies that addressed cholesterol in meat products indicated that the NIRS method does not work well for indicating cholesterol [2,15]. Preliminary research [2] focusing on the estimation of the quality of cooked ground beef patties showed that visible-NIR (VNIR) reflectance spectroscopy in combination with chemometric modeling holds great potential for rapid detection of fat and caloric content (Table 2). The R2 values were high and standard error of cross-validation (SECV) and prediction (SEP) were relatively low for both fat and calories. While the residual predictive deviation (RPD), an indicator of model performance, for fat, calories and MC for raw patties were all above 4 for raw patties under calibration and validation, the RPD for cooked ground beef patties were less than or equal to 3.0 under independent validation. A model with an RPD below 3 is considered unacceptable for online screening applications [37]. Therefore, the linear PLSR model for cooked ground patties was only borderline acceptable. Table 2. Performance of the full and refined PLSR models developed for predicting fat, calorie, cholesterol and MC in cooked ground beef patties Model Performance

Full PLSR model with 220 bands of difference spectra Fat % Calorie Chole MC Kcal/g mg/g % Calibration: Cross-validation R2-value 0.93 0.92 0.80 0.64 SECV 2.2 0.09 10.8 3.6 RPD 3.6 3.3 2.2 1.6

0.91 2.2 3.2

0.90 0.09 3.1

0.78 10.5 2.0

0.65 3.5 1.6

Independent Testing or Prediction R2-value 0.87 0.87 SEP 3.2 0.06 RPD 2.9 3.0

0.85 3.5 2.6

0.86 0.07 2.7

0.79 5.8 2.2

0.72 1.6 2.0

0.76 6.2 2.1

0.70 1.7 1.9

Refined PLSR with 50 bands of difference spectra Fat Calorie Chole MC % Kcal/g mg/g %

Copyright: Bajwa et al., 2009.

The denaturing of hemoproteins and changes in the appearance of the meat during cooking could change the spectral absorbance of patties. Another reason for the poor performance of the PLSR model for cooked ground beef patties was the decrease in the range and standard deviation of all four response variables for the cooked meat samples compared to the raw meat samples [2]. As the variability in the data set decreases, the performance of an empirical model deteriorates. Except for cholesterol, the standard deviation of cooked patty samples was approximately half as much as that of raw samples. Compared to raw meat samples, the coefficient of variation (ratio of standard deviation to mean) of cooked meat

Non-linear Modeling of Quality of Cooked Ground Beef Patties…

43

samples decreased by approximately 11% for fat, 6% for calories, 3% for cholesterol and 5% for MC. Another reason for the relatively poor performance of the PLSR model for cooked meat was the error in model specification for fat and calories in cooked ground beef patties. Almost all of the past studies have utilized either PLSR or mPLSR for developing empirical models to estimate the quality trait of interest in specific meat products (Table 1). The PLSR is a bilinear modeling method, that decomposes the high-dimensional spectral data into partial least squares or principal components, and develops an optimal model for predicting one or multiple response variables from the PLS factors. An analysis of the residuals of the PLSR model for fat and calories from [2] showed parabolic trends (Figure 1), indicating that the model requires higher order terms to adequately describe the relationship between response variables and spectral data. Based on the residuals, we hypothesized that the model relating fat and calories to spectral data should be non-linear for cooked ground beef patties. The common model structure used in PLSR is linear. However, it is possible to develop non-linear PLSR models mainly by transforming the Y variables to higher order terms. Although it is possible to also transform X variables into higher order terms in order to develop a non-linear PLSR model, the large number of X variables in spectral data makes this a quite complex and time consuming task.

Figure 1. Residuals of a linear PLSR model (Bajwa et al., 2009) developed for predicting fat, calories, MC and cholesterol of cooked ground beef patties.

Apart from PLSR and mPLSR, there are other methods for modeling the characteristics of a target material using high-dimensional spectral data. Radiative Transfer models (RTM) employ a process-based approach to study the chemical concentrations of compounds of interest in a target material. However, RTMs are too complex to be implemented in a processing line, or for quick nutrient profile analysis by non-technical users. Popular nonlinear empirical modeling methods that can be implemented for estimating the chemical

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Sreekala G. Bajwa and Jason K. Apple

constituent concentration in a target material from spectral data include (1) non-linear PLSR models that use transformed higher order X or Y variables, (2) artificial neural networks (ANN), and (3) support vector machines (SVM). There are only a few studies that have attempted the use of non-linear chemometric models for estimating the quality of meat products. One such study compared a linear PLSR model with back propagation (BP) and counter propagation type ANN models for estimating drip loss from pork muscle samples, and indicated that all three model performed similarly [38]. Another study used a combination of NIRS with principal component analysis and ANN modeling to successfully estimate the color and doneness of meat undergoing the cooking process [39]. This study was conducted with the objectives of testing two non-linear chemometric models, (1) a non-linear PLSR model with transformed higher order Y variables (fat and caloric content), and (2) a simple ANN model, for estimating the quality of cooked ground beef patties.

MATERIALS AND METHODS Lab Experiment A laboratory experiment was conducted with 8 different preparations of ground beef patties with fat content varying from 5 to 40% (wet basis). Ground beef patties were made by mixing lean muscle and fat at proportions of 95:5, 90:10, 85:15, 80:20, 75:25, 70:30, 65:35 and 60:40. The experiment was a completely randomized block design with 120 samples, which included 8 fat levels (treatments), 3 blocks and 5 replications under each treatmentblock combination. The beef patties were prepared by mixing peeled knuckles (quadriceps muscle) and fat trimmings in appropriate proportions, as explained in [2]. Weighed proportions of fat and lean muscle were coarse ground together through a 7.6-mm break plate and then fine ground through a 3.2-mm break plate in a Hobart grinder (Model 310; Hobart Inc., Troy, OH). The ground homogenized mixture was then fed into a Hobart patty machine (Model E-103; Hobart Inc., Troy, OH) to make patties of 151 g each. The raw patties were wrapped in a PVC film (O2 transmission rate of 1400 cc/m2/24h/atm) and stored frozen at -20ºC till cooking. Prior to cooking, the frozen patties were thawed to room temperature, and weighed to record the thawed weight. Then, they were pan-fried to an internal temperature of 71ºC according to AMSA cookery guidelines [40]. Patties were turned every 2 min during the cooking process, and subsequently cooled for 5 min before scanning. The experimental procedure is explained in detail in [2]. The same data set was used in this study.

Data Collection An ASD Field Spec Pro dual sensor spectro-radiometer (Analytical Spectral Devices, Inc., Boulder, CO) was used for spectral data collection. It had two sensors made of Si photodiode arrays, both with a wavelength range of 350-1100 nm, spectral resolution of 3 nm, and record interval of 1.5 nm. The two sensors simultaneously detected the reflected light

Non-linear Modeling of Quality of Cooked Ground Beef Patties…

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from a patty sample as well as from a reference panel called spectralon panel that had 98% reflectance in the 350-1100 nm range. The light reflected from the spectralon panel was used to estimate the incoming radiance. Spectral data from the 401 to 1064 nm range, sub-sampled at 3 nm interval was used in this study. Thus, reflectance recorded at 222 individual wavebands was used for building and testing the non-linear models to predict nutrient value of ground beef patties. A 14.5V, 50W artificial tungsten halogen light (LowelPro, Lowel Light Inc., Brooklyn, NY) was used as the light source. The light source was mounted at a distance of 1.2 m vertically above the sample such that the intensity of light over the patty surface was somewhat uniform. The cooked patties were longitudinally split to avoid the effect of surface abnormalities in the scan. Five NIR readings were measured from random locations on the inner side of each half. Cooked patties were packaged and stored at -20º C until preparation for chemical analyses. Both sensors were set to average 10 scans, and 5 dark readings for each data point. Each patty was scanned at 10 random points distributed throughout the inside surface of the cooked and split patty, and averaged to obtain a representative reading for each patty. Therefore, each radiance spectra representing a patty was an average of 100 readings. The simultaneous radiance measurements from the patty and the reference panel were radiometrically calibrated to radiance values using the calibration files provided by the instrument manufacturer. The absorbance of the patty was calculated using equation (1) below.

(1) where, A is the absorbance, IR and IO respectively are the reflected and incoming radiance in W/m2/sr, and R is reflectance as a ratio. The scanned samples of cooked patties were analyzed for fat and caloric contents at the University of Arkansas Central Analytical Lab (UACAL). The UACAL followed the ether extraction method (Method 920.39C AOAC, 1990) to determine the total crude fat. The MC of patty samples was determined by AOAC Method 934.01 [1]. Both fat and MC were calculated as a percentage of dry weight of samples. The caloric content of the samples were measured with a bomb calorimeter as cal per g of sample. The cholesterol content was measured in mg per g of dry sample using AOAC Method 976.26 [1] at the Missouri Agriculture Experimental Station Analytical Laboratory at Columbia in Missouri.

Data Analysis The absorbance values are sensitive to base effects such as thickness of patties and variation in particle size. To minimize the base effects, the absorbance spectra were converted to difference spectra using a finite difference method indicated in equation (2) below. A 15point high pass filter was used for calculating the difference spectra. The difference spectra had 220 bands, which were then used for model development.

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(2) where, A: absorbance, and dA: difference spectra, all at ith wavelength λi.

Non-Linear Partial Least Square Regression The PLSR method is a bilinear modeling method that has been widely used to develop empirical models relating chemical constituent concentration of a target object to spectral data. Spectral data are often high dimensional (220 difference spectra and 120 observations in this study) and multicollinear. The PLSR method decomposes the spectral data into orthogonal partial least square (PLS) factors such that the covariance between the response variables and the spectral data is maximized [41]. Similar to multivariate principle components, the PLS factors are computed as linear combinations of the spectral bands. Thus the PLS factors eliminate the multi-collinearity problem in the spectral data and reduce the number of independent variables to a manageable size. Another advantage of PLSR is that more than one response variable can be modeled simultaneously with one model. In this study, a PLSR model was developed with (fat2+fat), (calorie2+calorie), cholesterol and MC as response variables and difference spectra in 220 wavelengths as independent variables. Many transformations, including square, square root, logarithmic, exponential, etc were attempted using a trial and error method before choosing the above transform, which gave the best performance. Since the residual for cholesterol and MC in the original model showed a random distribution (Figure 1), and none of the attempted transformations improved the model performance for these two variables, they were left as such in the model. The data were processed in SAS using the PROC PLS option [42]. In the first run of PLSR, the number of PLS factors that significantly contributed to the model variability were identified through a full cross-validation. The initial PLSR model was further modified by eliminating the bands that had limited contribution to the model. Two factors, the regression coefficient and variable importance for projection (VIP) were used to evaluate the contribution of the 220 difference bands to the PLSR model. The VIP represents the value of each predictor in fitting the PLSR model to both predictor and response variables [43]. A predictor variable with a low regression coefficient close to zero and a low VIP is considered non-contributing or redundant, and eliminated from the model. The 120 samples were divided into two groups for calibration and testing. The calibration set included 96 observations and was used for calibrating the model. The remaining 24 observations were used for model validation. To select the validation set, the data set was first sorted by replication, block and then by treatment. Since there were 5 samples in each treatment-block combination, every fifth observation was selected for validation. After data decomposition into PLS factors in the refined model, the model was calibrated to optimize the number of PLS factors used in the model. A full cross validation procedure was used to identify the optimum number of PLS factors that will ensure stability of the model by avoiding over fitting [41]. The full cross validation method was used to identify the minimum number of PLS factors that explained significant (P<0.05) variability in the response variables. The stability and predictive ability of the models were also evaluated

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using the R2 value, standard error of calibration (SEC) or prediction (SEP), and RPD. They were calculated as shown in equations (3 – 5):

(3)

(4)

(5) where Yi is the nutrient content of samples from laboratory-based proximate analysis, Ŷi is the model predicted nutrient content, N is the number of samples, SDpop is the standard deviation of the population, and bias is the average error in the calibration model.

Artificial Neural Network (ANN) Method The ANN model used for this application was a feed-forward multi-layer perceptron (MLP) model that used error back propagation for training. This is one of the simplest and most commonly used neural networks. An ANN learns the same way the human brain learns information; by associating a certain input pattern with a certain output. For this type of learning, it is necessary to provide input patterns that correspond to every possible outcome, or at least a possible range of outcomes. More information about ANN can be obtained from [44]. A MLP model with 1 input layer, 1 hidden layer and 1 output layer with 4 neurons corresponding to fat, calories, cholesterol and MC was used in this study (Figure 2). For comparison purpose, only those first order difference bands that were used in the refined PLSR model were used in the ANN model. The first order difference spectra were initially transformed into principal components (PC). The number of principal components used as inputs to the model was selected to represent 99% of variability in the difference spectra. The first 11 principal components together represented 99% of variability in the spectral data, and hence were used as inputs to the ANN model. The activation functions were selected based on a trial and error method with various combinations of ‘logsig’, ‘tansig’ and ‘purelin’. The combination that gave the best performance was finally selected as the activation functions in

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Sreekala G. Bajwa and Jason K. Apple

the model. The best combination included the ‘tansig’ function at the hidden layer and ‘purelin’ at the output layer.

Figure 2. Topology of the artificial neural network model developed for estimating quality of cooked ground beef patties.

A trial and error optimization method was used to optimize the learning rate and number of neurons in the hidden layer. The learning rate was increased from 0.01 to 0.20 to select the optimal rate. A learning rate of 0.15 gave the best performance, and hence it was selected. A good starting point for the number of neurons in the hidden layer is sqrt(NI*NO), where NI is the number of inputs and NO is the number of outputs [39], which resulted in 6 neurons in this case. For optimization, the number of neurons in the hidden layer was varied from 6 to 25. The best model performance was obtained with 22 neurons in the hidden layer. The rule of thumb for maximum number of neurons in a hidden layer is that it should not exceed 2*NI [44] or 22 in this case. Since the number of neurons in the hidden layer selected by the trial and error process did not exceed the maximum recommended number of neurons, this network topology was considered acceptable. Similar to other network parameters, various training algorithms such as LevenbergMarquart (LM), gradient descent with momentum and/or adaptive learning, quasi-Newton, Bayesian regularization and conjugate gradient were also tested on a trial and error basis. The best performance was obtained when the LM training algorithm with Bayesian Regularization was used to train the network. This training algorithm is available in Matlab software through the function ‘trainbr’. The ANN model was implemented within the Matlab software.

Non-linear Modeling of Quality of Cooked Ground Beef Patties…

49

For comparison purpose, the training and test data used were roughly the same as in the case of the PLSR model. Once the model was trained with the training data to a satisfactory performance, the training was stopped. The criteria used to stop training included a maximum number of epochs of 1000 (selected based on the experience during optimization), a minimum mean square error (MSE) of 0.0001 and a minimum error gradient of 0.000001. If any one of these criteria was met, the training stopped. The trained model was then tested on the independent test data set to evaluate the performance of the model. The model performance was evaluated using R2 value, SEC, SEP and RPD, which were also used for evaluating the non-linear PLSR model.

RESULTS AND DISCUSSION Proximate Data Proximate data analysis showed that the samples represented a relatively broad range of fat (15-51%), calories (5.91-7.36 Kcal/g), cholesterol (173.1 to 779.4 mg/g), and MC (2756.3%) in the cooked patties (Table 3). The observed ranges were representative of the nutrient concentrations in cooked beef patties available on the market but considerably lower than the range of values reported for raw patties [2]. Also, the calibration and validation data showed similar distributions (comparable means and standard deviations) for all 4 meat quality characteristics. The validation data were within the range of calibration data for fat and calories. For cholesterol and MC, a few observations in the validation data fell outside the range of calibration data. All 4 quality characteristics were also normally distributed. Table 3. Results of proximate analysis on cooked ground beef patties showing data distribution for the calibration and validation sets Calibration Nutrient

N

Fat (% dw) Calorie (Kcal/g) Cholesterol (mg/g) MC (% dw)

96 96 96 96

Mean

Independent validation SD

Range

N

Mean

SD

Range

39.19 6.89

8.55 0.33

15.0-51.0 5.91-7.36

24 24

40.11 6.93

8.75 0.35

16.8-49.5 5.99-7.34

213.33

24.62

173.1-276.4

24

215.16

25.57

183.1-279.4

43.82

6.08

27.0-55.9

24

44.68

5.98

35.3-56.3

Copyright: Bajwa et al., 2009.

Absorbance Spectra of Cooked Ground Beef Patties The spectral absorbance of cooked ground patties showed that the absorbance increased as the amount of fat increased or the amount of lean muscle in the mixture decreased (Figure 3). The absorption peaks in the 400-635 nm region in meat is usually caused by various forms of Mb and hemoglobin in the meat [45-46]. The Soret peak at 410 nm, the metMb peaks at

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Sreekala G. Bajwa and Jason K. Apple

440 and 614 nm and cytochrome C peak at 548 nm were all very distinct in the cooked patties. The absorbance of cooked meat decreased as the wavelength increased from 400 to 900 nm. Beyond 700 nm, the absorbance spectra were relatively flat with low absorbance.

Figure 3. Spectral absorbance of cooked ground beef patties containing different proportions of lean and fat plotted against wavelength.

Non-Linear Partial Least Square Regression Model The non-linear PLSR model developed with transformed fat and calories performed better than the linear PLSR model reported by [2] (Tables 2 & 4). All performance parameters including R2, standard errors and RPD corresponding to fat and calories improved in the nonlinear PLSR model. The R2 values (≥ 0.92) and RPD (≥ 3.5) for fat and calories under calibration and cross-validation of the non-linear PLSR model indicated good performance, and potential for online screening applications [47-49]. The RPD for independent prediction was also above 3.0, with corresponding R2 values above 0.89. The SEP of 3.4% for fat and 0.11 Kcal/g for calories were considerably small compared to the mean values for these variables (Table 3). The performance of the non-linear PLSR model for estimating cholesterol was only fair, with R2 values of 0.8, 0.89 and 0.8, and corresponding RPD of 1.9, 2.1 and 2.2 for calibration, cross-validation and independent prediction (Table 4). The model also performed only fairly for estimating MC, with R2 values of 0.77 and RPD of 2.1 for cross-validation, and 0.79 (R2) and 2.2 (RPD) for independent prediction. The non-linear PLSR model performance for cholesterol and MC was similar to that of the linear PLSR model (Tables 2 and 4).

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51

Table 4. Performance of two non-linear models, a refined non-linear PLSR model and an ANN model with 11 principal components as inputs, for predicting quality of cooked ground beef patties. The SEC and SEP are expressed in same unit as the quality parameter Performance indicators

Calibration R2

Cross-validation

Prediction

SEC

RPD

R2

SEC

RPD

R2

SEP

RPD

Non-linear PLSR Model Fat

0.94

2.1

4.1

0.95

2.3

4.5

0.91

2.6

3.4

Calories

0.92

0.09

3.5

0.93

0.10

3.8

0.89

0.11

3.0

Cholesterol

0.80

11.3

2.2

0.89

12.1

3.0

0.80

11.5

2.2

MC

0.71

3.3

1.9

0.77

3.6

2.1

0.79

2.7

2.2

Fat

0.95

1.9

4.5

0.93

2.6

3.8

Calories

0.94

0.08

4.0

No cross-validation was performed

0.93

0.09

3.8

Cholesterol

0.83

10.8

2.4

0.81

10.6

2.3

MC

0.73

3.2

1.9

0.75

3.1

2.0

ANN Model

Examination of model residuals indicated that the degree of non-linearity in the residual has decreased (Figure 4) for the non-linear PLSR model compared to the linear PLSR model (Figure 1). However, the residuals for calories still showed a slight parabolic trend, indicating that the non-linear PLSR model was not able to capture the full degree of non-linearity between calories and spectral data. Also, the residuals for fat showed a dependency on the response variable, indicating that further transformation of the data may be required to improve the results. However, the various other transformations explored in this study had neither improved the model performance nor the distribution of the residuals with respect to the predicted values. As with the linear PLSR model, the residuals of MC and cholesterol showed an acceptable random distribution (Figure 4).

Figure 4. Distribution of model residuals for non-linear PLSR with transformed values for fat and calories plotted against predicted values of all four response variables (fat, calories, MC and cholesterol) for cooked ground beef patties.

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Artificial Neural Network Model The trained ANN model was used to simulate the outputs (fat, calories, cholesterol and MC) for both calibration and validation data. Performance functions such as R2, SEC and RPD indicated that the ANN model performed well on calibration data, particularly for fat and calories (Table 4). The calibration R2 values for fat and calories were 0.95 and 0.94 respectively, with corresponding RPD of 4.5 and 4.0, and SEC of 1.9% fat and 0.85 Kcal/g calories. The ANN model performance was slightly better than that of the non-linear PLSR model and significantly better than the linear PLSR model [2]. The RPD values of 4.5 and 4.0 are regarded as good according to the standards set by [47-48]. The independent validation results were similarly very good, with R2 value of 0.93 and RPD of 3.8 for both fat and calories. For cholesterol, the calibration R2 value of 0.83 and RPD of 2.4 were relatively low, which indicated that VNIR spectroscopy may be useful for rough screening of cholesterol in cooked beef patties. The model also performed only fairly for estimating MC, indicating that ANN model is no better than the linear and non-linear PLSR models for representing MC of cooked ground beef patties. The validation performance of ANN model was similar or slightly lower than the calibration performance. In general, R2 values and RPD were slightly less under validation compared to calibration data, which was expected. Analysis of the residuals resulting from the ANN model showed that most of the nonlinearity observed in the residuals of fat and calories when the linear PLSR model was used disappeared from the ANN model residual (Figure 5). Residuals were randomly distributed, which indicated that the ANN model was appropriate for representing the four response variables used in the ANN model.

Figure 5. Distribution of model residuals corresponding to the ANN prediction model plotted against predicted values of response variables (fat, calories, MC and cholesterol) for cooked ground beef patties.

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53

Discussion This study indicated that the two non-linear chemometric models based on PLSR and ANN are more appropriate than linear PLSR models for estimating quality of cooked ground beef patties, particularly, fat and calories. Both non-linear models resulted in high RPD (≥ 3.0) and R2 values (≥ 0.89) for fat and calories. The non-linear models decreased the SEP from 3.5% for the linear PLSR model to 2.6% for fat. There was no significant reduction in the SEP for calories. These results indicate that VNIR spectroscopy in combination with nonlinear chemometric modeling can be a reliable method for rapid estimation of the quality of cooked beef patties. The non-linear PLSR model performance for estimating fat in cooked ground beef patties was comparable to many of the linear PLSR models reported for various raw meat samples (Table 1). Regarding calories, only two other studies reported calories or gross energy of ground beef samples [2,13]. The performance of the non-linear PLSR model for estimating calories (R2=0.89) in cooked ground beef was slightly lower than the R2 values of 0.93 and 0.94 reported for linear PLSR models for raw ground beef [2,13]. An RPD of 1 indicates that the standard deviation of the response variable is the same as SEC or SEP, thereby indicating that the model did not explain much of the variation in the response variable. An RPD of 5 to 10 are adequate for online quality control. This will translate to corresponding R2 values of 0.96 to 0.99. In most VNIR spectroscopy applications, such high RPD and R2 values are rarely encountered. According to [48], an RPD greater than 2.5 for cross-validation is considered acceptable for screening applications. According to these standards, the PLSR model for fat and calorie in cooked ground beef patties are appropriate for screening applications. The non-linear PLSR model behaved very similar to that of the linear PLSR model for estimating both cholesterol and MC. This is understandable since these variables were represented in the model using linear terms. In general, the RPD for cholesterol varied from 2.0 to 2.4, and that for MC varied from 1.8 to 2.2 under calibration and validation. For cholesterol, a comparison with past studies indicated that the performance of the non-linear models was comparable to the R2 value of 0.8 reported for raw ground beef [2] and much better than the 0.34 reported for poultry [15]. A review of past research indicates that a majority of the chemometric models for estimating MC resulted in R2 values less than 0.8 [25, 27-29, 31-32]. However, [2, 6-7, 10,14, 25] reported R2 ≥ 0.9 for MC in raw meat products. Although RPD values below 2.5 is considered as relatively low, an RPD as low as 1.5 has shown appropriate for initial rough screening applications [49]. From this perspective, both non-linear models are appropriate for initial rough screening of cholesterol and MC. A comparison of the non-linear PLSR model with ANN model indicated that the ANN model was slightly better than the non-linear PLSR model in estimating calories, which was evident from the R2 values, RPD and standard errors (Table 4). The high degree of nonlinearity in the ANN model may be a reason for the slightly better performance of this model. Both non-linear models provided significant improvement over the linear PLSR model for estimating fat and calories.

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Sreekala G. Bajwa and Jason K. Apple

CONCLUSION Preliminary research on spectroscopic analysis of cooked ground beef indicated that the relationship between some of the response variables such as fat and calories, and spectral data were non-linear, and could not be captured completely with a linear partial least square regression model. Therefore, this study was undertaken to test two non-linear modeling techniques for estimating fat and calories of cooked ground beef patties. The two non-linear spectral modeling methods employed in this study included a non-linear PLSR model and an artificial neural networks model that accepted principal components of spectral data as inputs. The non-linear PLSR model used fat and calories transformed into second degree variables. The ANN model was a multi-linear perceptron model with 1 hidden layer (22 neurons), 4 output neurons. Both the non-linear modeling techniques performed better than the linear model for estimating fat and calories, indicating that they may be appropriate for screening purposes. The ANN model was slightly better than the non-linear PLSR for modeling fat and calories. Both non-linear models resulted in relatively low values for performance indicators such as RDP for estimating cholesterol and MC. The relatively low RPD values of 1.8 to 2.4 indicate that they may be appropriate for initial rough screening of cholesterol and MC.

ACKNOWLEDGMENT The authors would like to thank the Arkansas Agricultural Experiment Station for providing partial support for conducting the lab experiment, and Jayarani Kandaswamy for running the lab experiments that generated the data used in this study.

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AOAC, Official Methods of Analysis, 14th ed. Methods 24.002, 24.006, and 24.026, Arlington, Association of Official Analytical Chemists, Arlington, Virginia (1990). [2] S. G. Bajwa, J. Kndaswamy and J. K. Apple, J. Food Eng. 92, 454 (2009). [3] G. Ripoll, P. Albertí, B. Panea, J. L. Olleta and C. Sañudo, Meat Sci. 80, 697(2008). [4] A. J. Gaitán-Jurado, V. Ortiz-Somovilla, F. Espana-Espana, J. Perez-Aparicio and E. J. De Pedro-Sanz, Meat Sci. 78, 391 (2008). [5] S. Andres, I. Murray, E. A. Navajas, A. V. Fisher, N. R. Lambe amd L. Bunger, Meat Sci. 76, 509 (2007). [6] K. Khodabux, M. S.S. L’Omelette, S. Jhaumeer-Laulloo, P. Ramasami and P. Rondeau, Food Chem. 102, 669 (2007). [7] V. Ortiz-Somovilla, F. España-España, A. J. Gaitán-Jurado, J. Perez-Aparichio and E. J. De Pedro-Sanz, Food Chem. 101, 1031 (2007). [8] M. Pla, P. Hernandez, B. Arino, J. A. Ramirez and I. Diaz, Food Chem. 100, 165(2007). [9] M. Viljoen, L. C. Hoffman and T. S. Brand, Small Ruminant Res. 69, 88 (2007). [10] N. Barlocco, A. Vadell, F. Ballesteros, G. Galietta and D. Cozzolino, Animal Sci. 77, 111(2006).

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[11] L. Locsmándi, G. Kövér, G. Bázár, A. Szabó and R. Romvári, Acta Alimentaria 35, 455(2006). [12] N. Prieto, S. Andrés, F.J. Giráldez, A.R. Mantecón and P. Lavín, Meat Sci.74, 487(2006). [13] B. Sevenije, G. H. Geesink, J. G. P. van der Palen and G. Hemke, Meat Sci. 73, 181(2006). [14] M. Uddin, E. Okazaki, H. Fukushima, S. Turza, Y. Yumiko and Y. Fukuda, Food Chem. 96, 491(2006). [15] P. Berzaghi, A. Dalle Zotte, L. M. Jansson and I. Andrighetto, Poultry Sci. 84, 128(2005). [16] A.H. Hoving-Bolink, H.W. Vedder, J.W.M. Merks, W.J. H. de Klein, H.G.M. Reimert, R. Frankhuizen, W.H.A.M.van den Broek and en E. Lambooij, Meat Sci. 69, 417(2005). [17] I.T. Kadim, O. Mahgoub, W. Al-Marzooqi and K. Annamalai. Asian-Australian J. Animal Sci. 18, 1036(2005). [18] R.M. McDevitt, A.J. Gavin, S. Andres and I. Murray, J. Near Infrared Spectro. 13, 109(2005). [19] M. Prevolnik, M. Čandek-Potokar, D. Škorjanc, Š. Velikonja-Bolta, M. Škrlep, T. Znidaršič and D. Babnik. J. Near Infrared Spectroscopy 13, 77(2005). [20] M. Viljoen, L. C. Hoffman and T. S. Brand, Meat Sci. 69, 255(2005). [21] G. Xiccato, A. Trocino, F. Tulli and E. Tibaldi, Food Chem. 86, 275( 2004). [22] D. Alomar, C. Gallo, M. Castaneda and R. Fuchslocher, Meat Sci. 63, 441( 2003). [23] N.M. Anderson and P N. Walker, Trans. ASAE 46, 117 (2003). [24] G.H.Geesink, F.H. Schreutelkamp, R. Frankhuizen, H.W. Vedder, N.M. Faber, R.W. Kranen and M.A. Gerritzen. Meat Sci. 65, 661( 2003). [25] G. Togerson, J. F. Arnesen, B. N. Nilsen and K. I. Hildrum, Meat Sci. 63, 515 (2003). [26] W. R.Windham, K.C. Lawrence and P.W. Feldner, J. Appl. Poultry Res. 12, 69(2003). [27] D. E.Chan, P.N. Walker and E.W. Mills, Trans. ASABE 45, 1519(2002). [28] D. Cozzolino and I. Murray, J. Near Infrared Spectro. 10, 37 (2002). [29] D. Cozzolino, D. De Mattos, V. Martins, Animal Sci. 74, 477 (2002). [30] F. Abeni and G. Bergoglio, Meat Sci. 57, 133(2001). [31] J. Brondum, L. Munck, P. Hencke, A. Karlsson, E. Tornberg and S. B. Engelsen. Meat Sci. 55, 177(2000). [32] D. Cozzolino, I. Murray, J.R. Scaife and R. Paterson, Animal Sci. 70, 417(2000). [33] R. Rodbotten, B.N. Nilsen and K.I. Hildrum, Food Chem. 69,427(2000). [34] K.I. Hildrum, B.N. Nilsen, F. Westad and N.M. Wahlgren, J. Near Infrared Spectro. 12, 367(2004). [35] G. Togerson, T. Isaksson, B.N. Nilsen, E.A. Bakker and K.I. Hildrum, Meat Sci. 51, 97(1999). [36] N. Prieto, R. Roehe, P. Lavin, G. Batten and S. Andres. Meat Sci. 83, 175(2009). [37] P. Williams and K. Norris, Near Infrared Technology in the Agricultural and Food Industries (2001). .P.C. Williams, "Implementation of near-infrared technology", American Association of Cereal Chemists, St. Paul, Minnesota, pp 145–170. [38] M. Prevolnik, M. Čandek-Potokar, M. Novič and D. Škorjanc, Meat Sci. 83, 405 (2009).

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[39] M. O'Farrell, E. Lewis, C. Flanagan, W. B. Lyons and N. Jackman, Sensors and Actuators B: Chemical 107, 104 (2005). [40] B.W. Berry, A.E. Deithmers, T.A. Gillett, W.H. Marshall, W.G. Moody, D.G. Olson, B.A. Rainey and W.C. Schwartz, Proc. 56th Reciprocal Meat Conf. 36, 223(1995). [41] K.H. Esbensen, Multi-variate Data Analysis – In Practice,. CAMO AS, Oslo, Norway (2002). [42] SAS, SAS Version 9.3 Users’s Manual, SAS Institute, Cary, North Carolina (2009). [43] H. van de Vaterbeemd, Chemometric Methods in Molecular Design-Methods and Principles in Medicinal Chemistry (1994). S. Wold, " PLS for Multivariate Linear Modeling" Verlag-Chemie, Germany. pp195-218. [44] J. T. Heaton, Introduction to Neural Networks with Java, Heaton Research Inc., St. Louis, Missouri (2005). [45] Z.A. Obanu and D.A. Ledward, J. Food Technology 10, 675(1975). [46] K. Holownia, M.S. Chinnan, and A.E. Reynolds, J. Food Sci. 68, 742(2003). [47] P. Williams and K. Norris, Near Infrared Technology in the Agricultural and Food Industries, American Association of Cereal Chemists, St. Paul, Minnesota (2001). [48] P.C. William and D.C. Sobering, J. Near Infrared Spectro. 1, 25 (1993). [49] L.R. Schimleck, C. Mora and R.F. Daniels, Canadian J. Forest Res. 31,1671 (2003).

In: New Topics in Food Engineering Editor: Mariann A. Comeau

ISBN: 978-1-61209-599-8 © 2011 Nova Science Publishers, Inc.

Chapter 3

MOLECULAR SIZE DISTRIBUTION IN LONG-AGED FOOD BEVERAGES AND ALCOHOLIC DRINKS: A PRELIMINARY INQUIRY TOWARDS UNDERSTANDING PHYSICAL-AND SENSORYRELATED PROPERTIES Pasquale Massimiliano Falcone* and Paolo Giudici Department of Agricultural and Food Science - University of Modena and Reggio Emilia Kennedy, 17 - 42100 Reggio Emilia, Italy

ABSTRACT The present study supports the idea that physical- and sensory-related properties of long-aged beverages and alcoholic drinks containing reducing sugars would be described not by an unique value rather as a distribution of values due to the time-dependent increase of molecular heterogeneity in molecular sizes and structure. A wide range of beverages and alcoholic drinks obtained after different aging periods at room temperature were fractioned by Size-Exclusion Chromatography (SEC), then the elution profiles were analyzed by using a chemical-groups sensitive detector, i.e. an ultraviolet-visible (UVVIS), coupled to a mass-sensitive detector. i.e. a differential refractive device (DRI). The analysis of the probability density function as well as of the cumulative density function allowed comparing the distribution properties over a wide range among the investigated samples. This is because, unlike small molecules, such liquid matrices undergo accumulation of high molecular size biopolymers (melanoidins) throughout the aging period. In general, results proved that all the investigated matrices would be defined as heterogeneous mixtures of chromophore-labeled copolymers, uncolored and brown, *

Corresponding author: Complete Name: Pasquale Massimiliano Falcone Affiliation: Department of Agricultural and Food Science - University of Modena and Reggio Emilia Address: J. F. Kennedy, 17 – 42100 Reggio Emilia, Italy Phone: +39 522 522 057 Fax: +39 522 522 027 E-mail address: [email protected]

58

Pasquale Massimiliano Falcone and Paolo Giudici highly polydispersed with respect to their molecular size (ranging between 0.2kDa to over 2000kDa) and their chemical structure. In particular, the molecular size distribution of the end-products was attributed to the raw materials used for their production; while, the relative content of the biopolymers is strictly related to the extent of the thermal treatment applied along to the making process (when it is applied) as well as to the length of the storage time at room temperature.

Keywords: Aging, food beverages, alcoholic drinks, reducing sugars, biopolymers, distribution properties.

INTRODUCTION It is long time known that reducing-sugars containing foods when heated undergo to a complex series of chemical reactions, the so-called 'non-enzymatic browning' (NEB) including Maillard and caramellization reactions both leading to the formation of polymeric compounds affecting food quality (1). Many studies on model solutions showed that, due to high reactivity of the intermediates, complex dehydration, condensation and radicalic reactions can take place, resulting in a wide variety of products, whose functionalities strictly depend on the source of reactants and reaction conditions, e.g. medium composition, temperature, pH, water activity and other factors. Once they start, NEB reactions continue at room temperature. In a study conducted on long-aged vinegars from highly-concentrated grape must, polymerization and depolymerization reactions were proved to occur throughout the aging period leading to the accumulation mainly of uncolored biopolymers with highpolydispersity with respect to their composition and size in solution (2). Melanoidins may have effects on the human health and in end-quality of widely consumed dietary goods (e.g., coffee, cocoa, bakery products, malt and honey) thanks to their antioxidant properties (3, 4, 5, 6, 7), anti-microbial activity (6), anti-hypertensive properties (7), prebiotic activities (8), browning properties (9, 10), foam stability (11). Sometimes, melanoidins may show adverse effect on the human health because of their ability to contain potentially mutagenic compounds or to bind nutritionally important metals (12) and flavor compounds (13). In various studies as reported by Friedman (14), food melanoidins were proved to decrease food digestibility, to destroy or inactivate amino acids, including essential amino acids like lysine and tryptophan, inhibition of proteolytic and glycolitic enzymes, and interaction with metal ions. Authors (15) identified mutagenic compounds in instant and caffeine-free coffee including dicarbonyl compounds such as methylglyoxal, diacetyl and glyoxal, among which the methylglyoxal presented highest mutagenic activity; however, no quantitative correlation with carcinogenic properties was found. All these functionalities are derived from the fact that both the specific composition and structures arising in melanoidins formation is sufficiently diverse to have complex functional behavior, as reported by Ames, 2000. As widely proved for synthetic polymers, it is reasonable to hypothesize that all naturally occurring biopolymers such as polysaccharides, cellulosics as well as glycoproteins, tannins, and melanoidins may have more than one properties distributions, i.e. macromolecules of differing, each with a finite concentration, in their chemical composition, molecular weight, molecular size, branching, density, melt properties, specific heat capacity, viscosity, etc., all

Molecular Size Distribution in Long-aged Food Beverages and Alcoholic …

59

affecting the end-properties. In other words, we believe that, unlike small molecules, chemical and physical properties of biopolymers in foods are not unique rather a distribution of the properties, which in turn are expected to determine the wide spread of the endfunctionalities. Decoupling these biopolymers into discrete distributions and studying how and what macromolecules fraction may affect a target of desired and undesired endfunctionalities could be a promising approach to understand the molecular basis of biopolymer formation in foods and, therefore, predict the end-functionalities of food products as a function of raw materials, making process and storage conditions. Unfortunately, the fractioning of naturally occurring biopolymers from food matrices may be very difficult: with respect to melanoidins, for example, the main chemical and biological studies on their endfunctionalities have performed on food model systems. SEC is the method of choice for characterizing molecular size distributions of polymers in solutions. A first-attempt fractioning and characterizing by SEC the molecular size distribution of biopolymers from food origin was recently presented (2) about an Italian longaged sauce containing reducing sugars, i.e. the Traditional Balsamic Vinegar. Such viscous

liquids undergo sugar degradation reactions leading to the formation of biopolymers higly polydispersed in their molecular size and structure due to the specific making procedure and to the long-time requie for their ripening (32). To date, however, neither SEC nor other techniques have been used to study the distribution properties for biopolymers occurring in other food beverages or alcoholic drinks. As far as the food melanoidins are concerned, works reported in literature often proposed fractioning methodologies based on dual-grouping criterion leading to the high molecular weights (HMW) and low molecular weights (LMW) fractions according to the nominal cut-off value of dialysis systems or centrifugation used for their recovery from real foods. However, all methods used to study melanoidins do not yield information neither on how molecular properties are distributed with respect to their molecular size. This may be a major cause of the apparent adverse results reported in literature in terms of their mechanism of formation as well as of the end-properties of melanoidins. On the other hand, results obtained in buffers or synthetic media cannot always be extrapolated and applied to real foods, because various properties from more complex mixture may also combine synergistically to affect the food properties in a fashion that the individual properties distributions would not. All emphasize the importance of experiencing more in-depth the properties distribution of such biopolymers directly in real foods. The present work support the idea that relationships between sensory- and aging-related properties in a food rely on the distribution of a combination of chemical and physical properties, e.g. density, melt properties, specific heat capacity and viscosity, with respect to the molecular sizes characterizing raw materials or varied during making process and storage. The aim of this work was so double. In a wide range of food beverages and alcoholic drinks containing reducing-sugars, firstly the ability of SEC technique coupling UV-VIS to DRI detectors to fraction samples based on discrete distribution of molecular weights was first evaluated. Secondly, using the statistical approach usually followed in the polymer analysis was assessed to describe distribution properties with a view to their dependence on the starting material or aging.

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MATERIALS AND METHODS Sample Retrieval and Preparation A wide range of food beverages and alcoholic drinks contain reducing sugars naturally occurring or added during preparation was selected to be analyzed with respect to the starting materials or aging, i.e. Coffee extract, Tea Infusion, Draught Beer, Eucalyptus Honey, Chestnut Honey, Balsamic Vinegar, Aromatic Zhenjiang, Traditional Balsamic Vinegar, Aged Walnut Alcoholic Infusion, and Aged brandy. In addition, Aged Red Wine and White Wine were analyzed also, for which producers claim the absence of reducing sugars due to the quantitative bioconversion of the glucose and fructose to ethanol by fermentation of grape must (fructose in solution is in isomeric equilibrium with glucose). Draught Beer, White Wine, Balsamic Vinegar and Aromatic Zhenjiang were retrieved on the global market. Coffee and Tea, also retrieved on local market, consisted of blends from different plant species and they were undergo to the usual in-home procedures for the liquid coffee extraction by moka and tea infusion in hot water, respectively. Both Coffee extracts and Tea Infusions were sweetened adding the same amount of sucrose. With the purpose to evaluate the impact of the raw materials on the distribution properties, two kind of honey, i.e. Eucalyptus Honey and Chestnut Honey, were retrieved by a local producer which followed the same making procedure conditions, including honey bees and pasteurization treatment, i.e. heating at 80°C for 2 minutes, but using Eucalyptus’s or Chestnut’s flowers as starting material. With the purpose to evaluate the effect of aging alone on the distribution properties, two kind of aging procedure were accounted for, i.e. that semi-continuous followed to the Traditional Balsamic Vinegar production and the static one used for Aged Walnut infusions. As far as the Traditional Balsamic Vinegar is concerned, the actual age is the result of simultaneous microbial transformations and traditional semi-continuous refilling processes followed according to the specific law (16) during the first years of production and of the result of the refilling practice alone in the successive years of aging. Traditional Balsamic Vinegar with different age were withdrawn from a set of five wood barrels and then analyzed: the precise age of the samples was calculated according to the numerical procedure appointed by Giudici and Rinaldi (17). Walnut Infusions was produced by alcoholic infusion from unripened walnuts and then aged in a closed glass vessel at room temperature. Walnut Infusions was chosen together with Aged Red Wine, Aged Brandy for its high content in phenolics. All aged food beverages were long-term stored, until to about 30 years. All samples were investigated in triplicate as received.

Size-Exclusion Chromatography Due to the lack of knowledge about the distribution broadness of molecular sizes in the investigated foods, gel filtration chromatography was performed against water for a preliminary investigation (data not showed) using TSK-gel GMPWXL alone, i.e. a mixed-bed polymer-based column (30cm length, 7.8mm inner diameter, 13μm particle size, <100-1000Å pore size) expected to separate soluble polymers of molecular sizes in solution spreading

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from 50 to 8000kDa. Thereafter, a series of this column with a TSK-gel G3000PWXL (30cm length, 7.8mm inner diameter, 6μm particle size, 200Å pore size), connected to a TSK-gel PWH guard-column in a temperature controlled oven, was used to optimize the resolution within the observed range of hydrodynamic volumes. HPLC-grade water was used as eluent after 0.22μm filtering throughout Millipore cellulose-nitrate filers and ultrasound degassing. To avoid effects of polymer viscosity, the lowest detectable sample concentration was preferred, consequently, the sample preparation for the injection was obtained as follows: a weighted amount of samples of about 1g was diluted in 10ml of HPLC-grade water, then it was 0.22μm filtered with Millipore cellulose nitrate filers and degassed before use. no degradation was evident under the experimental conditions To ensure data reproducibility, acetone (0.03 vol %) was added to all samples as flow marker (35.5 minutes, negative peak).The prepared samples were then injected under 3 hours to avoid degradations. The optimum of fractionation conditions was experienced with an oven temperature, column pressure and isocratic flow rate of 30°C, 4Mpa, and 0.7mL/min, respectively. A Rheodyne volumetric valve injecting a volume of 20μl of sample, was used to start each gel filtration experiment with automatic zeroing for baseline signals. The pH of each investigated samples was determined before and after their aqueous dilution using a digital pH-meter (mod. 8417, Hanna Instrument, Padova, Italy). The observation of the fact that the pH did not changed significantly with respect to the initial value, likely due to the high content in organic acids having buffer capacity, allowed to suppose that all ionic links inter- and intra-macromolecules did was not modified with respect to those occurring into the original samples.

COUPLING OF UV-VIS AND DRI DETECTORS An intelligent UV detector (mod. UV-2070 Plus, Jasco Corporation, Tokyo, Japan) and a detector for differential refractive index (mod. DRI-2031Plus, Jasco Corporation, Tokyo, Japan) were used simultaneously in series as chemical chemical-group and mass concentration sensitive devices, respectively. In order to explore more than one chemical group, the UV detection was performed at two different wavelengths, i.e. 280 and 420 nm. The ultraviolet spectrophotorneter detector is limited to samples containing chromophores that will absorb in the UV region. The advantage of the DR1 detector rely on the fact that it offers a linear response over a wide range of concentrations with a relatively low sensitivity around 10-5 - 10-6g/mL (18) and for this reason it is widely considered a universal concentration detector.

Calibration Procedure Because of the fact that is to date unknown the structure of biopolymers within the investigated food products, which could arise from degradation reactions of phenolics and/or reducing sugars, including melanoidins, caramellization and other sugar degradation products, consistent standards simulating them were not available for HPLC calibration stage. Consequently, a secondary calibration based approach was carried out preferring standard

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mixtures of linear polyethilenglycole/polyethilenoxyde (PEG/PEO) polymers (Sigma Aldrich Production GmbH) due to their wide range of molecular weights, narrow molecular size distributions (polydispersity < 1.1) and the possibility to cover the entire permeation range of the used TSK-columns. A series of standard mixtures of ten PEG/PEO standards fractions were prepared overnight at ambient temperature and without agitation were injected into the HPLC system. Then logarithm of the peak molecular weight (MPEG/PEO), corresponding to the maximum of DRI response, versus peak retention volume (vi) data were fitted by a polynomial function (19):

Log [M PEG / PEO ,i ] = b + c ⋅ vi + d ⋅ (vi ) + f ⋅ (vi ) 2

3

(1)

where b, c, d, and f were treated as fitting parameters. Since gel permeation chromatography is governed by molecular size in solution, not molecular weight, as a function of both the molecule structure (linear, branched, star-like) and conformation (given by solvent and temperature) of the polymer to be separated, in this study, we decided to follow the "Universal Calibration" method trying to numerically correct the cubic curve used in the previous calibration step. An Universal Calibration is a plot of hydrodynamic volume Ji versus retention volume with the hydrodynamic volume defined by

J i = [η]i ⋅ M i

(2)

where [η]i is the intrinsic viscosity, Mi is the molecular weight at each retention volume. The solvent dependence (size in solution) for PEG/PEO linear polymers having narrow MWD (in our study PD<1.2) can be described by the empirical Mark-Houwink-Kuhn-Sakurada relationship (20)

[η]i = K ⋅ M ia

(3)

where, i.e. K and α are function of the polymer-solution interactions. Then, we calculated the molecular weight at each retention volume by:

J i = K ⋅ M ia +1

(4)

where Mi was obtained from the equation (1). In this work we used the Mark-Houwink parameters measured at 30°C by Cai (2004) for PEG/PEO aqueous solutions, i.e. 12.5mL/g and 0.78 for K and α, respectively. Again, we assumed that both the food polymers obey the Mark-Houwnik relationship, then the universal calibration curve from the investigated sample was obtained from:

{

+1⎞ ⎛ 1 ⎞ ⎡K ⎤ ⎛a 2 3 Log [M i ] = ⎜ ⎟ ⋅ log⎢ PEG / PEO ⎥ + ⎜ PEG / PEO ⎟ ⋅ b + c ⋅ vi + d ⋅ (vi ) + f ⋅ (vi ) a +1 ⎠ ⎝ a +1⎠ ⎣ K ⎦ ⎝

}

(5)

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using a numerical optimization search procedure. The proposed approach is widely considered able to take into account the solvent dependence (size in solution) for molecules of the same size. The Universal calibration allowed us to account also for the fact that the standards used for calibration were different from the food polymers to be analyzed.

Data Collection and Analysis Raw SEC data were acquired by Jasco-Borwin software package v1.5 and then processed with an advanced chromatographic software (Clarity ver. 2.5.6.99; DataApex Ltd. 2007, Prague, Czech Republic). The best-fit cubic function was obtained for the molecular weights calibration. After this curve was corrected by Mark-Howink parameters, it was then used to calculate for each investigated food the molecular weights as ‘PEG/PEO Linear Equivalent Molecular Weights’ (in the text referred as to LEMS) together with some statistics, e.g. Mn, Mw, Mv, Mz, and Mz1 (in the following referred as MWD averages) to characterize their distribution properties. The latter were calculated according to the classical approach used in polymer analysis, already applied with successful in the Traditional Balsamic Vinegar evaluation (2). These MWD averages were used to evaluate all differences in food properties distribution with a view to the physical and aging properties they are related to. In particular, Mn parameter is known to be sensitive to the presence of low molecular weight species and, therefore, to influence colligative properties, refractive index, density, and specific heat capacity of a sample. The Mw parameter is sensitive to the presence of the high molecular weight components and, therefore, influences properties that are affected by large chains such as melt and solution viscosity. Mz and Mz1 parameters are sensitive to the presence of very high molecular weight (at the peak tail) and, therefore, influence viscoelastic properties (timedependent mechanical properties) of the sample. The Mv parameter is sensitive to the presence of constituents with high intrinsic viscosity affecting the average viscosity of the sample. Finally, for each main discrete peak within the DRI profile, a Polydispersity Index, PDI[DRI], was calculated from SEC data as ratio between corresponding Mw to Mn values and then it was used to describe the heterogeneity degree (distribution) of molecular size for that peak. PDI is known to be sensitive to the mechanism of polymer formation (generally, for single MW polymers, PDI) 1; for condensation polymers, PDI ) 2; and for polymers from free radical polymerization, PDI > 2). Sampling and analysis were performed in triplicate. Significant differences between samples were analyzed at 99% of confidence limit (p < 0.01) by using Statistica for Windows software package ver. 7.0 (StatSoft; USA, Tulas).

RESULTS AND DISCUSSION Food samples were analyzed for the distribution properties both of the composition and molecular weights as revealed by SEC data. The basic idea was that, unlike small molecules, the molecular weight of polymeric compounds in food products, including melanoidins, is not unique rather a distribution of molecular weights, which in turn are expected to determine the

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wide spread of the end-functionalities. In particular, ultraviolet profiles were treated as a fingerprinting of the composition of 280nm-sensitive structures, e.g. conjugated double bonds, furan ring-containing compounds; visible profiles as fingerprinting of the composition of 420nm-sensitive structures such as brown polymers, enaminol intermediate products, lowmolecular-weight sugar analogues, unsaturated carbonyl products); finally, DRI profiles were used as a fingerprinting of the total distribution profile of sample composition. To make easy the discussion of results, in the next part of paper, we described in a detailed way the SEC profiles as detected by UV-VIS and DRI detectors corresponding to one kind of the investigated foods, i.e. Eucalyptus Honey; while, for the other food beverages, we limited the discussion on the distributions properties of their molecular weights with a view to the effect of starting raw materials, making process or aging.

SEC Profiles and Distribution Properties In Figure 1 the three SEC-profiles corresponding to the Chestnut Honey, as detected by UV-VIS detector at 280nm, 420nm and by DRI detector were represented. In this figure, the cubic curve used to LEMW calibrations after Mark-Howink correction was showed also. The multi-modality of SEC profiles represented in Figure 1 indicated the presence in the Chestnut Honey of some class, broadly or narrowly dispersed, of chromophore-labeled constituents with LEMW spreading from bioligomer weights of about 200Da to biopolymer weights with a great extent beyond 2000kDa. The range of molecular weights of the standards used for calibration limited the possibility to evaluate the precise upper limits for the molecular weights beyond 2000kDa. By subtracting numerically the 420nm-profile from 280nm-profile one can easily recognize the presence of four main discrete peaks. As can be observed in the figure, two narrow peaks were detected around 22 and 26 minutes of elution, both detected at 280nm and 420nm; while a broad peak was detected at 280nm around 29 minutes but not at 420nm. As can be inferred from Figure 1, again, was that the DRI detector provided a profile practically superimposable to that from UV-VIS ones. This finding reflected a difference in sample composition with respect to the molecular size in solution. With LEMW increasing, there was a comparatively higher signal intensity as detected at 420nm, a much more higher 280nm response, and a featureless DRI response. It was reasonably hypothesized that the polymer concentration decreased with the polymer weight increasing. At high elution time, LEMW decreasing, the situation was reversed: DRI detection was much more sensitive to trace the presence of low and medium molecular weights. LEMW determines the distribution of structural features, e.g. conjugated chromophore, into the molecular structure of constituents. A relative measure of the composition heterogeneity of the honey was indicated by the value ranging from 1.33 to 33.6 of PDI[DRI], calculate as ratio between Mw and Mn from DRI data. These data include those found in literature (21), who measured, in an water/ethanolic extract of several flower honey in which monosaccharides were previously removed, a polymerization degree ranging from 3 to 14. The presence of monosaccharides in the our sample may have determined the greater upper limit. Fractionation by size exclusion chromatography allowed us to prove that chromophore functional groups are covalently grafted to the macromolecular backbone rather than being merely trapped in the polymer matrix and, therefore, to recognized the investigated honey as a compositionally

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heterogeneous blend of chromophore-labeled copolymers together with other solutes having lower molecular weight. These biopolymers were uncolored and brown with molecular weights falling randomly between 0.2kDa to over 2000kDa, and highly polydispersed with respect to their composition and chemical structure. The other investigated food samples showed a similar chromatographic behavior. A comparative example was represented in Figure 2 showing the three SEC profiles corresponding to both the Chestnut Honey and Eucalyptus Honey as detected by UV-VIS detector at 280nm (a), 420nm (b) and by DRI detector (c).

Figure 1. Overlay of the SEC profiles corresponding to the Eucalyptus honey revealing the distribution of 280nm- and 420nm-sensitive functional groups (in the figure referred as red and blue lines respectively) and the overall composition distribution within honey sample as detected DRI device (black line). The cubic line represent the best fit function used for the molecular weights calibration. This curve was firstly corrected by Mark-Howink parameters was then used to calculate the sample molecular weights as ‘PEG/PEO Linear Equivalent Molecular Weights’ (data represented on the right axis of the plot). Vertical lines at 16.5min and 32 min indicate the integration limits used in determining MWD averages (see the Material and Methods section).

An attempt to discuss the fact that Honey samples should or not include melanoidins, phenolics and sugars degradation products within constituent microstructure was made analyzing simultaneously the ultraviolet and visible SEC profiles. Two narrow peaks were detected around 22 and 26 minutes of elution, both detected at 280nm and 420nm and a broad

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peak was detected alone at 280nm. This picture of honey composition lead us to recognize a role of phenolics comparable with that of sugar degradation products in determining the distribution properties of molecular sizes.

Figure 2. Chromatographic behavior of the Chestnut Honey (dotted line) and Eucalyptus Honey (bold line) as detected by UV-VIS spectrophotometer at 280nm (a) and 420nm (b) and by DRI detector reflecting changes in the differential refractive index (c).

On the one hand, in fact, the Maillard reaction is a complicated reaction that produces a large number of the so-called Maillard reaction products (MRPs) such as aroma compounds, ultra-

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violet absorbing intermediates, and dark-brown polymeric melanoidins (23). On the another hand, several phenolic acid and flavonoid, all absorbing ultraviolet radiation, were detected in honey from various floral sources (24). On the one hand, this finding was according to the fact that sugar degradation reactions were expected in both the honey samples due to the initial concentration of the reducing sugars (over 65% as glucose and fructose) and applied thermal treatment. Studies proved that heat-exposure of honey after to eliminate spoilage yeasts and deactivate polyphenol oxydase enzyme, results in sugar degradation, leading the level of sugar degradation products to increase (23), and browning to progress (26). On the other hand, an hyper-conjugation among double-bond systems may occur within backbone structure of biopolymers with high molecular weight resulting into the bathocromic shift from ultraviolet to visible field of radiation.

Molecular Weights Distribution (MWD) Analysis Since all molecules can affect in a way the refractive index, only DRI data were statistically treated to investigate the distribution properties of the overall molecular weights. With this aim, after the inter-detector delay was corrected, the MWD curves, Cumulative MWD curves and MW averages, i.e. Mn, Mw, Mv, Mz, and Mz1 were obtained and then analyzed. However, a disadvantage deriving from using the DRI as mass detector rely on the fact that its response is a function of the refractive index changes with a change in polymer concentration, i.e. the specific refractive index increment (dn/dc). The latter must be determined before a quantitative analysis: for the mass concentration (C) there is the relationship C = W /[b·(dn/dc)], where W is the DRI response. Moreover, when two polymer types co-elute at one particular retention time, each with different dn/dc values, the detector response will reflect the composition of the molecules as well as their total concentration. In our study, we assumed dn/dc constant across the DRI chromatograms but varying with respect the molecular weights: DRI data from successive dilutions of each investigated sample increased in a proportional way to the dilution factor (data not showed). The approach followed to compare the distribution properties among food samples were based on the integration of multiple peaks across DRI profiles as a single one within the calibration limits. In this way it was possible to get and compare a single MWD curve and a corresponding cumulative MWD curve among the samples. Figure 3 shows the MWD curve (a) of the Chestnut Honey together with its corresponding Cumulative MWD curve (b). MWD curve indicates the frequency of sample constituents within a given range of molecular weights; Cumulative MWD curve indicating the relative concentration of sample constituents falling under to a given level of molecular weight. As can be inferred from these curves, the molecular weights spread according to a log-normal function, indicating a great role of the constituents with higher molecular weights to the sample composition: cumulative curve showed the composition to include for the 30% HMW constituents. This finding support the idea that, as proved in other food products, phenolic constituents with high molecular weight play a very important role together with the sugar degradation products to determine the widely known spectrum of honey functionalities.

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Figure 3. MWD curve (a) obtained by integrating the whole DRI chromatogram as a single peak and the corresponding Cumulative MWD curve (b) relative to the Eucalyptus Honey. Both curves show a bi-modal profile indicating a role of two main distribution properties.

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Impact of Raw Material/Making Procedure on Properties Distribution Both the honey samples were prepared following the same making procedure conditions, including the honey bees and pasteurization treatment, i.e. heating at 80°C for 2 minutes, but using Eucalyptus or Chestnut flowers as starting material. As a consequence, all differences in terms of MW distribution as well as of MW averages were attributed only to the starting materials used to produce both the honey. It was showed that the source of honey determines many of the attributes of honey, e.g., aroma, flavor, color and composition (23). In Figure 4 the MW distribution curves for the Eucalyptus and Chestnut Honey were reported. These curves were calculated by integrating the corresponding DRI profiles considering each as a single peak. As can be observed from figure, a mono-modal distribution was obtained. In the same figure the MW distribution curve for the Aged Red Wine was represented also. It was well evident that was the integration method used to build the MWD curve was effectively to represent as the starting material used to produce food sample strongly impact the heterogeneities within the MW distribution, especially when one compare the honey with the red wine distribution properties. Both the honey samples have similar but quantitatively different profiles, with chestnut honey showing higher fractions of high molecular weights. On the contrary, more qualitative differences between the honey and red wine profiles can be observed and attributed to the phenolics arising from different sources, i.e. floral and grapes, respectively. Red wine showed a bimodal profile with the highest molecular weight biopolymers fraction, i.e. more than 20% of 90kDa compounds: recently, the fraction of polymeric phenolic compounds in red wine was proved to play the main role in its total radical-scavenging activity as observed by Fernández-Pachón ( 2004). Three kinds of vinegars were compared with respect to their distribution properties analyzing the MWD curves obtained following the one-step integration procedure. Figure 5 shows the MW distribution curves for Balsamic Vinegar, Traditional Balsamic Vinegar, two wine vinegars and Seasoned Aromatic Zhenjiang a rice vinegar. As known, wine vinegars derive both from grape cooked must but following a specific making procedure including different aging conditions. As can be inferred from Figure 5a, wine vinegars were characterized practically by different distribution properties with respect to their molecular weights. Two ranges of molecular weights were observed falling between 100Da to 10kDa and from 10kDa to a great extend beyond 2000kDa representing HMW and LMW fractions, respectively. Table 1 lists the corresponding distribution properties, i.e. MWD averages. were very different between the two samples. PDI data indicated that the broadness of molecular weights distribution followed the order: Traditional Balsamic Vinegar > Aromatic Zhenjiang > Balsamic Vinegar. Figure 5b showed great differences between the wine vinegars, resulting in the greatest relative concentration of macromolecules under 1000Da for the sample of the Traditional Balsamic Vinegar with respect to that relative to the Balsamic Vinegar, beyond 60% and 20% respectively. Aromatic Zhenjiang was composed of about the 30% of molecular weights under 60kDa. For all three samples, it was evident that with the molecular weights increasing the composition differences among samples vanished. These findings suggested that the polymerization reactions follow different mechanisms among samples. These differences were attributed to the fact that a cereal, i.e. the rice, was used on the behalf of the cooked grape must to produce this kind of vinegar.

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Figure 4. MWD curve (a) obtained by integrating the whole DRI chromatogram as a single peak and the corresponding Cumulative MWD curve (b) relative to the Eucalyptus Honey (yellow line), Chestnut Honey (red line), and Aged Red Wine (green line). The latter only showed a bi-modal profile.

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Figure 5. MWD curve (a) obtained by integrating the whole DRI chromatogram as a single peak and the corresponding Cumulative MWD curve (b) relative to the 14-years old sample of Traditional Balsamic Vinegar (black line), Balsamic Vinegar (red line), and Aromatic Zhenjiang (yellow line). All samples showed a bi-modal profile but with very different distribution properties for molecular weights.

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Table 1. Distribution parameters of LEMS relative to representative samples for two kind of wine vinegars (Balsamic Vinegar and Traditional Balsamic Vinegar) and a rice vinegar (Aromatic Zhenjiang). The DRI profiles was arbitrarily integrated as a single peak Peak (*) DRI profile Aromatic Zhenjiang as one peak DRI profile Bals. Vinegar as one peak T. Bals. Vinegar DRI profile (3-year old) as one peak

Mn

Mw

Mz

Mz1

Mv

PDI

Area [%]

1541 7967

76122

705860

7140

51,69

100,00

1137 3345

54346

503488

2724

29,41

100,00

1365 9807

143963

637565

7297

71,86

100,00

(°) Integration limits ranged within the calibration curve.

Figure 6 shows the MWD curves (a) and Cumulative MWD curves (b) for representative samples for each investigated food beverages and alcoholic drinks. In a comparative perspective, the Cumulative MWD curves offered a useful semi-quantitative picture of sample composition, indicating the relative concentration of food constituents falling under a given molecular weight. The distribution properties, i.e. MWD averages were listed in Table 2. Data from Figure 6 and Table 2 support the idea that polymerization reactions took place in all these samples, resulting in broad (PDI>1.2) and narrow (PDI<1.2) distributions of molecular weights. This polydispersity is expected to affect the end-physical properties. All functionalities are reasonably derived from the fact that both the composition and structures heterogeneities arising in biopolymer formation is sufficiently diverse to have complex functional behavior. We believe that the determination of the macromolecules distribution properties, together with the classic composition information on small molecules, will help in the future to better understand the molecular basis of the food in terms of its chemical, physical and sensory behavior on macroscopic scale.

Impact of Aging Time on Properties Distribution Figure 7 shows the MW distribution profiles (a) together with the corresponding Cumulative MWD curve (b) for Walnut Infusions both the 30-years and 16-years old samples. MWD averages listed in What worth to note is that, the extent of aging affected mainly the area under MWD curves, and therefore, the relative concentration of samples constituents with respect to a given molecular weight. Figure 7a showed that the relative concentration of lower molecular weights increased with age. Table 3, in fact, indicated with sample age that PDI and Mw increased while Mn, Mz and Mz1 decreased. Figure 8 shows the MW distribution profiles (a) together with the corresponding Cumulative MWD curve (b) for Traditional Balsamic Vinegars, the 14-years and 3-years old samples. Also in this case, the extent of aging affected mainly the area under MWD curves, and therefore, the relative concentration of samples constituents, while the MW distribution was the practically same spreading essentially from 10Da to 10kDa.

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Figure 6. MWD curve (a) obtained by integrating the whole DRI chromatogram as a single peak and the corresponding Cumulative MWD curve (b) relative to representative samples for each investigated food beverages and alcoholic drinks.

Table 2. Distribution parameters of LEMS relative to representative samples for each investigated food beverages and alcoholic drinks. The whole DRI profiles was arbitrarily divided into two groups of compounds: the first eluting was referred as to High Molecular Size (HMS) and the last eluting was referred as to Low Molecular Size (LMS)

Eucalyptus Honey Coffee Extract Tea Inf. Aged Brandy Aged Red Wine Aged Walnut Inf. (§) White Wine T. Bals. Vinegar (14-y. old) Bals. Vinegar Aromatic Zhenjiang

Peak (*) HMW LMW HMW LMW HMW LMW HMW LMW HMW LMW HMW LMW HMW LMW HMW LMW HMW LMW HMW LMW

Mn

Mw

Mz

Mz1

Mv

PDI

4.669E+04 0.136E+04 6.214E+04 0.134E+04 4.018E+04 0.044E+04 2.932E+04 0.123E+04 1.854E+04 0.039E+04 5.233E+04 0.093E+04 4.018E+04 0.044E+04 2.191E+04 0.122E+04 2.239E+04 0.146E+04 4.018E+04 0.044E+04

6.152E+04 0.193E+04 9.174E+04 0.232E+04 6.473E+04 0.128E+04 4.080E+04 0.191E+04 5.544E+04 0.123E+04 8.602E+04 0.123E+04 6.473E+04 0.128E+04 4.856E+04 0.139E+04 2.828E+04 0.195E+04 6.473E+04 0.128E+04

10.25E+04 0.248E+04 18.14E+04 0.302E+04 12.30E+04 0.255E+04 8.202E+04 0.249E+04 193.9E+04 0.366E+04 170.1E+04 0.015E+04 12.30E+04 0.255E+04 37.70E+04 0.155E+04 5.844E+04 0.290E+04 12.30E+04 0.255E+04

19.02E+04 0.299E+04 38.12E+04 0.342E+04 19.76E+04 0.352E+04 19.00E+04 0.292E+04 761.6E+04 0.574E+04 28.10E+04 0.175E+04 19.76E+04 0.352E+04 154.2E+04 0.172E+04 17.51E+04 0.508E+04 19.76E+04 0.352E+04

5.795E+04 0.185E+04 8.447E+04 0.219E+04 5.887E+04 0.113E+04 3.777E+04 0.182E+04 3.563E+04 0.104E+04 7.775E+04 0.118E+04 5.887E+04 0.113E+04 3.886E+04 0.137E+04 2.654E+04 0.186E+04 5.887E+04 0.113E+04

1.32 1.42 1.47 1.73 1.61 2.91 1.39 1.55 2.99 3.17 1.64 1.32 1.61 2.91 2.21 1.14 1.26 1.33 1.61 2.91

Area % 0.65 99.35 37.00 63.00 30.17 69.83 5.20 94.80 30.24 69.76 0.62 99.38 30.17 69.83 19.12 80.88 3.18 98.82 30.17 69.83

(*) Integration limits fall within the calibration range across the DRI chromatograms and they where between 16.5 minutes to 25 minutes for HMW fractions, and between 25 minutes to 31 minutes for LMW fractions (§) Samples aged for 16-years

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Figure 7. MWD curve (a) obtained by integrating the whole DRI chromatogram as a single peak and the corresponding Cumulative MWD curve (b) relative to the 16-years old (red line) and 30-years old (yellow) samples of Walnut Infusion. All samples showed a bi-modal profile but with very different distribution properties for molecular weights.

Table 3. Distribution parameters of LEMW for the two sample of Walnut alcoholic infusions with different age. The DRI profiles was arbitrarily integrated as a single peak Peak (*) Aged Walnut Inf. (16 years old) Aged Walnut Inf. (30 years old)

DRI profi le DRI profi le

Mn

Mw

Mz

Mz1

Mv

PDI

Area [%]

37 0

23 86

1174 76

4314 20

16 69

64,4 4

100, 00

21 8

27 42

1064 89

4257 61

19 38

125, 92

100, 00

(*) The integration limits fall within the calibration range.

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As far as the wine vinegar were concerned, polymerization reactions were observed as heatinduced reactions during cooking of grape musts (27) destined to the production of aged vinegar. Once they start, polymerization reactions continue to extent throughout the storage, incorporating progressively lower molecular weight into the backbone structure of biopolymers (Figure 8b). This finding was supported by the evidence that the viscosity of Traditional Balsamic Vinegar increases with aging (28) so that it is reasonable to hypothesize that LMW intermediate products from sugar degradation reactions are incorporated as formed. Table 4 lists the MW averages calculated for samples of Traditional Balsamic Vinegar with different age, i.e. 3-years, 6-years, 9-years and 14-years old: PDI decreased with aging indicating the decreasing of the ratio between high molecular weight components (as measured by Mw) and low molecular weight species (as measured by Mn).

Figure 8. MWD curve (a) obtained by integrating the whole DRI chromatogram as a single peak and the corresponding Cumulative MWD curve (b) relative to 3-years old and 14-years old samples of Traditional Balsamic Vinegar.

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Table 4. Distribution parameters of LEMW for the two samples of Traditional Balsamic Vinegar with different age. The DRI profiles was arbitrarily integrated as a single peak T. Bals. Vinegar

Peak (*)

Whole SEC profile Whole SEC 6-years profile Whole SEC 9-years profile Whole SEC 14-years profile 3-years

Mn

Mw

Mz

Mz1

Mv

PD

Area [%]

2.110E+02

1.572E+04 2.004E+06 5.144E+06 8.695E+03 74.40 100

2.170E+02

1.298E+04 1.742E+06 5.102E+06 7.550E+03 59.68 100

2.890E+02

0.604E+04 0.020E+06 0.048E+06 4.975E+03 20.93 100

2.280E+02

0.739E+04 0.112E+06 0.575E+06 5.604E+03 32.38 100

(°) Integration limits ranged within the calibration curve.

The fact that the aging affected adversely the PDI of the Walnut Infusion samples with respect to the Traditional Balsamic Vinegar ones allow to suppose different equilibriums between polymerization and depolymerization reactions resulting in different distribution properties.

CONCLUSION The present work supports the idea that aging-related properties of beverages and alcoholic drinks containing reducing sugars strictly depend on the distribution properties of molecular size and structure (molecular heterogeneity). This means that macroscopic physical properties such as refractive index, melt temperature, specific heat capacity as well as sensory-related properties such as viscosity and density and ohers would be described not by an unique value rather as a distribution of values. This is because, unlike small molecules, such liquid matrices undergo accumulation of polydispersed biopolymers (melanoidins) throughout the aging period. We used the Gel filtration chromatography technique coupling a dual detection system, i.e. UV-VIS and DRI detectors to fraction samples and characterize their composition with respect to the molecular size and chemical structure. Results from the probability density function analysis allowed us to recognize all the investigated foods as heterogeneous mixtures of chromophore-labeled copolymers, uncolored and brown, highly polydispersed with respect to their molecular size (ranging between 0.2kDa to over 2000kDa) and their chemical structure. Decoupling of food constituents into discrete distributions based on their molecular size and the analysis of the molecular size distribution allowed us to explain different quality attributes often recognized to vary randomly and adversely among the investigated matrices. We believe that the future availability of food-optimized SEC procedures will allow breaking new grounds both in food science and trade, first supporting scientists towards the understanding molecular basis of quality properties, and then supporting producers to design rationally and maximize food end-functionalities.

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ABBREVIATIONS USED High Performance Liquid Size-Exclusion Chromatography (SEC), Differential refractive index (DRI), Ultraviolet/Visible (UV/VIS), non-enzymatic browning reactions (NEB), high molecular weights (HMW) or low molecular weights (LMW), polyethilenglycole/ polyethilenoxyde (PEG/PEO), PEG/PEO Linear Equivalent Molecular Size (LEMS); molecular weights distribution (MWD)

REFERENCES [1]

Ames, J.M. Nonenzymatic browning. In “Encyclopedia of Food Sciences and Nutrition”, Caballero, B., Trugo, L. and Finglas, P. (eds.) Academic Press, London, 2003, pp. 665-672 [2] Ames, J. M.; Caemmerer, B.; Velisek, J.; Cejpek, K.; Obretenov, T.; Cioroi, M. The nature of melanoidins and their investigation. In Melanoidins in Food and Health; Ames, J. M., Ed.; 2000; Vol. 1, pp 13−29 [3] Falcone, P.M., and Giudici, P. (2008). Molecular Size and Molecular Size Distribution Affecting Traditional Balsamic Vinegar Aging. J. Agric. Food Chem., 2008, 56 (16), pp 7057–7066 [4] Bekedam, E.K., Schols, H.A., Cammerer, B., Kroh, L.W., van Boekel, M.A. J. S. , and Smit, J. Electron Spin Resonance (ESR) Studies on the Formation of Roasting-Induced Antioxidative Structures in Coffee Brews at Different Degrees of Roast. J. Agric. Food Chem., 2008, 56 (12), pp 4597–4604 [5] Borrelli, R.C., Visconti, A., Mennella, C., Anese, M., Fogliano, V. Chemical Characterization and Antioxidant Properties of Coffee Melanoidins. J. Agric. Food Chem, 2002, 50, 6527–6533. [6] Manzocco, L.; Calligaris, S.; Mastrocola, D.; Nicoli, M.C.; and Lerici, C.R. Review of non-enzymatic browning and antioxidant capacity of processed foods. Trends Food Sci. Nutr., 2001, 11, 340–346. [7] Qingping, X.; Wenyi, T.; and Zonghua, A. Antioxidant activity of vinegar melanoidins. Food Chem., 2007, 102, 841–849. [8] Del Castillo, M. D., Ferrigno, A., Acampa, I., Borrelli, R. C., Olano, A., MartınezRodrıguez, A., et al. In vitro release of angiotensinconverting enzyme inhibitors, peroxyl-radical scavengers and antibacterial compounds by enzymatic hydrolysis of glycated gluten. J. Cereal Sci., doi:10.1016/j.jcs.2006, 09.005. [9] Rufián-Henares, J.A.R., and Morales, F.J. Functional properties of melanoidins: In vitro antioxidant, antimicrobial and antihypertensive activities. Food Res. Int., 2007, 40, 995–1002. [10] Borrelli, R. C.; and Fogliano, V. Bread crust melanoidins as potential prebiotic ingredients. Mol. Nutr. Food Res., 2005, 49, 673–678. [11] Rizzi, G. P. Chemical structure of coloured Maillard reactions products. Food Rev. Int., 1997, 13, 1-28.

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[12] Hofmann, T. Studies on the relationship between molecular weight and the color potency of fractions obtained by thermal treatment of glucose/amino acid and glucose/protein solutions by using ultracentifugation and color dilution techniques. J. Agric. Food Chem., 1998, 46, 3891–3895. [13] D’Agostina, A; Boschin, G.; Bacchini, F.; and Arnoldi, A. Investigations on the High Molecular Weight Foaming Fractions of Espresso Coffee. J. Agric. Food Chem., 2004, 52, 7118–7125. [14] O'Brian, J.; Morrisey, P. A. Nutritional and toxicological aspects of the Maillard browning reaction in foods. Crit. Rev. Food Sci., 1989, 28, 211–248. [15] Hofmann, T.; Czerny, M.; Calligaris, S.; and Schieberle, P. Model studies on the influence of coffee melanoidins on flavor volatiles of coffee beverages. J. Agric. Food Chem., 2001, 49, 2382–2386. [16] Friedman, M. (1996). Food browning and its prevention: An overview. J. Agric. Food Chem. 44, 631-653 [17] Nagao, M., Takahashi, Y., Yamanaka, H. and Sugimura, T. (1979). Mutagens in coffee and tea. Mutat. Res. 68, 101-106 [18] European Council Regulation (EC), No. 813/2000 (17 April 2000). Official Journal of European Communities of April 20, 2000. [19] Giudici, P., and Rinaldi, G. A theoretical model to predict the age of traditional balsamic vinegar. J. Food Eng., 2007, 82, 121–127. [20] S. Lindsay, High Performance Liquid Chromatography, 2nd ed., London, 1992. [21] S. T. Balke, Quantitative Column Liquid Chromatography: A stavey of Chemometric Method, Elsevier, Amsterdam, 1984. [22] Brandrup, J. and Irnmergut, E. H. Poler Handbook, 2nd ed. John Wiley, New York, 1975 [23] Morales, V. , Sanz, M. L., Olano, A., and Corzo, N.. (2006). Rapid Separation on Activated Charcoal of High Oligosaccharides in Honey. Chromatographia, 64(3-4): 16. [24] Verzelloni, E., Tagliazucchi, D., Conte, A.. Relationship between the antioxidant properties and the phenolic and flavonoid content in traditional balsamic vinegar, Food Chem. doi: 10.1016/j.foodchem.2007.04.014. [25] Fernández-Pachón, M.S., Villano, D., Garcia-Parrilla, M.C., and Troncoso, A.M. (2004). Antioxidant activity of wines and relation with their polyphenolic compounds. Anal. Chem. Acta, 513: 113-118 [26] Wijewickreme, A. N., Kitts, D. D., and Durance, T. D.. Reaction conditions influence the elementary composition and metal chelating affinity of nondialyzable model Maillard reaction products. J. Agric. Food Chem., 1997, 45, 4577–4583 [27] Nele Gheldof, Xiao-Hong Wang, and Nicki J. Engeseth. 2002. Identification and Quantification of Antioxidant Components of Honeys from Various Floral Sources J. Agric. Food Chem., 50 (21): 5870–5877 [28] White, J. W., Jr. 1992. Quality Evaluation of Honey: Role of HMF and Diastase Assays. Am. Bee J.. 132 (11 & 12): 737-743, 792-794

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[29] R. Subramanian a; H. Umesh Hebbar a; N. K. Rastogi (2007). Processing of Honey: A Review. Int. J. Food Prop. 10(1): 127 - 143. [30] Piva, A.; Di Mattia, C.; Neri, L.; Dimitri, G.; Chiarini, M.; and Sacchetti, G. Heatinduced chemical, physical and functional changes during grape must cooking. Food Chem. 2008, 106, 1057–1065. [31] Falcone, P.M.; Verzelloni, E.; Tagliazucchi, D.; Giudici, P. A rheological approach to the quantitative assessment of Traditional Balsamic Vinegar quality. J. Food Eng., 2007, 86, 433–443. [32] Giudici, P., Solieri, L., Falcone, P.M. (2009) Traditional Balsamic Vinegar. Advances in Food and Nutrition Research, Vol. 58.

In: New Topics in Food Engineering Editor: Mariann A. Comeau

ISBN: 978-1-61209-599-8 © 2011 Nova Science Publishers, Inc.

Chapter 4

HYPERSPECTRAL WAVEBAND SELECTION FOR DETECTION OF ALMONDS WITH INTERNAL DAMAGE Songyot Nakariyakul* Department of Electrical and Computer Engineering, Thammasat University 99 Moo 18 Phaholyothin Rd., Khlongluang, Pathumthani 12120, Thailand

ABSTRACT Detection of concealed damage in almonds is an important production inspection application. Internally damaged almonds are not easily distinguished from normal ones by their external appearances, and, when cooked, they taste bitter. Prior study showed that using the whole spectrum of hyperspectral data from 700-1400 nm could distinguish internally damaged nuts from normal ones at an error rates as low as 12.4%. However, the hyperspectral system is rather slow and cannot achieve an inspection rate of 40 nuts/s required by almond processing plants. Thus, a feature selection algorithm is needed to choose only a small subset of useful wavebands from hyperspectral data for use in a realtime multispectral camera. In this study, we introduce two novel feature selection techniques; one method is developed to select an optimal subset of individual wavebands, while the other aims to find good sets of band ratios. We thoroughly discuss the advantages and disadvantages of both algorithms. Experimental results demonstrate that our proposed methods give higher classification rates than other state-of-the-art algorithms.

1. INTRODUCTION Detection of concealed damage in almond nuts is an important production inspection application. Internal damage in almonds is defined as a browning of the kernel interior after cooking [1]. It most likely occurs anytime after harvest when nut kernels are exposed to a warn and moist environment [2]. Internally damaged almonds are not easily distinguished from normal ones by their external appearances, and, when cooked, they taste bitter. Use of a *

E-mail: [email protected]

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hyperspectral system has been investigated for almond nut inspection, since it provides a noninvasive, accurate inspection system. It has been shown [3] that the near-infrared transmission could detect changes in sugars or oil oxidation levels of almond nuts after drying, which would help predict the internal damage in nuts after roasting. Hyperspectral (HS) data are high-dimensional data that contain more than a hundred responses of the object in narrowly spaced λ spectral bands. Use of an HS system for food and agricultural product inspection is promising, since the spectral information in HS data uniquely characterizes and identifies the chemical and/or physical properties of the constituent parts of an agricultural product [4]. HS applications in agricultural products include anomaly detection such as detecting skin tumors on chicken carcasses [5-7], surface defects on apples [8], and aflatoxin levels in corn kernels [9]. Furthermore, HS data have also been used for food quality measurement such as determining wheat grain quality [10], pork quality and marbling level [11] moisture in soybean seeds [12], and sugar content in potatoes [13]. The limitation of using HS data is that there are often a small number of samples compared with the number of spectral bands in the sample. It is well-known that if one wants to estimate the underlying probability density of unknown data, the number of training set data needed grows exponentially with the dimensionality of the feature space [14]. This phenomenon is known as the curse of dimensionality. It is generally accepted that the required number of training samples per class must be at least ten times the number of features [15] to have good generalization, i.e., the correct classification rate on training set data is comparable to that on test set data. Thus, in order to use the HS data effectively, it is necessary to reduce the number of features (wavebands) available by either feature extraction or feature selection techniques. Feature extraction refers to algorithms that map all of the original features into a few features (each of which is a function of all original features), while feature selection refers to algorithms that select a small subset of the input feature set (i.e. use of only several features) to use for classification. Feature extraction requires the use of all wavebands provided by the HS system; it is thus slow and cannot achieve an inspection rate of 40 nuts/s required by almond processing plants. Feature selection is preferable in practice because it leads to savings in measurement cost and speed; i.e., processing is faster and system cost is less when only several λ features are needed. Our goal is to investigate new feature selection methods to use for detection of almonds with internal damage. An inexpensive and fast inspection system can be fabricated using filters at only a maximum of four spectral bands out of all bands. Thus, our new feature selection algorithms are designed to select up to four wavebands. For the first part of this chapter, we consider selecting individual wavebands for classification. We propose a new adaptive branch and bound (ABB) algorithm to select an optimal subset of wavebands (features) and demonstrate its use on a HS almond database. Our ABB algorithm improves upon prior versions of the branch and bound algorithm. The second part of the chapter discusses the use of ratio features (the ratio of the responses at two different spectral bands) for classification. Ratio features have been successfully used in many product inspection applications because they are invariant to multiplicative scaling [16] that often occurs in HS data. We develop a new ratio feature selection method for internally damaged almonds that requires use of only two sets of ratio features for classification. We compare the classification results of our ratio feature selection method to those obtained using the best individual wavebands and extracted features chosen by other feature selection and feature extraction algorithms.

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The rest of this chapter is organized as follows. Section 2 describes the HS almond database used. Prior work is summarized in Section 3. We discuss our ABB feature selection algorithm and its test results in Sections 4 and 5. We then present our ratio feature selection algorithm and its test results in Sections 6 and 7. We compare the performance of our proposed methods with prior algorithms and advance conclusions in Sections 8 and 9, respectively.

2. THE HYPERSPECTRAL ALMOND DATABASE The database used in this chapter was provided by Dr. Tom Pearson from the Agricultural Research Service in Kansas, United States. The data set contains the transmission spectra of almond nuts from 700-1400 nm measured after the drying process. The central region of each almond was illuminated by a 100W quartz tungsten halogen lamp (Oriel, Stratford, CT, U.S.A.). Two different fiber optic transmission spectrometers were used to collect HS spectra; a silicon photodiode array sensor based spectrometer (Ocean Optics, Dunedin, FL, U.S.A.) was used to measure the spectrum from λ = 700-1000 nm in 0.48 nm intervals, and an InGaAs photodiode array spectrometer (Control Development, South Bend, IN, U.S.A.) was used to obtain the spectrum from λ = 950-1390 nm in 3.2 nm intervals. For each almond, ten complete transmission spectra were obtained and the average spectra from each spectrometer was used. Each spectrum was then smoothed by a 19 point Savitzky-Golay second-order filter [17], sampled at Δλ = 5 nm increments and combined to produce an HS spectra with 137 spectral samples from λ = 710 – 1390 nm.

Figure 1. Average HS spectral responses of the training set of normal and internally damaged almond nuts.

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Songyot Nakariyakul

For each almond sample, its spectral response was normalized by dividing the spectral response at each waveband by the average spectral response for that almond sample; this corrects for variations in skin quality, nut thickness, and nut shape [3]. The database contains the spectral responses for 454 almonds. The 454 nuts are divided into a training set of 183 nuts (139 normal and 44 internally damaged), a validation set of 45 nuts (34 normal and 11 internally damaged), and a test set of 226 nuts (172 normal and 54 internally damaged). The validation set is used to select algorithm parameters and sets of ratio features with the best generalization. The average transmittance spectra of the training set of normal and internally damaged almond nuts are shown in Figure 1. As seen, they are noticeably different and thus should be useful for classification. We now discuss the results of our proposed method.

3. PRIOR WORK We now detail prior work on detecting internal damage in almond nuts. Pearson [3] showed that, by using the whole spectrum of HS data from 700-1400 nm, he could distinguish internally damaged nuts from normal ones at an error rates as low as 12.4%. However, the HS system that measures the full transmission spectrum of whole almonds is slow and cannot achieve an inspection rate of 40 nuts/s required by almond processing plants. In another HS almond database, Casasent and Chen [18] used the KLD-MBB algorithm to select the best subsets of four and six separate wavebands chosen from all 137 wavebands. They first used the Kullback-Leibler distance (KLD) to reduce the number of original wavebands from 137 to 30; the modified branch and bound (MBB) feature selection algorithm [18] was then used to select the optimal subsets of four and six wavebands from the reduced 30-dimensional feature space. A nearest neighbor classifier was used as the classifier. However, the KLD-MBB algorithm is not guaranteed to provide the optimal subset. Prior work [9] on ratio feature selection for aflatoxin detection in an HS corn kernel database considered all ratio feature combinations for only every third spectral response; this significantly reduced the number of possible feature sets that had to be evaluated, but the classification performance was also limited. In another HS inspection application [19], the best 22 spectral bands were first chosen by the sequential forward selection algorithm [20], and an exhaustive search of all 231 single ratio feature combinations of these 22 spectral bands was then performed to select the best single ratio feature. However, the best ratio features are not necessarily the ratios of the best individual spectral bands.

4. OPTIMAL FEATURE SELECTION ALGORITHM 4.1. Various Branch and Bound Algorithms Among many feature selection techniques in the literature, the optimal branch and bound (BB) algorithm [21] is considered one of the most essential search algorithms in statistical pattern recognition. It requires that the criterion function J used satisfy the monotonicity property i.e. when a new feature is added to a feature set, the J value of the resultant feature

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set does not decrease. A subset with a larger J value is thus better than one with a smaller J value. To select the best set of m features out of n original features, the BB algorithm selects the n − m features to be discarded. It creates a search tree with n − m levels as shown in Figure 2, where the root represents the set of all n features and the leaves represent all possible subsets of m features. As the search traverses down the tree, the J value decreases because more features are omitted. The problem is to find the best leaf with the largest criteria function J value. (1,2,3,4,5) 1

2

(2,3,4,5) 2

(3,4,5) 3

4

(4,5) (3,5) (3,4)

3

(1,3,4,5) 3

4

(2,4,5) 5

LEVEL 0

4

(2,5)

3

(2,3,5) 5

(2,4)

(1,2,4,5)

(1,4,5)

4

LEVEL 1

4

(1,3,5)

(1,2,5)

5

4

5

5

5

(2,3)

(1,5)

(1,4)

(1,3)

(1,2)

LEVEL 2

LEVEL 3

Figure 2. The solution tree for the basic BB algorithm when m = 2 and n = 5.

The BB algorithm first analyzes all nodes at level 1, and the successor nodes (all nodes below a node) of the node with the largest J value are then analyzed further. The search continues until a leaf at the bottom of the tree (level 3 in our Figure 2 example) is reached; this provides an initial bound B for the criteria function J. The algorithm then backtracks to any unexplored nodes at level 2 and then those unexplored nodes at level 1. If the J value for a node is less than B, its successor nodes (leaves) at the bottom of the tree must have J values less than the bound B and cannot be the optimal subset. These nodes are omitted or cut off from the tree, and their J need not be calculated for them. If a new leaf with a J value larger than B is found, the bound B is updated with this new larger J value. The search and backtracking continues until all nodes in the tree are either explored or cut off. Several improvements have been made to the basic BB algorithm. In searching the rightmost path of the tree in Figure 2 that has only one branch, J needs to be calculated only once at node (1, 2) at the bottom leaf of the tree, but not at intermediate nodes (1, 2, 4, 5) and (1, 2, 5). By removing these single-branching intermediate nodes in the tree, one obtains the minimum solution tree [22] that saves many unnecessary J calculations. All BB algorithm experiments in this chapter use this minimum solution tree. The modified branch and bound (MBB) method [18] uses the sequential forward selection (SFS) method [20] to order the nodes in the tree such that the leftmost node at each level of the tree is more likely to have a J value less than other nodes at the same level. This is attractive, since when the leftmost nodes, which have more branches than any other nodes at the same level, can be cut off, many J calculations can be avoided. In addition, a good (large) initial bound B is obtained using the m best SFS features (i.e. omitting the n − m worst SFS features). The MBB algorithm also starts the search at level (n − m)/4 rather than at level 1,

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since nodes at levels near the top of the search tree are not likely to have J values less than B, since, for them, only a few features are omitted. If all nodes at this level (n − m)/4 have J values larger than B, the search jumps further down the tree and nodes at level (n − m)/2 are explored. If some nodes at level (n − m)/4 have J values less than B, the MBB algorithm stops jump searching, and the search proceeds from level (n − m)/4 as in the basic BB algorithm. Somol et al. [23] proposed the fast branch and bound (FBB) algorithm that uses a

l rather than calculating the true J for each statistical prediction mechanism to estimate J l is much faster than calculation of the true J criterion node. Calculation of the estimated J l for a node is less than the current bound B, the exact function. When the estimated J criterion function J for that node is computed, and whether that node is cut-off is based on its true J value and the present true bound B. Hence, the FBB algorithm selects the optimal l estimation. solution, since nodes are never cut-off based only on the J 4.2. Adaptive Branch and Bound (ABB) Algorithm Our adaptive branch and bound (ABB) algorithm [24] improves the search speed (number of J calculations necessary) of the BB algorithm in four new major aspects.

1). New Node Ordering Feature yj be the most significant feature in the set Y of all n features if removing it from the set Y gives the lowest J value for the resulting set of all n – 1 features. ABB first finds the most significant feature y1 in the full set Y of n features, and then the most significant feature y2 in the resultant set Y \{y1}of n − 1 features, and so on, where Y \{y1} denotes the set of features after removing feature y1 from the feature set Y. The nodes in the BB tree of Figure 2 are then replaced with these most significant features, i.e. feature 1 is replaced with feature y1, feature 2 with feature y2, and so on. This feature ordering in ABB puts nodes with smaller J values on the left side of the tree and requires only n(n + 1)/2 calculations of J to order the full set of n features. 2) A Large Initial Bound Binit Found by SFFS The second new aspect of our ABB algorithm is the use of the improved floating forward selection (IFFS) method [25] to compute a large initial bound Binit of m features. The IFFS method has been shown [25] to give the optimal or near-optimal solution in many applications. A large initial bound Binit allows many branches to be cut off early at the lower levels near the top of the tree, and thus the number of J calculations can be greatly reduced. 3) A Good Starting Search Level Kmin The ABB algorithm starts the search at level kmin (not necessarily level 1) in the tree. kmin is the minimum number of the most significant ordered features that can be removed from the full feature set Y so that J for the remaining n − kmin features is less than the initial bound Binit. This insures that J at the leftmost node at this kmin level (this node has the most successor nodes) is less than Binit and thus J at its successor nodes need not be evaluated.

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4) A New Prediction Mechanism to Jump Search The ABB algorithm adapts the jump search to select the next successor nodes and levels in the tree below kmin at which to evaluate J (the next level is not necessarily searched next). The motivation for this is that if J at some node is much larger than the current bound B, its successor nodes at the next level in the tree are not expected to have a J value less than B; thus the search should jump to a higher level further down the tree at which at least one node is expected to have J less than B (i.e. its successor nodes can be cut off). The prediction mechanism for J used to model the criterion function J at level k is

(

β Jl ( Y \Ψ k ) = J (Y )× 1 − ( k n )

)

(1)

l (Y \ Ψ ) is the predicted criterion function value at a node in level k when the where J k set Ψk of k features is removed from the entire set Y of all n features, and J(Y) is the true criterion function value with the full set Y. β for a node is calculated by substituting the true J value at that node into the left side of Eq. (1) and solving for β. The ABB algorithm starts the search by computing true J values for all nodes at the starting search level kmin and locating the node at this level with the largest true J value. β for this node is calculated and then used to predict the new level k to which to jump. This level k to jump to is calculated by setting the left side of Eq. (1) equal to the current bound B and solving for k. The true J values at successor nodes at the new level k are then computed, β is calculated for the node with the largest true J value, and the jump search continues. β is different for each node and thus the jump search algorithm is adaptive. The jump search and backtracking continues until all paths of the tree are explored or cut off. For more details about the ABB algorithm, see [24].

5. INDIVIDUAL WAVEBAND SELECTION RESULTS AND DISCUSSION In this section, we compare the performance of our ABB algorithm with other versions of the BB algorithm on the HS almond database. We use the Mahalanobis distance as the criterion function, J = (μ1−μ2)TC−1(μ1−μ2),

(2)

where μ1 and μ2 are the mean vectors for the training samples for the two classes in the problem (in our case, good-almond class and internally-damaged-almond class, respectively), and C is the covariance matrix for the training samples. The Mahalanobis distance is large if the mean difference between two classes is large. We use the Mahalanobis distance as the criterion function J, since it is monotonic, widely used in many prior HS applications [3], and computationally efficient. Since all versions of the BB algorithm are optimal and yield the same optimal feature subset, our only concern in comparing the different BB algorithms is their computational complexity. To compare the computational complexities of different algorithms, we show the number of J calculations and the execution time required by each

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algorithm as a function of the number m of selected features. All of our BB experiments were run on the pixel training sets using MATLAB7 on a Pentium IV-2.8 GHz computer with 4 GB of RAM. Table 1 shows the number of J calculations required by different optimal feature selection algorithms for the training set as the number m of selected waveband features increases (out of n = 137 available waveband features). When m = 1, an exhaustive search requires the smallest number of J calculations and is the fastest algorithm as expected due to its simpler implementation. When m = 4, the basic BB algorithm is slightly faster than the FBB and MBB algorithms, since it is designed to efficiently search the solution tree when m << n. We expect that when m increases (larger than we considered), the FBB and MBB algorithms will need fewer J calculations than the basic BB algorithm. The number of J calculations required by the MBB algorithm is larger than that required by our ABB algorithm as expected, since the feature ordering, the initial bound B, the starting search level, and the jump search strategy used in MBB are poorer than those in ABB. The FBB algorithm computes a large number of J calculations because its J estimation mechanism is not good, especially with the Mahalanobis distance measure used [24], and it does not use jump search in the tree search. It also requires many J calculations to order the nodes as it builds the tree. When m = 4, the ABB algorithm outperforms other optimal feature selection algorithms. It needs approximately 10 millions J calculations, whereas other versions of the BB algorithm require more than 14 millions J calculations. We expect that the advantage of the ABB algorithm will become very significant when m > 4. Table 1. The number of J calculations required by different optimal feature selection algorithms for the number m of selected wavebands for the almond training set m

Exhaustive Search

Basic BB

Fast BB

MBB

ABB

1 2 3 4

137 9,316 419,220

9,177 14,133 526,966

560 10,436 453,818

9,657 23,656 640,345

10,262 19,510 354,413

14,043,870

14,631,426

15,074,103

19,869,619

10,077,404

We now discuss the classification results using the optimal feature selection algorithm. The response of each sample at various numbers of selected features (wavebands) is fed to a support vector machine (SVM) classifier with a Gaussian kernel. The two parameters, C for error penalization in the SVM and the Gaussian kernel parameter σ are set to 100 and 0.25, respectively, since they yield the best generalization on the training and validation sets. Each sample is then classified as normal or internally damaged. Table 2 shows the wavelength (nm) selected by the ABB algorithm, the J values, and the PC scores (percentage of normal and damaged almonds correctly classified) for the training, validation, and test sets as the number m of final features increases. The false positive (normal nuts misclassified as internally damaged) and false negative (internally damaged nuts misclassified as normal) error rates for the test set are also included. We note that all versions of the BB algorithm select the same optimal feature subset and thus yield the same J value and PC score. As seen, the PC score for the training set increases as the J value increases. Thus, the Mahalanobis distance criterion function J we use is a good measure. When m = 1, the PC scores for the

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training and test sets are low. This clearly shows that use of only a single waveband is insufficient for this application, and use of multi-band data are needed. When more wavebands are used, the training set PC scores increase as expected. The PC scores for the training, validation, and test sets are very high when m ≥ 2. The validation set PC scores reach a peak at m = 2 and remain the same when m increases. Four wavelengths (features) should be used because the PC scores for the training and test sets are high, and generalization is best. When m = 4, only 2.91% (5 out of 172) of the normal almond nuts in the test set are misclassified as internally damaged and 14.81% (8 out of 54) of the internally damaged nuts are misclassified as normal. A high false negative error rate is obtained due to the small size of the training set for internally damaged nuts. Table 2. Feature selection results using the ABB algorithm for the HS almond database as the number m of selected features increases m

Wavelength (nm)

J Value

Training Set

Validation Set

Test Set

PC score (%)

PC score (%)

PC score (%)

False Positive error rate (%)

False Negative error rate (%)

1

{1160}

1.95

86.34

84.44

82.74

2.91

62.96

2

{755, 965}

9.61

94.50

93.33

93.36

3.49

16.67

3

{755, 965, 1375}

10.94

95.08

93.33

93.36

2.91

18.52

4

{760, 920, 935, 970}

11.96

96.17

93.33

93.33

22.91

14.81

6. THE RATIO FEATURE SELECTION METHOD As noted earlier in Section 1, a ratio feature is the ratio of the responses at two different spectral bands (λs). Using an exhaustive search to find the best single spectral band ratio feature requires (for our 137-feature database) that we evaluate performance of the classifier used for all training set samples for a total of ⎛⎜ 137 ⎞⎟ = 9,316 band ratios or sets of ratio ⎝ 2 ⎠ features. To select the best two pairs of ratio features requires a search of classification ⎛ 9316 ⎞

performance on the training set for ⎜ ⎟ = 43,389,270 combinations of two ratio features; ⎝ 2 ⎠ this is clearly excessive. Our ratio feature selection method reduces the number of combinations necessary and greatly improves the time required for training. Figure 3 shows the block diagram of our proposed method; it includes spectral band reduction, band ratio feature formation, ratio feature ordering and selection, and ratio feature classification.

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Figure 3. Block diagram of our ratio feature selection method.

6.1. Spectral Band Reduction The first step in the selection method is to remove a number of feature (spectral) bands with similar information. This should not significantly affect classification performance, since the discarded λ features do not give much new information expected to be useful for classification. We discard similar features before computing all possible ratio feature combinations to reduce the search time needed in training. Mean square error (MSE) is used to measure the similarity among all bands, and those bands with the highest degree of similarity are discarded. MSE is defined as

MSE ( λ a , λ b ) =

1 NT

NT

∑(λ i =1

(i ) − λ b (i) ) , 2

a

(3)

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91

where λa(i) is the spectral response in band a for the i-th training sample. This measure computes the mean over all NT training samples of the square of the difference of two spectral responses. The smaller the MSE, the more similar the two spectra are. The MSE values are entered as the elements of an Nλ× Nλ symmetric MSE matrix M (i.e., MSE(λa, λb) = MSE(λb, λa)), where Nλ is the total number of wavebands. The M(a, b) element is the MSE between the responses of the a-th and b-th wavebands over the training set, and all diagonal elements are zeros. To reduce the number of bands used, the non-zero minimum element of M is first determined; this defines the two wavebands with the most similarity. Assume bands a and b are most similar. To decide which band (a or b) to be discarded, we examine row a and row b of M (ignoring elements M(a, b) and M(b, a)), and the row with the remaining smallest nonzero element is the band removed. For our example, assume that band a is to be omitted, we thus set row a and column a of M to be zero. We repeat this procedure until the number of bands to be discarded is met. For our almond database, MSE values are calculated for all ⎛137 ⎞ = 9,316 possible pairs of two out of all 137 original λ bands, and 37 spectral bands are ⎜⎜ ⎟⎟ ⎝ 2 ⎠ omitted after this first step.

6.2. Band Ratio Feature Formation There are now 100 remaining spectral bands. All band ratios or ratio features of them are formed. In the ratio feature, we place the lowest numbered waveband in the numerator of the ratio feature; we do not consider both ratios of the same two wavebands, as they are both expected to have similar information. Thus, there are ⎛ 100 ⎞ = 4,950 combinations of two of ⎜ 2 ⎟ ⎝ ⎠

those 100 selected bands or 4,950 sets of ratio features to consider. Our goal is to select the best two pairs of ratio features to use for classification. An exhaustive search requires evaluating the performance of the classifier used for all training set samples for a total of ⎛ 4950 ⎞ ⎜ ⎟ = 12,248,775 combinations of two ratio features; this is clearly excessive. The next ⎝ 2 ⎠ step in our ratio feature selection method reduces this number and thus further improves training time.

6.3. Ratio Feature Ordering and Selection In this step, we order the set of ratio features and choose the final two sets of ratio features based on the ordered set. To order the ratio feature set, we apply the sequential forward selection (SFS) algorithm to the set of 4,950 possible ratio features to select the best subset of 50 ratio features. The Mahalanobis distance in Eq. (2) is again used as the criterion function J. To select the best subset of m ratio features out of n original ratio features, the SFS algorithm evaluates J for (2n−m+1)m/2 subsets of ratio features. For our example, to select the best subset of 50 ratio features out of 4950 total ratio features, the SFS algorithm requires evaluating J for (2×4950−50+1)×50/2 = 246,275 subsets of ratio features. After this, we

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obtain an SFS ordered set of 50 ratio features and the other set of the remaining 4,900 ratio features. We choose the SFS algorithm since it considers discrimination. Applying SFS to the set of ratio features versus waveband features is new. Other feature ordering methods can also be used. To select the final two best sets of ratio features, we make the assumption that at least one of the two best ratio features is one of the 50 SFS selected ones; this seems very likely. To select the second ratio feature set (recall, we desire two sets of ratio features), we could choose it from either the remaining first set of 50 SFS ordered ratio features or from the other 50 50 4900 ⎞ = 1,225 + 50×4,900 = set of 4,900 ratio features. To address both choices, ⎛⎜ ⎞⎟ + ⎛⎜ ⎞⎟⎛⎜ ⎜ 2 ⎟ ⎜ 1 ⎟⎜ 1 ⎟⎟ ⎝ ⎠ ⎝ ⎠⎝ ⎠ 246,225 combinations of two sets of ratio features must be examined. This is a reduction by a factor of approximately 60 compared to all 12 million combinations of two sets of ratio features needed to be exhaustively searched. Section 6.4 details how we examine these 246,225 combinations. We note that since our method does not consider all possible combinations of two sets of ratio features, our result is sub-optimal, i.e., there is no guarantee that the result will yield the optimal two sets of ratio features chosen by an exhaustive search.

6.4. Ratio Feature Classification In selecting the best two sets of ratio features out of 246,225 combinations, we proceed as follows. A support vector machine (SVM) classifier with a Gaussian kernel is used as the classifier. The two parameters, C for error penalization in the SVM and the Gaussian kernel parameter σ are set to 50 and 1, respectively; they are chosen ad hoc. For each set of two ratio features, the SVM is trained on the training set samples. The error rate for this SVM is then measured on the validation set for the same two sets of ratio features. These procedures are repeated for all 246,225 combinations of two sets of ratio features; and, the two sets of ratio features that yield the best classification rate on the validation set are selected as the final features.

7. RATIO FEATURE SELECTION RESULTS AND DISCUSSION In this section, we discuss the performance of our proposed ratio feature selection method. The first step in our method addresses the similarity among the responses in different wavebands over the training set samples. For the almond database, we found that the responses for many bands are essentially identical. For example, the MSE between the wavebands 1090 and 1095 nm is very small (7.22×10−6). Thus, the 1095 nm waveband could be removed without much loss of information. The 37 such bands are removed in this first step; all similar responses found were for adjacent bands. All 4950 combinations of ratio features of two bands for the reduced set of 100 bands are then formed in step 2. The SFS algorithm is used to rank the first 50 ratio features, and our 246,225 combinations of two sets of ratio features are fed to the SVM classifier after step 3. Table 3 shows the total correct classification PC rates (percentage of normal and damaged almonds correctly classified) for the training, validation, and test set obtained by our proposed

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method. To find the single best ratio feature to use (band ratio 925 nm/945 nm) involves measuring the classification rate for all 4950 ratio features on the validation set and selection of the best rate. We note that this single best ratio feature (using PC score) is also the best SFS-ordered ratio feature (using the criteria function J). From Table 3, the PC scores for the training, validation and test set seem to be comparable whether one or two ratio features chosen from the set of SFS features are used; thus, generalization is good. The best two sets of ratio features chosen (by our algorithm) from the 50 SFS-ordered set (925 nm/945 nm and 810 nm/815 nm) are not the two best features ranked by J in the SFS algorithm; rather, they are ranked first and thirty-fourth in the SFS-ordered set of 50. Although using two sets of ratio features from the set of 50 SFS-ordered features gives a larger PC score on the training set than using the best single ratio feature, they yield the same PC score on the validation and test sets. As seen, the best PC score is obtained when one ratio feature (850 nm/1210 nm) was chosen from the 50 SFS-ordered set and the other (1160 nm/1335 nm) from the remaining 4900 ratio features. Figs. 4a and 4b show plots of the training and test set samples using these two best sets of ratio features, respectively. Most of the normal nuts are seen to be separated in distance from the internally damaged nuts. The best ratio feature (850 nm/1210 nm) from the set of 50 SFSordered ratio features is ranked thirty-third in the 50 SFS ratio features. The test set PC score (91.15%) for this case is noticeably higher than that (86.73%) obtained by choosing both sets of ratio features from the 50 SFS-ordered set. For these best two ratio features, only 1.74% (3 out of 172) of the normal almond nuts in the test set are misclassified as internally damaged and 31.48% (17 out of 54) of the internally damaged nuts are misclassified as normal. The false positive error rate (1.74%) obtained by our ratio feature selection method is excellent for economic reasons, since only a few normal nuts would be misclassified as internally damaged and discarded from almond nut processors.

Figure 4. (Continued)

Table 3. Ratio feature classification results on our internally damaged almond database Model

Band Ratios

Training Set

Validation Set

Test Set

PC score (%)

PC score (%)

PC score (%)

False Positive error rate (%)

False Negative error rate (%)

Best single ratio feature

925 nm/945nm

85.25

88.89

96.73

1.16

51.85

Best 2 from 50 SFS ratio features

925 nm/945nm and 810 nm/815 nm

87.43

88.89

86.73

0.58

53.70

1 from 50 SFS ratio features and 1 from 4900 ratio features

850 nm/1210 nm and 1160 nm/1335 nm

90.16

93.33

91.15

1.74

31.48

Hyperspectral Waveband Selection for Detection of ….

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Figure 4. Plot of the training (a) and test (b) set samples for the two best sets of ratio features chosen by our method.

8. COMPARISON OF OUR PROPOSED METHODS WITH PRIOR ALGORITMS We compare our proposed methods with other feature selection algorithms on the same almond database. Casasent and Chen [18] used the KLD-MBB algorithm (see Section 3) to select two subsets of four and six separate wavebands for classification. A nearest neighbor classifier was used as the classifier. We also consider the performance of two feature extraction algorithms that use all available wavebands. Feature extraction results are useful as they show what performance one can obtain if all wavelength data are used. Pearson [3] used the principle components of the absorbance, the first derivative and second derivative spectra between 1000 and 1300 nm. In another feature extraction method, Casasent and Chen [18] applied the high-dimensional generalization discriminant (HDGD) feature extraction algorithm for internally damaged almond classification, since it was shown to give better PC performance than other well-known feature extraction algorithms such as principle component analysis and linear discriminant analysis in many applications. Table 4 shows test data results on the almond database using our two proposed methods and the four other algorithms noted above. As seen, our ABB algorithm using the optimal subset of four separate wavebands gives a higher classification rate by 11% and 8% compared to use of the subset of the best four and six separate wavebands chosen by the KLD-MBB algorithm, respectively. This is expected, since our ABB algorithm is optimal, while the KLD-MBB algorithm is not. None of the optimal four wavebands selected by ABB is used in the four wavebands chosen by the KLD-MBB algorithm. Furthermore, none of the best wavebands chosen by ABB and KLD-MBB is used in our selected two sets of ratio features. This clearly shows that the best individual wavebands do not necessarily give the best ratio

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Songyot Nakariyakul

features. As Table 4 shows, our proposed ABB and ratio feature selection methods, using a total of four wavebands, give higher classification rates than the principle component and HDGD feature extraction algorithms, which use all wavelength data. Compared to the classification result using four individual wavebands selected by the ABB algorithm, we found that using two sets of ratio features for classification gives a lower test set PC score by 3%. We believe this occurs since the SVM classifier (and any classifier) performs better with more features (four separate waveband features versus two ratio feature sets). Thus, use of individual wavebands is encouraging and should be considered for commercial online processing. Table 4. Test set PC comparison for different feature reduction algorithms on the almond database Test Set PC score (%)

Model

Features

KLD-MBB feature selection

{710 nm, 820 nm, 905 nm, 1095 nm} {765 nm, 880 nm, 905 nm, 935 nm, 975 nm, 1220 nm}

83.2

Principle component regression

8 principle components

87.6

HDGD feature extraction

4 HDGD extracted features

90.7

Our ABB algorithm

{760 nm, 920 nm, 935 nm, 970 nm}

94.2

Our ratio feature selection method

{850 nm/1210 nm, 1160 nm/1335 nm}

91.2

85.9

9. CONCLUSION We proposed two new feature selection algorithms for classification of almonds with internal damage. The optimal ABB feature selection algorithm was developed to select an optimal subset of individual wavebands, while the ratio feature selection method was designed to choose two good sets of ratio features. Initial results on an internally damaged almond database showed that our two proposed methods using only a total of four wavebands gave higher classification rates than did use of feature extraction algorithms using all spectral responses. Using four individual features gave a higher test set PC score than using two sets of ratio features for classification. We found that none of the best separate wavebands in feature selection was present in our ratio features. Our ABB method is promising for commercial online processing because the responses at only four different wavebands can be recorded by many fast multispectral sensor systems. It is thus very useful and could be easily applied to other agricultural product inspection applications.

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ACKNOWLEDGMENT The authors would like to thank Dr. Tom Pearson of the Agricultural Research Service in Kansas, United States for providing the HS almond database used in this chapter.

REFERENCES [1]

[2]

[3] [4] [5] [6] [7] [8]

[9]

[10]

[11]

[12] [13] [14] [15]

Reil, W., Labavitch, J. M. & Holmberg, D. Harvesting. In: Micke, W. C., editor. Almond Production Manual. Oakland, CA: University of California, Division of Agriculture and Natural Resources; 1996; 260-264. Kadar, A. A. & Thompson, J. F. Postharvest handling systems: Tree nuts. In: Kader, A. A., editor. Postharvest Technology of Horticultural Crops. Oakland, CA: University of California, Division of Agriculture and Natural Resources; 1992; 254. Pearson, T. (1999). Spectral properties and effect of drying temperature on almonds with concealed damage. Lebensmittel-Wissenschaft und-Technologie, 32, 67-72. Lu, R. & Chen, Y. R. (1999). Hyperspectral imaging for safety inspection of food and agricultural products. Proceedings of SPIE, 3544, 121-133. Kong, S. G., Chen, Y.R., Kim, I. & Kim, M. (2004). Analysis of hyperspectral fluorescence images for poultry skin tumor inspection. Applied Optics, 43(4), 824-833. Nakariyakul, S. & Casasent, D. (2007). Fusion algorithm for poultry skin tumor detection using hyperspectral data. Applied Optics, 46, 357-364. Nakariyakul, S. & Casasent, D. (2009). Fast feature selection algorithm for poultry skin tumor detection in hyperspectral data. Journal of Food Engineering, 94, 358-365. Kleynen, O., Leemans, V. & Destain, M.-F. (2005). Development of a multi-spectral vision system for the detection of defects on apples. Journal of Food Engineering, 69(1), 41-49. Pearson, T., Wicklow, D. T., Maghirang, E. B., Xie, F. & Dowell, F. E. (2001). Detecting aflatoxin in single corn kernels by transmittance and reflectance spectroscopy. Transactions of the ASAE, 44(5), 1247-1254. Dowell, F., Ram, M., & Seitz, L. (1999). Predicting scab, vomitoxin, and ergosterol in single wheat kernels using near-infrared spectroscopy. Cereal Chemistry, 76(4), 573576. Qiao, J., Ngadi, M. O., Wang, N., Gariepy, C. & Prasher, S. O. (2007). Pork quality and marbling level assessment using a hyperspectral imaging system. Journal of Food Engineering, 83(1), 10-16. Lamb, D. & Hurburgh, C. (1991). Moisture determination in single soybean seeds by near-infrared transmittance. Transactions of the ASAE, 34(5), 2123-2129. Mehrubeoglu, M. & Cote, G. (1997). Determination of total reducing sugars in potato samples using near-infrared spectroscopy. Cereal Foods World, 42(5), 409-413. Bishop, C. M. (1995). Neural networks for pattern recognition. New York, NY: Oxford University Press. Jain, A, Ruin, R. & Mao, J. (2000). Statistical pattern recognition: a review. IEEE Trans. Pattern Analysis and Machine Intelligence, 22, 4-37.

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[16] Keshava, N. (2004). Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries. IEEE Transactions on Geoscience and Remote Sensing, 42(7), 1552-1565. [17] Hruschka, W. R. Data analysis: wavelength selection methods. In: Williams, P., Norris, K., editor. Near-Infrared Technology in the Agricultural and Food Industries. St. Paul, MN: American Association of Cereal Chemists, Inc.; 1987. [18] Casasent, D. & Chen, X.-W. (2003). Waveband selection for hyperspectral data: optimal feature selection. Proceedings of SPIE, 5106, 259-270. [19] Delwiche, S., Stephen, R., & Kim, M. (2000). Hyperspectral imaging for detection of scab in wheat. Proceedings of SPIE, 4203, 13-20. [20] Whitney, A. W. (1971). A direct method of nonparametric measurement selection. IEEE Transactions of Computers, 20, 1100-1103. [21] Narendra, P. & Fukunaga, K. (1977). A branch and bound algorithm for feature subset selection. IEEE Transactions on Computers, C-26, 917-922. [22] Yu, B. & Yuan, B. (1993). A more efficient branch and bound algorithm for feature selection. Pattern Recognition, 26, 883-889. [23] Somol, P., Pudil, P. & Kittler, J. (2004). Fast branch & bound algorithms for optimal feature selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, 900-912. [24] Nakariyakul, S. & Casasent, D. (2007). Adaptive branch and bound algorithm for selecting optimal features. Pattern Recognition Letters, 28, 1415-1427. [25] Nakariyakul, S. & Casasent, D. (2009). An improvement on floating search algorithms for feature subset selection. Pattern Recognition, 42, 1932-1940.

In: New Topics in Food Engineering Editor: Mariann A. Comeau

ISBN: 978-1-61209-599-8 © 2011 Nova Science Publishers, Inc.

Chapter 5

HIGH FREQUENCY ULTRASONIC TECHNIQUES DEDICATED TO FOOD PHYSICAL PROPERTIES ASSESSMENT D. Lauxa*, J. Y. Ferrandisa, V. Cereser Camaraa, H. Blascoa and M. Valenteb a

Institut d’Electronique du Sud. MIcro and Rheo-Acoustics group (MIRA) UMR CNRS 5214. CC 082. University Montpellier II. Place Eugène Bataillon. 34095 Montpellier b CIRAD, UMR Qualisud, TA B-95/16 - 73 rue J-F.Breton. Montpellier, F-34398 Cdx5 France

ABSTRACT Usually, viscoleastic properties of materials (complex shear moduli and viscosity) are evaluated with rheometers which can give G’, G’’ for instance, on wide bandwidths thanks to the Time Temperature Superposition principle. It is clear that the knowledge of such properties is very interesting on a fundamental point of view because information on material microstructure can be deduced from master curves. On a more practical point of view, it can be used to improve fabrication processes, to perform quality controls, especially in food industry and engineering. This powerful method can fail to give large bandwidth information in several cases: phase transition of the material with temperature, volume to be analyzed too small, bad morphology of the sample. In order to overcome such a difficulty, it is possible to use ultrasonic waves. This paper, dedicated to high frequency methods (from a few kHz to many MHz) presents a review of existing methods and improvements developed in our labs. Several applications in the domain of food engineering will be given in order to prove the huge interest of high frequency approaches which are sometimes neglected compared to classical low frequency rheology.

*

Corresponding author : [email protected]

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INTRODUCTION Measurement of material properties using ultrasound has intensively been studied for decades. This technique has been demonstrated to be very efficient for non destructive examination. The advantage of this method is the direct coupling between ultrasonic quantities and mechanical properties. So, ultrasonic measurements can be considered as continuation of rheology for high frequencies. Hence, these high frequency mechanical vibrations can be used to perform “micro-rheometers”. Some authors sometimes speak of piezo-rheometer. Several experimental techniques (Ultrasonic Pulse Transmission technique, Thickness Shear Mode, Bulk Acoustic wave, Ultrasonic Shear Wave Reflected Method …) based on longitudinal or shear waves already exist and are widely detailed in literature [1-9] In this paper we focus our attention on three approaches, which are for us the most interesting for food physical properties estimation: longitudinal velocities and attenuation evaluation with echographic method, complex reflection coefficient evaluation at an interface “elastic solid / viscoelastic material ” with reflectometry technique, resonance frequency and quality factor of kHz-range acoustic resonator in contact with viscoelastic media analysis. These three approaches will be first presented on a theoretical point of view. Then, we will show how they are applied in our labs to obtain accurate results on food. At last, major results on various foods will be detailed: measurement of the master curve of honey from a few Hz to hundreds of MHz, ultra-precise evaluation of moisture content at room temperature in several honeys and potential applications to non destructive quality controls, mechanical properties of sugars such as Sorbitol near glass transition and evaluation of textural properties of mangos. Regarding these various approaches we will discuss at the end of our paper on other potential applications in food industry and engineering.

1. BASICS: THEORETICAL BACKGROUND 1.1. Simple Case of Elastic Solids [10] In the case of linear elasticity, strain and stress are linked via tensors, which, in the most general case, are constituted with 81 coefficients Cijkl. The knowledge of such coefficients totally determines the elastic behavior of the material. Thanks to symmetries and to crystallographic considerations, this high number can be largely reduced. For instance, for an isotropic material only two coefficients are needed. One can choose Lamé’s constants λ and μ or the Young and shear modulus E and G respectively. The Young modulus represents the ratio between a traction stress and the resulting deformation of the sample. The shear modulus quantifies the angular deformation when the sample is submitted to a tangential stress. One can also define the bulk modulus K and the Poisson’s ratio ν. This Poisson’s ratio is a very relevant quantity. It links the reduction of section of the sample during a traction test. The bulk modulus, quantifies the reduction of volume due to a hydrostatic pressure. Some relationships between these constants are given above:

High Frequency Ultrasonic Techniques Dedicated …

101

K=

G.E 9.G − 3E

(1)

ν=

E − 2G 2G

(2)

Then, thanks to the link between mechanical characteristics and ultrasonic waves which can propagate into an isotropic solid (longitudinal and shear waves), relationships exist between E, G, ν, VL and VS where VL and VS are the velocities of longitudinal and shear waves respectively (in these expressions, ρ is the density).

G = ρVS

2

E = ρVS

2

(3) 2

3VL − 4VS 2

VL − VS

⎛V 1 − 2⎜⎜ S ⎝ VL ν= ⎛ ⎛V 2⎜1 − ⎜⎜ S ⎜ ⎝ VL ⎝

⎞ ⎟⎟ ⎠

2

⎞ ⎟⎟ ⎠

2

2

2

⎞ ⎟ ⎟ ⎠

(4)

(5)

Hence, the measurement of longitudinal and shear wave velocities will lead to the assessment of mechanical constants. At last, we will see further that it can be very useful to introduce the acoustical impedance which represents the ratio between the acoustical pressure and the velocity of the particles which are moving around their equilibrium position during the wave propagation. For a homogeneous material this acoustical impedance Z is equal to ρV where V can represent VL or VS, depending on the nature of the wave. For elastic solids, Z is a real quantity.

1.2. Viscoelastic Materials [10] When mechanical waves such as ultrasound propagate in a viscoelastic material, they are submitted to a very high attenuation. This attenuation becomes enormous for high frequencies (over MHz) and shear waves. For instance, for frequencies around 10 MHz, a shear wave does not propagate over a few hundreds of µm in honey around room temperature [11]. In order to take into account this damping, one usually writes the wave vector k as a complex number k* as follows:

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102

k* =

ω − j.α V

6)

In these expression, α is the ultrasonic wave attenuation and ω the radian frequency (ω=2πf). V is the velocity of the mechanical wave. It can be a longitudinal or a shear wave. For viscoelastic materials, it is more interesting to study the material with shear solicitations because viscoelastic effects are emphasized. So, we will now use the subscript “s” to define the complex shear wave number kS* and a complex velocity associated (VS*) with the help of VS and αS.

kS* =

ω ω − j.α S = VS VS *

(7)

With 2

VS * =

V αω VSω2 + j 2 s S2 2 2 2 2 ω + VS αS ω + VS αS

(8)

Then, regarding equation (3), it becomes natural to define a complex shear modulus G*= ρ(VS*)². This leads to:

G* =

ρVS

2

jα V ⎞ ⎛ ⎜1 − S S ⎟ ω ⎠ ⎝

2

= G '+ jG ' '

(9)

G’ and G’’ represent the conservation and loss shear moduli and are generally measured with rheometers in the small frequency domain (< 1 kHz). At last, G’ and G’’ can be written as follows: [12]

G ' = ρVS

⎛α V ⎞ 1− ⎜ S S ⎟ ⎝ ω ⎠

2

2

⎛ ⎛ α S VS ⎞ 2 ⎞ ⎜1 + ⎜ ⎟ ⎟ ⎜ ⎝ ω ⎠ ⎟ ⎠ ⎝

2

and G ' ' = ρVS

2

2α S VS ω ⎛ ⎛ α S VS ⎞ 2 ⎞ ⎜1 + ⎜ ⎟ ⎟ ⎜ ⎝ ω ⎠ ⎟ ⎝ ⎠

2

(10)

The same approach could be applied on longitudinal waves and would lead to the definition of E*, E’ and E’’ [12]. As for elastic materials, one can define the acoustical impedance which is now a complex number. For a shear wave in a viscoelastic material, it will be :

Z* = ρVs *

(11)

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In the next part we will show how it is possible to assess in the high frequency domain, G* with ultrasonic waves depending on the studied material. Three approaches will be developed: echography, reflectometry and resonant techniques.

2. EXPERIMENTAL METHODS 2.1. Direct Measurement of Velocities and Attenuation: Echography This well known method is essentially based on two simple facts: when an ultrasonic wave propagates on a distance d with a velocity V, the time needed is t = d/V. If attenuation occurs during propagation, a simple sinusoidal mechanical wave can be written as :

u = uoe

ω i ( ωt − x ) v

e − αx

So, in order to evaluate the velocity, one has to measure the propagation time versus the thickness of the sample. For attenuation assessment, the variation of wave amplitude versus thickness leads to the estimation of α.

2.1.1. Data Acquisition Depending on the nature of the material (liquid or solid), two methods have to be distinguished. For solid materials, one can a priori operate in reflection or transmission mode. For solids, we prefer the transmission method because it only uses one path in the sample and the data treatment is easier if attenuation is high. In practice, two ultrasonic transducers are settled on the both sides of the sample (plate with parallel face and thickness d). One transducer acts as emitter and the other as receiver. For various thicknesses the ultrasonic signal transmitted is acquired on a computer before treatment. For solid materials, ultrasonic attenuation is generally not enormous and consequently it can be applied with longitudinal or shear waves. On the contrary, as already mentioned in part 1.2 and in [13], in viscoelastic liquids, the shear attenuation is so high for frequencies around the MHz that echographic methods cannot be applied easily. So, in this paragraph we only present a method dedicated to longitudinal waves. In literature many papers have described methods in reflection or transmission in order to assess the ultrasonic attenuation and velocity. We present here the reflection one because it is used in our labs and gives very accurate and relevant results (for results see part 3.3.): consider an arbitrary distance, Zo (along a vertical axe), between an ultrasonic transducer and the bottom of a tank containing the liquid. This distance is progressively reduced using a stepper motor. At each step, the ultrasonic signal reflected on the bottom of the container after propagation in the liquid is acquired (see Figure 1).

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Figure 1. Signal acquisition for ultrasonic velocity and attenuation measurement in liquids.

2.1.2. Signal Processing For viscoelastic materials, all the ultrasonic parameters depend on frequency. Hence a special treatment has to be used. Generally, ultrasonic sensors excited with electrical pulse have got a quite large bandwidth. So, in order to extract the velocity and the attenuation for a chosen frequency, the following treatment is used: in a first step, the Fast Fourier Transform of each echo acquired is performed to obtain the modulus and the phase of the FFT. Let A the amplitude of the FFT modulus for the chosen frequency fo. We plot 20Log(A) versus the distance z (for liquids) or versus the thickness d (for solids). Then, the slope is equal to -2α (for reflection method on liquids) or equal to -α (for solids study in transmission), where α is the attenuation in dB per unit of distance. For the frequency fo we also plot the FFT phase divided by 2πfo versus z or d. The slope of this linear trend is then equal to 2/V (for liquids and reflection method) or to 1/V (for solids studied in transmission mode) (see Figure 2). V can represent VL or VS, but for liquids we recall that it is generally VL because shear attenuation is too high for the considered frequencies (over a few MHz). At last, following the procedure presented in reference [14], the effects of diffraction due to the finite size of the transducer are corrected on both velocity and attenuation. So, at the end of the signal processing, we obtain the ultrasonic velocity and the absorption (attenuation corrected from diffraction) in decibels per unit of distance.

Figure 2. Signal processing for viscoelastic liquids velocity and attenuation assessment.

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2.2. Reflectometry This experimental approach is especially dedicated to shear waves and constitutes an interesting way to obtain G’, G’’, Vs and αS when echography fails due to a too high attenuation [15-19]. According to the theory of wave reflection, for the normal incidence of shear waves at a boundary between two elastic materials, the reflection coefficient can be defined as,

r=

A reflected Z 2 − Z1 = A incident Z 2 + Z1

(12)

where r is the wave reflection coefficient in pressure or stress, defined as the amplitude ratio between the reflected and incident waves, which can be obtained directly from experimental measurements. Z1 and Z 2 are the shear acoustical impedances of the materials. In the case of pure elastic materials, acoustical impedance and r are real parameters. If the viscoelastic behavior is considered, the acoustical impedance is also a complex quantity which can be written as (see part 1):

Z* = R + jX

(13)

Considering now that the material 1 is elastic and that the material 2 is viscoelastic, the reflection coefficient can be generalized in a complex format: (14) r* = r0e jΦ = r0 (cos Φ + j sin Φ ) where Φ is the phase shift between the incident and the reflected wave, and ro is the magnitude or modulus of the reflection coefficient. This means that, when an ultrasonic wave propagating in material 1 is reflected on the interface (1 / 2), its amplitude decreases and the wave undergoes a phase shift. The real (R) and imaginary part (X) of the shear impedance of the viscoelastic material, given in Eqs. (15) and (16), can be obtained using Eqs. (13) and (14):

R = Z1

1 − r02 1 − 2r0 cos Φ + r02

(15)

X = Z1

2r0 sin Φ 1 − 2r0 cos Φ + r02

(16)

For ultrasonic shear waves, the complex acoustic impedance Z* is related to the storage (G’) and the loss (G”) moduli through the following relationship:

Z* = ρG * = (ρ(G '+ jG ' ' )) 1 2

(17)

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where ρ is the density of the studied material. Then, both the storage and loss shear modulus can be expressed as a function of the real and imaginary part of the shear impedance as shown in equations (18) and (19) [12].

R 2 − X2 ρ 2RX G' ' = ρ G' =

(18) (19)

We can now rewrite the storage and loss shear modulus by replacing the equations (15) and (16) in the equations (18) and (19), where ρ is the density of the studied material, ρ1 the density of the elastic medium 1 and VS1 the shear ultrasonic velocity in 1.

G ' = (ρ1 VS1 )

2

(r02 − 1) 2 − 4r02 sin 2 Φ ρ(2r0 cos Φ + 1 + r02 ) 2

G ' ' = −4(ρ1 VS1 )

2

(r02 − 1)4r0 sin Φ ρ(2r0 cos Φ + 1 + r02 ) 2

(20)

(21)

At last, using relations (10) it is possible to deduce VS and alpha αs in the viscoelastic medium 2. Experimentally, an ultrasonic transducer, made of a piezoelectric crystal and a delay line in silica or glass for instance, is used. It represents the material 1 in the previous paragraph. The material 2 is constituted by the viscoelastic liquid which is studied. For some polymers the phase shift undergone by the ultrasonic wave is measurable on the first echo reflected on the viscoelastic interface. Hence, the method can be used. We can cite studies of polydimethylsiloxane polymers and curing epoxy systems [18][19]. However, in many cases, the phase shift is so small that no measurement is possible on the first echo. But, as the ultrasonic attenuation is small in the delay line, multiple reflections are possible leading to the existence of many echoes. Using this fact, we have proposed a multiple ultrasonic reflection device (MUR) [11]. We worked with the multiple reflections instead of a simple one, at the interface between the delay line and the material. Then, the complex reflection coefficient, r* was calculated with the following relationships, where: An is the amplitude of the echo number n, in the case of the interface (Delay Line /air), Bn is the amplitude for the interface (Delay Line / material), Δtn is the time shift between these two echoes and f is the operating frequency (See Figure 3) .

⎛B ro = ⎜⎜ n ⎝ An

1

⎞n ⎟⎟ ⎠

(22)

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⎛ Δt ⎞ Φ = −2πf ⎜ n ⎟ ⎝ n ⎠

107 (23)

If n echoes are used, the time-lag or phase shift to be measured, is multiplied by a factor n.

Figure 3. Schematic representation of the Multiple Ultrasonic Reflection method.

On an experimental point of view, the shear transducer is excited using a pulser /receiver (Panametrics Olympus 5800 PR) and digitized by an oscilloscope (Tektronix TDS 3032). All the measurements are made in a refrigerated incubator (BINDER KB 53) with an accuracy of ± 0.1°C. The signals are recorded on a computer using a standard IEEE GPIB interface. Before each test, the free surface of the sensor is cleaned with ethanol. Then, the transducer is introduced in the incubator and the reference signal (interface delay line / air) is recorded. After, the material to be studied is deposited on the free surface of the system and the echoes in the delay line are again acquired. For each set of measurements, several echoes are recorded. ro and the phase shift Φ are determined with a program elaborated under Labview©. Concerning the signal processing, (A1…An) and (B1…Bn) are simply evaluated using the minima and maxima on the echoes. Then, following the procedure given in [11] we plot ln(Bi/Ai) versus i, for i ranging from 1 to n. A linear adjustment of this curve directly leads to ln(ro). Hence, ro is deduced. For the phase shift evaluation, we use a cross correlation procedure [20] between the echo i for the interface between delay line and air and the echo for the interface between delay line and viscoelastic material, leading to Δti. Then a linear adjustment of Δti versus I, for i ranging from 1 to n, leads to Φ deduction.

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Remark : Recently [21] we have also demonstrated that it is preferable to use delay lines in polymers in order to create a more important time shift. For instance with a delay line in Polyurethane, the time shift is multiplied by ten when compared to a delay line in glass or silica. With such delay lines, the multiple ultrasonic reflection method overpass oblique incidence methods proposed in literature and which are more complex to design and use [12][17].

2.3. Approaches Using Resonant Acoustic Sensors 2.3.1. Background and Classical Horns for Newtonian Fluids Evaluation The resonant acoustic method uses the alterations in the resonance state of a sensor (see Figure 4) to determine the properties of the medium in which the sensor tip is immersed or inserted. A piezoelectric element serves as both input and output port. The input signal is a frequency sinusoidal signal that excites extensional acoustic waves in the resonant system. The dimensions of the sensor define the frequency range working. Typically it is between 10kHz to 50kHz. The amplitude of the vibration at the end of the tip is around 100 nm. Because the tip diameter of the sensor is much smaller than the wavelength of the acoustic waves, the interaction of the tip with the sample occurs in a confined energetic-field around the tip. The medium interacts with the sensor tip and exerts a mechanical constraint, hence modifying the resonance. The response of the acoustic sensor is its impedance (ratio between voltage and intensity) variation as a function of the frequency. The sensor is composed of two stainless steel parts, glued by epoxy to a piezoelectric element (See figure 4). The piezoelectric element excitation produces extensional oscillations of the main resonator, which is free at the upper end and coupled to the smaller resonator (the tip) at the lower end. The fundamental mode corresponds approximately to a half wavelength mode. The tip, which is nearly free at the lower end and is strongly coupled to the large mass at the other end, oscillates in a quarter wavelength mode. As the length of the main cylinder (l2+l3) is roughly equal to twice that of the tip (l1), the two oscillators are strongly coupled and vibrate in the same frequency range.

Figure 4. Schematic representation of the resonant acoustic sensor.

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The tuning of the two oscillators ensures that the acoustic load acting on the tip is efficiently transferred to the electrical system. The sensor is designed to be dipped or inserted into the medium to be tested. This is a major difference from many devices designed to measure media properties, where the piezoelectric element is totally immersed or where the medium must completely fill the measurement cell. The acoustic sensor interacts with the medium in two ways: through pressure waves radiated through the medium from the flat end of the tip, and through shear waves [22]. In the case of measurements on liquids, the measurement parameters are Δf , the frequency in the analyzed medium minus the frequency in air and Δ(1/Z), the inverse of the value of the electrical impedance Zelec measured when the tip is in the medium minus the inverse of the value of Zelec in air. The coordinates of the maximum of the resonance curve, needed to determine Δf and Δ(1/Z), are calculated using a numeric interpolation method. Generally, the sensor used is composed of two metal parts (stainless steel), glued by epoxy to a PZ ceramic element. The complex electrical impedance Zelec of the sensor is measured using the electrical circuit shown in Figure 4. With Newtonian liquids, the forces exerted on the probe modify the resonance and can be related to the density ρ and the dynamic viscosity η of the sample by the relations [22]:

Δf = Aρ + B(ρη)

0. 5

(24)

1 0. 5 Δ ( ) = C(ρη) Z (25) The constants A, B and C are related to the sensor and the experimental set-up. In fact, the density arises because the probe does not vibrate freely after immersion or insertion, but drives an extra mass of liquid instead. This decreases the resonance frequency. Laterally, the shear of the fluid involves energy losses and attenuation and a broadening of the resonance curve. So, for Newtonian liquids it is clear that this technique can give the viscosity regarding relations (24) and (25). When the rheological behaviour of the material is unknown, it is not so simple to determine the viscoelastic properties. But one can follow the variations of Δf and Δ(1/Z) as a function of an external parameter. Such an approach will be applied on mangos in part 3.4. Recently, this approach has been improved to viscoelastic materials and is presented in the following paragraph.

2.3.2. Resonant Approach based on a Vibrating Plate for Viscoelastic Materials Evaluation The main principle is identical to the previous paragraph but here, the tip is a thin plate in glass. Excited on one extremity with longitudinal waves, the plate generates in the surrounding fluid pure transverse waves. Such a configuration has been described in literature [12] and coupled with a stroboscopic photoelastic observation system. Then G’ and G’’ are

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deduced from a direct observation of the shear wave propagating in the viscoelastic medium. In our system, we deduce G’ and G’’ from Zelec measurement as presented in 2.3.1. Let us now consider the system presented in figure 5a.

Figure 5a. Resonant system using a plate for shear waves generation.

In the following part we will use a small part of the plate with dimensions (dx, e, l) as presented in figure 5b.

Figure 5b. Details of the plate’s dimensions and small volume considered for calculations.

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The dimensions are chosen so that λplate >>e. Under this assumption, the waves generated in the viscoelastic medium are purely transverse. Hence, the shear velocity in the viscoelastic medium is directly linked to G’, G’’ and Z* with the relationships (11) and (17). Let define S = (l.e), S’=(l.dx), E the glass Young modulus (real constant) and G* the complex shear modulus of the viscoelastic material surrounding the plate. If a longitudinal wave generated by the piezoelectric element is propagating in the plate and if “u” is the longitudinal displacement created in the small width dx, two forces (F1 and F2) are acting in the small volume (dx, e, l).

F1 = ES

du dx

F2 = −G *S'

(26)

du dz

(27)

Using the second Newton’s law, one finds :

Ee

∂²u ∂²u ∂u = ρ glass .e. + 2Z* v ∂x ² ∂t ² ∂t

(28)

where (Z*v) is the complex impedance of the viscoelastic material If one reports the solution as a propagative and attenuated ultrasonic wave, it is possible to define an effective propagation vector keff :

k eff = k glass ² −

2 jωZ*V 2

ρ glass Vglass e

(29)

where : Vglass is the shear velocity in glass Using this effective propagation vector, the local impedance at the abscissa x defined as the ratio between strain and velocity, can be written as : 2

Z( x ) = j

ρglass Vglass k eff ω

tan( k eff x )

(30)

At last, using the relation between an impedance at the abscissa x and the impedance at the abscissa (x+a) [23] and the expression of electrical impedance of a piezoelectric element surrounded with two charges [24], we can write the electrical impedance of the piezoelectric element as :

Z elec = with :

1 jωC o

⎡ Ω jΔ ⎤ ⎢⎣1 + ω Π ⎥⎦

(31)

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Ω=

h ²εZ p

(32)

L p ²k p

(

)

Δ = Z *V (L + L 1 ) + Z *V L 2 sin( k p L p ) − 2 Z p (1 − cos (k p L p ))

(

(

)

(

(33)

)

)

Π = Z 2p + Z*V (L + L1 ) + Z*V L 2 sin(k p L p ) − j Z*V (L + L1 ) + Z*V L 2 Z p cos(k p L p ) (34) In these relationships, subscript “p” refers to the piezoelectric element. h, Co and ε are fundamental constants of the piezoelectric element. This Zelec, measured with the electrical device presented in figure 4 is a function of piezoelectric constants (evaluated with an experiment in air) and of (Z*v) which is directly linked to G*. So, thanks to the adjustment of the measured Zelec and the theoretical value (equation 31), G’ and G’’ of the viscoelastic material are obtained.

3. RESULTS 3.1. Examples of Master Curves 3.1.1. Glycerin In order to validate the multi-frequencial approach developed with ultrasonic waves, we present in this part results on a well known and simple material: glycerin. This material which is Newtonian should have, on a very large frequency range, a storage modulus G’ equal to zero and a loss modulus G’’ equal to ωη where η is the viscosity and it is constant on the whole frequency range. First, rheological measurements of G’’ have been performed on a rheometer AR 2000 TA Instruments. Working at 20°C, G’’ has been obtained from 0.16 to 16 Hz (See Figure 6). No value of G’ has been measured and the linear evolution of G’’ versus frequency is clear. Furthermore steady state measurements of viscosity have shown that it remains constant versus shear rate. Hence glycerin is clearly Newtonian for small frequencies. Then, ultrasonic methods have been tested with the resonant plate and Multiple Ultrasonic Reflection Technique. For resonant plate we worked at 25 kHz. For reflectometry we have used two sensors (5 and 10 MHz) and Time Temperature Superposition principle from 5° C to 30 °C. The linear trend from 0.16 Hz to 30 MHz is perfect and clearly demonstrates the Newtonian behavior of this material. The adjustment leads to a constant viscosity of 1.3 Pa.s. This value is in good agreement with the value obtained with steady state measurements. It is also in good agreement with values given in literature and which can vary, depending on the relation proposed by authors to rely viscosity to temperature [25].

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Figure 6. Master curve obtained for glycerin at 20°C.

3.1.2. Honey The same works, performed on honey (Lune de Miel ©) has given the following master curve. We used two resonant sensors working at 13 and 57 kHz especially designed for Honey studies.

Figure 7a. Master curve of honey measured with a rheometer and ultrasonic methods. T=20°C.

With classical rheology, the beginning and the end of the rubbery plateau are clearly visualized. With reflectometry, the glass transition is well observed. Regarding this master curve it is clear that honey, generally studied with very low frequencies and which is sometimes said to be Newtonian is clearly non Newtonian and appears as an entangled polymer. The rubbery plateau region points out that honey presents reversible loose network, thermorheologically simple, which gives the illusion of an entanglement network. Hence, honey appears as a complex structured fluid : one may advance that hydrogen-bonded structures are responsible for the existence of the plateau [11]. With this master curve,

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transverse velocity and attenuation have been calculated with (10). The results are presented in Figure 7b. The velocity reaches a plateau for high frequencies. This behaviour is also observed on G’. Concerning attenuation, the global behavior is similar to G’’ : rapid decrease after glass transition. For higher frequencies G’’ tends to zero and G’ does not evolve. Hence the viscoelastic material reaches the behavior of a solid material. This high frequency behavior could be well described with a Maxwell element.

Figure 7b. Transverse attenuation and velocity calculated versus frequency. T=20°C.

With the values of attenuation α , the penetration depth of the ultrasonic or mechanical wave can be defined as 1/α. For ultrasonic frequencies (over 10 kHz), this value ranges from 400 µm to 5 µm for f = 100 MHz. Regarding these values it is clear that performing echography is totally impossible and reflectometry or vibrating methods can be said to be the unique ways to obtain high frequency information (around glass transition) for viscoelastic materials with measurements performed around room temperature. The interest of such measurements is presented in the next part for honey.

3.2. Moisture Content in Honey As fermentation reduces considerably consumer acceptability, a precise determination of moisture content is fundamental. Generally, honeys are classified as follows: for moisture content less than 18 %, no fermentation occurs if the yeast does not exceed 1000 cfu.g-1 (colony forming unit per gram of honey). Between 18 and 19 %, no fermentation occurs if the yeast does not exceed 10 cfu.g-1. Over 19 % of moisture content, a danger of fermentation exists in all cases. In Europe, honey composition as well as manufacture is controlled by the Council Directive 74/409/EEC of 22 July 1974 [26][27][28]. Over 20 % of moisture content, the product is not considered as honey. Experimentally, moisture content can be evaluated using optical refractometers because of the direct relationship between refractive index and moisture content [29] or with Differential Scanning Calorimetry. With DSC, the parameter

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which is studied is the glass transition temperature which is around -40°C but can vary a lot depending on moisture content [30][31]. As the density ρ of honey depends on moisture content, the calculation of G’ and G’’ is not possible if the moisture content is unknown. So, the parameters G’ and G’’ are not interesting. We will use the quantity Tan(δ) defined as G’’/G’ and which can be obtained without the knowledge of the density regarding equations (20) and (21). If Tan(δ) >> 1 the viscoelastic material is said to be more viscous than elastic . Inversely, if Tan(δ) << 1, the elastic behavior of the material is more important. For a given material it is possible to go from Tan(δ)<1 to Tan(δ)>1 with a temperature increase . For high frequencies investigations of honey (over a few MHz), the temperature where Tan(δ)=1 corresponds to the beginning of the vitreous plateau. The beginning of this plateau is directly linked to the reduction of the free volume for entangled viscoelastic materials. As the introduction of water molecules will impact the free volume and hydrogen bounds, one can imagine that the beginning of the vitreous plateau will be very influenced by water content. Furthermore, this point has already been observed with DSC [30][31]. We worked with various kinds of honeys and with a 10 MHz ultrasonic shear transducer. For each honey sample, we varied temperature in order to determine the temperature where Tan(δ)=1. This temperature was called TVPB (Temperature for the Vitreous Plateau Beginning). In order to find a calibration law, the moisture content was also measured with an optical refractometer (Atago© pocket refractometer). For all the experiments, the temperature was precisely controlled (± 0,1 °C) with a BINDER KB 53 incubator. In details, the experiment was realized as follows: first, all honey samples were heated at 50°C for 1 hour before ultrasonic measurements to melt any crystals present and to remove the air micro bubbles. Then, water content was measured with the refractometer. The ultrasonic sensor was introduced in the incubator and the echoes in the delay line were acquired on the computer. Honey was then settled on the sensor, the echoes in the delay line were again acquired. At last, the echoes were treated on the computer in order to obtain r*, ρ.G’ and ρ.G’’. The temperature was modified, and the experiment again performed until G’=G’’. Hence, the temperature TVPB was obtained. A plot of TVPB versus moisture content gave a linear relationship. The best-fitting trend line through all the experimental points is shown in Figure 8 [32], where TVPB (Temperature of Vitreous Plateau Beginning) is plotted vs. moisture content. In this figure, the errors have not been reported for more clarity, however, for our measurements, we have an uncertainty of 0.5 °C for the temperature and an uncertainty of 0.2% for water content. If we use the best fitting (R2=0.99) as a calibration law, the following equation can be proposed: TVPB(°C) = -3.95 M + 85.5

(34)

where M is the moisture content in %. Using the previous relation and the fact that the uncertainty on TVPB is equal to 0.5°C, the uncertainty on M is equal to 0.13 %. Hence the method proposed in this paper is very accurate to distinguish very small variations of moisture content in honey.

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Figure 8. TVPB versus moisture content measured with ultrasonic shear waves.

TVPB would be a valuable quality control parameter because honeys with a TVPB less than 5°C (> 20% moisture content) can be considered of poor quality according to the European standards, while good quality honeys will have a TVPB around 14°C (< 18 % moisture content). Using the ultrasonic shear reflectivity method, the water content of honey samples was related to the temperature of the vitreous plateau beginning for a constant operating frequency of 10 MHz. This ultrasonic shear method is extremely sensitive because it is possible to separate two honeys whose water content varies from less than 0.2 %.

3.3. Viscoelastic Properties of Water / Sorbitol Solutions Historically, this study has begun in order to find a coupling fluid well adapted for longitudinal and shear waves. We present here major results because they clearly demonstrate the tight complementarities between longitudinal and shear high frequency data around glass transition. More details can be found in references [13] and [33]. In literature, the maximum longitudinal velocity found for pure liquids is 1980 m.s-1 for glycerin [34]. Weissler et al. in the short note [35] obtained an “unusually high velocity” for Sorbitol : 3000 m.s-1. In order to check and complete the above short note, we prepared Water /Sorbitol solutions for both longitudinal and shear investigations. We used Delalande Sorbitol manufactured by Sanofi synthelabo for solutions preparation. For 0 % (pure water) to a concentration of 85 % of Sorbitol in mass, we just put Sorbitol powder into water and mixed until we obtained a homogeneous liquid. From 85 to 100 % of Sorbitol in mass it was not possible to apply the same procedure because the dissolution was too slow. In this case we heated the solutions. In all the cases a homogeneous transparent highly viscous liquid was obtained. Results are presented in Figures 9 and 10.

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The rapid increase of longitudinal velocity is clearly explained regarding the values of G’ and G’’ versus Sorbitol content in water. Over 85 % of Sorbitol in water, G’ becomes greater than G’’. Hence the solution tends to a solid behavior for high frequencies. So, even if it could be surprising, this solution which appears as a liquid during the experiments, acts as a material over the glass transition for high frequency ultrasonic waves. Over glass transition the attenuation will decrease as presented in figure 7b for honey, and Sorbitol will become an ideal coupling fluid with high acoustical impedance and small attenuation. This special behavior for high frequency experiments around room temperature would not be the same for smaller frequencies.

Figure 9. Longitudinal 5 MHz velocity in Water /Sorbitol solutions. T=25°C.

Figure 10. G’ and G’’ values measured at 25°C for an operating frequency of 5 MHz from 75 to 100% of sorbitol concentration in the aqueous solutions.

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3.4. Mangos [36] The objectives of the present study were: (1) to examine the potential use of resonant acoustic method presented in 2.3.1 to characterize mango tissue firmness; (2) to compare and to correlate the acoustic parameter values obtained from a batch of mangoes with a wide range of maturity, to the related firmness values measured by a puncture test.   Tests  were  performed  on  commercial mango fruit (Mangifera  indica  L., cv. Amelie) imported from the Ivory Coast and purchased on wholesale market. Forty-five fruit were selected, covering a wide range of maturity. Pulp firmness Fp (N) was assessed by driving a flat-tipped 2 mm diameter cylindrical plunger into the mesocarp of the equatorial fruit slice. The depth and rate of penetration of the plunger into the pulp tissue were 10 mm and 1 mm/s, respectively. Special care was taken to penetrate the pulp perpendicularly to the cut surface. Penetrometer measurements were carried out using a TA-XT2 texture analyzer (Stable Micro Systems, Surrey, U.K.). The firmness index of the pulp was the average force Fp measured between 1 and 10 mm penetration depth, so as to take local pulp heterogeneity due to mango fibers into account. This parameter was calculated from the experimental force/displacement curve. Acoustic measurements were made by inserting the sensor tip (see paragraph 2.3.1.) into the mango up to 7 mm depth. Penetration depth was controlled using a micrometer (10 µm resolution). Each acoustic measurement point was about 4 mm from the adjacent puncture point. Average values were calculated for these parameters to characterize each fruit. Δf  was very sensitive to the quality of the probe-medium interface, especially for unripe fruit, and poorly correlated with puncture test values. In contrast, the acoustic parameter Δ(1/Z) was significantly correlated with mango pulp firmness Fp assessed by puncture test (Figure 11). Hence, resonant acoustic technique in its basic form (no viscoelastic model known) constitutes an interesting tool for mangos and certainly for fruit texture evaluation during storage or ripening.

Figure 11. Correlation between the acoustic parameter Δ(1/Z) and pulp firmness.

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CONCLUSION In this chapter we have presented three different ultrasonic approaches (echography, reflectometry, resonant method) especially dedicated to viscoelastic properties of material determination. Regarding results presented in this work and literature it is clear that enormous complementarities exist between classical rheological methods and ultrasonic approaches. Ultrasonics techniques have been applied on fruits (mangos) or liquid materials (Glycerin, Honey, Sorbitol). The method to be employed is adapted to the nature of the sample. Furthermore, as matter is non opaque to ultrasonic waves if the frequency is correctly chosen, investigations can be planed through pipes or storage tanks during fabrication process. Then, results obtained in a research context could be at the cost of minor modifications transferred to industrial media when food is prepared, transferred (in pipes for instance) or conditioned. Regarding the calibration law obtained on Honey versus moisture content it is also clear that ultrasonic waves could be usefull for non destructive quality controls.

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[19] Challis, R.E; Unwin, M.E; Chadwick, D.L; Freemantle, R.J; Partridge I.K; Dare, D.J; Karkanas, P.I. Journal of Applied Polymer Science, 2003, vol. 88, 1665-1675. [20] Hull, D.R; Kautz, H.E; Vary, A. Material Evaluation 1985, 43, 985. [21] Cereser Camara, V; Laux, D; Arnould, O. Ultrasonics 2010. Accepted. To be published. doi:10.1016/j.ultras.2010.02.007 [22] Lévêque, G ; Ferrandis, J.Y. ; Van Est, J. ; Cros, B. Rev. Sci. Instrum. 2000, vol. 71, 1433. [23] Blackstock, D.T. Fundamental of physical acoustics. Ed. Wiley Interscience, 2000. [24] Masson, W. Proceeding of the Institute of Radio Engineers 1935, vol. 23, 1252. [25] Viswanath, D.S; Ghosh, T.K.; Prasad, D.L; Nidamarty, V.K. ;Dutt N.V.K. Viscosity of Liquids.Springer 2007. [26] Marini, F.; Magrì, A. L.; Balestrieri, F.; Fabretti, F.; Marini, D.(2004). Analytica Chimica Acta 515(1), pp. 117-125. [27] Radovic, B. S.; Goodacre, R.; Anklam, E. (2001). Journal of Analytical and Applied Pyrolysis 60(1), pp. 79-87. [28] Zhenbo Wei; Jun Wang; Wenyan Liao.(2009). Journal of Food Engineering. Volume 94, Issues 3-4, October 2009, Pages 260-266. [29] Wedmore, E. B. (1955). Bee World, 36(11), pp. 197-206. [30] Kantor, Z.; Pitsi, G.; Thoen, J. (1999). Journal of Agricultural Food Chemistry, 47, pp. 2327–2330. [31] Sopade, P. A.; Bhandari, B.; Halley, P.; D’Arcy, B.; Caffin, N. (2001). Food Australia 53, pp. 399–404. [32] Cereser Camara, V; Laux, D. J. Food. Engeneering. 96 (2010) 93-96 [33] Laux, D . ; Levêque, G.; Cereser Camara, V. Ultrasonics 49 (2009) 159-161. [34] Kinsley, L.E. Fundamentals of acoustics. Third Edition. 1984. [35] Weissler, A.; Del Grosso, V.A. The Journal of the Acoustical Society of America. Vol 23, number 5. September 1951. [36] Valente, M; Ferrandis, JY. Postharvest Biology and Technology, 2003, vol. 29, 219228.

In: New Topics in Food Engineering Editor: Mariann A. Comeau

ISBN: 978-1-61209-599-8 © 2011 Nova Science Publishers, Inc.

Chapter 6

TRENDS IN HIGH PRESSURE PROCESSING OF FOODS: FOOD QUALITY AND BIOACTIVE COMPONENTS Shirani Gamlath1 and Lara Wakeling2 1

School of Exercise & Nutrition Sciences, Faculty of Health, Medicine, Nursing & Behavioural Sciences, Deakin University, Victoria, 3125, Australia 2 School of Science & Engineering, University of Ballarat, Ballarat, Victoria, 3353, Australia

ABSTRACT High pressure processing (HPP) is a non-thermal food processing technology that offers great potential for the processing of a wide range of food products. Application of HPP can inactivate micro-organisms, affect food-related enzymes and modify structures with minimal changes to nutritional and sensory quality aspects of foods. The effects of high pressure on the inactivation of micro-organisms in food have been thoroughly reviewed. Recent research on HPP has mainly focused on fruits and vegetables with an emphasis on food quality and bioactive components. This chapter highlights the current trends in HPP research and provides a summary of the available findings on the effect of HPP on chemical, nutritional and bioactive components and health related properties of a wider range of commodities. Strategies to maintain the quality attributes and health related components in HPP foods and identification of the gaps for future research in HPP are also discussed.

INTRODUCTION High pressure processing (HPP) has gained much attention in recent years due to a number of advantages: the retention of fresh taste and texture in products such as fruit and vegetable juices, shell fish, sauces and guacamole; an increase in microbiological safety and shelf-life by inactivation of pathogens, spoilage organisms and some quality related enzymes; production of novel products by modifying the existing food structures; low energy

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consumption and labour requirements, and uniform isostatic pressure distribution throughout the product, irrespective of size and geometry (Patterson, 2006; Rastogi et al., 2007). A number of publications have demonstrated the application of HPP in relation to microbiological safety and keeping quality of foods, with optimal pressure and exposure times to validate the process conditions for fruits, vegetable, dairy and meat products. Some quality attributes, particularly the effect on quality related enzymes in food matrices and buffer systems, have also been extensively studied. There is a significant commercial interest in both the local and global markets for new food products particularly fresh-like food products with extended shelf life and high quality. The interest of consumers in the consumption of fresh fruit and vegetables with high antioxidant levels has also become a current trend. High pressure processing has attracted local and global interest due to its mild effect on food products, while maintaining high nutritional value and fresh like properties compared to thermal processing. However, the existing knowledge on pressure stability of nutritional and bioactive components in HPP foods and the optimum conditions to retain the beneficial properties of foods are limited. This chapter aims to explore the current literature around the effect of HPP on nutritional, chemical and bioactive components and health related properties of a wider range of commodities with an emphasis on identifying suitable HPP regimes to retain the nutritional and healthful properties of foods.

EFFECT OF HPP ON FOOD QUALITY It is now well known that HPP can be used to inactivate micro-organisms, while maintaining organoleptic and nutritional qualities of the food. Much of this quality work has focussed on colour and texture in food, as these are the properties that consumers base their food choices on. The effect of HPP on colour and texture has been extensively covered in fruit and vegetables over many years, with comprehensive reviews available by GuerreroBeltran et al., (2005), Rostagi et al., (2007) and Norton and Sun (2008). Most texture related investigations have focussed on the effect of high pressure on enzymes such as pectin methylesterase and polygaluctonase, although interest in assessing texture by instrumental means is now increasing (Norton and Sun, 2008, Perera et al., 2010). In muscle foods increased pressures are thought to result in changes to the muscle proteins, resulting in changes to functional properties and therefore to texture. This was found to be the case in fish where pressures ≥450 MPa resulted in higher hardness, gumminess and chewiness (Yagiz et al., 2007). Colour has been assessed via either instrumental techniques or individual pigments, such as the carotenoids, have been assessed. Table 1 summarises some very recent findings in this area. There is a decrease in redness in all muscle foods after HPP (Andres et al., 2006, Cava et al., 2009) with a corresponding increase in lightness of fish, often associated with a cooked appearance (Ramirez-Suraz and Morrissey, 2006, Yagiz et al., 2007). A decrease in the lightness of HPP cow’s milk and ewe’s milk has also been observed (San Martin-Gonzalez et al., 2006). It has been suggested that the changes in the lightness of these animal products are due to protein structural changes that occur after HPP.

Table 1. The effect of HPP on colour- examples from recent studies Commodity

Method used

HPP conditions*

Iberian ham

Colour

200 & 400 MPa/20 °C/15 min

200 & 300 MPa/15-30 min

200 & 300 MPa/15-30 min

Iberian loin

Rainbow trout (whole muscle)

Colour (up to 6 days)

150, 300, 450, 600 MPa/RT/15 min

Mahi Mahi (whole muscle)

Colour (up to 6 days)

150, 300, 450, 600 MPa/RT/15 min

Albacore tuna (minced muscle) Cows and Ewe’s milk

Colour

275 & 310 MPa /RT/2, 4, 6 min

Melon

Colour

∆E 600 MPa/RT/10 min

Compared to the experimental control Lower initial redness (a*) then stable Lightness ↑ with storage (39 days at 5 °C)

Reference Andres et al., 2006

Cava et al., 2009 ↓ redness; lightness and yellowness unchanged. Similar effect after 90 days storage (4 °C) Cava et al., 2009 ↓ redness; lightness and yellowness unchanged. Similar effect after 90 days storage (4 °C), except yellowness ↑ ↓ redness (a*) >150 MPa cooked appearance (↑ lightness) ↓ redness (a*) Slight ↑ in lightness & yellowness with pressure. Lighter and whiter with ‘cooked appearance’; (L* and b* ↑; a* ↓) ↓ in lightness ↑ in lightness, redness and yellowness

Yagiz et al., 2007

Yagiz et al., 2007

Ramirez-Suraz and Morrissey, 2006 San Martin-Gonzalez et al., 2006 Wolbang et al., 2008

Table 1. (Continued) Commodity

Method used

HPP conditions*

Apple – Granny Smith

∆E

600 MPa/ 22 °C/1-5 min (medium: pineapple juice)

Pink Lady Blackberry puree Strawberry puree

Colour & ∆E

400, 500 & 600 MPa/10-30 °C/15min

Tomato puree Carrot puree

Colour

400-600 MPa/20 °C/15 min

Tomato juice and Carrot juice

∆E

250 MPa/35 °C/15 min

* RT = Room temperature

Compared to the experimental control ↓ at HPP for 3 & 5 min + 25 & 50 % pineapple juice ↓ at HPP for 1 min + 25 % pineapple juice Minor changes. Redness ↓; 96 % retention in strawberries at 600 MPa Redness and colour intensity retained. Lightness ↑ in carrots but ↓ in tomatoes Minor change with HPP. Change ∆E≤15 units after storage for 30 days (4 & 25 °C)

Reference Perera et al., 2010

Patras et al., 2009a

Patras et al., 2009b

Dede et al., 2007

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Minimal changes occur to the colour of HPP fruits (Dede et al., 2007; Patras et al., 2009a,b; Perera et al., 2010), although changes may occur in individual pigments. Further information about the affect of HPP on individual pigments is given in the section on bioactive compounds. It is generally accepted that HPP does not alter the flavour of food. However a recent application of HPP regarding flavour has been the combined use of enzyme immobilisation and HPP to de-bitter grapefruit juice (Ferreira et al., 2008). A pressure of 160 MPa/37 °C/20 min resulted in a reduction in the naringin content and therefore a reduction in bitterness.

EFFECT OF HPP ON CHEMICAL AND NUTRITIONAL COMPONENTS IN FOOD Macronutrients Some recent studies highlighting the effect of HPP on macronutrients in food are shown in Table 2. Generally the total protein and total lipid contents are not affected by HPP. Lipid tends to be of greatest interest in terms of the effect of HPP on lipid oxidation. Total sugars, sucrose, glucose and fructose are not affected by HPP. However sucrose levels dramatically decreased in pressure-treated (600 MPa/25 °C/6 min) raspberry puree, with a corresponding increase in glucose and sucrose levels, most likely due to invertase activity (Butz et al., 2003). Very few studies are available that have investigated the effect of HPP on dietary fibre. In cabbage, no effect of HPP (up to 500 MPa and 80 °C) on total dietary fibre was evident, however soluble fibre increased, while insoluble fibre decreased at 400 MPa (Wennberg and Nyman, 2004). A similar effect was evident in longan fruit pericarp for water soluble polysaccharides, while other fibre components were not affected by HPP (Yang et al., 2009). HPP has been shown to enhance glucose retardation/binding in tomato puree (Fernandez Garcia et al., 2001; Butz et al., 2002a), suggesting that this technique might be used to develop diabetic foods. Overall, as HPP has a limited effect on covalent bonds, macro- and micro-nutrients tend to not be affected by HPP except at conditions of high pressure and high temperature.

Micronutrients The effect of HPP on vitamins has mainly focussed on ascorbic acid (Vitamin C) content, although thiamine (B1), riboflavin (B2), pyridoxal (B6) and folate have also been investigated. The majority of these studies have been in fruits and vegetables or model systems. Generally all of these water soluble vitamins are stable to high pressure processing, with folate showing higher sensitivity (Sancho et al., 1999; Indrawati et al., 2002; Oey et al., 2008; Sanchez-Moreno et al., 2009). A summary of recent studies assessing ascorbic acid content is shown in Table 3.

Table 2. The effect of HPP on Macronutrients Commodity

Macronutrient

HPP conditions

Beef

Total lipid

Ham (frozen)

Total protein

Albacore tuna

Total lipid

0.1, 200 & 800 MPa/60 ºC/20 min 400 & 600 MPa/~15 °C/10 min 275 & 310 MPa/2, 4, 6 min

Milk

Total lipid Total protein

400 MPa/20 °C/20 min

No change No change

Cheese

Total lipid Total protein

400 MPa/20 °C/20 min

No change No change

Cheese – using HP milk

Total lipid Total protein Glucose binding (retardation)

400 MPa/20 °C/20 min 600MPa/20 °C/60 min

Significantly ↓ Significantly ↑ Significant retention (28 %)

600MPa/25 °C/60min

↑ of 14.34-27.13 %

Butz et al., 2002a

0.1, 400 & 500 MPa/20, 50 & 80 °C/10 min

Slight change in TDF. Soluble fibre - ↓ all temp at 400 MPa; Insoluble fibre – concurrent ↑

Wennberg and Nyman, 2004

Tomato puree

White cabbage

Dietary fibre

Compared to the experimental control No change

Reference

Tume et al., 2010

No significant change

Serra et al., 2007

Significantly ↑

Ramirez-Suarez and Morrissey, 2006 Sandra et al., 2004

Fernandez Garcia et al., 2001

Longan fruit pericarp

Raspberry puree

Strawberry Raspberry Orange juice

Orange/lemon/carrot mixed juice

Water-soluble polysaccharide Alkali-soluble polysaccharide Cellulose Lignin Sucrose

0, 200, 300, 400, 500 MPa/25 °C/30 min

↓ with ↑ pressure

Yang et al., 2009

No significant difference

600 MPa/25 °C/6 min 600 & 800 MPa/44 °C/6 min

Glucose & fructose

600 Mpa/25 °C/6 min

Sucrose

600 MPa/25 °C/6 min

Fructose, glucose, sucrose, total sugar

500 & 800 MPa/20 °C/ min

Total sugar

No significant difference No significant difference >90 % loss during storage at 4 °C for 30 days at 600 MPa

Butz et al., 2003

40 % and 60 % ↑ during storage at 4 °C for 30 days at 600 MPa 100 % retained >95 % retained No difference

Butz et al., 2003

500 & 800 MPa/20 °C/ min

100 % retention after 21 days storage (4 °C)

Butz et al., 2002b

Sucrose

600 MPa/25 °C/6 min

100 % retention

Butz et al., 2003

Sucrose

600 MPa/25 °C/6 min

100 % retained

Butz et al., 2003

Butz et al., 2002b

Table 3. The effect of HPP on Micronutrients Commodity

Micronutrient

HPP conditions*

Beef

Tocopherol

0.1, 200 & 800 MPa/60 ºC/20 min

Fatty acids

Iberian ham

α-tocopherol

200 & 400 MPa/20 °C/15 min

Dry-cured pork loin

Total free amino acid

Orange juice

Ascorbic acid

300, 350 & 400 MPa/20 °C/10 min 600 MPa/40 °C/4 min

Compared to the experimental control No change Significant ↑ myristic (14:0); palmitic (16:0); trans-vaccenic acid (18:1trans-11) Significant ↓ linoleic acid (18:2n-6), cis-vaccenic acid (18:1cis-11) No effect of HPP ↓ correlation with ↑ TBARS after 39 days storage (5 °C) No change

Reference Tume et al., 2010

Andres et al., 2006

Campus et al., 2008

Retained

Polydera et al., 2005

500 & 800 MPa/20 °C/ min

No change. 90% retention after 21 days storage (4 °C)

Butz et al., 2002b

100 MPa/60 °C/5 min 350 MPa/30 °C/2.5 min 400 MPa/40 °C/1 min

90 % retention No change

Sanchez-Moreno et al., 2003a

93 % retention. No further loss after 10 days storage (4 °C)

Orange/lemon/carrot mixed juice Tomato juice Carrot juice

Tomato puree Carrot puree Cowpea

Melon

Blackberry puree Strawberry puree * RT = Room temperature

Ascorbic acid

Ascorbic acid – 4 days germination 6 days germination Ascorbic acid

Ascorbic acid

600 MPa/25 °C/6 min

95 % retained

Butz et al., 2003

600 MPa/25 °C/6 min 250 MPa/35 °C/15 min

>95 % retained No change. After 30 days storage (4 & 25 °C) 70 % & 45 % retained respectively

Butz et al., 2003 Dede et al., 2007

400-600 MPa/20 °C/15 min 300, 400 & 500 MPa/RT/15 min

90 % retained

Patras et al., 2009b

600 MPa/RT/10 min

400, 500 & 600 Mpa/10-30 °C/15min

Doblado et al., 2007 90-72% retained 91-59% retained 50 % retained. Cultivar dependent with 15-74 % retention. No significant change (>90 % retained)

Wolbang et al., 2008

Patras et al., 2009a

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Ascorbic acid has been shown to be stable to high pressure processing, unless it is subjected to high pressure and high temperature (>65 °C) conditions, where oxidation reactions are enhanced (Oey et al., 2008). In addition to its role as a vitamin, ascorbic acid also plays an important role as an antioxidant. Ascorbic acid has been shown to protect folate against the effects of pressure and heat (Oey et al., 2008). Further information about the antioxidant role of ascorbic acid can be found in the section on HPP and antioxidant activity. Most studies of the effect of HPP on fat soluble vitamins have been limited to carotene, Vitamin A (retinol) and Vitamin E (tocopherol). Again, HPP has minimal effect on these vitamins (Table 2) (Indrawati et al., 2002; Oey et al., 2008). The effect on carotenoids is expanded upon in the section on antioxidant activity. There is still limited information available about the effect of HPP on niacin, cobalamin, and Vitamins A, D, E and K. Information about the effect of HPP on minerals is scarce, with most discussion of minerals focussing on possible relationships between iron and lipid oxidation. This is an area that requires further investigation. As with the other micronutrients mentioned, few studies have been reported that investigate amino acid or fatty acid content except in meat products. HPP (up to 400 MPa) of dry-cured pork loin has no effect on the total free amino acid content (Campus et al., 2008). Some variation in individual fatty acids occurs when beef is subjected to HPP (up to 800 MPa) (Tume et al., 2010). Further investigation into the effect of HPP on individual fatty acids is necessary, especially in relation to essential fatty acid content.

Proteins and Enzymes Proteins are usually denatured by high pressure however the protein type, processing conditions and pressures applied are all important considerations. Generally, at low protein concentrations and low pressures (<300 MPa), reversible pressure-induced denaturation occurs, while higher pressures (>300 MPa) induce irreversible and extensive effects on proteins (Guerrero-Beltran et al., 2005; Rastogi et al., 2007). HPP is most commonly used to inactivate deleterious enzymes, thereby ensuring the high quality characteristics of the food are maintained (Rastogi et al., 2007), but it can also be used to stabilise and activate other enzymes (Eisenmenger and Reyes-de-Corcuera, 2009). These changes tend to have little effect on the nutritional content of the food, but are important with respect to food quality especially in relation to colour (polyphenoloxidase) and texture of food (pectic enzymes), plus some influence lipid oxidation (lipase and lipoxygenase). Thus most investigations have focused on monitoring the effect of high pressure on these quality related enzymes, and there are a number of comprehensive review articles available summarising this work (Guerrero-Beltran et al., 2005; Rastogi et al., 2007; Eisenmenger and Reyes-de-Corcuera, 2009). Table 4 shows a summary of the effect of HPP on proteins and enzymes in various commodity groups. Future work may focus on expanding the knowledge of HPP on antioxidant enzymes as another means of maintaining food quality and nutrition.

Table 4. The effect of HPP on Proteins Commodity

Protein type

HPP conditions*

Cows Milk

Casein miscelle

Whey proteins

Goats milk

β-lactoglobulin α-lactalbumin BSA Casein miscelle

Soy milk

Lipase Lipoxygenase

250 MPa >400 MPa 500 & 800 MPa 500 & 800 MPa ≤400 MPa/RT/60 min 300-350 MPa / 45 °C 400-500 MPa 300-400 MPa/0-180 min >800 MPa 600 MPa/60 °C ≤300 MPa/9°C/20 min

Cold-smoked salmon Lupin Meta (bovine) Ham (frozen)

Dry-cured pork loin

Proteolytic enzymes (cathepsin and calpains) Protein digestibility (invitro) Antioxidant enzymes (superoxide dismutase, catalase, glutathione peroxidise) Proteolytic enzymes (cathepsin B, cathepsin B+L) Cathepsins

amino-peptidases dipeptidyl peptidases

* RT = Room temperature

Compared to the experimental control Size temp dependant Size altered 70 % & 90 % denatured 10 % & 50 % denatured Not denatured ↑ Size Smaller size ↑ activity Inactivated Inactivated Reduced activity

Reference

200 & 500 MPa/10 °C/10 min

Most hyrolysed at 500 MPa

De Lamballerie-Anton et al., 2002

400 & 600 MPa/~15 °C/10 min

Significant ↓, but still active

Serra et al., 2007

San Martin-Gonzalez et al., 2006 San Martin-Gonzalez et al., 2006 San Martin-Gonzalez et al., 2006 Van der Ven et al., 2005 Lakshmanan et al., 2005

No significant change

300, 350 & 400 MPa/20 °C/10 min

20 % ↓ for 400 MPa; Significant ↓ after 45 days storage Significant ↓ Significant ↓

Campus et al., 2008

Table 5. The effect of HPP on Lipid Oxidation Commodity

Lipid oxidation test

HPP conditions*

Chicken (minced thighs)

TBARS (to 9 days)

500 MPa/50 ºC/30 min (air storage)

TBARS (7 days) Beef

TBARS (up to 6 days)

500 MPa/-10, 5, 20, & 50 ºC/30 & 60 min (vacuum stored)

Raw – no significant difference after 9 days

0.1, 200, 400, 600 & 800 MPa/20-70 °C/ 20 min 0.1, 200 & 800 MPa /60 ºC/20 min

↑ at >400MPa regardless of temperature ↑ in 6 day samples Low at 200 MPa

Reference

Beltran et al., 2004

Ma et al., 2007 Tume et al., 2010

↓ at 6 days

Peroxide Value

Iberian ham

Compared to the experimental control Raw – significant ↑ at 6 & 9 days Overcooked – significant ↑ from day 1

TBARS (7 days)

0.1, 200, 400, 600 & 800 MPa/20-70 °C/ 20 min

TBARS (90 days storage)

200-300 MPa/20-30 min

TBARS (39 days)

200 & 400 MPa/20 °C/15 min + MAP

At 20 & 40 °C - 5 fold ↑ at ≥400MPa. Little additional effect at > temperatures No differences

Significant ↑ at 39 days (5 °C) for 400 MPa No O2 = low TBARS

Ma et al., 2007

Andres et al., 2006

Dry-cured pork loin

TBARS (up to 45 days storage)

300, 350 & 400 MPa/20 °C/10 min

No difference

Campus et al., 2008

Ovine Milk

Free Fatty Acid value

500 MPa /4, 25, 40 °C

No change or ↓

Norton and Sun, 2008

Rainbow trout (red muscle)

TBARS (up to 6 days)

150, 300, 450, 600 MPa/ 5 min

Yagiz et al., 2007

Mahi Mahi (red muscle)

TBARS (up to 6 days)

150, 300, 450, 600 MPa/RT/15 min

Sardine (cold smoked)

TBARS & Peroxide Value TBARS (22 days/4 °C & 93 days/-20 °C)

300 MPa/20 ºC/20 min

No change at 150 MPa ↑ with ↑ pressure 300 MPa ideal ↑ with ↑ pressure, with max at 300MPa then ↓; 450 MPa ideal Little change over 2 wk storage (air) Lower immediately after HPP. Slight increase with storage

Albacore tuna (minced muscle) * RT = Room temperature

275 & 310 MPa/2, 4, 6 min

Yagiz et al., 2007

Gomez-Estaca et al., 2007 Ramirez-Suraz and Morrissey, 2006

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Lipid Oxidation The effect of HPP on lipid oxidation is important from a nutritional perspective due to the loss of polyunsaturated lipids and the production of undesirable oxidation products. Most studies have focussed on meat, some on seafood, and a few on dairy products. The focus on muscle foods is primarily due to reports that lipid oxidation is enhanced at processing pressures >400 MPa and is more prevalent in red meat due to the higher myoglobin levels (Ma et al., 2007). It has also been suggested that free metal ions are released by the pressure treatment, again promoting lipid oxidation. Some recent studies investigating lipid oxidation in HPP foods are summarised in Table 5 and they reiterate the importance of keeping the processing pressure at <400 MPa to reduce lipid oxidation. However they also highlight that by vacuum storing the pressure processed muscle food, lipid oxidation is minimised (Beltran et al., 2004; Andres et al., 2006). In the case of fish, lower processing pressures seem to result in lower levels of lipid oxidation. The actual effect on individual fatty acids, particularly polyunsaturated fatty acids requires further investigation.

EFFECTS ON BIOACTIVE COMPONENTS IN FOODS Food commodities or ingredients that provide health-related benefits beyond their basic nutrient supply have gained wide spread acceptance by consumers. There is a vast range of functional foods in terms of their chemical nature, target biomarker and target health benefit. The beneficial effects of foods are associated with the bioactive components present in them including plant pigments (anthocyanin, carotenoids), phenolic compounds, vitamins, fatty acids and peptides. Current trends in the functional food area are towards retaining the maximum level of bioactive components while maintaining fresh-like qualities of foods. With this in mind studies have focused on the effects of HPP on carotenoids, total phenolic compounds, anthocyanins and flavonones mainly in fruits and vegetables and in a few other commodities, as detailed in Table 6. Among the wide range of bioactive components in fruits and vegetables, HPP treatment has shown remarkable benefits in retaining or increasing the levels of total carotenoids in foods (Table 6). A significant increase in all types of carotenoids has been observed in tomato puree and orange juice (Sanchez-Moreno et al., 2005 & 2006), papaya slices (De Ancos et al., 2007) and melon (Woolbang et al., 2007) around pressures of 400-600 MPa. Broccoli and carrots have retained a maximum of 95-100% of α- and βcarotene at similar pressures, while lycopene (tomato puree) and lutein (in broccoli and green beans) also showed very high stability (100% retention) after HPP. The effect of HPP on anthocyanin and total phenolic compounds in berry fruits and a few tropical fruits has also been studied. Anthocyanin in strawberry, raspberry and blackberry juices after HPP showed 100% retention (400-600 MPa at room temperature) and increased pressures led to an increase in stability. However, the retention percentage varied in different fruit matrices being 54% in muscadine grape juice (550 MPa) (Del Pozo-Insfran et al., 2007) and 50-42% in blackcurrant (800 MPa) (Kouniaki et al., 2004). Anthocyanin retention during HPP seems to relate to other phenolic compounds, vitamin C content and PPO activity in fruits. Elevated temperatures during HPP and storage conditions seem to reduce the anthocyanin content. Anthocyanin present in HPP strawberry, blackcurrant and raspberry was

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most stable when a pressure of 800 MPa for 15 min was applied (Tiwari et al, 2009) however, anthocyanin degradation in HPP processed juice was observed when HPP combined with heat. This decrease could be related to condensation reactions with other flavanols or organic acids in juices, and better stability at higher pressures may be attributed to complete inactivation of the PPO enzyme (Tiwari et al., 2009). Other phenolic compounds such as phenolic acids and flavonoids seem highly stable or in fact increased during HPP. A significant increase in total phenolics has been reported in strawberry (9%), blackberry (5%), onion (12%) and longman fruit, while litchi had no significant changes (Table 6). High pressure enhances mass transfer rates which increase cell permeability leading to an increased extraction of cellular components and increasing levels of pigments in the juice. Small molecules such as pigments and volatile flavour compounds connected with the sensory quality of foods are unaffected by HPP (Cheftel, 1995). HPP treated (400 MPa/40 °C/1 min) orange juice (which is a very rich source of flavanone glycosides which are degraded to aglycones by human intestinal flora after ingestion) showed an increase in levels of naringenin (20%) and hesperetin (39%), but insignificant changes at 4 ºC for 10 days storage. It is suggested that at around 400 MPa, some structural changes in the cell walls of the orange juice sacs may have led to a release of phenols from protein, and consequently an increase in the extraction of flavanones. (SanchezMoreno et al., 2005). A study on the effect of HPP on isoflavones (complex glucosides and bioavailable forms of aglycone) in soybean seeds and soymilk reported insignificant changes to total isoflavone content and better retention of isoflavones compared with conventional thermal processing (Jung et al., 2008). However, the isoflavone profile was significantly modified due to the adiabatic heating occurring during pressurization combined with mild temperature treatment. Apart from fruits and vegetables, limited studies have also focused on bioactive components in dairy (immunoglobulin, lactoglobulin and lactoferrin) and fish (omega-3 fatty acids). Immunoglobulin A in human milk and cow’s milk showed better stability at 400 MPa with retentions of 75-86% (Viazis et al., 2007). However, significant changes in immunoglobulin protein in goat’s milk has been reported at 500 MPa and in bovine colostrum at 400 MPa and above, due to the denaturation of proteins. Pressures of 200 MPa applied for 2 hours may be used to retain at least 85% of the immunoglobulin activity, although the microbial load of colostrum is reduced by less than 2-log cycles which is insufficient to maintain the shelf-life of the product (Palmano et al, 2010). Lactoferrin, a bioactive milk peptide, seems to be highly pressure stable at pH 4.5 around 400 MPa. Yoghurt with added lactoferrin showed 80% retention of Lactoferrin after HPP (Carroll et al., 2010).

EFFECTS ON ANTIOXIDANT ACTIVITY (AA) Numerous plant constituents, such as phenolic compounds, vitamin C and carotenoids in fruits and vegetables, have been recognised as natural antioxidants that possess beneficial effects against free radical related oxidative damage associated with a number of diseases (Prasad et al., 2009). The pressure stability of antioxidants present in fruits and vegetables compared to other conventional processes is of great interest.

Table 6. The effect of HPP on Bioactive Components Compared to the experimental control

Commodity

Bioactive compound

HPP conditions

Tomato Puree

Total Carotenoid (mgl-1) α carotene β carotene Lycopene Lutein Vitamin A(RAE/litre)

400 MPa/25 °C/15 min

Significant ↑ in all types 82% 36% 48 % 71% 39%

Sanchez-Moreno et al., 2006

Tomato puree

Lycopene

600 MPa/25 °C/ 60 min

100% retention

Butz et al., 2002

400 MPa/25 °C/ 15 min

49% ↑

Sanchez-Moreno et al., 2006

400-600 MPa/25 °C/2 min

100% retention

McInerney et al., 2007

Carrots (Whole)

α carotene and β carotene

Carrot Juice Broccoli (Whole)

Carotenoids α carotene and β carotene Lutein

300 MPa/ 50 °C

Green beans (Whole)

Lutein

400-600 MPa/25 °C/2 min

Reference

Kim et al., 2001 Highest stability 95- 83% retention

McInerney et al., 2007

100-90% retention 400-600 MPa/25 °C/2 min

100% retention

McInerney et al., 2007

Onion

Total phenol content Flavonol content Total quercetin

100 MPa 50 °C/5 min 400 MPa/5 °C/5 min 100-400 Mpa/5 °C/5 min

26% ↑

%↑ 44 75 30 33

Sanchez-Moreno et al., 2005

β carotene

600/MPa /20°C/1 min storage 4 & 10°C

No significant difference after HPP and storage

Bull et al., 2004

Flavonones

100-450 MPa /30-40°C /12.5 min

↑ at 350-450 MPa

Sanchez-Moreno et al., 2005

↑ 9.8% in strawberry and 5% in blackberry at 600 MPa

Patras et al., 2009b

Zeaxanthin Lutein α carotene β carotene

400-600 MPa/20 °C/15 min

100% retention in both

Anthocyanin 800 MPa/18-22 °C/15 min Anthocyanin

No significant change after HPP

Garcia-Palazon et al., 2004

Greater stability at 800 MPa

Sulthangjai, et al., 2005, Zabetakis, et al.,

200- 800 MPa/20 °C/15 min Anthocyanin

Roldan-Marin et al., 2009

400 MPa/40°C/1 min

Orange juice

(mg/100g DW) Total phenols

12% ↑

Table 6. (Continued)

Commodity

Bioactive compound

HPP conditions

Compared to the experimental control

Reference

2000 Kouniaki, et al., 2004

Blackcurrent

Anthocyanin

200-800 MPa/20 °C/15 min

Muscadine grape Juice

Anthocyanin

400 & 550 MPa/15 min

Melon

β carotene

600 MPa/20 °C/10 min

Little change soon after HPP at 600 MPa; 70% retention after five days in storage (4 °C). 50-58% ↓ at 800 MPa Better retention at higher pressures. 30% at 400 MPa and 54% at 550 MPa Significant ↑

Papaya slices

Caroteinoids

400 MPa/25 °C/1 min

56 % ↑

De Ancos et al., 2007

Longman fruit

Total phenolics (TP)

200-500 MPa/30°C/2.5- 30 min

Prasad et al., 2009a

Litchi fruit

Total Phenolics

200-500 MPa/30°C/2.5- 30 min

Significant ↑ as pressure ↑. Higher extraction of TP in HP extracted juice than solvent extracts. Time had no significant effect No significant difference.

Del Pozo-Insfran et al., 2007

Woolbang et al., 2008

Prasad et al., 2009c

Soymilk

Isoflavone

300-700 MPa

Human milk

Immunoglobulin A

Yogurt

Lactoferrin (added)

Salmon (Atlantic)

Omega 3 n-3 PUFA and n-6PUFA

400 MPa/30 °C/30-120 min HPP treatment (500 MPa) for milk before yogurt preparation  150-300MPa/15min

No significant change in total isoflavone at 750 MPa/25 °C Glucoside content ↑, total aglycon content ↓ Retention 86-75% from 30-120 min. 80% retention

No significant difference after HPP. Greater retention than cooking at 72 °C

Jung, et al., 2008

Viazis, et al., 2007 Carroll, et al., 2010

Yagiz, et al., 2009

Commodity

Carrots Whole

Table 7. The effect of HPP on Antioxidant Activity Method used HPP conditions Compared to the experimental control

Reference

FRAP (Fe+2/Kg)

400-600 MPa/25 °C/2 min

Modest ↓ at 400 but no difference at 600 MPa

McInerney et al., 2007 Butz et al, 2002

Puree

TEAC index

800 MPa/20 °C/5 min

Slight but significant difference

Patras et al, 2009a

Puree

Anti Radical Power (g/l-1)

400-600 MPa/250 °C/15 min

33% ↑ at 600 MPa

Indrawati et al, 2004

100-800 MPa/30-65 °C/max 90 min

↑ by HPP but ↓ above 40 °C at higher pressures

Dede et al, 2007

150-250 MPa/5-35 °C/5-15 min

TEAC index Juice DPPH Radical scavenging activity

DPPH Radical scavenging activity

400 MPa/25 °C/15 min

No significant difference up to 250 MPa/35°C/15 min. Significant but slight change after that and during storage (80% retention) Significant ↓

Puree

TEAC Index

500-800 MPa/20 °C/5 min

Insignificant ↓ at 800 MPa

Butz et al., 2002

Puree

Anti Radical Power

400-600 MPa/25 °C/15 min

Significant ↑

Patras et al., 2009a

Puree

ABTS+ (TEAC index)

500-800 MPa/20 °C/5min

Minimal initial loss

Fernandez Garcia et

Juice

Tomato puree

Sanchez –Moreno et al., 2006

(70% retained)

al., 2001

No significant difference up to 250 MPa/25°C/15 min. No significant effect

Dede et al., 2007

DPPH Radical scavenging activity

150-250 MPa/5-35 °C/5-15 min

Broccoli

FRAP (Fe+2/Kg)

400-600 MPa/25 °C/2 min

Green Beans

FRAP (Fe+2/Kg)

400-600 MPa/25 °C/2 min

Water soluble antioxidants ↑ at both levels

McInerney et al., 2007

Onion

DPPH Radical scavenging activity

100 MPa/50 °C/5 min 400 MPa/5 °C/5 min

↑ trend with ↑ pressures. No significant effect at 400 MPa/5 °C

Roldan-Marin et al., 2009

Cowpea (Legume) Raw Germinated Orange Juice

TEAC Index

300- 500 MPa/room temperature/15 min

Doblado et al., 2007

TEAC Index

800 MPa/20 °C/5min

Slight ↓ 90-85% retention 80% retention No great difference after HPP; 10% loss during three weeks storage

TEAC Index

100-600 MPa/65°C/90min

↓ with ↑ pressure

Indrawati et al., 2004

ABTS+ (TEAC index)

500-800 MPa/20 °C/5min

85% retention after 21 days storage at 4 °C

Fernandez Garcia et al., 2001

ABTS

600 MPa/40 °C/4 min

Better retention of

Polydera et al., 2005

Juice

McInerney et al., 2007

Butz et al., 2002

Commodity

Method used

Table 7. (Continued) HPP conditions

Compared to the experimental control

Reference

antioxidant activity than thermal treatment

Berries Strawberry

DPPH Radical scavenging activity

100-450 MPa/30-40 °C/1-2.5 min

No change after HPP. 95% retention at 4 °C & 10 days storage

Sanchez-Moreno, et al., 2003a

Free radical scavenging activity as Anti radical power ARP(g/l-1)

400- 600 MPa/20 °C/15 min

Significant ↓

Patras et al., 2009b

67% ↑ at 600 MPa

Blackberry Apple and Peach

TEAC Index µmol/ml

60 MPa

No significant change

Butz et al., 2003

Longman fruit

DPPH Radical scavenging activity

200-500 MPa/30 °C/2.5- 30 min

Prasad et al., 2009a Prasad et al., 2009 b

DPPH Radical scavenging activity

200-500 MPa/30 °C/2.5- 30 min

↑antioxidant activity in HP extracted juice compared with solvent extracts No significant change

Litchi fruit

Prasad et al., 2009c

Trends in High Pressure Processing of Foods

143

Recent research has mainly focused on a few commodities such as orange juice, carrot puree and tomato puree, while limited studies have focused on broccoli, green beans, onion, cowpea, berry fruits, apples, litchi and longman fruit using a range of analytical methods to test the antioxidant capacity as detailed in Table 7. In general, the effect of HPP on the antioxidant activity is not the same among the food products and depends on the vitamin stability and the extraction yield of existing bioactive compounds, quality related enzymes, such as PPO levels, and pH conditions of the matrices. Antioxidant activity of several fruits and vegetables showed insignificant changes after the application of pressures around 600-800 MPa (Butz et al., 2003). Orange juice showed a good retention of ascorbic acid after HPP around 500-800 MPa at room temperature and for shorter time periods (5 min) however, a slight reduction (10-15%) has been reported during three weeks storage at 4 °C. As the exposure time increased (90 min) the reduction of ascorbic acid accelerated at higher pressures (Indrawati et al., 2004). Among compounds exhibiting antioxidant activity in orange juice, vitamin C is the most important accounting for 68-90% of total antioxidant capacity (Polydera et al., 2005), therefore the retention of vitamin C during HPP is more important than the other compounds. Carrots and tomato showed a small reduction in ascorbic acid in most of the studies except in one study where a significant increase of 33% at 600 MPa was found, which was reflected by the increased levels of carotenoids and better retention of vitamin C after HPP (Patras et al., 2009a). High pressure processed fruit purees had significantly higher antioxidant capacities when compared to thermally treated samples (Patras et al., 2009b).

OTHER HEALTH RELATED EFFECTS In recent years attention has also been paid to potential anticarcinogenic components from fruits and vegetables. Studies have also reported the retention of higher levels of anticariogenic (Prasad et al., 2009c), allergenic (Indrawati et al., 2002) and anti-inflammatory properties of HPP foods. To date, there are no published reports about toxicity in HPP foods. Further investigations into both the effects of HPP on allergenicity and toxicity are necessary. Reports show that high hydrostatic pressure enhances whey protein digestibility to generate whey peptides that improve anti-inflammatory activity. The altered profile of low molecular weight peptides, isolated from hydrolysates of pressurized (400 & 550 MPa, 3-4 min) whey proteins, has indicated a strong upward trend in the total and reduced GSH content in cultured mutant CFTR cells in comparison to peptides released from native whey hydrolysates (Vilela et al., 2006). In another study, HPP orange juice (400 MPa/40°C/1 min) consumption improved the plasma vitamin C, antioxidative status and inflammatory markers in healthy humans (Sanchez-Moreno et al., 2003b) In-vitro experimental systems also show that flavonoids possess anti-inflammatory, antiallergic, antiviral, hypocholesterolemic, and anticarcinogenic properties. Even though there is not enough research with HPP in relation to each and every healthful property of foods, the retention or increased levels of flavones in orange juice and soybeans (healthful properties of isoflavones such as lowering cholesterol levels, preventing both prostate and breast cancers, attenuating bone loss in postmenopausal women and alleviating menopausal symptoms), carotene and lycopene levels in carrots and tomatoes, predict the benefits of HPP in

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maintaining or enhancing the healthful attributes of a wider range of commodities. As a consequence, HPP can be regarded as an alternative or complementary technology to thermal processing.

CONCLUSION From a nutritional perspective, high pressure processing is an excellent food processing technology which has the potential to retain compounds with health properties. Macronutrients and most micronutrients do not appear to be affected by HPP. Careful selection of pressures around 400-600 MPa and shorter exposure times (5-15 min) are essential to retain maximum levels of bioactives which exert antioxidant, anti-carcinogenic and anti-inflammatory properties. These conditions are also recommended to minimise the incidence of lipid oxidation in food after HPP. Lower pressures of around 100 MPa have little effect however, pressures around 400 MPa enhance the cell permeability and structural changes leading to improved extraction of nutrients and bioactive components in food matrices. HP treatment at extreme pressure and temperature combinations could result in degradation of vitamin and bioactive components

LIMITATIONS AND RECOMMENDATION This chapter has revealed a number of limitations in current knowledge of the effect of HPP on particular compounds in foods. Generally, investigations into the effect of HPP on dietary fibre have been fairly limited, suggesting the greatest effect is on water soluble fibre components. Further investigations using a broader variety of foods would be useful, especially as dietary fibre is considered to play an important role in human health. In addition, the pressure stability of the B-group vitamins, particularly niacin and cobalamin, the fat soluble vitamins E and D, and minerals is currently almost unknown. It is known that lipid oxidation can be induced following HPP, yet few studies have investigated the effect on individual fatty acids, particularly essential fatty acids in foods. An expanded knowledge of the effect of HPP on antioxidant enzymes would help in maintaining food quality. Information about how HPP affects toxins is rare, as is information about the possible allergenicity of HPP food. The fate of antioxidants in other HPP fruits and vegetable products, dairy, fish and meat products, is another area requiring investigation.

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In: New Topics in Food Engineering Editor: Mariann A. Comeau

ISBN: 978-1-61209-599-8 © 2011 Nova Science Publishers, Inc.

Chapter 7

THERMODYNAMIC AND KINETIC CRITERIA TO STUDY THE STABILITY OF DRIED FOODS Cesar I. Beristain1, Eduardo J. Vernon-Carter2 and Ebner Azuara1 1

Instituto de Ciencias Básicas, Universidad Veracruzana, Av. Dr. Rafael Sánchez Altamirano s/n, Col. Industrial-Animas, Apdo. Postal 575, Xalapa, Veracruz., 91000, México 2 Depto. Ingeniería Química, Universidad Autónoma Metropolitana-Iztapalapa, San Rafael Atlixco 186, México, D.F., 09340, México

ABSTRACT In order to assure the quality of dry foods it is important to maintain a strict control over the moisture content and temperature conditions during storage. Quality loss in dry foods can be due to enzymatic and non-enzymatic browning, lipid oxidation, loss of nutrients, loss of flow and microbial contamination, among other factors properties. Determining the optimum moisture content and temperature conditions that minimize the detrimental processes of foods is a difficult task that requires a profound understanding of the interactions of water with other food ingredients. Despite the use of increasingly sophisticated analytical techniques that seek to shed information regarding water-food interactions and water-water interactions, the water sorption mechanism in foods is not still understood wholly. The majority of foods can be considered as complex colloidal systems in which amorphous and crystalline regions occur, with water acting as a plasticizer. Water is a solvent that may take part in detrimental reactions, but in most cases, acts as a medium that provides mobility to reactants allowing them to come into close contact and react. Thus, it is convenient to control the participation of water within foods. Up to date there is still not a 100% reliable method for predicting and controlling food stability. Both, the water activity and the glass transition, are parameters that have been accepted as of food stability criteria worldwide. However, several scientific studies have demonstrated that both parameters exhibit great limitations, and that it is necessary to approach this problem from a fresh point of view. Regarding this, two factors are worthwhile considering: (1) equilibrium or thermodynamic considerations, and (2) rate or kinetic processes. A reaction may not take place if the thermodynamic parameters are unfavorable. Thus, management of the thermodynamic parameters that describe the state of a food is a good starting point for achieving a better understanding of the stability of

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stored food. On the other hand, even when a reaction is thermodynamically feasible, it cannot occur if the process kinetics does not occur at a feasible rate. In this chapter the thermodynamic and rate processes are proposed as criteria for establishing the stability of stored dry foods. It will also be shown that these two factors are closely interrelated, as the equilibrium state of a system depends in great measure of the kinetics followed to reach it. Furthermore, it will be established that it is possible to improve food stability by controlling its microstructure.

INTRODUCTION Microbiological contamination, colour changes (browning), texture changes (caking), nutrient content and lipid oxidation are some of the quality criteria traditionally used for evaluating the shelf life of a product upon storage. Food products acceptance by consumers is a subjective judgement based mainly on a visual inspection followed by a sensory evaluation. Thus, an evaluation of stability must include subjective testing such as colour, flavour and texture, but also objective criteria such as microbial growth and physical changes. However, all of these quality parameters are influenced by the interactions of water with the polymeric matrix of a foodstuff. Research dealing with the thermodynamics of dehydrated foods has experienced a boost during the last few years, because the thermodynamic functions help to explain the behaviour and structure placed by water in the surface and within foodstuffs [1,2,3]. The stability of a food depends on great measure on its moisture sorption characteristics. Sorption isotherms are useful for modelling water content changes and for computing the differential and integral thermodynamic properties. These data can be employed for selecting an appropriate package, and for determining storage conditions that allow for optimum aroma, flavour, colour, texture and nutrient retention in the food [4,5,6]. The integral minimum entropy can be considered as the water activity at which the foodstuff exhibits maximum stability [3,6,7,8,9]. Likewise, the enthalpy-entropy compensation has been extensively observed in physics, chemistry, biology and thermal analysis areas. Problems that could arise by applying the compensation law to reactions related to foods, such as microorganism’s thermal inactivation, protein denaturalization and ascorbic acid degradation have been described by Labuza [10]. Beristain et al. [11] demonstrated that the enthalpy-entropy compensation is a useful tool for obtaining information regarding the mechanisms that control water vapour sorption in foods. Recent studies using integral thermodynamic properties have confirmed that the minimum integral entropy, proposed as the point of maximum stability of dry foods [3], occurs when the water molecules adsorb in the micro pores [12]. It is crucial for designing and optimizing new methods for stabilizing dry foods to understand the relationship that exists between the thermodynamic equilibrium and the kinetics of the water vapour sorption process. Furthermore, is necessary to study the relationship that exists between the thermodynamic states and the way these states were arrived to over time. The effect of pore blockage on iodine anomalous dynamic adsorption over activated charcoal has been investigated by Bathia et al. [13]. The results of these researchers suggest that sorption kinetics were strongly influenced by the formation of a resistance at the mouth of the pores.

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The objective of this work is to propose a new stability criterion, where the water vapour sorption kinetics process is related to the thermodynamic equilibrium, taking as departing point the changes in enthalpy, entropy and microstructure observed in dry foods.

THEORETICAL FRAME In accordance to their shelf life foods can be categorized as: perishable, semi-preserved and shelf-stable. Perishable foods are those that have a short shelf life, i.e., of just a few days and their main cause of spoilage are due to microbial contamination. Examples of these types of foods are fresh products such as milk, meats and fish. Semi-preserved foods are those that are subjected to some kind of processing for preserving them and include pasteurized milk products, cured meats, salted fish, pre-cooked refrigerated produce, etc. The stability of most these foods depends on various factors as are moisture content, processing history, storage temperature and the inclusion of food additives such as stabilizers, preservatives and antioxidants. Stable foods can be considered non-perishable when stored during large time periods at controlled temperature. Within this category are dry foods, which usually do not suffer microbial contamination but are subjected to physical and chemical changes that are dependent of the interactions that water molecules undergo with other components of the food matrix. To preserve the quality of dry foods during storage it is of the utmost importance to maintain a strict control over their moisture content and water activity. Dry foods undergo losses in quality due to browning, mainly non-enzymatic, oxidation, nutrient loss, loss of flow properties, and to a lesser degree microbial contamination. Browning and oxidation happen over a wide range of water activities and the rate of reaction is a function of the water activity of the product. On the other hand, the loss in flow properties of the powders and microbial growth take place at specific water activities, so that at aw below a certain specific value these deleterious processes do not take place [14]. Water activity is an equilibrium thermodynamic parameter related to the water content of specific food matrices. Moisture sorption isotherms are relationships between the equilibrium moisture content and the water activity, which describe the water molecules sorption characteristics in a given product. In practice, most materials have the ability to sorb water exhibiting a certain degree of hysteresis [15]. Hysteresis is a manifestation of one or several irreversible stages that occur during the sorption process [2]. There is an ongoing controversy of whether it is the adsorption or desorption isotherm which represents the true state of thermodynamic equilibrium. Nevertheless, we can at least suppose the existence of a pseudoequilibrium state [7].Despite that the numerical values of the enthalpies and entropies are not exact, these values provide a good estimate. On the other hand, Beristain et al. [16] found out that although the value of the integral entropy obtained from an adsorption isotherm was different from that obtained from a desorption isotherm, their curves shapes were similar, and the minimum entropy was located in the same moisture range. Van den Berg [17] established that stability predictions cannot be made based on aw only, due to the great variability of foods, and that the concept of glass transition can provide an additional useful tool when studying mainly dry and frozen foods. The importance of glass transition on the stability of amorphous foodstuffs has been intensively studied since the 1980’s. The premise of these studies is that the physical state of a food has important implications on its processing and

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storage stability [18]. There is general agreement that glass transition influences physical changes such as structure collapsing and crystallization, but the use of a simple glass transition temperature (Tg) has been criticized [19]. Ma et al. [20] found that the oxidation of oil entrapped in maltodextrin occurred when the polymeric matrix was in its glassy state. Bell and Hageman [21] showed that aspartame degradation rate depended more aw than on the state of the system, and that the mobility found at the glass transition temperature did not control de degradation rate. Chirife and Buera [22] examined the value of the glass transition temperature as indicative of microbiological stability and found that Tg was not better than aw. Beristain et al. [6] reported that the rate of oxidation of encapsulated orange oil occurred rapidly when the mesquite gum matrix was in the glassy state. However, these same microcapsules stored at aw corresponding to the zone of minimum integral entropy showed the best stability against oxidation. Similar results were obtained when macadamia nuts were stored in the zone of minimum integral entropy during 7 weeks at 35°C [9]. All of the above indicates that the changes in some thermodynamic properties related to the moisture content can provide a good description about the sorption mechanisms taking place and can be used for estimating transition points between the mechanisms [2]. Besides, the hydrophobic and hydrophilic interactions between water and other molecules can be explained through entropy and enthalpy mechanisms [11,23,24], and also from water-food matrix pores [25]. The aw and Tg parameters are extensively accepted as stability criteria. However, several scientific studies have shown that both parameters present severe limitations and that it is necessary to approach the problem of food stability from a new point of view. Consideration of these limitations indicates that thermodynamics and moisture sorption kinetics can provide a more solid and reliable scientific criterion for studying the stability of dry foods.

MATERIALS AND METHODS Sorption Isotherms The experimental moisture sorption isotherms data were reproduced using the Guggenheim-Anderson-de Boer (GAB) equation [26]:

M=

M o CKa w (1 − Ka w )(1 − Ka w + CKa w )

(1)

where: M is the moisture content (g water/100 g dry solids), aw is the water activity and

M o (g water/100 g dry solids) , C and K are constants. Theoretically M o is the monolayer moisture content and C and K are related to temperature: C= c exp[(hm-hn)/RT]

(2)

K= k exp[(hi-hn)/RT]

(3)

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where c and k are entropic accommodation factors; hm, hn and hi are the molar sorption enthalpies (J/mol) of the monolayer, multilayer and bulk liquid, respectively; and R (J/mol K) and T (K) are the ideal gas constant and the absolute temperature, respectively.

Determination of Thermodynamic Parameters The free energy for water adsorption (ΔG) was calculated using the equation of Gibbs [27,28]: ΔG = R T ln aw

(4)

The isosteric heat of sorption is a differential molar quantity derived from the temperature dependence of the isotherm, and it represents the energies for water molecules binding at a particular hydration level, in contrast to the integral heat, which is the average energy of all molecules already bound at that level. The differential and integral entropies are obtained from their differential and integral heats respectively. The usual entropy discussed qualitatively or quantitatively (statistical mechanics) in terms of order-disorder of the adsorbed molecules is the integral entropy and not the differential entropy [2,29]. Changes in differential enthalpy at the water-solid interface at different stages of the adsorption process were determined using Othmer’s equation [30]:

⎛ H (T ) ⎞ ⎟ ln P o + C1 ln Pv = ⎜ v v ⎜ H o (T ) ⎟ ⎝ v ⎠M

(5)

o where Pv is the vapor pressure of water in the food, Pv is the vapor pressure of pure o water at the same temperature, H v (T) is the isosteric heat for water adorption, H v (T ) is the heat of condensation of pure water, and C1 is an adsorption constant. o o A plot of ln Pv vs ln Pv , gives a straight line if the ratio H v (T) / H v (T) is maintained constant in the range of temperatures studied. The net isosteric heat of adsorption or differential enthalpy is defined by equation (6).

⎛ H (T ) ⎞ − 1⎟ H ov (T ) ( ΔH dif ) T = ⎜ v ⎜ H o (T ) ⎟ ⎝ v ⎠M

(6)

o Calculating H v (T) / H v (T) with equation (5) and substituting into equation (6), it is possible to estimate the net isosteric heat of adsorption at different temperatures using steam tables.

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With values obtained for enthalpy changes, the variation in the molar differential entropy

(ΔSdif ) T may be estimated using equation (7): (ΔSdif ) T = S1 − SL =

− (ΔH dif ) T − RT ln a w T

(7)

where S1 = (∂S / ∂N 1 ) T ,P is molar differential entropy of water adsorbed in the food,

S L is molar entropy of pure water in equilibrium with the vapor, S is total entropy of water adsorbed in the food, N 1 is number of moles of water adsorbed in the food and T is temperature (K). The integral enthalpy changes (ΔH int ) T (J/mol) at the water-food interface, at different stages of the adsorption process, were determined using the equation of Othmer [30]:

d ln Pv d ln Pvo

=

H v (T ) H ov (T)

(8)

where: the adsorbed substance is water; Pv (atm) is the vapor pressure of water over the o adsorbent; Pv (atm) is the vapor pressure of pure water at the temperature of sorption;

H v (T) (J/mol) is the integral molar heat of sorption, and H ov (T) (J/mol) is the heat of condensation of pure water. Since all these terms are temperature-dependent, the equation can be integrated:

⎛ H (T ) ⎞ ⎟ ln P o + A ln Pv = ⎜ v v ⎜ H o (T ) ⎟ ⎝ v ⎠Φ

(9)

where: A is the adsorption constant, and Φ (J/mol) is the pressure of diffusion or surface potential. o o A plot of ln Pv versus ln Pv gives a straight line if the ratio H v (T) / H v (T) is constant within the range of temperatures used. The molar integral enthalpy (ΔH int ) T can be calculated using equation (10), at a constant pressure of diffusion [8,31]:

⎛ Hv(T ) ⎞ ( ΔH int ) T = ⎜ − 1⎟ H ov (T) ⎜ H o (T ) ⎟ ⎝ v ⎠Φ

(10)

Thermodynamic and Kinetic Criteria to Study the Stability of Dried Foods

Φ = μ ap − μ a = RT

Wap a w

∫

Wv 0

Md ln a w

157

(11)

where: μ ap (J/mol) is the chemical potential of the pure adsorbent; μ a (J/mol) is the chemical potential of the adsorbent participating on the condensed phase; W ap (g/mol) is the molecular weight of the adsorbent, and Wv (g/mol) is the molecular weight of the water. o By calculating H v (T) / H v (T) from equation (9) and substituting it into equation (10) it becomes possible to calculate the integral enthalpy at different temperatures, provided that a o good means of estimating H v (T) is available, such as that proposed by Wexler [32]: 3

H ov (T) J/mol = 6.15 x 104 – 94.14 T + 17.74 x 10-2T2 – 2.03 x 10-4T

(12)

Using the values obtained for (ΔH int ) T changes, the molar integral entropy (ΔSint ) T can be estimated using the equation [29]:

(ΔSint ) T = SS − S L =

− (ΔH int ) T − R ln a w T

(13)

where: SS = S/N1 (J/mol K) is the integral entropy of water adsorbed in the foodstuff; S (J/mol K) is the total entropy of water adsorbed in the foodstuff; N1 is the moles of water adsorbed in the foodstuff, and SL (J/mol K) is the molar entropy of pure liquid water in equilibrium with vapor.

Calculation of Moisture Content Corresponding to the Micropore Volume The Dubinin-Radushkevich model is still the most widely used for the fractional pore filling of the micropores [33]. Therefore the moisture content corresponding to the micropore volume (no) was obtained using the Dubinin-Radushkevich equation [34]:

⎛ Po ⎞ log n = log n o − B log 2 ⎜ v ⎟ ⎜ Pv ⎟ ⎝ ⎠

(14)

where: n (g water/100 g dry solids) is the amount of moisture adsorbed; no (g water/100 g dry solids) the amount of moisture adsorbed corresponding to the micropore volume, and B a constant related to the microporous structure of the adsorbent.

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Compensation Theory Values for (ΔHint ) T and (ΔSint ) T were correlated with the law of compensation [11]:

(ΔH int ) T = TB (ΔSint ) T + ΔG B

(15)

where: TB (K) is the isokinetic temperature, and ΔG B (J/mol) is the measure of the free energy at TB . The mean harmonic temperature ( Thm ) was defined as [35]:

Thm =

N N

(16)

∑ (1 / T) 1

where N is the total number of isotherms used. An approximate (1-α)100 percent confidence interval for TB may be calculated from:

TB = TB ± t m − 2,α / 2 V (TB )

(17)

where:

TB =

∑ ((ΔH int)T − (ΔHint )T )((ΔSint )T − (ΔSint )T ) ∑ ((ΔSint ) T − (ΔSint ) T ) 2

((ΔH int ) T − ΔG B − TB (ΔSint ) T ) 2 ∑ V(TB ) = (m − 2)∑ ((ΔSint ) T − (ΔSint ) T ) 2

(18)

(19)

and m is the number of ((ΔH int ) T , (ΔSint ) T ) data pairs, (ΔH int ) T is the average integral enthalpy and (ΔSint ) T is the average integral entropy.

Pore Blockage Kinetic Theory Bathia et al. [13] studied the effect of pore blockage in the anomalous dynamic adsorption of iodine on activated carbon. The activated carbon used had a microstructure formed by micropores (< 2 nm) and mesopores (2-50 nm). Their results indicated that sorption kinetics was strongly influenced by the formation of a resistance at the pores mouth.

Thermodynamic and Kinetic Criteria to Study the Stability of Dried Foods

159

Kinetic equations developed for determining the water vapour sorption kinetics in foods when a resistance arises at the pores mouth are given below as well as their implications on the equilibrium states. The sorption process can be described by the following reaction: Ka

(Sd – Sa) + Wa ↔

Sa

Kd

(20)

where: Sd is the total number of adsorption sites of the food, Sa is the number of sites where already a water molecules has adsorbed (occupied sites), Wa are water molecules from the surrounding environment available for adsorption, Ka is an adsorption kinetic constant and Kd a desorption kinetic constant. As Wa is directly proportional to the relative humidity (R.H.), the rate of water adsorption is given by:

dM ∗ = Ka ( R .H.)(1 − M ∗ ) − Kd ( M ∗ ) dt where M ∗ =

Mt Meq

(21)

, Mt is the moisture sorbed by the food at time t, Meq is the

equilibrium moisture that corresponds to the relative moisture at which the experiment is carried out, and t is the sorption time. The integrated equation for the adsorption process is:

− ln(1 − M ∗ ) = Ka (R.H.)( t )

(22)

and that for the desorption process is:

− ln M ∗ = Kd( t )

(23) ∗

Plotting − ln(1 − M ) or − ln M

∗

vs t, depending on whether the experiment carried

out is an adsorption or desorption one, pore blockage is observed when an abrupt decrease in Ka or Kd is observed.

Sorption Kinetics A Dynamic Vapour Sorption (DVS-2000) equipment was used for obtaining the experimental sorption kinetics in this work.

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Cesar I. Beristain, Eduardo J. Vernon-Carter and Ebner Azuara

RESULTS AND DISCUSSION Gibb’s Free Energy, Differential Enthalpy, Differential Entropy and Integral Entropy A quantitative measure of the dry food-water affinity is the free energy necessary for transferring a water molecule from the vapour state to the adsorbed state. The degree to which water-food interactions predominate (or are stronger) over interactions between water molecules can be determined by the relative change in enthalpy. Likewise, the conditions of maximum food stability during storage and the interpretation of processes such as dissolution, crystallization, and swelling can be inferred from the relative change in entropy. Figure 1 shows the usefulness of the ∆G vs Moisture Content curves for studying food hygroscopicity. In this specific case, it can be observed that freeze-dried banana is more hygroscopic than dehydrated pea. On the other hand, it is widely accepted that the isosteric heat (differential enthalpy) is useful for determining the occurrence of endothermic processes during the exothermic adsorption of water vapour. In products rich in sugars, the isosteric heat crosses over the zero moisture content where the sugars dissolution commences, and afterwards negative heats occur because the exothermic adsorption is overtaken by the endothermic heat of dissolution (Figure 2). Besides of these frequent interpretations, it is possible to obtain information from the integral thermodynamic properties applicable to achieve control over food stability. A crossover between the integral and differential entropy curves occurs in the minimum in integral entropy (Figure 3), but the differential entropy cannot be interpreted in terms of the order-disorder of the system’s molecules [29]. The differential entropy represents the algebraic sum of the integral entropy at a specific level of hydration, plus the change in order or disorder that happens alter the system adsorbs new water molecules at the same hydration level. Thus, the integral entropy is the adequate thermodynamic function for studying the water molecules ordering during sorption. The minimum integral entropy can be considered the point of maximum stability, as the water molecules are more ordered within the food matrix. Here stronger bonds exist between the adsorbate and the adsorbent [3,7], and water is less available for taking place in detrimental reactions [6]. The criterion of minimum integral entropy (MIE) as the point of maximum stability has been confirmed experimentally in peas [3], whole green coffee bean and decaffeinated coffee [8], orange peel essential oil microencapsulated in mesquite gum [6] and with macadamia nuts [9]. The zone of MIE was determined for macadamia nut (Figure 3) and afterwards it was stored at three different relative humidities corresponding to water activities before, within, and after the MIE zone. Quality parameters such as the peroxide value (Figure 4), hue angle (Figure 5) and texture (Figure 6), were evaluated during 7 weeks, and it was demonstrated that macadamia nut maximum stability was achieved when stored in the MIE zone.

Thermodynamic and Kinetic Criteria to Study the Stability of Dried Foods

161

0

-1000

ΔG (J / mol)

-2000

-3000

-4000

-5000

-6000 0

5

10

15

20

25

30

Moisture content (g water / 100 g dry solids) Pea at 25ºC

Banana at 22ºC

Figure 1.Variation of the Gibb’s free energy as a function of the moisture content, for dehydrated pea and freeze-dried banana [3]. 10000 288 K(15ºC) 303 K(30ºC) 318 K(45ºC) 6000

333 K(60ºC)

dif T

Enthalpy (ΔH ) (J / mol)

8000

4000

2000

0

-2000 0

10

20

30

40

50

60

70

80

Moisture Content ( g water / 100 g dry solids) Figure 2.Differential enthalpy of adsorption as a function of moisture content for apricots [11].

162

Cesar I. Beristain, Eduardo J. Vernon-Carter and Ebner Azuara 0 Differential Integral

(J/mol K)

-10

Entropy ( Δ S)

T

-20

-30

-40 zone of minimum integral entropy -50 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Water activity

Figure 3. Differential and integral entropy changes of adsorbed water on macadamia nut as a function of water activity at 35 ºC [9]. 140 w

a = 0.436 w

a = 0.628 w

100

2

Peroxide value (meq O / kg nut)

a = 0.215 120

80

60

40

20

0 0

1

2

3

4

5

6

7

8

Time (weeks) Figure 4. Effect of water activity on the peroxide value of macadamia nut stored at 35 ºC [9].

Thermodynamic and Kinetic Criteria to Study the Stability of Dried Foods

163

Enthalpy-Entropy Compensation Ferro-Fontan et al. [36] suggested the existence of a linear relationship between the enthalpy and entropy for water sorption in some foods. Aguerre et al. [31] applied enthalpy-entropy compensation to derive a two-parameter sorption equation which took into account the effect of temperature on water sorption isotherms of some food products. Beristain et al. [11] used thermodynamic differential properties and found that water adsorption in foods rich in sugars exhibit only one compensation straight line, and that the process is enthalpy controlled (Figure 7), whereas in foods rich in starch, two compensation straight lines occur, and the process is entropy controlled at low moisture contents but enthalpy controlled at high moisture contents (Figure 8). Leffler [37] proposed that if TB > Thm the process was enthalpy controlled, but if TB< Thm the process was entropy controlled. Even though these results are interesting, for them to be useful in the study of dry foods stability, it is still necessary to obtain the compensation between the integral enthalpy, which can be interpreted as a latent heat, and the integral entropy, which is related to the order of the adsorbed water molecules in the food. The compensation obtained from plotting the integral enthalpy vs integral entropy of four different yogurts, showed that water vapour adsorption was controlled at low moisture contents by entropy, but at high moisture contents by enthalpy [12]. Processing induced changes in the microstructure of natural yogurt (Y), concentrated yogurt (CY), freeze-dried yogurt (FDY) and concentrated freeze-dried yogurt (FDCY) powders, producing four different isokinetic temperatures in the moisture content zone controlled by entropy, but only one isokinetic temperature in the moisture content zone controlled by enthalpy (Figure 9). These results indicate that the enthalpy controlled zone depended on food composition, whereas the entropy controlled zone was a function of food microstructure. The enthalpyentropy compensation plots of the four yogurt powders showed that the change in the control mechanism from entropic to enthalpic occurred at the point of minimum integral entropy, suggesting that it is possible to produce stable foods with higher moisture contents, if we are able to increase the moisture content range that corresponds to the entropy controlled zone.

Hue angle H* (°)

80

60

a

40

a a

w w w

= 0 .2 1 5 = 0 .4 3 6 = 0 .6 2 8

20

0 0

1

2

3

4

5

6

T im e ( w e e k s )

Figure 5. Effect of water activity on the hue angle of macadamia nut stored at 35 ºC [9].

7

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Cesar I. Beristain, Eduardo J. Vernon-Carter and Ebner Azuara

In order to prove that entropy barriers exist in the low moisture contents zone, Azuara and Beristain [12] calculated the micropores volume of the four dry yogurt products and compared them with the moisture contents corresponding to the minimum integral entropy (Table 1). Their results showed that the minimum integral entropy occurred when the food micropores (< 2 nm) filled-up, so that in these small pores the steric and other effects associated with the close proximity to pore walls (entropic effects) are predominant and diffusion is controlled by interactions between the water molecules and the pores walls [34]. 3.5 a = 0.215 w

3

a = 0.436 w

a = 0.628 w

Penetration (mm)

2.5 2 1.5 1 0.5 0 0

1

2

3

4

5

6

7

8

T im e (w eeks)

Figure 6. Effect of water activity on the texture of macadamia nut stored at 35 ºC [9]. 5000 288 303 318 333 288 303 318 333

-5 00 0 -1 00 0 0

dif T

Enthalpy (ΔH ) (J / mol)

0

K K K K K K K K

P ru n es

A p ricots

-1 50 0 0

T = 31 7.6 ± 4 .6 K B

2

r = 0 .99 6

-2 00 0 0 -2 50 0 0 -3 00 0 0 -3 50 0 0 -1 00

-8 0

-6 0

-4 0

E n trop y (Δ S )

d if T

-2 0

(J / m o l K )

Figure 7. Enthalpy-entropy relationship for water sorption in dried fruits [11].

0

20

Thermodynamic and Kinetic Criteria to Study the Stability of Dried Foods

165

0 293 303 323 333 313 323 333 343

-10000

dif T

Enthalpy (ΔH ) (J / mol)

-5000

K M acadam ia N uts K T = 382.5 ± 7.3 K K B 2 K r = 0.997 K Potatoes K K K T = 265.0 ± 18.8 K B

-15000

2

r = 0.999

-20000 T = 272.0 ± 57.7 K B

2

-25000

r = 0.987

-30000 -80

-70

-60

-50

-40

-30

-20

-10

0

Entropy (ΔS ) (J / mol K) dif T

Figure 8. Enthalpy-entropy relationship for water sorption in potatoes and macadamia nuts [11].

Effect of Pore Blockage For designing and optimizing the water vapour sorption in foods it is of the utmost importance to know and understand the sorption kinetics process. Pore sizes in foods influence the sorption kinetics because it determines the relative humidity (RH) at which a resistance is formed at the pores mouth (or opening).The effect of pore blockage in foods has not been studied previously as far as we know, and this theory probable allows to explain the relationship between the sorption kinetics at different RH with the equilibrium state and stability of the foods. The stability of the food will depend on the amount of water sorbed in the surface interacting with the more active sites of the polymeric matrix, which forms a protective layer against oxidation, and that on turn serve as a medium for allowing other detrimental reactions. Food micropores finish filling-up at a specific water activity, and from that moment a resistance is created at the pores mouth, which diminishes the water molecules sorption rate. At the same time, at the pores mouths, water molecules begin to interact at a lower energy level with other water molecules, forming a second layer that possesses higher mobility which increases the integral entropy. From the above explanation, we may assume that as filling up of the micropores proceeds, diffusion is controlled by the interactions between the water molecules that are diffusing and the pores walls; i.e., adsorption occurs by entropic mechanisms and the driving force is the difference between the water activity in the food and in the environment (RH). Within the micropores the water molecules will accommodate themselves in an orderly fashion, at least while enough micropores volumes are available, decreasing their integral entropy. The minimum in integral entropy will occur at the RH where all the food pores fill-up, and immediately afterwards, and increase in resistance at the

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pores mouths will happen, decreasing the velocity of water vapour sorption. The RH and moisture contents values at which the minimum integral entropy occurs will depend on food microstructure. It goes without saying that during all the sorption process enthalpic mechanisms (attraction bonds or forces due to the chemical nature of water and food) also take place, but that at low moisture contents entropic mechanisms prevail. After the filling-up of the micropores, the water molecules interact with other water molecules at the pores mouths, and the filling-up of the mesopores falling within a range of 2 nm to 5 nm commences, with surface and capillary forces controlling the diffusion. In this zone the integral entropy begins to increase and the process is controlled by enthalpic mechanisms. Macropores with sizes larger than 5 nm affect very little the adsorption process. Some foods rich in sugars, such as dry fruits, present only one enthalpy-entropy compensation straight line, where the adsorption is controlled by enthalpy because they possess few micropores. In order to experimentally validate the pore blockage theory in foods, it is necessary that the RH corresponding to the minimum integral entropy obtained with thermodynamic 10000 Y

-10000

FDY

293 K

308 K

308 K

323 K

323 K

CY 293 K

FDCY 293 K

308 K

308 K

323 K

323 K

T

B2

B2

Y

) Enthalpy ( Δ H

= 327.4 ± 0.7 K T

int T

(J / mol)

0

293 K

CY FDCY

-20000

Y: T = 208.7 ± 4.8 K

FDY

B

CY: T = 178.9 ± 7.1 K B

-30000

FDY: T = 241.1 ± 1.4 K B

FDCY: T = 216.1 ± 3.1 K B

-40000 -120

-100

-80

-60

Entropy (Δ S )

int T

-40

-20

(J / mol K)

Y= Yogurt, CY= Concentrated yogurt, FDY= Freeze dried yogurt, FDCY= Freeze dried concentrated yogurt

Figure 9. Enthalpy-entropy relationship for water sorption in yogurt products [12].

0

Thermodynamic and Kinetic Criteria to Study the Stability of Dried Foods

167

Table 1. Dubinin-Radushkevich parameters for sorption isotherms of yogurt products and moisture content at the minimum integral entropy at 35°C [12]

Product

no (g water/100 g dry solids)

B

r

Moisture at the minimum (ΔSint)T (g water/100 g dry solids)

Yogurt Concentrated Yogurt Freeze-Dried Yogurt

9.3 4.3 5.6

0.3273 0.3995 0.2900

0.995 0.996 0.993

9.0 3.8 5.7

Freeze-Dried Concentrated Yogurt

3.5

0.3829

0.995

3.3

2

equations be equal to the RH where pore blockage occurs during adsorption. Figure 3 shows that for macadamia nut the thermodinamically calculated minimum integral entropy occurs at RH= 0.4 at 35°C. Applying the pore blockage kinetic theory to water vapour adsorption in macadamia nut by plotting − ln(1 − M ∗ ) against t for different RH, in accordance to equation (22), we can observe in Figure 10 that the kinetic adsorption data fit straight lines, except when RH=0.4, where a sharp decrease in the straight line slope indicates the fill-up of the micropores and the appearance of a resistance at the pores mouths. The pores volume in macadamia nut calculated with the Dubinin-Radushkevich (equation (14)), was of 1.4 g H2O/100 g D.S which corresponds to aw= 0.4. It is important to notice that for this product the thermodynamic criterion and the kinetic criterion indicate that the maximum stability during storage was achieved at RH= 0.4 at 35°C. Occasionally, when food microstructure is very heterogeneous, the filling-up of the pores is not likely to occur simultaneously, which causes a gradual slow increase in the resistance, making difficult to observe with clarity the pore blockage. Δ 3 R H = 0 .1 0 R H = 0 .2 1 2 .5

R H = 0 .3 1

*

-ln (1-M )

R H = 0 .4 3 P o r e b lo c k a g e

2

1 .5

1

0 .5 -1 0

0

10

20

30

40

T im e (m in )

Figure 10. Effect of pore blockage during water adsorption on macadamia nut at 35°C.

50

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CONCLUSION The minimum integral entropy and the pore blockage kinetic theory are two useful methodologies for determining the point of maximum stability of dry foods during storage. The enthalpy-entropy compensation is an important tool for recognizing the mechanisms that control water sorption in foods. It is possible to enhance the stability of foods without modifying their composition, if we are capable of modifying their microstructure producing a larger zone where sorption is controlled by entropic mechanisms.

REFERENCES [1] [2] [3] [4]

[5]

[6]

[7] [8]

[9]

[10] [11] [12] [13]

Hill, P.E. & Rizvi, S.S.H. (1982). Thermodynamic parameters and storage stability of drum dried peanut flakes. Lebensmittel-Wissenschaft und-Technologie 15, 185-190. Rizvi, S.S.H. & Benado, A.L. (1984). Thermodynamic properties of dehydrated foods. Food Technology 38(3), 83-92. Beristain, C.I. & Azuara, E. (1990). Maximal stability of dried products. Ciencia(México) 41: 229-236. Diosady, L.L., Rizvi, S.S.H., Cai, W. & Jagdeo, D.J. (1996). Moisture sorption isotherms of canola meals, and applications to packaging. Journal of Food Science, 61, 204-208. Gabas, A.L., Menegalli, F.C. & Telis-Romero, J. (2000). Water sorption enthalpyentropy compensation based on isotherms of plum skin and pulp. Journal of Food Science 65, 680-684. Beristain, C.I., Azuara E. & Vernon-Carter E.J.(2002). Effect of water activity on the stability to oxidation of spray-dried encapsulated orange peel oil using mesquite gum (prosopis juliflora) as wall material. Journal of Food Science 67, 206-211. Nunes, R. & Rotstein E. (1991). Thermodynamics of water-foodstuff equilibrium. Drying Technology 9, 113-117. Beristain, C.I., Díaz, R., García, H.S. & Azuara, E. (1994). Thermodynamic behavior of green whole and decaffeinated coffee beans during adsorption. Drying Technology 12, 1221-1233. Domínguez, I.L., Azuara, E., Vernon-Carter, E.J. & Beristain, C.I. (2007). Thermodynamic analysis of the effect of water activity on the stability of macadamia nut. Journal of Food Engineering , 81, 566-571. Labuza, T.P. (1980). Enthalpy/entropy compensation in food reactions. Food Technology 2, 67-77. Beristain, C.I., García H.S. & Azuara E. (1996). Enthalpy-entropy compensation in food vapor adsorption. Journal of Food Engineering 30, 405-415. Azuara, E. & Beristain, C.I. (2006). Enthalpic and entropic mechanisms related to water sorption of yogurt. Drying Technology, 24, 1501-1507. Bathia, S.K., Liu, F. & Arvind, G. (2000). Effect of pore blockage on adsorption isotherms and dynamics: anomalous adsorption of iodine on activated carbon. Langmuir, 16, 4001-4008.

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[14] Mannheim, C.H., Liu, J.X. & Gilbert, S.G. (1994). Control of water in foods during storage. Journal of Food Engineering 22, 509-532. [15] Johnston, K.A. & Duckworth, R.B. (1985). The influence of soluble components on water sorption hysteresis. In D. Simatos and J.L. Multon (Eds.), Properties of water in food (pp. 65-82). Dordrecht, Netherlands: Martinus Nijhoff Publishers. [16] Beristain, C.I., Azuara, E. & Vernon-Carter, E.J. (2002). Thermodynamic analysis of the sorption process of mesquite gum. Chemical Engineering Communications, 189, 115-123. [17] Van den Berg, C. (1986). Water activity. In Concentration and Drying of Foods, ed. D. Mac Carthy, 11-36. London: Elsevier. [18] Slade L. & Levine, H. (1991). Beyond water activity: recent advances based on an alternative approach to the assessment of food quality and safety. Critical Reviews in Food Science and Nutrition 30, 115-360. [19] Peleg, M. (1997). A dissenting view on glass transition summary. Food Technology 51, 30-31. [20] Ma, Y., Reineccius, G.A., Labuza, T.P. & Nelson, K.A. (1992). The stability of spraydried microcapsules as a function of glass transition temperature [Abstract]: In : IFT Annual Meeting Book of Abstracts; 1992 June 20-24; New Orleans, La. Chicago, Institute of Food Technologists, p 217, Abstract nr 858. [21] Bell, N.L. & Hageman, M.J. (1994). Differentiating the effects of water activity and glass tran sition dependent mobility on a solid-state chemical reaction: aspartame degradation. Journal of Agricultural and Food Chemistry, 42, 2398-2401. [22] Chirife, J. & Buera M.P. (1994). Water activity, glass transition and microbial stability in concentrated/semimoist food systems. Journal of Food Science 59, 923-926. [23] Lum, K., Chandler, D. & Weeks, J.D. (1999). Hidrophobicity at small and large length scales. Journal of Physical Chemistry B 103, 4570-4577. [24] Chandler, D. (2002). Two faces of water. Nature 417: 491. [25] Sansom, M.S.P. & Biggin, P.C. (2001). Water at the nanoscale. Nature 414, 156-159. [26] Bizot, H. (1983). Using the G.A.B. model to constructing sorption isotherms. In Physical Properties of Foods, ed. R. Jowitt, F. Escher, B. Hallstrom, H.F.Th. Meffert, W.E.L. Spiess and G. Vos, 43-54. New York: Applied Sciences Publishers. [27] Rockland, L.B. (1969). Water activity and storage stability. Food Technology 23,12411248. [28] Iglesias, H.A., Chirife, J. & Viollaz, P. (1976). Thermodynamics of water vapor sorption by sugar beet root. Journal of Food Technology 11,91-101. [29] Hill, T.L., Emmett, T.L. & Joyner, L.G. (1951). Calculation of thermodynamic functions of adsorbed molecules from adsorption isotherm measurements: Nitrogen on graphon. Journal of the American Chemical Society 73, 5102-5107. [30] Othmer, D.F. (1940). Correlating vapor pressure and latent heat data. A new plot. Industrial and Engineering Chemistry 32, 841-856. [31] Aguerre, R.J., Suarez, C. & Viollaz, P.E. (1986). Enthalpy-entropy compensation in sorption phenomena: Application to the prediction of the effect of temperature on food isotherms. Journal of Food Science, 51, 1547-1549.

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[32] Wexler, A. (1976). Vapor pressure formulation for water in range 0 to 100oC. A Revision. Journal of Research of the National Bureau of Standards. A. Physics and Chemistry 80, 775-785. [33] Sonwane, C.G. & Bhatia, K. (2000). Characterization of pore size distributions of mesoporous materials from adsorption isotherms. Journal of Physical Chemistry B 104, 9099-9110. [34] Fletcher, A.J. & Thomas, K.M. (2000). Compensation effect for the kinetics of adsorption/desorption of gases/vapors on microporous carbon materials. Langmuir 16, 6253-6266. [35] Krug, R.R., Hunter, W.G. & Grieger, R.A. (1976). Enthalpy-entropy compensation. 2Separation of the chemical from the statistical effect. Journal of Physical Chemistry 80, 2341-2351. [36] Ferro-Fontan, C., Chirife, J., Sancho, E. & Iglesias, H.A. (1982). Análisis of a model for water sorption phenomena in foods. Journal of Food Science, 47, 1590-1594. [37] Leffler, J.E. (1955). The enthalpy-entropy relationship and its implications for organic chemistry. Journal of Organic Chemistry 20, 1202-1231.

In: New Topics in Food Engineering Editor: Mariann A. Comeau

ISBN: 978-1-61209-599-8 © 2011 Nova Science Publishers, Inc.

Chapter 8

DEVELOPMENT OF VACUUM SPRAY DRYING SYSTEM FOR PROBIOTICS POWDER Yutaka Kitamura and Yukari Yanase* Graduate School of Life and Environmental Sciences, University of Tsukuba, Ibaraki, Japan

1.1. INTRODUCTION In recent years, health consciousness has grown in relation to health hazards associated with lifestyle and living environment, and various types of functional foods are attracting attention (Matsuda, 2002; Bureau of Citizens, Culture and Sports, Tokyo Metropolitan Government, 2005; Sashihara et al., 2005). In addition, the functionality of food is currently an important added value for the food industry, and the development and applications of functional foods are being actively advanced. Among various functional foods, probiotics is one of the materials that have attracted considerable attention (Sashihara et al., 2005). Probiotics are living microbes that exert healthful effects on the living body, lactic acid bacteria being a typical example. The wide-ranging health effects of various types of bacteria have been reported, as shown in Table 1.1. Further, epidemiological studies on probiotics for disease prevention and scientific study and commercial development involving useful lactic acid bacteria are being pursued (Watanabe et al., 2005). Probiotic foods are able to ameliorate the effects of environmental factors, and are useful in disease prevention; establishing the safety of probiotics is easy as compared to doing so for medical chemicals, because the bacterial cells of probiotics have long been used for storing and improving the flavor of food. Therefore, various probiotic foods are being researched, developed, and produced. These will be indispensable functional ingredients in the future food industry in Japan, where health consciousness is increasing. Drying is an important operation to increase the added value, such as the storability, transportability, and convenience, of food containing functional ingredients. First, one purpose of drying food, including functional food, is to ensure that the food is of a quality that * Present: Nisshin Flour Milling Inc., 25, Kanda-Nishiki-cho 1-chome, Chiyoda-ku, Tokyo 101-8441, Japan

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Yutaka Kitamura and Yukari Yanase

satisfies consumer requirements. The other benefits are food storage stability, wide-area transportability, safety, and sanitization. Drying also results in convenience in terms of the place of consumption and distribution, along with giving products commercial value and having positive sales effects (Kimura et al., 1993). Furthermore, in order to give added value to target food, it is also important to be able to retain functionality during drying and subsequently, retain the effectiveness and functionality of the food for a long time. Therefore, the importance of processing functional components of food highly efficiently will surely increase (Sawaoka, 1997). It is an important challenge for the future food industry to overcome existing problems related to drying technology and equipment in dealing with a wide variety of functional foods for drying. Table 1.1. Health effects of probiotics (excerpt from Sashihara et al., 2005) Effect Improvement of constipation or diarrhea Prevention for infection (Investment of immunity) Prevention and improvement of allergy Reduction of stress Bowel disease Enhancement of infant growth Improvement of fat metabolism Inhibition of cavity formation Aniti-virus

Probiotics L. rhamnosus 19070-2, L. reuteri DSM 12246 B. lactis HN019 L. rhamnosus GG L. casei DN-114001 B. breve, B.bifidum, L.acidphilus YIT 0168 B. bifidum Bb 12 L. acidophilus 145, B. longum 913 L. rhamnosus GG L. gasseri OLL2716

L,: Lactobacillus , B,: Bifidobacterium

1.2. CURRENT SITUATION AND CHALLENGES OF SPRAY-DRYING AND FREEZE-DRYING METHODS Spray drying is very important in the food industry because it is essentially suited to the powderization of liquid food containing thermosensitive ingredients. Spray drying is a method used to obtain dry powder by allowing hot air to directly make contact with small droplets in a drying tower and vaporize water instantly (Okawara, 1993a; Matsuno et al., 1991a;; Figure 1.1). Spray drying can process liquid raw materials quickly and continually, and is characterized by excellent drying and cost efficiencies. It is widely used for the production of products such as powdered milk, powdered seasonings, powdered spices, and starch (Tsujimoto, 2003). It is considered that spray droplets come into contact with hightemperature hot air in the early phase of drying, and that the dry particle temperature lowers as the hot air temperature decreases through the drying process. Clearly, the drying time of droplets is strongly influenced by how the sprayed droplets are heated in the hot air in the drying tower. There may be undesirable changes, that is, the degradation or the loss of thermosensitive ingredients, such as the depletion of volatile (fragrant) ingredients, denaturation of protein, and oxidation of lipids (Yamamoto, 2003; Matsuno et al., 1991a). It is considered that the heat denaturation of thermosensitive ingredients occurs due to the high temperature of the dry air used in spray drying. The spray droplet is instantly dried near the nozzle, and is exposed to a high exit temperature; therefore, the target ingredient decreases

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173

before it is collected by the cyclone. If the exit temperature is high, the powder collection rate increases; however, the target ingredient decreases significantly. Therefore, it is important to set an optimized exit temperature that can retain the activity of target ingredients (Yoshii et al., 2007). Feed Blower

Air filter Heater

Spray nozzle

Cyclone

Drying tower Air filter

Micro powder

Heater

Product Vibrating cooler Air filter

Cooler

Figure 1.1. Schematic of spray dryer (excerpt from Matsuno et al., 1991a)

With regard to powdering thermosensitive ingredients using spray drying, mixing them with various glucide compounds, gum, protein, modified starch, or synthetic polymers is a useful way of retaining target ingredients. For example, hydrophobic flavor or liquid lipids are converted into an O/W emulsion using an emulsifier such as Arabian gum, modified starch, or milk protein and then mixed with an excipient such as maltodextrin, before being spray dried (Furuta et al., 2005). For the nonvolatilization of volatile ingredients, we spray dry a liquid mixture in which a clathrate compound is formed with cyclodextrin, which has large intermolecular spacing, and takes in liposoluble materials in the hydrophobic molecules within molecules (Kitabatake et al., 2003). Further, Yoshii et al. (2007) conducted a study on maintaining enzyme stability until after spray drying by mixing in trehalose, which has a cluster structure resembling that of water. Watanabe et al. (2006) spray dried a mixture in a suspended form of nongelatinized starch and bifidus culture (to suppress gelatinization), and efficiently obtained bifidus powder with a good survival rate. Another measure has been to develop a spray nozzle as part of a spray dryer to suppress the loss of thermosensitive ingredients. This method uses a 4-fluid nozzle spray dryer that can spray micron-sized droplets (Mizuguchi et al., 2002). In this method, contact with dry air is more efficient because of the large surface area per unit weight of sprayed droplets. The time required for drying the droplets is less, the drying temperature can be set lower than that for existing dryers, and the loss of target ingredient (bifidus) is low. In fact, although spray

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drying was achieved at lower temperatures in their study, a remarkable increase in the survival rate of bifidus was not demonstrated. Thermosensitive ingredients are also produced by freeze drying. Freeze-dried, highquality powders are produced using long and costly drying and equipment specifications, and the resulting products are considered to have high added value. In the freeze-drying method, various types of materials are frozen at a temperature lower than freezing point, and dried as is by sublimation (Matsuno et al., 1991b; Figure 1.2). Few physical and chemical changes are induced in the materials by this method, and the food experiences almost no material change due to heat, loss of flavor of ingredients, or enzyme deactivation. In addition, water is removed under the frozen condition, and the dried product becomes porous; therefore, the original quality can often be restored by simply adding water. However, the drying speed is very low because of the low drying temperature; usually, more than a day is required for drying the product. In addition, due to factors such as the cost of operation and equipment, the freeze-drying method is costly; in other words, the product price is high as compared to that of products produced using other drying methods (Matsuno et al., 1991b; Table 1.2).

Vacuum pump Condenser

Freezer

Drying Chamber Water tank

Heater Material plate

Figure 1.2. Schematic of freeze dryer (excerpt from Matsuno et al., 1991b)

Table 1.2. Cost comparison of spray drying and freeze drying (excerpt from Hayashi, 1992)

Initial cost (Yen/k-vapor) Energy cost (Yen/kg-vapor) Energy ratio (%) Drying cost ratio (-)

SD 26 38 59 1

FD 270 63 19 5.2

Although there have been many reports on various ways of treating raw materials or additives to retain thermosensitive ingredients, there have been few studies on improving spray-drying or freeze-drying techniques to retain thermosensitive ingredients effectively. It is necessary to develop processing techniques and equipment for the production of powdered liquid food containing thermosensitive ingredients efficiently and inexpensively.

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1.3. PREVIOUS STUDIES ON FOOD USE OF LACTIC ACID BACTERIA In recent years, health consciousness has increased in relation to issues such as the prevention of lifestyle related diseases. A typical practice is the intake of various types of lactic acid bacteria with the function of probiotics. Among various types of useful fermented foods produced using various types of lactic acid bacteria (Figure 1.3; Table 1.3), yogurt is expected to exhibit effects such as the regulation of intestinal functions, relief from pollinosis symptoms, and cancer prevention. Yogurt is a popular health food, and many yogurt products are sold as such; in particular, some popular products are marketed as “food for specific health uses” (MyVoice Communications, 2005; Do House, 2005). milk

Inoculate with Lactococcus lactis and Lactococcus cremoris

Inoculate with Lactobacillus bulgaricus and Storeptococcus thermophilus

Inoculate with Lactococcus cremoris and Lactococcus lactis subsp. diacetylactis

Press curd to remove whey

Stir fruit or other flavoring into the coagulated milk

Stop fermentation by cooling to 5℃ (prevents coagulation)

Ripen (may involve salting)

Package (fresh cheese)

Package (yogurt)

Package (buttermilk)

Package (e.g., cheddar) Figute 1.3. Milk-fermented foods using lactic acid bacteria (quoted from Johnson-Green, 2002)

Lactic acid bacteria, including yogurt starters, are stored for long periods by methods such as sub-cultivation, freeze drying, or L-drying. In a subculture, bacteria are repeatedly transferred onto a new nutrient medium every two or three months in a refrigerator. The freeze-drying technique is adopted in many types of culture collection; while storage over 30 years is possible, this requires equipment such as a vacuum dryer and an ampoule filling and sealing machine. In the L-drying method, cells are not frozen and water is directly vaporized for drying; this technique is applied to microbes to which freeze drying cannot be applied without difficulty. In all of these methods, loss or denaturalization of the target material during storage should be avoided, and the material should be preserved for long periods and retain proper functions after drying (Yaeshima, 2002).

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Yutaka Kitamura and Yukari Yanase Table 1.3. Probiotics used in dairy products (quoted from Yaeshima, 2002)

Dairy product Species Fermented milk, Fermented milk beverage Streptococcus salivarius subsp. thermophilus Lactococcus lactis subsp. lactis Lactococcus lactis subsp. cremoris Leuconostoc mesenteroides subsp. cremoris Lactobacillus delbrueckii subsp. bulgaricus Lactobacillus helveticus Lactobacillus acidophilus Lactobacillus gasseri Lactobacillus johnsonii Lactobacillus casei Lactobacillus rhamnosus Lactobacillus reuteri Lactobacillus plantarum Bifidobacterium bifidum Bifidobacterium longum Bifidobacterium breve Bifidobacterium lactis Streptococcus salivarius subsp. thermophilus Cheese，Fermented butter Lactococcus lactis subsp. lactis Lactococcus lactis subsp. cremoris Leuconostoc mesenteroides subsp. mesenteroides Leuconostoc mesenteroides subsp. cremoris Lactobacillus delbrueckii subsp. delbrueckii Lactobacillus delbrueckii subsp. bulgaricus Lactobacillus delbrueckii subsp. lactis Lactobacillus helveticus Lactobacillus casei

As a thermosensitive biological material with food functionality ingredients, lactic acid bacteria are classified into mesophilic and thermophilic families depending on their optimum temperature. The powderization of a culture fluid of lactic acid bacteria using spray drying allows for high-efficiency, low-cost drying; however, many studies, pioneered by Rogers in 1914, reported low survival rates and the storage instability of lactic acid bacteria in drying (Teixeira et al., 1995; Table 1.4). For example, it has been reported that in the powderization of liquid containing lactic acid bacteria using spray drying, the number of living bacteria in the powder increases by lowering the exit temperature; however, when this technique is used, the water content in the powder is high (Johnson et al., 1995; Lian et al., 2002; Ananta et al., 2005). Furthermore, there have been many studies on drying media (drying substrates) and protective agents to shield the target lactic acid bacteria from heat (Table 1.4). In this approach, the sensitivity depends on the species or the strain of lactic acid bacteria, even if the drying temperature is low or a protective agent is added, and the loss of target lactic acid bacteria cannot be prevented (Conrad et al., 2000; Desmond et al., 2002).

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Table 1.4. Studies on the spray drying of probiotics (quated from (1) Johnson et al., 1995; (2) Kim et al., 1990; (3) To et al.; (4) Lian et al., 2002; (5) Ananta et al., 2005; (6) Gardiner et al., 2002; (7) Corcoran et al., 2004; (8) Desmond et al., 2002; (9) Fu et al., 1995; (10) To et al., 1997a) Probiotics

Diluent

L. helveticus CNRZ-32

[1]

19% maltodextrin solution

S. thermophilus[2]

*1

L. bulgaricus[2]

*1

L. cremoris D11[3]

30% maltodextrin solution

S. thermophilus CH3TH[3]

30% maltodextrin solution

L. pseudoplantarum UL137[3] 30% maltodextrin solution B. longum B6[4] B. infantis CCRC14633[4] L. rhamnosus GG ATCC53103[5]

10% gelatin 10% gum arabic 10% RSM 10% gelatin 10% gum arabic 10% RSM 20% RSM

L. paracasei[6] L. rhamnosus GG[7] L. rhamnosus E800[7] L. salivarius UCC500[7] L. paracasei NFBC338[8]

20% RSM 10% RSM / 10% polydextrose 10% RSM / 10% oligofructose 20% RSM 20% RSM 10% RSM / 10% polydextorose 20% RSM 10% RSM / 10% polydextorose 20% RSM 10% RSM / 10% polydextorose 20% RSM 10% RSM / 10% gum acacia

L. lactis C2[9] B. linens ATCC9174

30% RSM [10]

30% RSM

Inlet temp./Outlet temp. (℃) 220/82 220/120 150/*1 170/*1 *1/60 *1/90 150/*1 170/*1 *1/60 *1/90 220/65 220/90 220/65 220/90 220/65 220/90 100/60 100/60 100/60 100/60 100/60 100/60 *1/70 *1/100 *1/80 *1/80 *1/80 175/68 170/85-90 170/85-90 170/85-90 170/85-90 170/85-90 170/85-90 170/95-100 170/100-105 170/95-100 170/100-105 220/77 220/120 220/70* 220/90* 220/60** 220/90**

Survival ratio (%) 0.08 15 3 1.8 0.01 3 1.6 0.6 0.0003 1 3 0.3 30 5 15 0.5 8.2 23 63 0.01 0.06 1.64 70 5 65 55 65 84.5 50 31 25.4 41 0.7 0.12 1.7 0.01 1.4 0.9 4.3 0.2 20 1.5 80 5

RSM: Reconstituted skim milk, *1: not described, *; 4.5 L glass chamber, **; 12.5 L glass chamber

Because the freeze-drying of lactic acid bacteria is a gentler process than spray drying, a high percentage of lactic acid bacteria are retained in the form of freeze-dried powder, depending on the species or strain of the bacteria and the type of protective agent added (Champagne et al., 1996). However, it has been reported that the damage to lactic acid

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bacteria that occurs through freezing or drying can be further suppressed through various techniques such as rapid freezing at the first stage of freeze drying and a smaller size of bacteria (Meng et al., 2008). Although both spray drying and freeze drying are established methods for producing lactic acid bacteria powder, such processes may subject live bacteria to considerable great stress because of the use of high or low temperatures, exposure to oxygen, and osmotic pressure. It is necessary to control conditions such as the drying of the culture fluid, protective agent, and humidity in order to ease such stress (Meng et al., 2008); however, a quality powder retaining lactic acid bacteria could be produced if a good drying process that will minimize the stress on living bacteria is developed, as discussed below.

1.4. VACUUM SPRAY DRYING (VSD) In this study, a vacuum spray-drying (VSD) technique is proposed to powderize liquid food containing thermosensitive ingredients. In VSD, evaporated water is removed while the drying tower is decompressed using a vacuum pump with a cold trap. The drying temperature is controlled by a far-infrared heater and a heating system with an outside jacket. The raw materials are sprayed from a two-fluid nozzle at the top of the drying tower and powdered in the drying tower. The details of VSD are discussed in Chapter 2.

Solution 溶液

噴霧乾燥経路 Drying pathway in SD VSDpathway 経路

溶解曲線 curve Dissolution

Temp. (℃) 凍結乾燥経路 Drying pathway in FD

0

Drying food

Rubber area

Freezing curve

Glassガラス転移曲線 transition cutve

Water 100%

Glass ガラス Solute 100%

Water＋Solute (%) Figure 1 4. Model food state diagram (based on Takai, 2000)

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In decompressing the drying tower, the VSD method realizes drying at relatively low temperatures (40–60°C) as compared to existing methods (Figure 1.4). It is expected that the loss (or extinction) of thermosensitive ingredients will be smaller than in the existing spraydrying method, and the technique can provide efficient and inexpensive drying as compared to even freeze drying. Honda (2005) and Kitamura et al. (2006) stated that VSD is suited for powdering liquid food containing thermosensitive ingredients. They constructed a VSD system to powderize fermented milk; the analysis of the influence of temperature on the fermented milk powder showed that approximately 80% of the lactic acid bacteria were retained at a drying temperature of 35°C. However, they also mentioned problems such as too much inner-wall adhesion in the drying tower, low powder recovery rates, and high water content in the powder. Therefore, they have suggested that it will be necessary to improve the system. As a result, in this study, the relationships among the VSD conditions, recovery rate, and powder properties are examined in order to understand the physical phenomena governing these problems and to provide solutions for the same.

1.5. PURPOSE OF THIS STUDY In order to clarify the relationships among VSD conditions, the recovery rate, and powder properties, experiments are conducted to powderize liquid food and obtain basic data on the design and operation of a VSD system used to retain the functional food ingredients. First, using sugar-free condensed milk as a target material, the techniques used to construct a vacuum spray-drying system, and the influence of factors such as drying tower capacity, drying temperature, spray pressure, and spray liquid concentration, on the powder properties are examined. Furthermore, using sugar-free condensed milk containing lactic acid bacteria as a target material, a retention test for functional food ingredients was conducted to evaluate the applicability of VSD.

2. DEVELOPMENT OF A VACUUM SPRAY-DRYING TECHNIQUE 2.1. Construction of Vacuum Spray-Drying System In this study, a vacuum spray-drying (VSD) method is proposed as a drying technique that will suppress the damage or loss of thermosensitive functional ingredients such as biological materials, vitamins, and enzymes. Via the decompression of the drying tower (Figure 1.4), this method allows for drying at lower temperatures (40–60°C) as compared to existing spray-drying systems. Therefore, the loss of thermosensitive ingredients is smaller than in the spray-drying method, and an efficient and inexpensive drying system will be realized even in comparison with the freeze-drying method.

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Figure 2.1. SD-10001)

A VSD system was constructed by improving the spray dryer used for research purposes (Tokyo Rikakikai, SD-1000). Figures 2.1 and 2.2 show a photograph and the schematics of the SD-1000. The equipment is a two-liquid nozzle spray dryer in which atomization is induced by causing both compressed air and the material to flow simultaneously. The operation panel, as shown in Figure 2.3, controls the flow rate of the supply fluid and the spray pressure.

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Compressed air

Inlet

Exhaust

Outlet

1 2 3 4 5 6 7 8 9 10

Spraying air line valve (Solenoid valve) Three way solenoid Regulator Pressure sensor (Spraying air) Spray nozzle Feeding pressure sensor Feeding pump Magnetic stir Aspiration filter Air flow sensor

11 12 13 14 15 16 17 18 19 20

Heater Inlet temp. sensor Drying chamber Cleaning hatch Separator Outlet temp. sensor Cyclone Receiving bin Aspiration blower Exhaust filter

Figure 2.2. Flow sheet of SD-10002)

The system was modified as described below. Figures 2.4 and 2.5 show a photograph and the schematics of the constructed VSD system. The major modifications/improvements can be seen in the heating system and the decompression system.

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Figure 2.3. Operation panel

Figure 2.4. External appearance of the VSD system. 1. Feeding material, 2. Stir with heater, 3. Degital monometer, 4.

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Figure 2.5. Schematic of decompression spray drying equipment

2.1.1. Heating System In VSD, where spray drying is conducted at low temperatures under reduced pressure, water does not evaporate unless latent heat is supplied for evaporation, even if the temperature of this heat is low. However, if we use hot air, as in existing systems, the drying tower becomes an open system, and decompression becomes more difficult. We need a new system to supply heat in the drying tower using a method other than hot air. In this study, two heating methods are used together. One involves winding a vinyl tube around the drying tower wall; warm water flows through the tube and gently heats the drying tower. The other method involves placing a far-infrared heater inside the drying tower to heat the drying tower from the inside. The advantages of far-infrared heating are as follows (Amemiya et al., 1990): •

• •

The heating effect is greater in far-infrared heating than in indirect heating, where the air, the heat medium, is heated just as in the hot air method and then heat is conducted from the air to the target material. Heating through irradiation accelerates water evaporation, even in a vacuum. The energy level of the electromagnetic wave is too low to cause chemical action; heating does not cause a change in the properties of the target material. There is no power, as in ultraviolet rays, that can destroy DNA; thus, the method can safely deal with microbes.

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• • •

Heating does not appreciably depend on the hue of the heated material, and the heating is stable under any hue conditions. Drying does not cause a loss in minor ingredients or flavor, nor does it cause brown discoloration, and burning finishes without an appreciable amount of water evaporation. The recovery rate is high. Further, many studies on far-infrared rays have shown that far-infrared drying influences food in various favorable ways (Shibukawa, 1990; Shimizu et al., 1986; Sugiyama et al., 1993; Yoshikawa et al., 1993). Heating is pollution-free, hygienic, and clean. The method does not create waste gas, soot, or a gas smell. Far-infrared heaters are long-lived, durable, and convenient to use. Hot-wind heating heats up everything. In contrast, far-infrared rays are linearly emitted, and heat is conducted only to a target directly facing the heat source.

The far-infrared heater supplies heat from the upper part of the drying tower (Figure 2.6), and the vinyl tube that circulates warm water for heating is installed to heat the central and lower parts of the drying tower, which are difficult for the heat from the far-infrared heater to reach (Figure 2.7). The temperature is measured inside the tower using thermocouples placed from the outside of the drying tower into the inside; thus, the temperature is measured at several positions (Figure 2.8). In addition, a temperature controller is situated so that the farinfrared heater is cut off if the measurement of the temperature sensor in the upper part exceeds a preset level.

Figure 2.6. Far-infrared heater on the upper part of drying tower

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Figure 2.7. Warming system for VSD

Figure 2.8. Thermocouple arrangement for temperature measurement

2.1.2. Decompression System In the decompression system, an exhaust hose is connected to the lower part of the drying tower, and the inside of the tower is decompressed using an oil-sealed rotary vacuum pump (Ulvac Kiko, GLD-101). A cold trap (Tokyo Rikakikai, UT-1000) is installed between the pump and the drying tower to capture the evaporated water; an improved lid (with a nozzle attached), which is designed based on a lid dedicated to use with a cold trap, is employed in the experiment. A leak valve is installed between the cold trap and the pump; it is used to return the decreased pressure in the drying tower to atmospheric pressure (Figure 2.9). Pressure in the drying tower is measured using a digital manometer (SHIBATA, DM-10) connected to the upper part of the drying tower.

Figure 2.9. Decompression system for VSD

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2.1.3. System for Supplying and Spraying Liquid The raw material is preheated to 35–40°C using a heater stirrer, and the liquid is sent to the atomization system using a peristaltic pump. The surface area of the liquid is increased by atomization to such a degree that drying is instantaneous (Okada, 1984a). There are three types of atomization devices used for spray dryers: the rotary disk, pressure nozzle, and twofluid nozzle type; the former two are used more often for practical applications (Okada, 1984b). In this study, compressed air and supply liquid are sent through the nozzle simultaneously, and a two-fluid nozzle is used in which the shear force between liquid and air is used for spraying. In the two-fluid nozzle, steam collides at high speed with the liquid flow to tear up the liquid column for atomization, and small droplets of 10–65 µm3) are obtained even with a high-viscosity liquid (Matsuno et al., 1991a). 2.1.4. Drying and Separation System In the SD-1000 spray dryer, the standard drying tower is made of hard glass of φ 15 cm × 50 cm (height) in the cylinder portion and 5 cm in the conical portion. In the drying tower, thermocouples are installed to measure the inside temperature of the drying tower, as will be discussed below. A vinyl tube is wound around the drying tower to warm it, as discussed above. A cyclone separator can be installed in the drying tower. A cyclone separator is a dust collector that centrifuges dust from the dust-containing airflow (mixture of solid particles and gas) (Ogawa, 1980). A vinyl tube circulating warm water is also installed around the cyclone separator. 2.1.5. Temperature Measurement System Temperatures are measured at the upper, central, and lower parts of the drying tower, and at the upper and lower parts of the cyclone separator. Using model T thermocouples and a temperature measurement system (Handy Logger MR2041-MU, CHINO), the temperature is measured at the four positions shown in Figure 2.8 at the central and lower portions of the drying tower, and at the upper and lower parts of the cyclone separator. The positions are 40, 10, 30, and 3 cm from the lower part of the drying tower or cyclone separator. The temperature sensor on the upper part of the drying tower is connected to the temperature controller in the subsequent stage of far-infrared heating.

2.2. Operation of VSD System The VSD system is operated by the procedure shown in Figure 2.10 and 2.11: (1) First, the far-infrared heater and warm water circulator operate until the tower reaches a preset temperature. (2) The cold trap is used to cool the water condensation unit sufficiently, and (3) the vacuum pump utilized for decompression is operated until the internal pressure of the drying tower becomes stably low. (4) A beaker containing water is preheated to approximately 40°C with a heater stirrer. (5) The switches of the atomizer (for spray-pressure adjustment) and fluid flow are turned on to start spraying. (6) After confirming the spray at the nozzle, (7) the raw material, approximately 200 g in weight and preheated to approximately 40°C, is sent out. (8) After confirming that all raw materials left in the tube have been sent out for the spray, (9) the fluid flow, atomization, and far-infrared heating are

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brought to a halt. (10) The vacuum pump is brought down, and the valve is thrown open to let the pressure in the drying tower rise back to the atmospheric pressure. (11) The cyclone separator and the drying tower are removed from the dryer, and the powder is collected using a silicon spatula (Figure 2.12). In this study, what is referred to as “powder” is only the material collected by sliding the spatula along the inner wall of the drying tower. (The solid body adhering to the inner wall of the drying tower cannot be removed with a spatula.)

Figure 2.10. VSD startup operation

Figure 2.11. VSD closing operation

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Figure 2.12. Silicon spatula

3. CHARACTERIZATION OF POWDERIZATION BY VACUUM SPRAY DRYING 3.1. Introduction The purpose of this study is to powder liquid food under various drying conditions to examine the characteristics of powderization by vacuum spray drying (VSD). In particular, we discuss the design and the specifications of VSD and the influence of drying temperature, spray pressure, and spray liquid concentration on the physical and chemical properties and the recovery rate of the produced powder.

3.2. Materials and Techniques 3.2.1. Spray Liquid To clarify the characteristics of VSD, in this study, we use milk powder, which powderizes easily and has a long history as a dry powder food. Powdered milk products include whole milk powder, powdered skim milk, and baby milk powder. Milk, among other liquid food products, is relatively easy to powder. Sugar-free condensed milk (Evamilk, Snow Brand Milk Products) is chosen as a test material. Usually, condensation is used as a preprocessing step before powderizing liquid food; we use condensation in VSD, as discussed below. Sugar-free condensed milk having a concentration of approximately 26% is used in this study to reduce the condensation time. 3.2.2. Drying Tower Two types of drying towers are used. One is the standard spray dryer, and the other is the trial type. Here, the standard spray dryer is called drying tower A, and the trial type is called drying tower B. Table 3.1 shows the specifications of drying towers A and B. The cylinder portions of drying towers A and B are φ 15 cm × 50 cm (height) and φ 29 cm × 25 cm (height), respectively, and the conical section of drying tower B is 25 cm in height. Therefore, in drying tower B, the distance that a spray droplet covers from the nozzle to the inner wall of

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the drying tower in the barrel portion is twice that of a spray droplet in drying tower A, and approximately 1.3 times the distance in the conical section (Figure 3.1). Therefore, a spray droplet can stay in drying tower B longer than in drying tower A. It is expected that dry powder can be collected without sticking to the inner wall in drying tower B, even under conditions where powderization is difficult in drying tower A. Table 3.1. Specifications of the two types of drying towers

Cylindrical part Conical part Volume Material Inner vacuumed pressure Inner vacuumed and sprayed pressure *

*

Tower A φ15 cm×hight50 cm 5 cm 9.6 L Hard glass 2～5 kPa

Tower B φ29 cm×hight25 cm 25 cm 25.2 L Hard glass

13～16 kPa

22 kPa

約6 kPa

：Feeding rate ca. 2 mL/min，Spray pressure 100 kPa

10 cm Figure 3.1. Projected spray pattern of drying towers A and B

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On the other hand, the capacities of drying towers A and B are 9.6L and 25.2L, respectively. The capacity of drying tower B is 2.6 times greater than that of drying tower A, creating a difference in the level of decompression. In other words, drying towers A and B are maintained at 2–5 kPa and 6 kPa, respectively, if the tower is decompressed by a vacuum pump with a displacement of 100 L/min. Furthermore, 13–16 kPa and 22 kPa are maintained in drying towers A and B respectively, if the minimum flow rate of the spray liquid is used at a spray pressure 100 kPa. In the preliminary experiment, as shown above in relation to the VSD temperature, the tower’s internal pressure is greater in drying tower B, and the relationship[ between spray pressure and tower internal pressure is different between the two drying towers. Although the drying conditions may be different in the two drying towers depending on the tower’s internal pressure, we adopt the spray pressure as the primary experimental parameter.

3.3. Measurement and Analysis 3.3.1. Preparation and Measurement of Physical/Chemical Property of Spray Liquid

Figure 3.2. Experimental procedure

As shown in Figure 3.2, the procedure of this experiment is as follows: [1] Measurement of concentration of soluble solid content First, a sample of 2–3 g is placed in an aluminum can, heated at 105˚C in a drying machine for more than 3 h, and cooled in a desiccator; then, the weight of the residual solid content is measured. Assuming that the weight loss is due to water, the concentration of soluble solid content (w/w %) is calculated by:

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C=

W × 100 W0

191 3-1,

where C is the concentration of soluble solid content (%); W, the mass (g) of the sample after drying; and W 0 , the mass (g) of the sample before drying. [2] Control of concentration of soluble solid content The concentration of the soluble solid content is controlled using a rotary vacuum evaporator (Tokyo Rikakikai, N-1000S) for decompression and condensation. The condensation conditions are a water bath temperature of 40°C and a coolant water temperature of 5°C. After condensation, the concentration of the soluble solid content is measured using the abovementioned method, and the concentration is controlled using pure water to realize solid contents of 30, 40, and 45% for the experiment. [3] Measurement of density

Figure 3.3. Pycnometer

The density of the sample is measured against the temperature using a Gay-Lussac pycnometer (Figure 3.3). First, the volume of the pycnometer is measured using pure water, and the density (g/cm3) is obtained from the mass of the liquid sample of the same volume as

ρ=

W − W0 × ρw W w − W0

3-2,

where ρ is the density (g/cm3) of the sample; W 0 , the mass (g) of the pycnometer container; W w , the mass (g) when the pycnometer is filled with pure water; W , the mass (g) when the pycnometer is filled with the sample; and ρ w , the density of pure water (g/cm3).

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Using an Ubbelohde viscometer (SU-7811, SU-4133, Figure 3.4), a capillary tube viscometer, the temperature dependence of kinetic viscosity is measured, and the viscosity is calculated using the density value.

NM L

m1 m2

m3 m4

Figure 3.4. Ubbelohde viscometer

First, the sample is sent from pipe L up to the middle of the gauge lines m3 and m 4 (approximately 15mL), and dipped into the constant temperature water tank to set the temperature. Then, after pipe M is closed, the sample is pulled up across the gauge line m1 through pipe N. Pipe N is closed and pipe M is opened. If the sample begins to flow from the bottom end of the capillary tube, pipe N is opened to let the sample flow naturally. The time required for the sample to move from gauge line m1 to m 2 is called the efflux time. From the efflux time and the viscometer constant for the viscometer, the kinetic viscosity is calculated as

v = R × Ft

3-3,

where v is the kinetic viscosity (mm2/s); R, the viscometer constant(-); and Ft , the efflux time (s). Then, viscosity (mPa·s) is calculated from the derived kinetic viscosity and density, as measured above. The viscosity is calculated by

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η = v× ρ

193 3-4,

where η is the viscosity (mPa·s); v, the kinetic viscosity (mm2/s); and ρ, the density (g/cm3).

3.3.2. Powderization Experiment By VSD In drying tower A, two drying temperatures (40 and 60°C), three spray pressure conditions (20, 60, and 100 kPa), and three spray liquid (sugar-free condensed milk) concentration conditions (30, 40, and 45%) are adopted, for a total of 18 conditions. In drying tower B, two drying temperatures (40 and 60°C), two spray pressure conditions (60 and 100 kPa), and three spray liquid (sugar-free condensed milk) concentration conditions (30, 40, and 45%) are adopted, for a total of 12 conditions (Table 3.2). In drying tower B, because the internal pressure of the drying tower is too great for drying large-size spray droplets, a powderization experiment at a spray pressure of 20 kPa has not been conducted; it has been assumed that it would be too difficult to obtain a meaningful recovery rate. Table 3.2. Experimental conditions Drying temp. (℃)

Spray pressure (kPa) 20* 60

40

100

20* 60

60 100

Solution Concn. (%) 30* 40* 45* 30 40 45 30 40 45 30* 40* 45* 30 40 45 30 40 45

＊：Only in tower A

The spray liquid (sugar-free condensed milk) is powderized following the VSD operation method described in Chapter 2.

3.3.3. Evaluation Techniques Of Powder Products [1] Measurement of moisture content Powder (2–3 g) is placed in an aluminum can and heated at 105°C in a drying machine for more than 3 h; the weight of the sample measured after drying is divided by that before drying, and the water content is given as

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Wc =

Wb × 100 Wa

3-5,

where Wc is the moisture content of the powder (%); Wb, the weight (g) of the powder before drying; and Wa, the weight (g) of the powder after drying. [2] Calculation of recovery rate The recovery rate is the ratio of powder collected from the drying tower using a spatula when the spray liquid is powderized to the amount of spray liquid. The recovery rate is obtained from the solid content of the powder product and that of the spray liquid, excluding the water content, and is given by

Y=

Ps ×100 Fs

3-6,

where Y is the recovery rate (%); Ps, the solid powder content (g); and Fs, the solid content of the spray liquid (g). [3] Measurement of size distribution The size and weight basis distributions are obtained by screening using a set of ready-toassemble sieves with polypropylene frames, caps, and saucers and a phosphor bronze mesh. Eight types of mesh are adopted: 25, 35, 45, 60, 80, 120, 170, and 230 mesh/inch. The mesh opening is 710, 500, 355, 250, 180, 125, 90, and 63 µm, respectively, in conformity with the Japanese Industrial Standards. First, a sample (approximately 5 g) is thrown onto the uppermost sieve, and sieved by vibration and tapping. Then, the weight of the content of each sieve is measured to obtain the cumulative undersize distribution. By plotting the values against the particle size on the x-axis and the cumulative weight percent on the y-axis, a double-logarithmic Rosin-Rammler distribution is obtained. The Rosin-Rammler distribution is a distribution function expressed by Eq. (3-7), and the cumulative undersize distribution should give a straight line on the Rosin-Rammler plot. In order to compare the particle size of the prepared powder, the size D50, which gives a 50% cumulative distribution, is defined as the typical particle size (Arai et al., 1976; Hirota, 2006). n ⎪⎧ ⎛ x ⎞ ⎪⎫ Q( x) = exp⎨− ⎜ ⎟ ⎬ ⎪⎩ ⎝ x e ⎠ ⎪⎭

3-7.

Here, Q(x) is the cumulative weight percent (-); x, the particle size (µm); xe, the characteristic coefficient of granularity (-); and n, the shape parameter (-).

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Figure 3.5. Sieve

[4] Measurement of water activity Water activity is closely related to the storage stability of food. As shown in Table 3.3, water takes two forms in food: bound water and free water. Bound water directly combines with a hydrocolloid such as protein and stabilizes the colloid; free water is the remaining water that is not in a bound form and is relatively close to pure water in terms of its characteristics. Because the water content of a material depends on the environment, the water content of food is expressed by water activity, Aw, which is based on a concept similar to that of relative humidity (Eq. 3-8; Murao et al., 2003; Nagashima, 1983). This is given by

Aw =

P Rh = . P0 100

3-8.

Here, AW is the water activity (-); P, the water vapor pressure of the material in an airtight container (Pa); P0, the maximum vapor pressure of water at the temperature (Pa); and Rh, the equilibrium relative humidity (%) in the container.

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Table 3.3. Degree of water bonding in food and living bodies (based on Kumagai et al., 2005a; Nagashima, 1983) Type of water combination Condition of water Capillary condensation area (Fully Pure water active) Physically captured water among membranes, Capillary condensation area (Slightly lower activity) capillaries (φ1 μm)；close to ideal solution Molecular multi layers absorption Directly hydrated water in solute，multiarea (Low activity and strong layered hydrated water near to solute, water in binding energy） capillary (φ1 μm)；far from ideal condition Molecular single layer absorption Chemically hydrated water,water hydrated to area (Lower activity and stronger ，hydrated water strongly bound to dipole ion binding energy）

Water activity <1.0

0.8~0.99 0.25~0.8

0~0.25

Figure 3.6. Relation between water content and water activity (based on Nagashima, 1983)

Microbes, which need water to grow, can use only free water for growth. Water in a material with a small water content and water activity exists as monomolecular adsorption water or bound water; this cannot be used by microbes and cannot be a medium for enzyme reaction or browning reaction. From the viewpoint of drying food, corruption due to microbes and the deterioration reactions of food can be suppressed by drying it to the water content W1 (Figure 3.6). However, with a water content of less than W1, food becomes susceptible to an oxidation reaction such as the oxidation of fat oil; it is best to dry food down to water content W1, where food is covered by a monolayer of water. In fact, there exist powder foods with a water content of W1; the water activity of powdered milk is generally less than 0.2 (Kumagai et al., 2005a). Water activity (Aw) is extracted by the graph insertion method using a Conway diffusion analysis unit (Figure 3.7). First, a sample of approximately 1 g is placed in an aluminum can, and placed in the inner chamber of a glass container. A chemical reagent of which the water

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activity is known is placed in the outer chamber of the glass container and saturated by adding a small amount of water (Figure 3.8).

Figure 3.7. Measurement procedure of water activity

The adopted chemical reagents are lithium chloride dihydrate (Kanto Chemical), potassium acetate (Kanto Chemical), magnesium chloride hexahydrate (Kanto Chemical), magnesium nitrate hexahydrate (Kanto Chemical), chlorination strontium hexahydrate (Kanto Chemical), sodium chloride (Kanto Chemical), and potassium chloride (Kanto Chemical), of which the water activities are 0.11, 0.22, 0.33, 0.53, 0.71, 0.75, and 0.84, respectively.

Figure 3.8. Conway unit

Each unit is left as it is at 25ºC for more than 4 h. After measuring the weight of the aluminum can of each unit, the change in sample weight before and after 4 h of storage is

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converted to a value per 1 g. Then, the variation of the sample (y-axis) is plotted against the water activity of the chemical reagents (x-axis). A straight line is drawn against each data set, and the intersection with the x-axis, which corresponds to zero sample weight variation, is defined as the water activity of the sample (Figure 3.9).

Figure 3.9. Extraction of water activity

3.4. Results and Discussion 3.4.1. VSD Characteristics with Drying Tower A Table 3.4 shows the VSD powderization characteristics for drying tower A as dependent on drying temperature, spray pressure, and spray liquid concentration. At a drying temperature of 60°C, as the spray liquid concentration increased, the recovery rate increased 40% to 70% at a spray pressure of 20 kPa, from 57% to 64% at a spray pressure of 60 kPa, and from 43% to 70% at a spray pressure of 100 kPa. Furthermore, as the spray liquid concentration increased, the water content decreased from 6.2% to 4.2% at a spray pressure of 20 kPa, from 3.9% to 2.9% at a spray pressure of 60 kPa, and from 4.7% to 3.1% at a spray pressure of 100 kPa. Moreover, as the spray liquid concentration increased, the water activity decreased from 0.32% to 0.21% at a spray pressure of 20 kPa and from 0.26% to 0.17% at a spray pressure of 100 kPa. The results suggest that, as the spray liquid concentration increases, the water content of droplets decreases, the evaporation of water becomes more efficient, the recovery rate increases, and the water content and water activity decrease. The highest recovery rate, 70%, was obtained at a spray pressure of 100 kPa and a spray liquid concentration of 44.2%. However, under the condition of 100 kPa spray pressure and spray liquid concentrations of 30% and 40%, the recovery rates were 43 and 59%, respectively; these values are smaller than the recovery rates of 57% and 65% with a spray pressure of 60 kPa and spray liquid concentrations of 30% and 40%, respectively. The result

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suggests that as the spray pressure increases, the particle size of spray droplets decreases, the evaporation of water is accelerated, and therefore, the collection rate and the physical and chemical properties of the powder are improved. However, as the spray pressure increases, the internal pressure of the drying tower also increases (Table 3.5), and the evaporation of water becomes more difficult in relation to the preset temperature. Table 3.4. VSD powderization characteristics of drying tower A Temp. （℃）

20 40

60 100 20

60

Concn. （%）

Spray pressure (kPa)

60 100

31 36 46 30 38 44 35 40 46 34 38 46 31 38 44 32 42 44

Recovery ratio （%） 24 35 57 0 10 63 57 40 30 40 50 70 57 65 64 43 59 70

Moisture （%） 6.2 5.7 4.8 n.d. 7.3 4.9 5.6 5.8 6.2 6.2 3.3 4.2 3.9 2.9 2.9 4.7 3.5 3.1

Water activity (-) 0.30 0.26 0.24 n.d. 0.39 0.21 0.24 0.30 0.32 0.32 0.21 0.21 0.19 0.12 0.19 0.26 0.22 0.17

n.d. : not determined

At a drying temperature of 40°C, the recovery rate was low and water content and water activity were high as compared to those at a drying temperature of 60˚C. At a drying temperature of 40°C, the drying temperature is low against the internal pressure of the drying tower (Table 3.5), and the evaporation of water before arriving at the wall of the drying tower is probably insufficient. It is assumed that powder collection becomes difficult because the half-dried powder particles stick to the drying tower wall surface, and adhere to it, heated by the heat conduction from the wall surface. Furthermore, when the powderization characteristics at a drying temperature of 40°C are viewed comprehensively, the result appears to be relatively unstable, suggesting that this drying temperature may be close to the minimum for the water in the droplet to be able to evaporate under the internal pressure of the tower. This is understandable, for example, from comparisons of the conditions among a drying temperature of 40°C, spray pressure of 60 kPa, and spray liquid concentration of 44%, versus a drying temperature of 60˚C, spray pressure of 60 kPa, and spray liquid concentration of 44%. The former exhibited a powder recovery rate of 63%, water content of 4.9%, and water activity of 0.21, whereas the latter exhibited a powder recovery rate of 64%, water content of 2.9%, and water activity of 0.19. This suggests that because both the water content and the water activity are large at a drying temperature of 40˚C, water evaporation is slow in the drying tower, and therefore, droplets are dried less efficiently.

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If the powder recovery rate is small, drying becomes inefficient and the spray droplets tend to stick to the bottom of the drying tower. As the recovery rate decreases, the area of adhesion extends from the bottom of the drying tower toward the central and the upper portions (data not shown), decreasing the area for powder collection. A probable mechanism responsible for this is that water evaporation from large spray droplets becomes inefficient and the droplets, remaining heavy, drop onto the bottom of the drying tower. Furthermore, when drying is slow due to the test conditions (drying temperature, tower internal pressure, spray pressure, and spray liquid concentration), the drying of spray droplets becomes more difficult as the droplet size increases. A similar low recovery rate is seen in drying tower B, very probably due to a similar mechanism. Table 3.5. Internal pressure of drying tower A under test conditions Temp. （℃）

40

60

Spray pressure (kPa) Concn. 31 36 20 46 30 38 60 44 35 40 100 46 34 20 38 46 31 60 38 44 32 42 100 44

（%）

Inner pressure (kPa)

7.8～9.4 9.3～10.8 5.6～7.7 8.6～9.4 3.9～5.0 11.1～14.4 13.3～14.3 14.2～15.1 9.4～10.9 9.7～12.3 10～10.5 9.8～13.8 12.1～14.1 13.9～16.8 15.0～19.0 13.8～18 20.0～30.0 15.3～20.7

3.4.2. VSD Characteristics with Drying Tower B Table 3.6 shows the VSD powderization characteristics for drying tower B as dependent on the test conditions (drying temperature, spray pressure, and spray liquid concentration). Table 3.7 shows the internal pressure in drying tower B under each test condition. Due to the result of VSD using drying tower A, where the powder recovery rate was low at a spray pressure of 20 kPa, VSD using drying tower B was conducted under only two spray pressure conditions, 60 and 100 kPa. At a drying temperature of 60°C, as the spray liquid concentration increased, the powder recovery rate increased from 25% to 83% at a spray pressure of 60 kPa, and from 77% to 87% at a spray pressure of 100 kPa. Furthermore, as the spray pressure increased, the powder recovery rate increased from 25% to 77% at a spray liquid concentration of approximately 30%, from 68% to 78% at a spray liquid concentration of approximately 40%, and from 83%

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to 87% at a spray liquid concentration of approximately 45%. On the other hand, although the water content and water activity could be affected by, for example, the humidity of the room, the water content of the powder decreased in relation to the spray liquid concentration, from 4.3% to 3.6% at a spray pressure of 60 kPa, and from 3.4% to 3.1% at a spray pressure of 100 kPa. The water activity was stable under all conditions. Therefore, it is concluded that powder is obtained with low water content, low water activity, and high recovery rate if both the spray pressure and spray liquid concentration are high. Table 3.6. VSD powderization characteristics of drying tower B Concn. （%）

Spray pressure Temp. （℃） (kPa) 30 40 45 30 40 46 29 42 46 28 38 45

60 40 100 60 60 100

Recovery ratio （%） 58 53 48 64 26 82 25 68 83 77 78 87

Moisture （%） 7.3 5.8 5.1 8.3 6.8 4.0 4.3 4.3 3.6 3.4 6.5 3.1

Water activity (-) 0.42 0.28 0.24 0.46 0.33 0.20 0.19 0.2 0.19 0.21 0.22 0.15

Table 3.7. Internal pressure of drying tower B under test conditions Temp. （℃）

Spray pressure (kPa) 60

40 100

60 60 100

Concn. 30 40 45 30 40 46 29 42 46 28 38 45

（%）

Inner pressure (kPa) 18.8～21.7 19.2～22.7 20.9～21.9 24.6～26.1 25.6～25.9 24.7～26.0 13.4～14.7 18.2～20.6 18.9～24.8 26.0～27.6 24.6～25.8 21.8～25.5

At a drying temperature of 40˚C, no spray pressure or spray liquid concentration dependence was observed in the recovery rate. As in the case of drying tower A, at a drying temperature of 40°C, the recovery rate was low and the water content and water activity were high as compared to those at a drying temperature of 60°C. At a drying temperature of 40°C, it is likely that the drying temperature is too low in relation to the internal pressure of the

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tower, and the evaporation of water before arriving at the drying tower wall surface is inefficient. Powder particles stick to the wall surface of the drying tower, are dried and adhere to it, and the collection of powder becomes more difficult. As the spray liquid concentration increased, the powder water content rate decreased from 7.3% to 5.1% at a spray pressure of 60 kPa, and from 8.3% to 4.0% at a spray pressure of 100 kPa. Similarly, the water activity decreased from 0.42 to 0.24 at a spray pressure of 60 kPa, and from 0.46 to 0.20 at a spray pressure of 100 kPa. Therefore, low water content and water activity of the powder are achieved if both spray pressure and spray liquid concentration are high. As in the case of drying tower A, a drying temperature of 40°C may be close to the minimum temperature for water in the droplets to be able to evaporate under the internal pressure of the tower. This is suggested, for example, from the comparison of powderization characteristics between the condition of a drying temperature of 40°C, spray pressure of 60 kPa, and spray liquid concentration of 46% versus a drying temperature of 60°C, spray pressure of 60 kPa, and spray liquid concentration of 45%. The former exhibited a powder recovery rate of 82%, water content of 4.0%, and water activity of 0.20, whereas the latter exhibited a powder recovery rate of 87%, water content of 3.1%, and water activity of 0.15. Because the water content and water activity are larger at a drying temperature of 40°C, it is likely that at this drying temperature, the water evaporation in the drying tower is slower and the droplets are less efficiently dried.

3.4.3. Comparison of VSD Characteristics in the Two Types of Drying Towers The capacities of drying towers A and B are 9.6L and 25.2L, respectively, implying that the capacity of drying tower B is greater than that of drying tower A. The experimental result shows that the powder recovery rate is higher in drying tower B than in drying tower A. Furthermore, even at a drying temperature of 40°C, the recovery rate is higher in drying tower B than in drying tower A. In some experimental conditions, the average particle size is found to be smaller in drying tower B. A good result was obtained, probably because the supply speed of spray liquid is small in relation to the capacity of the drying tower, and the capacitive load is small, leading to a faster drying speed at lower temperatures (Hayashi, 2005). Moreover, as shown in Figure 3.1, because the distance between the spray nozzle and the inner wall of the drying tower is twice as large in the cylinder portion, and approximately 1.3 times longer in the conical section of drying tower B, the staying time of the spray droplets in the tower is longer, accelerating the drying of the spray droplets. In this study, powder water content, which affects the powder recovery rate, is high as compared to the reported optimum values for powdered food. There could be four reasons for this. First, the far-infrared heater may be a factor. In far-infrared heating, the temperature is maintained low around the food, and this effectively suppresses changes in quality and the burning of the surface. However, airless drying and short processing times tend to decrease water evaporation from food (Hirano, 1993). Therefore, in order to improve the efficiency of drying spray droplets by far-infrared heating in VSD, installing an aluminum or stainless steel board below the drying-tower ceiling to reflect far-infrared rays may be a solution (Amemiya et al., 1990).

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Figure 3.10. Drying characteristics (quoted from Matsuno et al., 1991a)

Second, we assume that droplets arrive at the inner wall of the drying tower earlier in the falling-rate drying period. The falling-rate drying period is a period in which the drying speed decreases because the resistance against water movement inside the droplet increases as the water content in the material decreases. The water content finally becomes equal to that in dry air, and drying stops (Figure 3.10; Figure 3.11). In this period, water movement inside the material controls the rate of reaction, and there would be no free water on the material surface. Therefore, in a material with high water content, as was used in this study, the situation may not be favorable for powdered food, although the evaluated water content is lower than the critical water content. It is likely that the dry material adhering to the inner wall of the drying tower are those droplets that arrive at the inner wall of the drying tower before the constant-rate period ends and before the critical water content is reached. Therefore, for our purpose, a new drying tower is needed in which the staying period for spray droplets is long, whereas the internal pressure of drying tower is low in relation to the drying temperature (Matsuno et al., 1991a).

Figure 3.11. Three drying phases with material temperature and water content (quoted from Matsuno et al., 1991a)

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Third, it is possible that the equilibrium water content of droplets is high under the drying temperature and humidity conditions in the drying tower. In the end of drying, the equilibrium water content is determined by temperature and pressure. If the equilibrium water content is reached, the drying ends, and the water content does not decrease further, even if the operation continues (Yano, 2002). The drying of droplets is affected by many factors such as the temperature and humidity of dry air, spray droplet size, and wet bulb temperature (Figure 3.12). By spray drying, if we pursue a low moisture content region for drying, it is necessary to increase the hot air temperature or decrease the hot air humidity to reduce the equilibrium water content. Therefore, in our VSD, it is necessary to develop a technique to lower the humidity in the drying tower (Goula et al., 2005).

Figure 3.12. Dynamic equilibrium of water droplet evaporation (quoted from Matsuno et al., 1991a)

Finally, even if powder is successfully produced with low moisture content, we must not forget that after the powder is taken out, the water content increases through absorption of moisture from the air. If the powder surface absorbs moisture, it becomes sticky through the plasticization effect of water, and the powder particles stick to and fuse with each other (Kumagai et al., 2005b). This problem could be solved by improving the air conditioning system for the ambient air surrounding powder collection (Kubota, 1995).

3.4.4. Typical Particle Size of VSD and Commercial Powder Food Table 3.8 and 3.9 show the typical particle size of the produced powder and the test conditions. The typical particle size of commercial powder food is shown in Table 3.8.

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Generally, the particle size of powder food is designed depending on the use. The detailed manufacturing method is not clear for the commercial powder food in Table 3.10. An average typical particle size, 405 µm, for granulated powder food (lactose-free diets, allergenremoved food milk, powdered skim milk, food containing milk as the main raw material, powder refreshing drinks A–F, instant coffee), which we drink with water, will be regarded as the typical particle size of powder, and is compared with the values in Tables 3.8 and 3.9. The typical particle size of powder made by VSD was always greater than 405 µm. This suggests that VSD powder particles stick and fuse to each other. In terms of the powderization characteristics of VSD, the water content is high as compared to that of general powdered food. We now discuss whole milk powder. If it has plentiful lipids, a small particle size, and a water content greater than 6% (at a relative humidity of 50–70%), it tends to soften depending on the conditions of amorphous lactose and water or because the fusible ingredients of powder dissolve in excess water. The powder’s surface becomes sticky and powder particles stick and fuse to each other (Rennie et al., 1999). In summary, due to these mechanisms, the typical particle size for the powder made by VSD could be greater than that of general powdered food. Table 3.8 Typical powder particle size by VSD in drying tower A (D50) Temp. （℃）

Spray pressure (kPa) 20

40

60 100 20

60

60 100

Concn. （%） 31 36 46 30 38 44 35 40 46 34 38 46 31 38 44 32 42 44

D50 (μm) n.d. 593 616 n.d. n.d. 605 607 536 578 553 552 624 626 717 614 722 846 702

n.d. : not determined

In the case of a drying temperature of 60°C for drying tower A, a two-fluid nozzle makes the spray droplet size smaller in relation to the spray pressure. In this study, whereas the powder particle size decreases in theory in the order of spray pressures of 20, 60, and 100 kPa, the powder particle size experimentally decreased in the order of spray pressures of 100, 60, and 20 kPa (Figure 3.13). At a drying temperature of 60°C, the powder particle size is relatively large at a spray pressure of 20 kPa. It is assumed that only small-size liquid droplet particles, which could be dried easily, are dried, and the powder particle size decreases. At

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spray pressures of 60 and 100 kPa, spray liquid droplets are easily dried because the particles are small. However, at a spray pressure of 100 kPa, the airflow into the drying tower is large, and the internal pressure of the tower increases in relation to the drying temperature. Then, water content and water activity in the dried powder increase, even though the drying speed is low, and the powder particles easily stick and fuse to one another. Therefore, the greatest powder particle size is realized at a spray pressure of 100 kPa. Table 3.9. Typical powder particle size by VSD in drying tower B (D50) Temp. （℃）

Spray pressure (kPa) 60

40 100 60 60 100

Concn. （%） 30 40 45 30 40 46 29 42 46 28 38 45

D50 (μm) 982 537 526 657 518 444 542 550 624 546 418 462

Table 3.10. Typical particle size of commercial powder foods (D50) Items Food without lactose Food without milk allergy Skim milk Foods made of milk Beverage powder A（Tea） Beverage powder B（Fruit） Beverage powder C（Fruit） Beverage powder D（Fruit） Beverage powder E（Fruit） Beverage powder F（Fruit flavor） Instant coffee Instant tea Amino acid contained food Vinegar powder for Sushi

D50 (μm) 370 442 291 416 454 388 405 404 419 355 514 196 1031 353

At a drying temperature of 40°C, in drying tower A, there were conditions wherein powder was not obtained because the drying temperature was too low. Powder was not collected at a spray pressure of 60 kPa and spray liquid concentration of 30%; furthermore, a sufficient amount of powder was not collected and particle size distribution was not measured at a spray pressure of 20 kPa and spray liquid concentration of 31%, or at the spray pressure of 60 kPa and spray liquid concentration of 38% (Figure 3.13). When powder was

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successfully collected, the powder particle size was similar between the test conditions. This is probably because at a drying temperature of 40°C, only small spray droplets could be dried in relation to the drying speed. In the case of a drying temperature of 60°C, in drying tower B, the typical particle size for the spray liquid concentration of 45% became larger than that for spray liquid concentrations of 30% and 40% (Figure 3.14). As discussed above, this is probably because adhesion and fusion between powder particles occurred due to a large quantity of lipids in the spray liquid of concentration 45%.

(b) Figure 3.13. Spray liquid concentration dependence of powder particle size in drying tower A with spray pressure as a parameter; a: Drying temperature 40°C, b: Drying temperature 60°C. a：Drying at 40°C，b：Drying at 60°C

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In the case of the drying temperature of 40°C, in drying tower B, the powder particle size for the spray liquid concentration of 30% was greater than that for spray liquid concentrations of 40 and 45% (Figure 3.14). This is probably because the low drying temperature made the water content and water activity larger, and the powder surface softened to accelerate the adhesion and fusion between powder particles. In addition, as in the case of a drying temperature of 60°C, the powder particle size decreased with spray pressures of 60 and 100 kPa, as theoretically predicted; the drying of the spray droplets was probably so smooth because the volume loading speed of drying tower B was small. (a)

(b)

Figure 3.14. Spray liquid concentration dependence of powder particle size in drying tower B with spray pressure as a parameter; a: Drying temperature 40°C, b: Drying temperature 60°C. a：Drying at 40°C b：Drying at 60°C

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3.4.5. Proof Test of VSD Using Drying Tower B The result of the proof test using drying tower B is shown in Table 3.11. Table 3.11. Proof test by drying tower B Run No. 1 2 3 4

Room temp. (%) 55 48 54 41

Recovery rate (%) 50 61 62 65

Moisture (%) 3.9 2.9 3.7 3.3

Water activity (-) 0.23 0.18 0.19 0.18

D50 (μm) 578 605 677 606

Conditions: Drying temp. 60°C Spray pressure 100 kPa，Concn. of solution 26 %

A comparison of the result of spray tests No. 1, 2, 3, and 4 indicates that the powder recovery rates were 50, 61, 62, and 65%, and the water activities were 0.23, 0.18, 0.17, and 0.18, respectively. In spray tests No. 2, 3, and 4, the result is considered to be relatively uniform. However, the powder’s water contents were 3.9, 2.9, 3.7, and 3.3%, respectively; furthermore, the average particle sizes of the powders made in the spray test were 579, 611, 693, and 620 µm, respectively; thus, the results are less uniform. There are three ways to explain this. First, because the powder is collected manually using a spatula after removing the drying tower, the powder could be affected by the humidity of the room, or damaged by the handling during collection. Second, the humidity of air may not be uniform throughout the experiment; the air of the compressor is used as is, and the room air can flow into the drying tower when the reduced pressure returns to the atmospheric pressure. Third, across the experiments, there could be variations in conditions such as spraying, temperature, and pressure in the tower. For example, it is confirmed in our study that if the nozzle is clogged up, the spray angle becomes nonuniform and the spray is intensively directed in one direction. Finally, the temperature is maintained in the tower using only warm water circulation, and can decrease due to the evaporation of water. Therefore, it is necessary to stabilize the equipment conditions, temperature, and other factors, in order to stably produce powder by VSD. If these problems are solved, it will be possible to produce powder products that are better suited for storage. This study involved a basic drying test to clarify which methods are feasible or promising, and which are uncertain or difficult. It used basic data to help estimate plausible equipment specifications for scale-up, and the basic research of the present phase has an important meaning. However, at the time of scale-up, it will be difficult to assume complete similarity in any model from the viewpoint of the similarity rule. In solving this type of problem, it becomes important to physically understand the phenomena observed in the proof test of the scale-up model, and to pay attention to similarity rules in the scale-up model. Therefore, it is necessary to accumulate basic data by repeating the tests to examine the influence of drying temperature, spray pressure, and spray liquid concentration on the powder recovery rate and the physical and chemical properties of the powder, as done here, in order to further clarify what can be realized or is promising in this process, as mentioned above.

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3.4.6. Points of Improvement In VSD The points of improvement needed in VSD are summarized as follows. 1. When the reduced pressure returns to the atmospheric pressure, we need a mechanism to keep moist air from flowing into the drying tower. With this, it should become possible to prevent the produced powder from absorbing moisture in the drying tower. 2. To improve the powder recovery rate, it is necessary to conduct the spray test using a drying tower with a greater height. 3. It is necessary to place aluminum or stainless steel boards in the upper part and the surroundings of the drying tower for the reflection of far-infrared rays. 4. Because the vinyl tube around the drying tower is made of chloroethylene, it expands and becomes loose when warm water is circulated, and finally drops off due to gravity. 5. Because powder is not collected from the cyclone separator, it is necessary to improve the method of using the cyclone separator. 6. It is necessary to dehumidify the compressed air if raw materials are repeatedly sprayed using the same two-fluid nozzle. This should improve the possibility of realizing dry powder production. 7. It is necessary to prevent dry powder from entering the vacuum pump and the cold trap. 8. In collecting powder, a silicon spatula is used to manually scratch off powder. Improvement is needed to eliminate the possible influence of the humidity of the room.

3.5. Summary In this study, using sugar-free condensed milk as a test material, the influence of the drying temperature, spray pressure, and spray liquid concentration of VSD on the physical and chemical properties of the test material and the recovery rate of powder were examined. As a result, it is confirmed that powder with low water content and water activity is obtained with a high recovery rate if the capacity of the drying tower is sufficiently large and the drying temperature, spray pressure, and spray liquid concentration are all large. If the drying tower is of a small capacity, the internal pressure of the tower increases with the spray pressure, the drying speed decreases, powder particles with large water content and water activity stick and fuse to one another, and the powder particles become larger. In a largecapacity drying tower, powders with small particle sizes are obtained at high spray pressures; however, it is very likely that adhesion and fusion occur between powder particles, as suggested by the comparison with the typical particle size of general powdered food. The present study presents basic data for estimating the equipment specifications for scale-up; moreover, the study has an important meaning at this stage. In the future, it will be necessary to accumulate basic data for scale-up through repeated tests using various materials and conditions in order to further clarify what can be realized or what is promising in this process.

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4. RETENTION OF FOOD FUNCTIONALITY INGREDIENTS BY VSD 4.1. Introduction It is expected that by using VSD in order to protect thermosensitive ingredients from heat and let them retain activity, powder can be obtained rapidly and inexpensively, retaining many thermosensitive ingredients, as compared to powder obtained through the spray-drying and freeze-drying methods. Preparing a spray liquid containing lactic acid bacteria as thermosensitive ingredients, and retaining lactic acid bacteria using VSD, powder products were produced and collected. The applicability of VSD to the powderization of thermosensitive ingredients was evaluated by clarifying its influence on the collection rate, water content, water activity, and the number of lactic acid bacteria before and after powderization. The process flow of this study is shown in Figure 4.1.

Figure. 4.1. Experimental procedure

4.2. Materials and Techniques 4.2.1. Materials 4.2.1.1. Spray Liquid We used the lactic acid bacteria contained in fermented milk (Meiji Bulgaria yogurt LB81, Meiji Dairies), approved as Food for Specified Health Uses, as the thermosensitive ingredient for VSD. The lactic acid bacteria are Lactobacillus delbrueckii subsp. bulgaricus 2038 and Streptococcus salivarius subsp. thermophilus 1131. It is known that there are symbiotic relationships between L. bulgaricus and S. thermophilus: The amino acid detached from L. bulgaricus with proteolysis activity helps S. thermophilus grow, whereas the formic acid, a metabolic product of S. thermophilus, helps L. bulgaricus grow (Hashiba, 1996).

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The optimum temperatures for L. bulgaricus and S. thermophilus are 37–50°C and 30– 37°C, respectively (Yaeshima, 2002), and therefore, these lactic acid bacteria are assumed to be thermosensitive ingredients in this study. Fermented milk was also chosen due to the following two points. Lactic acid bacteria can live favorably in fermented milk; if appropriately produced in a factory, as shown in Figure 4.2, fermented milk is a convenient living environment for bacteria: a sufficient amount of lactic acid bacteria, over 108 CFU/g, can survive. Another important point was that fermented milk has a high glass transition temperature and is relatively easily powdered because it is plain yogurt, which is simply milk fermented in the presence of lactic acid bacteria, containing no additives such as sugar or spices (Health and Nutrition Institute, Snow Brand Milk Products, 1995).

Figure 4.2. Fabrication process of fermented milk (quoted from Hashiba, 1996)

If the concentration of spray liquid is low in VSD, it is difficult to improve the recovery rate (Chapter 3: 3.5.1 and 3.5.2). The concentration of soluble solid content in fermented milk is approximately 10%, too low for powderization by VSD. Therefore, for the powderization of fermented milk, powdered skim milk (Skim Milk, Morinaga Milk Industry) is added to increase the concentration of fermented milk. In the culture of lactic acid bacteria, powdered skim milk is widely used to increase the content of fat-free mass. Due to the buffer action of the milk ingredient in powdered skim milk, the rapid decrease in pH is suppressed in the milk nutrient medium, and the growth of lactic acid bacteria is advanced. The number of living bacteria increases linearly with the content of fat-free mass in the 2–20% range, whereas the growth speed increases linearly with the content in the 8–12% range but levels off beyond that. Up to 12–13%, the time needed to reach a level of acidity is reduced in inverse proportion to the content. However, the growth speed decreases under the influence of osmotic pressure or ionic strength if the content of fat-

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free mass becomes too large (Hashiba, 1996; Yaeshima, 2002). However, because the culture of lactic acid bacteria in itself is not our objective, the solid content of fermented milk is quickly increased to produce dry powder by VSD.

4.2.1.2. Plate-Count Agar with Bromocresol Purple Plate-count agar with Bromocresol Purple (BCP) (Merck; Table 4.1) was used to measure the number of lactic acid bacteria. Plate-count agar with BCP is a standard culture medium, as prescribed in the “Ordinance of the Ministry of Health and Welfare on the ingredients of milk and dairy products,” based on the Food Sanitation Act. L-cysteine and Tween 80 are reinforced, and we can even grow the type of Lactobacillus for yogurt that demands pantethine and oleic acid (Yaeshima, 1996). Each lactic acid bacterium cell is buried in the middle of the agar flat plate to form a white colony. The shape is like a convex lens, sphere, hair bulb, or konpeito (candy), and the size is less than 1 mm (Uchimura et al., 1992). Table 4.1. Composition of plate-count agar with BCP Composition Yeast extract Peptone Glucose L-cycteine Tween 80 Bromocresol purple Agar powder

Conc. (g/L-media) 2.5 5.0 1.0 0.1 1.0 0.04 15.0

4.2.2. Techniques 4.2.2.1. Preparation of Spray Liquid The concentration of spray liquid was determined in a preliminary experiment in which the dependence of the number of lactic acid bacteria and pH on the concentration of spray liquid as well as time, was examined. As a result, the solid content of the spray liquid was specified to be 30%. The content was controlled by mixing fermented milk and reconstituted skim milk. Reconstituted skim milk in paste form with a solid content of 50% was made by dissolving powdered skim milk (65 g) in distilled water (55 g). Further, a mixed solution (200 g) of fermented milk and reconstituted skim milk with solid content of approximately 30% (in the following, lactic acid bacteria solution) was made by mixing reconstituted skim milk (100 g) and fermented milk (100 g). Unopened fermented milk within the expiration date was used in each experiment. 4.2.2.2. Measurement of Physical/Chemical Properties of Spray Liquid [1] Measurement of the concentration of the soluble solid The method discussed in Chapter 3, 3.3.1, is used. [2] Measurement of density The method discussed in Chapter 3, 3.3.1, is used.

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4.2.2.3. Powderization of Lactic Acid Bacteria Solution Two experimental conditions were used: a drying temperature of 60°C, spray pressures of 60 and 100 kPa, and a spray liquid concentration of 30%. Spray tests were repeated twice for each test condition. The powderization of the lactic acid bacteria solution and the collection of the produced powder (in the following, VSD lactic acid bacteria powder) were carried out using drying tower B in accordance with the VSD operation described in Chapter 2, 2.3. 4.2.2.4. Evaluation Techniques for VSD Lactic Acid Bacteria Powder [1] Measurement of powder water content The method discussed in Chapter 3, 3.4.3, is used. [2] Calculation of powder recovery rate The method discussed in Chapter 3, 3.4.3, is used. [3] Measurement of powder water activity The method discussed in Chapter 3, 3.4.3, is used.

4.2.2.5. Storage Test of VSD Lactic Acid Bacteria Powder VSD lactic acid bacteria powders, 1 g each, were put into polyethylene bags with zippers and sealed (Figure 4.3). The bags were wrapped in aluminum foil and kept in a desiccator until just before the measurement (Figure 4.4; Figure 4.5). The storage temperature was 25°C, and the change in the number of living bacteria was monitored against the number of storage days.

Figure 4.3. Sample configuration for the storage test of lactic acid bacteria powder

Development of Vacuum Spray Drying System for Probiotics Powder

Figure 4.4. Sample configuration for the storage test of lactic acid bacteria powder

Figure 4.5. Storage of lactic acid bacteria powder

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4.2.2.6. Freezing and Freeze Drying of Lactic Acid Bacteria Solution In order to compare this and other processing methods with respect to the change in the number of living bacteria in VSD lactic acid bacteria powder, the lactic acid bacteria solution was frozen and freeze dried. For the freezing (FR), fermented milk (20 g) and lactic acid bacteria solution (20 g) stored at –18°C were defrosted for 40 min in flowing water before the test. The number of lactic acid bacteria was monitored for the cryopreserved samples in relation to the number of elapsed days. For the freeze-drying (FD), fermented milk and lactic acid bacteria solution were injected into a test tube of 20mL, the orifice of the test tube was sealed with Parafilm, the sample was frozen for a day in a freezer at –18°C, attached to a freeze dryer, and dried for a day. After the FD, the sample was immediately placed in a desiccator, and the dried material was collected for the measurement of water content and the number of lactic acid bacteria. Furthermore, to examine the daily change in the number of lactic acid bacteria in relation to the daily change in the freeze-dried powder, the respective freeze-dried powders, approximately 1 g each, were put into polyethylene bags with zippers and kept at 25°C in a desiccator until the number of lactic acid bacteria was measured. 4.2.2.7. Measurement of Number of Living Lactic Acid Bacteria in Fermented Milk, Lactic Acid Bacteria Solution, VSD Lactic Acid Bacteria Powder, FR Solution, and FD Lactic Acid Bacteria Powder To measure the number of living lactic acid bacteria in fermented milk and lactic acid bacteria solution, the following operation was conducted according to the agar flat board preparation technique using the pour-plate method, and the colony count technique was used to measure the number of living bacteria (Uchimura et al., 1992; Figure 4.6).

Figure 4.6. Pour-plate method with agar flat board

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Plate-count agar with BCP (12.3 g) was added to 500 mL purified water, resolved by heating, and autoclave sterilized 121°C for 15 min. After sterilization, it was left at 60°C in a thermostatic oven until just before use. The number of living lactic acid bacteria in fermented milk, lactic acid bacteria solution, and FR solution was measured as follows. The sample (10 g) was diluted 10 times with sterile physiological saline (90mL) and stirred adequately. Then, 1ml was collected and mixed with salt water (9mL) in a hard glass test tube with a screw cap, and stirred. The 10-times dilution was repeated to obtain a 10–8 dilution. This was repeated twice by the experiments described in 4.3.2.3 and 4.3.2.5 and three times by the experiment described in 4.3.2.6. Each diluted solution (1mL) was placed in a Petri dish, and a medium of approximately 15mL was added. The liquid was stirred lightly in a circular pattern, and then left to stand. After confirming that the agar had hardened, the dishes turned back; the samples were cultured at 37°C for 72 ± 3 h (Figure 4.7). Based on the dilution stage in which 30–300 colonies were formed per dish, the total number of lactic acid bacteria was measured (Eq. 41).

a

b

Figure 4.7. Plate-count agar with BCP a: Before culture, b: After culture (Yellow point is a colony)

The measurement of the number of living lactic acid bacteria in VSD lactic acid bacteria powder and FD lactic acid bacteria powder was conducted as follows. Just after drying, powder (5 g) was diluted 10 times with sterile physiological saline (45mL), and 1 g of the stored powder was diluted 10 times with sterilized saline (9mL) and stirred adequately. From here, the 10-times dilution was repeated to obtain a 10-8 dilution by the abovementioned method. This was repeated twice by the experiments in 4.3.2.3 and 4.3.2.5, and three times by that in 4.3.2.6. Like in the abovementioned method, the number of total lactic acid bacteria was obtained by

N =C×D.

4-1.

Here, N is the number of lactic acid bacteria per 1 g (CFU/g) of sample; C, the number of colonies (CFU/g); and D, a dilution factor (-).

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4.2.2.8. Calculation of Survival Rate of Lactic Acid Bacteria The number of living bacteria was calculated from the number of living bacteria per 1 g of spray liquid and of dried powder. Then, the survival rate of lactic acid bacteria after drying was calculated by

S=

NS × 100 . N S0

4-2.

Here, S is the survival rate of lactic acid bacteria (%); NS, the number of living lactic acid bacteria in dried powder (%) per 1 g of solid content; and NS0, the number of living lactic acid bacteria in spray liquid (%) per 1 g of solid content.

4.3. Results and Discussion 4.3.1. Powderization Characteristics of Lactic Acid Bacteria Solution Table 4.2 shows the powderization characteristics of the lactic acid bacteria solution. In the case of a spray pressure of 100 kPa, the powder recovery rate was 43%, and the water content of the powder was 4.8%; in the case of a spray pressure of 60 kPa, they were 30% and 6.1%, respectively. These results suggest that a higher spray pressure makes the spray droplets smaller and dry faster; in addition, we can produce powder with less water content. Further, if the droplets dry quickly, powder sticks less to the inner wall of the drying tower, and the recovery rate increases. The water activities were 0.17 and 0.18 against spray pressures of 60 and 100 kPa, respectively; these are suitable values for powderization. The water content was higher and the water activity was lower in the case of a spray pressure of 60 kPa than in the case of 100 kPa. It is assumed that if the spray pressure is high, the spray droplet size is small, and therefore, drying is relatively easy. Table 4.2. VSD powderization characteristics of lactic acid bacteria solution Spray (kPa)

pressure Run No. 1 2 1 2

60 100

Powder recovery (%) 30 23 43 34

Moisture (%) 6.1 4.6 4.8 4.5

Water activity (-) 0.17 0.20 0.18 0.18

Table 4.3. Spray pressure dependence of tower internal pressure Spraying pressure (kPa) 60 100

Run No. 1 2 1 2

Drying tower pressure (kPa) 20.0～21.5 17.0～19.0 28.5～30.6 20.0～26.0

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Table 4.3 shows the spray pressure dependence of tower internal pressure. From the VSD powderization characteristics of the lactic acid bacteria solution, it is concluded that the tower internal pressure hardly influences the powder.

4.3.2. Comparison Between VSD Sugar-Free Condensed Milk Powder and VSD Lactic Acid Bacteria Powder Table 4.4 compares the nutrient ingredients of sugar-free condensed milk and lactic acid bacteria solution used in this study. The value per undiluted solution (100 g) is shown for the sugar-free condensed milk, and the value converted to the mixture of fermented milk and reduced nonfat milk (100 g) is shown for the lactic acid bacteria solution. Table 4.4. Nutrient ingredients of spray liquid used in this study (per 100 g)

Energy (kcal) Protein (g) Lipid (g) Carbohydrate (g) Sodium (mg) Calcium (mg)

Condensed milk 144 7.4 8.2 10.1 87 250

Lactic acid former microorganism slurry 120 10.7 1.7 15.5 135.5 354.5

From the VSD powderization characteristics of sugar-free condensed milk, as discussed in Chapter 3, the water content of powder was 4.3% under the condition of a drying temperature of 60°C, spray pressure of 60 kPa, and spray liquid concentration of approximately 30%, whereas it was 3.4% at a spray pressure of 100 kPa. On the other hand, from the VSD powderization characteristics of lactic acid bacteria solution, the water contents of the powder were 6.1 and 4.6%, respectively, at a spray pressure of 60 kPa, and 4.8 and 4.5%, respectively, at a spray pressure of 100 kPa. The water content is higher in VSD lactic acid bacteria powder because sugar-free condensed milk has more lipids and less protein and carbohydrates than the lactic acid bacteria solution (Table 4.4). It is known that equilibrium water content increases with the content of carbohydrates and protein, and lipids tend to lower the equilibrium water content (Matsuno et al., 1991a), whereas biopolymers such as protein and carbohydrates exhibit low water activity and relatively high water content (Nagashima, 1983). Furthermore, it is known that the viscosity of liquid food with a large content of carbohydrates and protein increases exponentially in relation to the solid content, and we have difficulty in atomizing a high-viscosity liquid (Hayashi, 1992). Actually, the viscosity of lactic acid bacteria solution is higher than that of sugar-free condensed milk (Figure 4.8). Therefore, the spray droplet size was large and because the content of protein and carbohydrates was large, the recovery rate of powder for lactic acid bacteria solution was much smaller than that of sugar-free condensed milk. Moreover, in the VSD of the lactic acid bacteria solution, powder adhered thinly over the entire inner wall of the drying tower and the recovery rate was low as compared to that in the VSD of sugar-free condensed milk. We assume that although the glass transition temperature of lactic acid bacteria solution is high, the drying of the spray droplets is not efficient due to the mechanism discussed earlier, and the surface of the spray droplets does not become glassy, but rather remains rubber-like when sticking to the inner wall of the drying tower. Although the recovery rate may be low, powder

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collection is possible, suggesting that small spray droplets are dried. Therefore, in order to lower the water content and obtain a larger amount of powder through the VSD of lactic acid bacteria solution, it is necessary to decrease humidity in the drying tower during the VSD operation. The increase in viscosity due to the increase in solid content could be dealt with by adding an emulsifier.

Figure 4.8. Viscosity of sugar-free condensed milk and lactic acid bacteria solution

4.3.3. Retention of Number of Living Lactic Acid Bacteria in VSD Lactic Acid Bacteria Powder Figure 4.9 shows the relationship between the spray pressure and the survival rate of lactic acid bacteria in VSD lactic acid bacteria powder after powderization, and Figure 4.10 shows the daily change in the number of living lactic acid bacteria in VSD lactic acid bacteria powder. In Figure 4.9, in spray tests No. 1 and 2, the survival rates of lactic acid bacteria were 48% and 94%, respectively, at a spray pressure of 60 kPa, and 106% and 200%, respectively, at 100 kPa. In these powder products, the powder water content and water activity were 6.1% and 0.17, and 4.6% and 0.20, at a spray pressure of 60 kPa, and 4.8% and 0.18, and 4.5% and 0.18 at 100 kPa. A decrease in the number of living bacteria was not seen at a spray pressure of 100 kPa, whereas a decrease in the number of living bacteria was seen in spray test No. 1 at a spray pressure of 60 kPa. It is believed that this is due to the influence of water activity. Teixeira et al. (1995) reported that the survival rate of lactic acid bacteria in powder is high if the powderization temperature is low and water activity is high. Therefore, in this study, the low survival rate of 48% would be attributed partly to the low water activity in relation to the drying temperature, and partly to the difference in test conditions.

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Figure 4.9. Survival rate of lactic acid bacteria before and after drying

Figure 4.10. Variation of survival rate of lactic acid bacteria during the storage of VSD lactic acid bacteria powder, Y: Fermented milk, YS: Mixed solution of fermented milk and reduced nonfat milk

The survival rate (%) of lactic acid bacteria, as shown in Figure 4.10, was calculated against the number of living lactic acid bacteria at the 0th day after drying. The number of living bacteria rapidly decreased in a day after drying at a spray pressure of 100 kPa (▲), and decreased slowly afterwards. On the other hand, at a spray pressure of 60 kPa (◆), there was no rapid change; however, a slow decrease was shown starting from 1 day after drying. At 4– 15 days after drying, the number of bacteria decreased in a similar manner at spray pressures of 60 and 100 kPa. At 15 days after drying, the number of living bacteria in powder was smaller at a spray pressure of 60 kPa than at 100 kPa. However, we assume that the influence

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of spray pressure on the VSD lactic acid bacteria powder is not large because living bacteria of 108 CFU/g are retained at 60 kPa and 100 kPa. It is also shown in other studies that spray pressure hardly affects the survival of lactic acid bacteria. In Fu et al. (1995), the decrease in cell survival rate after drying is attributed to the cell damage and the drying of the cell due to heating near the nozzle. In VSD, the heating temperature near the nozzle is not high for lactic acid bacteria as compared to that in Fu’s experiment. Therefore, it is likely that the decrease in the survival rate of lactic acid bacteria in this study is mainly due to the drying of the cells. It is widely accepted that spray pressure does not greatly affect the survival of bacteria; however, from the report that droplets of small size are susceptible to heat damage (Lian et al., 2002), the rapid decrease in the number of living bacteria at a spray pressure of 100 kPa in this study may be attributed to the high spray pressure. This suggests that lactic acid bacteria may be retained effectively by suppressing the rapid decrease in living bacteria by means of VSD with a controlled spray pressure and by adding a protective agent. The number of living bacteria can be retained in storage through spray drying with a low exit temperature, controlling the powder water content to within 1–4%, and controlling the storage temperature (Fu et al., 1995). While spray drying was conducted at a low temperature in this study, the number of living bacteria could be retained better by appropriately controlling the powder’s water content and choosing an appropriate storage temperature. The numbers of living lactic acid bacteria in frozen fermented milk and frozen lactic acid bacteria solution were examined. In the case of frozen fermented milk (○), the number decreased from 79% after 1 day to 16% after 15 days. In the case of lactic acid bacteria solution (●), the decrease was small, from 89% after 1 day to 87% after 15 days. This is probably because the lactic acid bacteria in fermented milk, which are vulnerable to low temperatures, were protected from low-temperature injury by the reduced nonfat milk. On the other hand, in terms of the number of living lactic acid bacteria in the freeze-dried materials(×), the retention rate was only 1% after 15 days in the case of freeze dried fermented milk, whereas it decreased to 47% after 15 days in the case of freeze-dried lactic acid bacteria solution (data not shown). This suggests that lactic acid bacteria, which are vulnerable to dry ambient, were protected by the reduced nonfat milk. Therefore, as compared to the lactic acid bacteria powder made by VSD and the numbers after 15 days, a spray pressure of 60 kPa (survival rate 44%) exhibited a result similar to that of freeze-dried lactic acid bacteria solution, and the spray pressure of 100 kPa (survival rate 17%) exhibited a result similar to that of freeze-dried fermented milk. Based on the above result, the relationship between the number of living lactic acid bacteria and the drying method will be discussed. First, because freeze drying requires a drying time of more than one day, powder production is faster with VSD assuming the same survival rate of lactic acid bacteria. Furthermore, because freezing increases cost and necessitates more storage space, powder production by VSD is more economical and superior with respect to transportability and storability, assuming the same survival rate of lactic acid bacteria. Earlier studies on the spray drying of probiotics, as shown in Table 1.4, helped us to think about the effectiveness of VSD in retaining the number of living bacteria. However, a direct comparison is difficult because the experimental conditions are different, and because some bacteria or strains might be sensitive to the spray-drying temperature. Table 1.4 shows the conditions and the “survival rate” obtained in each study; however, the number of bacteria is

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also important in discussing the survival rate. For example, a survival rate of 10% implies that the number decreases by one order of magnitude, whereas a survival rate of 1% implies that the number decreases by two orders of magnitude. On this account, it is true that a survival rate of 50% is obtained in the studies of Ananta et al. (2005), Gardiner et al. (2002), Corcoran et al. (2004), and To et al. (1997a), where the exit temperature is low; however, it is also true that VSD clearly exhibits good results when compared with earlier studies. In addition, from the studies of Desmond et al. (2002) and Kim et al. (1990), it is clear that the applicability of VSD may be better evaluated by using liquid containing specific lactic acid bacteria, species or strain, that exhibit less than 10% survival rates even when a drying matrix and protective agent are added. The results are summarized as follows. By VSD, as compared with the other studies discussed above, the lactic acid bacteria retain a level of over 108 CFU/g several days after drying (Figure 4.11), and a large decrease in the number of living bacteria is not seen. Although longer-term storage tests and tests using other species would be desirable in the future, it is concluded that lactic acid bacteria are retained in powder by VSD.

Figure 4.11. Variation of the number of lactic acid bacteria during the storage of VSD lactic acid bacteria powder

In this study, lactic acid bacteria were assumed as thermosensitive ingredients, and only lactic acid bacteria called L. bulgaricus and S. thermophilus were used; the number of lactic acid bacteria retained by VSD was shown. However, it is necessary to further examine aspects of the functions of lactic acid bacteria. For example, tests are needed to determine whether the dried lactic acid bacteria can generate lactic acid or proliferate. It is also necessary to examine the applicability of VSD using vitamins, enzymes (proteins), other lactic acid species, or stocks of bacteria or other biotic materials as test materials. This is because the materials properties, shear force of the nozzle, spray pressure, and drying temperature may affect test materials in some cases. For such tests, we can search for the operation conditions in a preliminary experiment. A similar consideration would also apply

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for existing spray-drying and freeze-drying methods. Therefore, it is expected that VSD can be practically applied to retain thermosensitive ingredients.

4.4. Summary Using spray liquid containing lactic acid bacteria as thermosensitive ingredients, the production and collection of powder were tested for their retention of lactic acid bacteria in VSD. The applicability of VSD to the powderization of thermosensitive ingredients was evaluated by examining its influence on the collection rate, water content, water activity, and the number of lactic acid bacteria. Using fermented milk mixed with reduced nonfat milk, a survival rate of lactic acid bacteria of more than 40% was obtained by VSD at a drying temperature of 60°C. Furthermore, the powder retained 108 CFU/g in the storage test at 25°C for 15 days. It is concluded that VSD is suitable for the powderization of liquid containing thermosensitive ingredients such as lactic acid bacteria. To more fully examine the applicability of VSD, an extended study is desirable using vitamins, enzymes (proteins), other lactic acid species, or stocks of bacteria or other biotic materials as test materials. It can be said that VSD can efficiently retain thermosensitive ingredients such as lactic acid bacteria as compared to the spray-drying, freeze-drying, and freezing methods.

5. OVERALL SUMMARY In recent years, health consciousness has been increasing in relation to the health hazards associated with lifestyle and living environments, and various types of functional foods have attracted attention. Therefore, the functionality of food is currently an important added value for the food industry, and the development and applications of functional foods are being actively advanced. Among various functional foods, probiotics is one of the materials that are attracting considerable attention. The improvement of environmental factors using probiotics foods is good for disease prevention; establishing the safety of probiotics is easy as compared to that of medical chemicals, because the bacterial cells for probiotics, which favorably influence the human body through oral intake, have long been used for storing and improving the flavor of food. Various probiotics food have been researched, developed, and produced. Probiotics are indispensable functional ingredients in Japan, where health consciousness is increasing, and will continue to be important ingredients for the food industry in the future. Drying is an important operation to give added value such as storability, transportability, and convenience to food containing functional ingredients. The purpose of drying food is, beyond satisfying the quality requirements of consumers, to achieve storability, transportability, and safety/sanitation for wide-area distribution, as well as convenience for consumption and sales, salability, and sales promotion effects. In order to add value to target products, it is also important to retain the functionality of raw materials during the drying process, and to retain their effectiveness and functionality for a long time. The efficient processing of functional food ingredients using drying techniques will become increasingly important; therefore, it is important to solve the problems of the drying technologies and equipment to be used for drying various functional foods.

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In this study, a vacuum spray-drying technique, VSD, is proposed as a way of powdering liquid food. In this technique, drying is conducted at relatively low temperatures (40–60°C) as compared to existing spray drying methods, by decompressing the drying tower. We expect that VSD will be fast and cost-effective as compared to even freeze drying, without the loss (or extinction) of thermosensitive ingredients unlike that which occurs with existing spraydrying techniques. The purpose of this study is to obtain basic data on the design and operation of VSD systems for retaining the food functionality of ingredients through experiments involving the powderization of liquid foods. In Chapter 1, the background and the purpose of this study were described. In Chapter 2, the constitution and characteristics of VSD were described in detail. VSD is a technique in which the inside of a drying tower is decompressed using a vacuum pump and a cold trap to remove evaporated water. The drying temperature is controlled using a heating system with an outside jacket and a far-infrared heater, and the raw material is sprayed from a two-fluid nozzle at the top of the drying tower, and then powdered in the drying tower. Chapter 3 clarified the characteristics of powderization by VSD by powdering liquid food under various drying conditions. In particular, the influence of the design and specifications of VSD, drying temperature, spray pressure, and spray liquid concentration on the physical and chemical properties of powder products, as well as the recovery rate, was examined. Using sugar-free condensed milk as the material, the influence of drying temperature, spray pressure, and spray liquid concentration of VSD on the physical and chemical properties of the powder was assessed. Powder with low water content and water activity was obtained with a higher recovery rate using a larger-capacity drying tower and with a higher drying temperature, spray pressure, and spray liquid concentration. The powder recovery rate was improved by approximately 20% by expanding the drying tower capacity by approximately 2.6 times at a drying temperature of 60°C, spray pressure of 100 kPa, and spray liquid concentration of approximately 45%. Even at a drying temperature of 40°C, spray pressure of 100 kPa, and spray liquid concentration of approximately 45%, we saw an approximately 50% increase in powder recovery rate, an approximately 2% decrease in powder water content, and a decrease of approximately 0.1 in the water activity. In a drying tower of smaller capacity, the drying speed decreased due to the increase in the tower’s internal pressure caused by the increase in spray pressure, and the powder size of the powder product was large because the powder particles, possessing large water content and water activity, adhered and fused to each other. In a drying tower of larger capacity, the powder particles were small, even at high spray pressures, because the staying period of the spray droplets in the drying tower was longer than in the drying tower of smaller capacity. However, the comparison with the average particle size of general powdered food suggested the existence of adhesion and fusion between powder particles. In Chapter 4, we discussed earlier studies on the food use of lactic acid bacteria and the importance of this study. Preparing spray liquid containing lactic acid bacteria as a thermosensitive ingredient, a powder product was produced and collected by VSD, retaining the lactic acid bacteria. The applicability of VSD to the powderization of thermosensitive ingredients was evaluated by examining its influence on the recovery rate, water content, water activity, and number of lactic acid bacteria. Using lactic acid bacteria mixed with reduced nonfat milk, a survival rate of lactic acid bacteria of more than 40% was obtained by spray drying at 60°C under decreased pressure. Furthermore, it was confirmed through the storage test that the powder retains 108 CFU/g at 25°C for 15 days. It was also confirmed that

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the spray pressure does not appreciably influence the quality of the lactic acid bacteria powder. Therefore, VSD can be applied to the powderization of liquid containing lactic acid bacteria. It can also be said that lactic acid bacteria are retained with high efficiency by VSD as compared to the spray-drying, freeze-drying, or freezing method. This study involved a basic drying test that aimed to clarify which methods are feasible or promising, and which are uncertain or difficult. The present study will create basic data for estimating the equipment specifications for scale-up, and the study at this stage has an important significance. However, at the time of scale-up, it will be difficult to assume complete similarity in any model from the viewpoint of the similarity rule. To solve this type of problem, it is important to physically understand the phenomena to be observed in the proof test of a scale-up model and to ease similarity rules in the scale-up model. It is necessary to accumulate basic data by repeating tests using various materials and conditions in order to better clarify what can be realized or what is promising for scale-up. In this study, lactic acid bacteria were used as thermosensitive ingredients, and only L. bulgaricus and S. thermophilus were used; it was found that a high number of lactic acid bacteria were retained by VSD. However, it is necessary to study the aspects of functions of lactic acid bacteria to confirm whether, for example, dried lactic acid bacteria can generate lactic acid or proliferate. Further study is desirable using vitamins, enzymes (proteins), other lactic acid species or stocks of bacteria, and other biotic materials as the test materials to examine the applicability of VSD in greater detail. This is because the material properties, elasticity of the nozzle, spray pressure, and drying temperature may possibly affect test ingredients in some cases. For such tests, we can try to determine the operation conditions for the material in a preliminary experiment. A similar consideration would also apply to the existing spray-drying and freeze drying-methods. Therefore, it is expected that VSD can be practically applied to retain thermosensitive ingredients.

ACKNOWLEDGMENT We are deeply grateful to Ichiro Kagawa, General Manager of Tokyo Rikakikai Co., LTD., and Takashi Tabira, Director of Tokyo Rikakikai Co., LTD., for their cooperative research support.

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to Kansou), Matsuno, R., Nakamura, K., Furuta, T., Tamon,. T. (eds). KORIN PUBLISHIG Co., Ltd., Tokyo, Japan, pp. 244-259 (in Japanese) Meng, X. C., Stanton, C., Fitzerald, G. F., Daly, C., Ross, R. P. (2008). Anhydrobiotics: The challenges of drying probiotics cultures. Food Chemistry, 106, 1406-1416. Mizuguchi, T., Arai, T., Hirayama, K. Dewatered Microorganism by Spray Drying (Funmu Kanso niyoru KintaiKansoubutsu), Published Patent 2002-17337. 2002-1-22. (in Japanese) Murao, S., Arai, M. (2003). Applied Microbiology, BAIFUKAN Co., Ltd., Tokyo, Japan. pp. 325. (in Japanese) My voice com (2005). General statistical yearbook data 2005 for diet (Syokuseikatsu Data Sougou Toukei Nenpo 2005), Life information center (Eds). Information Service Center, Japan, Tokyo, Japan, pp. 284-285. (in Japanese) Nagashim, S. (1983). Advance of science and technology in food industry 1. The Japanese society for food science and technology, KORIN PUBLISHING, Tokyo, Japan, pp. 116. (in Japanese) Ogawara, M. (1993). New food processing technologies and machines: The development and advance (Atarashii Syokuhin Kakou Gizyutu to Souchi: Sono Kaihatsu to Sinpo), Industry Research Encyclopedia Publishing Centre, Tokyo, Japan, pp, 246. (in Japanese) Okada, M. (1984a). Encyclopedia of dried foods, Asakura Publishig Co., Ltd, Tokyo, Japan, pp. 74. (in Japanese) Okada, M. (1984b). Encyclopedia of dried foods, Asakura Publishig Co., Ltd, Tokyo, Japan, pp.75. (in Japanese) Ogawa, A. (1980). Cyclone separator (Cyclone Bunriki), Eirth Company, Tokyo, Japan, p.1. (in Japanese) Rennie, P. R., Chen, X. D., Hargreaves, C., Mackereth, A. R. (1999). A study of the cohesion of dairy powder. Journal of Food Engineering, 39, 277-284 Sawaoka, M. (1997). New food ingredients and function (Atarashii Syokuhin Sozaito Kinou), BIO INDUSTRY Editorial Department (eds). CMC, Tokyo, Japan, pp. 1-11. (in Japanese) Sibukawa, S. (1990). Cooking equipment Part 3, Type of heater and infrared heating (Kanetsu Chouriki Part 3, Heater no Syurui to Ensekigaisen Kanetsu), Chouri Kagaku, 23, 37-43. (in Japanese) Simizu, K., Watanabe, A. (1986). Utilization of infrared to food cooking and processing (Syokuhin no Kakou・Chouri eno Ensekigaisen no Riyou), Chouri Kagaku, 19, 236241. (in Japanese) Sugiyama, K., Miyasaki, Y., Sibukawa, S. (1993). Effect of wavelength distribution on the emission heating of food (Syokuhin no Housya Kanetsu niokeru Hachou Bunpu no Eikyou), Nihon Kasei Gakkaishi, 44, 923-928. (in Japanese) Takai, M. (2000). Food and glass translation・Crystallization technology (Syokuhin to Glass Teni/ Kessyouka Gizyutsu), Murasa, N. (eds.), Sciece Forum, Tokyo, Japan, pp. 15-21. (in Japanese) Teixeira, P. C., Castro, M. H., Malcata, F. X., Kirby, R. M. (1995). Survival of Lactobacillus delbrueckii ssp. bulugaricus following spray-drying. Journal of Dairy Science, 78, 1025-1031.

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To, B. C. S., Etzel, M. R. (1997a). Survival of Brevibacterium linens (ATCC 9174) after spray drying, freeze drying, or freezing. Journal of Food Science, 62, 167-170. To, B. C. S., Etzel, M. R. (1997b). Spray drying, freeze drying, or freezing of three different lactic acid bacteria species. Journal of Food Science, 62, 576-578. Tokyo Bureau of Citizens and Cultural Affairs. (2005). General statistical yearbook data 2005 for diet (Syokuseikatsu Data Sougou Toukei Nenpo 2005), Life information Center (Ed.), Information Service Center, Japan, Tokyo, Japan, p. 341-468. (in Japanese) Tujimoto, S. (2003). Encapsulation technique of high functional food powder (Shyokuhin no Kouhinsitsu Funmatsuka・Kapuseruka Gizyutsu), Furuta, T., Murasei, N., Adachi, S., Tsujimoto, S., Nakamura, T. (eds). Science Forum Inc., Tokyo, Japan, pp. 303. (in Japanese) Uchimura, Y., Okada, S. (1992). Lactobacillus experimental manual - from isolation to identification - (Nyuusankin Zikken Mannual - Bunri kara Doutei made -), Ozaki, M. (Supervised), Asakura Publishig Co., Ltd, Tokyo, Japan, pp. 6-17. (in Japanese) Watanabe, E, Sirakawa, T. (2005). Development of allergy-preventation/ Potential of probiotics (Allergy Yobou heno Tenkai～Probiotics no Kanousei), Chounai Saikin Gakkaishi, 19, 31-38. Watanabe, M., Nakajima, K., Yosioka, T. Bifidus bacteria powder (Bifidus Kin Kintai Funmatsu), Published Patent 006-280263. 2006-10-19. (in Japanese) Yaejima, T. (1996). Science and Technology of Lactic Acid Bacteria, Chapter Board of lactobacillus story collection, Japan Scientific Societies Press, Tokyo, Japan, pp. 18-19. (in Japanese) Yaejima, T. (2002). Science of fermented milk – function and healthy effect of lactic acid forming bacteria (Hakkounyu no Kagaku - Nyuusankin no Kinou to Hokenkouka -), Hosono, A. (eds). I & K Corporation, Tokyo, Japan, pp. 212-216 (in Japanese) Yano, T. (2002). Food engineering, biochemical engineering - an engineering and scientific point of view and philosophy (Syokuhin Kougaku / Seibutsu kagaku Kougaku Kagakuteki・Kougakuteki na Monono Kangaekata to Mikata), Maruzen, Co., Ltd, Tokyo, Japan, pp. 137-143. (in Japanese) Yamamoto, S. (2003). Encapsulation technique of high functional food powder (Shyokuhin no Kouhinsitsu Funmatsuka・Kapuseruka Gizyutsu), Furuta, T., Murasei, N., Adachi, S., Tsujimoto, S., Nakamura, T. (eds). Science Forum Inc., Tokyo, Japan, pp. 167. (in Japanese) Sakihara, N., Ikegami, S. (2005). Studies on probiotic function Part 1: Studies on health function of lactic acid bacteria and the commercial development (Probiotics no Kinoukenkyuu Part 1: Nyusannkin no Hokenkinou ni Kansuru Kenkyuu to Sono Syougyoukaihatsu), Food Industry, 48, 27-32. (in Japanese) Snow Brand Milk Products Co., Ltd. Research Institute Healthy Life Guide (eds)., (1995). How to make yogurt and its type (Yogurt no Seihou to Syurui), SHOKABO PUBLISHING Co., Ltd., Tokyo, Japan, pp. 40-44. (in Japanese) Yoshii, H., Buche, F., Takeuchi, N., Terrol, C., Ohgawara, M., Furuta, T. (2007). Effects of protein on retention of ADH enzyme activity encapsulated in trehalose matrices by spray drying. Journal of Food Engineering, In Press. Yosikaasa, M., Chaya, N., Hatakeyama, S., Nisino, Y., Yamahara, S., Murakami, K. (1993). Processing of crude drugs with infrared processing (Part 2) Ginger (Zingiberis

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Rhizoma) component variations in the drying process (Ensekigaisen wo Mochiita Syoyaku no Kakouchousei (Part 2) Syoukyou (Zingiberis Rhizoma) no Kansoukatei niokeru Seibun Hendou), YAKUGAKU ZASSHI, 712-717. (in Japanese) URL Tokyo Rikakikai Co., Ltd., Spray Dryer SD-1000，http://www.eyela.co.jp/products/sd1000/ index.shtml, 2006-4-18． Tokyo Rikakikai Co., Ltd., Spray Dryer SD-1000，http://www.eyela.co.jp/products/sd1000/ program.shtml, 2006-4-18． Spraying Systems Co., Japan, Two Fluid Nozzle Air Atomizing Nozzle, http://www.spray. co.jp/products/2r_ryusikei.html, 2007-12-25．

In: New Topics in Food Engineering Editor: Mariann A. Comeau

ISBN: 978-1-61209-599-8 © 2011 Nova Science Publishers, Inc.

Chapter 9

APPLICATION OF VACUUM IMPREGNATION AND EDIBLE FILMS TO IMPROVE THE QUALITY OF RAISIN-CEREAL SYSTEMS Pau Talens* and María José Fabra Departamento de Tecnología de Alimentos. Instituto de Ingeniería de Alimentos para el Desarrollo Universidad Politécnica de Valencia. Camino de Vera, s/n 46022 Valencia, Spain

ABSTRACT The shelf life of multicomponent food systems depends, among others things, on how fast the water transfer between components takes place. This moisture migration can result in undesirable physical and chemical changes in the system, affecting its quality and shelf life. Several factors influence the amount and rate of moisture migration in multicomponent foods. However, water activity equilibrium and rate of diffusion are the two main factors. To control this migration, several principles can be utilized. A raisincereal mixture is one of the multicomponent food systems whose quality and shelf life is affected by moisture migration between components. In this system, water is transported from the raisin to the cereals, resulting in quality deterioration to both components. One way to reduce the moisture migration in this system is to reduce the water activity of raisins. The problem is that the raisin texture, one of most important factors governing their quality, becomes unacceptably hard when their water activity decreases below 0.40. Another possibility for controlling moisture migration is to add an edible barrier between components. In this study, in order to try to reduce the water activity of raisins, while maintaining their softness, raisins were infused with a glycerol-water solution (5:1) applied by vacuum impregnation. Mechanical properties, weight changes and water activity were evaluated before and after infusion to study the effect of the glycerol on raisins. On the * Corresponding author Pau Talens Tel.: +34 963879836 Fax: +34 963877369 E-mail address: [email protected]

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other hand, in order to try to reduce the moisture transfer between components, the raisins were coated by directly applying starch and beeswax to them or by using different film forming emulsions made with whey protein, beeswax and glycerol at different protein:lipid:plasticizer ratios. The results of these application procedures have shown that the use of combined solute infusion and vacuum impregnation methods are an effective way of reducing the water activity properties of the raisins as well as serving as an alternative formula towards maintaining raisins at their best possible conditions for keeping a homogenous raisin-cereal system. After 1 hour of treatment with glycerol, testing revealed that the water activity of the raisins decreased, thus decreasing the gradient of water activity between both components. Furthermore, testing has indicated that this treatment does not affect the texture of raisins. The hardness of the raisin skin was not influenced by the presence of glycerol, allowing for a produced softness inside the raisins. As regards to the testing of various coating effects, the direct application of beeswax seemed to be the best protection from water loss. Among the film forming emulsions based on whey protein, the higher plasticizer content lead to a decrease in the water loss protection; although, these differences were not significant. In spite of the fact that the tested films have different water vapour permeabilities when they are treated as independent structures, it seems that the interactions that transpired, between these films and the surface of the product, eliminated the above mentioned differences.

INTRODUCTION Water content has a decisive effect on the properties of most foods. In multicomponent foods like dry cereal with intermediate-moisture raisins, frozen pizza crust with sauce, or ice cream in a cone, the components have different water activity (aw) values which will result in moisture migration from the higher water activity (aw) to the lower aw food component. The moisture migration ceases when the difference in aw no longer exists (Risbo, 2003). This moisture migration can result in undesirable physical and chemical changes in the system, affecting its quality and shelf life. Several factors influence the amount and rate of moisture migration in multicomponent foods. However, aw equilibrium (thermodynamics) and rate of diffusion (dynamics of mass transfer) are the two main factors (Labuza & Hyman, 1998). To control this migration, several principles can be utilized. These include formulating the components to obtain as close as possible water activity parallelism through the use of selected solutes. For example, the close as possible adjustment of the aw of intermediatemoisture fruit and dry cereal to each other reduces the driving force for moisture migration so that the cereal does not pick up much moisture and loses crispness. Another possibility for controlling moisture migration is to add an edible barrier between components (Labuza & Hyman, 1998). Watter and Brekke (1961) studied methods of retarding the moisture transfer in a cereal-raisin system by dipping the raisins in various compounds. Of those tested, the only material they found that had a significant effect was beeswax. As mentioned earlier, a raisin-cereal mixture is one of the multicomponent food systems whose quality and shelf life is affected by moisture migration between components. In this system, water is transported from the raisin to the cereals, resulting in quality deterioration to both components (Sapru & Labuza, 1996). When 16% moisture (aw = 0.51-0.62) raisins are packaged in a sealed container with 3% (aw = 0.20-0.28) moisture cereal, the system

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equilibrates, with the raisins becoming hard and chewy and the cereals flakes losing some of their crispness (Bolin, 1976). One way to reduce the moisture migration in this system is changing the water activity of raisins and/or cereals, in order to reduce the water activity gradient between these components. The water activity of cereals and raisins is 0.20-0.28 and 0.51-0.62, respectively (Bolin, 1976; Payne, 1987; Lagrane & Payne, 1988). Crispness, a salient textural characteristic in dry cereals, is lost if water is gained to aw > 0.28-0.55 for breakfast cereals (Peleg, 1994; Labuza & Hyman, 1998), with resulting rejection by consumers (Nielsen, 1979). For this reason, a way to maintain good quality in a cereal-raisin system is to reduce the water activity of raisins. The problem is that the raisin texture, one of most important factors governing their quality (Lewicki & Spiess, 1995), becomes unacceptably hard when their water activity decreases below 0.40 (Lewicki & Wolf, 1995). In this sense, it is desirable to reduce the water activity of the raisins while, as much as possible, maintaining their mechanical properties at the same time. Infusion is an approach method for reducing the water activity of raisins while maintaining an acceptable soft texture. Infusion resembles a process known as osmotic dehydration. Osmotic dehydration removes substantial amounts of water from a product while adding minimal solids. As an alternative to only removing water, infusion maximizes the osmotic movement, in both directions, so solutes move into the food. This yields a different set of characteristics in the finished product. Infusion takes advantage of the existent concept that within a multi-phase food system, both solutes and water, seek equilibrium. The use of an applied vacuum is recommended to shorten the time needed to reach equilibrium (Iglesias & Chirife, 1976). Vacuum impregnation (VI) consists of exchanging the internal gas or liquid occluded in open pores for an external liquid phase, due to the action of hydrodynamic mechanisms promoted by pressure changes (Fito,1994; Fito & Pastor, 1994).The operation is carried out in two subsequent steps after the product immersion has taken place inside the tank containing the liquid phase. In the first step, vacuum pressure must be imposed for a short time (for this specific research analysis, the pressure applied was 80 mbar during 15 minutes) in the closed tank, thus promoting the expansion and outflow of the product’s internal gas. The process of releasing this internal gas transfers the innate liquids out from the pores of the product along with this corresponding gas emission. In the second step, atmospheric pressure must then be restored inside the tank for the predisposed period of time determined, in turn having effects resulting in compression, which leads to a great volume reduction of the remaining gas in the pores and consequently in the subsequent inflow of the external liquid in the porous structure (Fito, Chiralt, Barat, Andres, Martinez-Monzo & Martinez-Navarrete, 2001). When the water activity and solute concentrations between the food and infusing mixture reach equilibrium, both water and solute migration stop. By using infusion, water can be encouraged to migrate out of a piece of food while at the same time allocating space for it to be replaced by solutes, these usually being some type of soluble carbohydrate or carbohydrate derivative. Smaller molecules are used for infusion, since higher molecular weight compounds migrate into the product more slowly and at lower levels, increasing the tendency toward osmotic dehydration rather than infusion. Among the different solutes used in infusion, some control water activity better than others. Humectants ingredients; such as, sugar alcohols, glycerol, propylene glycol, and similar ingredients, have a marked effect on water activity and can change the product texture. Altering the types of solutes and their ratios can create a product that is very plastic, or rubbery, or with alternative variables, something that is more firm or soft.

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The main interest in edible films and coatings is generally based on their potential to control moisture, oxygen, carbon dioxide, lipid, flavor and/or aroma transfer; either between food components or to/from the atmosphere surrounding the food, all desired controlled properties resulting in an increase of quality and in shelf-life. The film-coating composition is chosen as a function of the desired application of the edible film selected for the food in mind; for example, when the purpose is to control the moisture balance within a heterogeneous food, as raisin-cereal systems, hydrophobic materials are required to make a film with good water barrier properties. The aim of this work was to apply a solute infusion to reduce the water activity of raisins, while maintaining their softness. Accomplishing this would contribute to obtaining a cerealraisin system with better sensory characteristics, by reducing the driving force for moisture migration from the raisins to cereal flakes. On the other hand, in order to try to reduce the moisture transfer between components, the raisins were coated by directly applying starch and beeswax to them or by using different film forming emulsions made with whey protein, beeswax and glycerol at different protein:lipid:plasticizer ratios.

MATERIALS AND METHODS Materials Sun-Maid® Natural California Raisins were used as raw material for the experiments. Glycerol, C3H803 (Panreac Quimica, S.A., Castellar Del Vallés, Barcelona, Spain) was used as infusion agent. Whey protein isolate (Llorella S. A., Barcelona, Spain), beeswax (Brillocera S. A., Valencia, Spain), Glycerol and Starch (Roquette Laisa, S.A., Valencia, Spain) were used for the preparation of the coating forming solutions. Magnesium Nitrate Mg(NO3)2.6H2O (Panreac Quimica, SA, Castellar del Vallés, Barcelona), Potassium Carbonate K2CO32H2O (Panreac Quimica, SA, Castellar del Vallés, Barcelona) and Potassium Acetate CH3COOK (Panreac Quimica, SA, Castellar del Vallés, Barcelona) were used to create a saturated salt solutions to keep chambers for pre-conditioning samples at 50%, 40% and 20% Relative Humidity, respectively.

Infusion Treatment of Raisin Samples Infusion treatment was carried out at 25 ºC in a glycerol solution made with 5 parts of glycerol to 1 part of water. The infusion solution:sample weight ratio was high enough (10:1) to avoid any significant change in the infusion solution concentration. Prior the infusion the raisin samples were pre-conditioned by adjusting their water activity to a starting desired level of 0.5. The conditioning took place in a closed chamber over saturated Magnesium Nitrate salt solutions (Spiess & Wolf, 1983). The pre-conditioned raisins, with aw = 0.500 ± 0.006 at 25º C, were immersed in the glycerol solution with a aw = 0.402 ± 0.001 at 25 º C. A vacuum pressure of 80 mbar was applied for the first 15 minutes of treatment. Afterwards, atmospheric pressure was restored; and after different times of treatment (15 minutes and 1, 2, 4, 6, 8, 24, 48 and 72 hours), the raisins were withdrawn from the solution. To perform the experiments, 2 batches of raisins were used for each condition. Weight changes, water activity values, and mechanical properties were analyzed at each time variable in triplicate.

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The weight changes were determined using a using an analytical balance (± 0.001 g). Water activity (aw) was measured using a dew point hygrometer (Decagon®, model Aqualab CX2, Decagon Devices, Inc., Pullman, Wash., U.S.A.). Mechanical properties of fresh and impregnated samples were analyzed through a penetration test (strain rate 2.5 m/s) by using a universal test machine (TA.XT2 Texture Analyser, Stable Micro Systems, Haslemere, England). Each measurement was carried out for 6 samples at 25 ºC. The force(F)-distance(d) curves obtained from the penetration test were transformed into force(F)-relative deformation(ε) curves (Dobraszczyk & Vincent, 1999).

Preparation of Coating Forming Solutions Coating forming solutions with different whey protein isolate (WPI): beeswax (BW): glycerol (Gly) ratios (1:1:1; 1.5:1.5:1; 2:2:1, 2.5:2.5:1, and 3:3:1) were prepared according to the method described previously by Talens & Krochta (2005). An aqueous solution of 10% (w/w) WPI were prepared and heated in a 250-mL Erlenmeyer flask for 30 min in a 90 °C water bath to denature the protein. The amount of BW required was melted in the hot denatured protein solution, and each solution was homogenized with a high-shear probe mixer (Ultraturax T25, Janke & Kunkel, Germany) for 1 min at 15000 rpm, followed by 3 min at 22000 rpm. The temperature of homogenization was 90 °C. The emulsions were then placed in an ice bath for cooling. Gly was added in the amount required to get the desired final film composition, and the film-forming solutions were degassed at room temperature with a vacuum pump. Coating of Raisins Samples The raisins were coated by directly applying starch and beeswax to them or by using the different coating forming solutions described previously. 140 raisins were used for the experiment: 70 fresh raisins and 70 raisins submitted to an infusion treatment during 4h. Before coating the samples, the fresh and the infused raisins were pre-conditioned by adjusting their water activity to a starting desired level of 0.5 and 0.4, respectively. The conditioning took place in closed chambers over saturated Magnesium Nitrate and Potassium Carbonate salt solutions (Spiess & Wolf, 1983). When the samples were conditioned, 10 samples of fresh raisins and 10 samples of infused raisins were used as control specimens (uncoated samples), 10 samples of fresh raisins and 10 samples of infused raisins were coated with starch and were dipped in melted BW for 2 minutes, and 50 samples of fresh raisins and 50 samples of infused raisins were dipped in five different coating forming solutions (1:1:1; 1.5:1.5:1; 2:2:1, 2.5:2.5:1, and 3:3:1 WPI:BW:GLY) for 2 minutes. The applied coatings were dried by natural convection at room temperature and all the samples were stored again in the closed chambers to make sure that the water activity values of the raisins were 0.5 and 0.4, respectively. Half of the coated and uncoated raisins were transferred to a cabinet with 20% relative humidity supported by a Potassium Acetate-saturated solution and homogenized constantly by fan (CPU COOLER. Y.S. TECHs). Before being placed in the same cabinets, the other half of the coated and uncoated raisins were placed in a gyratory drum spinning at controlled rotations, this was done with the objective of providing the test material with conditions similar to the shocks, accidents and possible damage suffered by raisins inside the cereal packaging in which they are prepared for the consumer and in which they are transported during their whole logistic chain of storage and distribution. After different time

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intervals were set and checked (24, 48, 72, 96, 372, 554 y 734 hours), the raisins were withdrawn from the cabinet and were weighed. Figure 1 shows a scheme of the experiment procedure for the reduction of moisture transfer.

Figure 1. Scheme of the experiment procedure for the reduction of moisture transfer.

Changes in total mass were calculated by applying the equation 1, where mt and m0 indicates the sample weight at time t and 0, respectively.

ΔM =

m t − m0 m0

(1)

Statistical Analysis Statistical analysis of data was performed through analysis of variance (ANOVA) using Statgraphics Plus for Windows 5.1 (Manugistics Corp., Rockville, MD).

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RESULTS AND A DISCU USSION In nfusion Treeatment Study. Figure 2 shhows the channges of weightt for raisins suubmitted to thee infusion treaatment. The raaisins gain weeight at each tiime interval, indicating i thatt the samples gain glycerol faster than loose water. Thaat means thatt glycerol is a better infusiion agent thann an osmotic agent. The w water activity of o raisins withh glycerol-wateer infusion solution, were monitored m throoughout the 1,, 2, 4, 6, 8, 24, 2 48 and 722 hour intervaals of the expperiment. The average valuues, for the raaisins used in the experimennts, are plotteed in Figure 3. The line inddicates the waater activity vaalues for the infusion solutiion. Figure 3 illustrates i the development of aw within the samples annd their corressponding valuues of glycerol-water infusion solution. The T same valuue for both, raaisin sample and a infusion solution, s weree reached verry quickly at about 1 h off treatment. A After that, the water activityy of the raisinns was constannt at each tim me interval. Thhese results inndicate that thhe infusion soolution:samplee weight ratio was enough to avoid any significant chhange in the infusion soluution concentrration and that the vacuum m impregnatioon process faavors the gain of glycerol diiminishing thee water activityy of the raisins.

Fiigure 2. Percenttage of weight gain g for raisins after infusion trreatment.

In order to o determine thhe effect of the t glycerol innfusion on thhe texture of raisins, r the mechanical pro m operties of frresh and infuused raisins were w measureed. Figure 4 shows the chharacteristic penetration p currves of untreaated raisins andd the characteeristic penetrattion curves off infused raisinns, having waater activities of o 0.500 ± 0.0006 and 0.402 ± 0.001, respeectively. In thhese curves, th he first peak shows s the skinn rupture poinnt and the seccond peak reppresents the m moment that the t raisin is fully penetraated. Betweenn these peakss, the curve shows the peenetration of the t internal raaisin flesh. The mechanical parameters annalyzed were: the failure foorce at the firsst peak (forcee values at thee skin rupture,, Fskin); the faiilure force at the second peeak (F2Peak); thhe force valuee required to penetrate p the raisin r (Fflesh); the relative deformation d inn the first peaak (ε); the areea until the second peak iss reached (A)); and the ratiio of force

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reequired to peenetrate the flesh f and thee force reachhed at the moment m of skkin rupture (((Fflesh/Fskin). Taable I shows thhe values of all a these param meters for freshh and infused raisins. An annalysis of variiance (ANOV VA) for these studied s valuess, at each timee interval, shoows that no siignificant diffe ferences were observed betw ween the failuure force at thee first peak moment m and att the second peak p momentt, and neither were any siggnificant diffeerences observved for the reelative deform mation values at a their first peeak moment. These T results indicate i that thhe infusion trreatment does not affect the skin of the raisin. A decreaase in the Ffleshh, A and Fflesh/F Fskin values w observed during was d the infuusion treatmennt. This decrease is more efffective at thee beginning off the process.

Fiigure 3. Changees in the water activity a of raisinns during the innfusion treatmennt. The illustrateed line inndicates the watter activity valuues for the infusiion solution.

Fiigure 4. Characteristic penetrattion curve for fresh fr and infusedd raisins.

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Table 1. Mea an values and d standard deeviation of meechanical parrameters anallyzed for fresh an nd infused raiisins Fskkin (N)

F2Peak (N) 2

ε

Ffleesh (N)

Fflesh / Fskin f

A (J) Area

Frresh

4.22 (0.8)a

2.6 (0.6)a

0..6 (0.2)a

1.88 (0.1)a

500 (7)a

12.7 (1.0)a

IV V

4.66 (1.1)a

2.8 (0.9)a

0..6 (0.1)a

1.88 (0.2)a

433 (8)a

12.6 (1.4)a

1hh

4.66 (1.0)a

2.0 (0.6)a

0..6 (0.1)a

1.77 (0.2)ab

399 (5)ab

11 (2)ab

2hh

4.55 (0.7)a

2.44 (0.7)a

0..6 (0.2)a

1.66 (0.2)ab

366 (4)b

11 (2)ab

4hh

4.44 (0.8)a

2.3 (1.0)a

0..6 (0.1)a

1.55 (0.3)bc

344 (5)bc

10 (2)ab

6hh

4.22 (0.5)a

2.3 (0.8)a

0..6 (0.1)a

1.33 (0.3)bc

322 (5)bc

9.8 (0.7)b

8hh

4.11 (0.7)a

1.7 (0.6)a

0..6 (0.1)a

1.33 (0.2)c

311 (4)c

9.8 (1.0)b

244h

4.22 (0.7)a

1.9 (0.5)a

0..7 (0.1)a

1.33 (0.2)c

311 (3)c

10 (2)b

488h

4.22 (0.5)a

2.1 (1.0)a

0..6 (0.1)a

1.33 (0.1)c

322 (3)c

9.9 (1.2)b

722h

4.22 (0.4)a

2.5 (1.5)a

0..6 (0.1)a

1.33 (0.2)c

300 (5)c

10.3 (1.2)b

a–cc

Different supeerscripts within a column indiccate significant differences d amoong samples (p < 0.05).

Figure 5 shows s the meean force valuues at the skkin rupture pooint for untreeated raisin saamples compaared to infusion-treated raiisin samples at a each infusiion time. No significant diifferences amo ong all samplees were observved.

Fiigure 5. Force values v in the skiin rupture pointt for untreated and a treated sampples throughoutt time inntervals measureed.

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In order to determine if the t glycerol innfusion affecteed the internall raisin flesh, the ratio of foorce required to t penetrate thhe flesh (Ffleshh) was dividedd by the force in the skin (F Fskin). High vaalues for this parameter woould mean thaat the raisin flesh f became tough, t while low values w would mean th hat the raisinn became sofft. Figure 6 shows s the rattio Fflesh/Fskin values for unntreated and treated t raisins. Two groups of samples can c be observeed. The untreaated raisins (sshown as “Fresh”) and the innfused raisinss under observvation for up too four hours of o treatment (sshown as barss VI, 1h, 2h and 4h) dennote one of thhese groups; and, the infuused raisins obbserved until the end of thee study denotees the other group g (shown as bars 6h, 8hh, 24h, 48h annd 72h). Untreated raisins show s the highhest Fflesh/Fskinn values, whichh decrease proogressively w when the glyceerol solution is i infused intoo the raisins. After A 4 hours of treatment,, this value reemained statisstically constaant until the end e of the expperiment. Thiss behavior inddicates that gllycerol affecteed the flesh of o raisins. As observed earrlier in Figuree 5, Fskin is constant for trreated raisins throughout thhe study. Thhus, the decreease in Fflesh/F Fskin shown inn Figure 6 inndicates that th he raisin flesh softens due too glycerol infuusion, even as aw decreases. This study w done for tw was wo batches of raisins, and sttatistically no significant diffference betweeen batches w found. was To confirm m that the raisiin flesh was sooftened due too infusion, thee area betweenn the initial pooint of observ vation and the second peak was w determineed for untreateed and treated samples. If raaisins were softened due to infusion, the area betw ween the skinn rupture peaaks should deecrease. Figurre 7 shows thaat untreated raaisins and raissins after vacuuum infusion and during thhe following 4 hours had the highest area values. Afterwards, the area decrreased and reemained consttant during thee remaining tim me of the expeeriment. Thuss, both the Fflessh/Fskin ratio annd the area between b skin penetration peaks indicatte that approxximately afteer 4 hours, inncreased raisin n softness is achieved. Affter this time,, the internal raisin texturee does not chhange. Thus, additional a infuusion time is not n necessary. The low moleecular weight of glycerol deecreases the molecular m weigght of the soluutes in the liquuid phase diminnishing the viscosity and thhe mechanical resistance in the system.

Fiigure 6. Fflesh/Fskin values for unntreated and treaated raisins throoughout time inntervals. s

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The infusio on treatment study s shows thhat glycerol iss a good infussion agent for decreasing thhe water activvity of raisinns, being onee alternative for f maintaininng raisins at their best coondition, and preserving a homogenous h r raisin-cereal system. After 1 hour of treaatment with gllycerol, the water activity of o raisins decreeased, thus deecreasing the gradient g of waater activity beetween both components. At A this one houur time intervaal, Fflesh/Fskin annd area valuess were 38.7 ± 5.3 and 11.0 ± 1.9 J, reespectively. However, H Fflessh/Fskin and arrea values coontinued to th urably up untill the 4 -hour interval, whenn the values were w 33.8 ± 4.99 and 9.9 ± deecrease measu 1..8 J, respectively. This treattment does nott affect the texxture of raisins which increaases during thhe first 4 hou urs and keepss constant aft fter this time.. For this reaason, the mosst efficient trreatment woulld be to keep raisins in thee solution witth glycerol forr up to 4 houurs, thereby siimultaneously reducing the raisin water activity a and maintaining m or increasing itss properties off softness wheen compared too its untreatedd raisin counteerpart.

Fiigure 7. Area vaalues for untreaated and treated samples througghout time interrvals.

C Coating of Raisins R Samp ples w the waterr loss of the samples) s of Figure 8 shhows the weight loss (relatted directly with frresh and infused raisins storred at 20ºC inn a cabinet with 20% relativve humidity. The T weight looss of fresh raaisins was higgher than the weight w loss of o infused raisins, probably due to the exxposure of thee fresh raisinss to a higher average a RH thhan that foundd with the infuused raisins (550-20% vs.: 40-20%) 4 and because b glyceerol has the capacity c of maaintaining waater content (L Lee, Dangarann and Krochta, 2002) and thhat can contribbute towards decreasing d thee water loss off the raisin. The weight loss calculattion was not taken t into connsideration duue to the valuee of the m0 beeing higher in n infused raissins than the value found in fresh raisiins; therefore,, the water vaapour resistannce (WVR) of the uncoated and coated raiisins was estim mated (Equatioon 2) using

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thhe weight losss versus storrage time datta calculationn and Fick’s modified difffusion law (A Avena-Bustillo os, Cisneros-Z Zevallos, Krocchta, & Saltveit, 1993). In thhis equation, WVR W is the w water vapour resistance, r h//cm; aw the water w activityy of raisins; RH R the cabinnet relative huumidity; Pv the water vapour presssure, mmHgg; R the universal u gass constant 3 (m mmHg·cm /g·K K); T the cabiinet temperatuure, K; A the average a surfacce area of raissins, cm2; J thhe slope of thee weight loss curve, c g/h.

RH ⎞ ⎛ ⎜aw − ⎟PV A 100 ⎠ ⎝ WVR = RT J

(2)

Nevertheleess, due to the difficulty of determining d thhe area of the raisin (the dennsity of the raaisin could be measured andd made it relaative to its vollume, then thee area of the raisin r could haave been calcuulated and determined by means m of undeerstanding its mass, assumiing that the geeometric shap pe of a raisin is calculated as that of a sphere); s and, the t possible error e in the esstimation of th he WVR, the slopes of thee weight loss versus storage time data cuurves were ussed where anaalyzed. Workiing with the hypothesis thatt the area of a raisin was sim milar in all thhe samples, thhe first segm ment of the slope s was theen correlated with the waater vapour reesistance of th he samples, where w we hennce, after anallysis, reached the conclusioon that the hiigher the slopee of a curve, thhe higher the water w vapour resistance behhavior.

Fiigure 8. Weightt loss of fresh annd infused raisiins stored at 20ººC in a cabinet with w 20% relatiive huumidity.

Figure 9 an nd 10 show the t slope valuues in g/h of the t different raisins r studiedd. Figure 9 shhows the values of fresh raiisins, those without manipuulation (a) andd those manipuulated with shhock movemeent (b); wherreas figure 10 1 shows thee values of infused i raisinns, without m manipulation (aa) and those manipulated m w shock movvement (b). Inn the first casee (figure 9), with

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one can observe that the best treatment to apply is to directly coat the fresh raisins with starch and BW, being that this coating offers the lowest slope values to commence the study; meanwhile, the fresh (untreated with starch and BW) raisins have the least water vapour resistance attributes. No significant differences were observed among the different emulsions used. On a different note, the shocks provoked by the gyratory spinning drum did not seem to affect the interaction between the coating and the surface of the raisin. In the second case (figure 10), can be observed that the presence of glycerol infused in the raisin made the effect of the starch-BW coating (directly applied over the glycerol infused raisin) to be less effective. This effect could be due to the glycerol causing an increased hydrophilic surface on the raisin, thus making adhesion more difficult between the beeswax and the surface of the raisin. It was also observed that the shocks provoked on the glycerol infused raisins by the spinning gyratory drum did seem to affect the interaction between the coating and the surface of the glycerol infused raisin. This appears to indicate that induced shocks most likely broke up the wax coating over the surface of the glycerol infused coated raisins; and in this case study, provoked a more rapid rate of water loss on the samples under observation. The problem observed when analyzing these slopes is that these values are not able to compare the effect derived from the glycerol infusion treatment, being that the driving force behind the glycerol infused treatment process is different. Therefore, making the hypothesis that the interchange area (skin of the raisin) is not modified by the infusion treatment (effect justified in the infusion treatment study), and by substituting, in Equation (2), the values corresponding to the driving force of the yield process for each example, equation 3 can be obtained. This equation permits us to compare the water vapour resistance values of untreated and infused raisins.

WVRFresh = β ·WVRInfused

(3)

For untreated raisins, and for the raisins coated with the different emulsions, the set value of β is equal or higher than 1. This value indicates that the raisin skin resistance to water vapour decreases as an effect of the infusion treatment and that the coating emulsions are not affected by the presence of glycerol on the surface of the raisins. In contrast, the effect of directly applying the starch and beeswax coating is quite more perceptible (set value of β is equal to 3) when demonstrating reduced adhesive qualities of the beeswax due to the presence of glycerol on the raisin skin. These evaluated results showed that the glycerol infusion treatment decreased the driving force of water transfer within the system, but also seemed to contribute to the decrease of the water vapour resistance of the skin, a loss of water vapour resilience that did not improve when the tested coatings were applied. The different coating emulsions did not seem to affect the water vapour resistance of the samples in a distinguishable manner being that the slopes observed were very similar. Even though these films present distinct permeable qualities when they are treated as independent structures (Talens & Krochta, 2005), it seems that the interactions that take place between these and the surface of the studied product diminish those very same considered permeable distinctions. Each emulsion has its own unique adhesive quality relative to its mixture composition and applicable in a specific/dissimilar manner depending on the type of raisin it coats; emulsions all having specific details reaffirming the necessity of continued investigation regarding microemulsion formulations

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with smaller sized particles or bi-layered coatings that will achieve better water barrier qualities for food products. (a) Raisins (aw=0.5)

(b) Shocked Raisins (aw=0.5)

8

7 BW

6 3:3:1

0

8

BW 7

6 3:3:1

5 2.5:2.5:1

4 2:2:1

0,0

3 1.5:1.5:1

0,0

2 1:1:1

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Fresh 1

0,1

5 2.5:2.5:1

0,2

4 2:2:1

0,2

0,3

3 1.5:1.5:1

0,3

0,4

2 1:1:1

0,4

Fresh 1

Slope·-10-3 (g/h)

0,5

0

Slope·-10-3 (g/h)

0,5

Figure 9. Values of the slopes (g/h) for fresh raisins, without (a) and with shocks (b). (b) Shocked Raisins (aw=0.4)

0,5

0,5

0,4

0,4

Slope·-10-3 (g/h)

0,3 0,2 0,1 0,0

0,3 0,2 0,1

8

7 BW

6 3:3:1

5 2.5:2.5:1

4 2:2:1

3 1.5:1.5:1

2 1:1:1

Fresh 1

8

BW 7

6 3:3:1

5 2.5:2.5:1

4 2:2:1

3 1.5:1.5:1

2 1:1:1

Fresh 1

0

0,0

0

Slope·-10-3 (g/h)

(a) Raisins (aw=0.4)

Figure 10. Values of the slopes (g/h) for infused raisins, without (a) and with shocks (b).

WVRFresh = β ·WVRInfused

(4)

CONCLUSION The use of combined solute infusion and vacuum impregnation methods are an effective way of reducing the water activity properties of the raisins as well as serving as an alternative formula towards maintaining raisins at their best possible conditions for preserving a homogenous raisin-cereal system. After 1 hour of treatment with glycerol, testing revealed that the water activity of the raisins decreased, thus decreasing the gradient of water activity between both components. Furthermore, testing has indicated that this treatment does not affect the texture of raisins, which increases in area during the first 4 hours and keeps constant after this time. The hardness of the raisin skin was not influenced by the presence of glycerol, allowing for a produced softness inside the raisins. As regards to the testing of

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various coating effects, the direct application of beeswax seemed to be the best protection from water loss. Among the film forming emulsions based on whey protein, its higher plasticizer content lead to a decrease in its water loss protection; although, these differences were not significant. In spite of the fact that the tested films have different water vapour permeabilities when they are treated as independent structures, it seems that the interactions that transpired, between these films and the surface of the product, eliminated the above mentioned differences.

ACKNOWLEDGMENT The authors acknowledge the financial support from the Conselleria de Educacion de la Comunidad Valenciana through the Project GVPRE/2008/355 and from Vice-rectorate for Research from UPV through the project PAID-06-08-3242.

REFERENCES Avena-Bustillos, R.J., L.A. Cisneros-Zevallos, J.M. Krochta & M.E. Saltveit. (1993). Optimization of edible coatings on minimally processed carrots using response surface methodology. Transactions of the ASAE (American Society of Agricultural Engineers) 36(3), 801-805. Bolin H.R. (1976) .Texture and crystallization control in raisins. Journal of Food Science, 41, 1316-1319. Dobraszczyk, B. J.; Vincent, J. F. V. (1999). Measurement of mechanical properties of food materials in relation to texture: The materials approach. In: A., Roosenthal (Ed.), Journal of Food Texture: Measurement and Perception. Chapter 5 (pp. 99-147): An Aspen Publication. Fito P. (1994).Modelling of Vacuum Osmotic Dehydration of Food. Journal of Food Engineering,22, 313-328. Fito P & Pastor R. (1994). On some non-diffusional mechanism occurring during vacuum osmotic dehydration. Journal of Food Engineering,22, 313-328 Fito P.,Chiralt A., Barat J.M., Andres A., Martinez-Monzo J. & Martinez-Navarrete N. (2001). Vacuum impregnation for development of new dehydrated products. Journal of Food Engineering, 49, 297-302. Iglesia H & Chirife J.(1976). Equilibrium moisture contents of air dried beef.Dependence on drying temperature. Journal of Food Technology, 11, 556. Lagrange V.& Payne J.T. (1988). Shelf- Life Extension of Food Products Containing Raisins and Raisins Products. Cereal Food World, 33(2), 211-214. Labuza T.P.& Hyman C.R.1998.Moisture migration and control in multi- domain foods. Trends in Food Science & Technology, 9, 47-55. Lee S.Y., Dangaran K.L. & Krochta.J.M. (2002). Gloss Stability of Whey-Protein/Plasticizer Coating Formulations on Chocolate Surface. J Food Sci.,65 (4):658-662. Lewicki P.& Spiess W.E.L.(1995).Rheological Properties of Raisins: Part I. Compression test. Journal of Food Engineering, 24, 321-338.

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Lewicki P. & Wolf, W. (1995). Rheological Properties of Raisins: Part II. Effect of Water Activity. Journal of Food Engineering, 26, 29-43. Nielsen A.C.Company. (1979). Product and Package Performance:The consumers View. A.C. Nielsen Company, Northbrook, IL. Payne J.T. (1987).The Role of Raisins in High-Fiber Muesli-Style Formulations. Cereal Food World, 32(8), 45-547. Peleg M. (1994). A Mathematical Model of Crunchiness/Crispness Loss in Breakfast Cereals. Journal of Food Science,25, 403-410. Risbo J.(2003). The dynamics of moisture migration in packaged multi-component food systems I:shelf life predictions for a cereal- raisin system. Journal of Food Engineering,58, 239-246 Sapru V.& Labuza T. (1996). Moisture Transfer Simulation in Packaged Cereal- Fruit Systems. Journal of Food Science, 27, 45-61. Spiess, W.E.L. and Wolf, N.R. (1983). In: R. Jowitt, F. Escher, B. Hallstrom, H.F.Th. Meffert, W.E.L. Spiess and G. Voss, Editors, Physical Properties of Foods, Applied Sci. Publ., London , pp. 65–87. Talens, P., & Krochta, J. M. (2005). Plasticizing effects of beeswax and carnauba wax on tensile and water vapor permeability properties of whey protein films. Journal of Food Science, 70 (3), E239–E243. Watter G.G & Brekke J.E. (1961). Stabilized raisins for dry cereal products. Food Technology, 15 (5), 236-238.

In: New Topics in Food Engineering Editor: Mariann A. Comeau

ISBN: 978-1-61209-599-8 © 2011 Nova Science Publishers, Inc.

Chapter 10

FOOD PACKAGING: INNOVATIVE CONCEPT AND NECESSITIES Kelen Cristina Dos Reis Dept. of Food Science, Universidade Federal de Lavras, Lavras, Brazil

1. INTRODUCTION The type of packaging used has an important role in determining the shelf life of a food and the main purpose of food packaging is to protect the food from microbial and chemical contamination, oxygen, water vapour and light. Innovative food packaging concepts has been introduced as a response to the continuous changes in current consumer demands and market trends. The growing demands for ready-to-eat and easy to consume products are enhancing the necessity to increase the control on quality and food safety, and then a new tendency in food technology packaging emerging and it consist in using active packaging. Beyond their action as a barrier against external agents, they interact with the product in a manner to preserve the quality, reducing risks of pathogens and prolonging the shelf life of food. The active packaging can act absorbing oxygen, humidity and ethylene, incorporating taste and flavor, and reducing/inhibiting the microbial activity. The next generation of food packaging may include materials with antimicrobial properties. These packaging technologies could play a role in extending shelf-life of foods and reduce the risk from pathogens. Antimicrobial polymers may find use in other food contact applications as well [1]. In addition, actually a new concept with food-packaging materials that are more natural, disposable and biodegradable have a great interest in the world. Therefore, it is evident that the interest in active food-packaging systems in which the polymer matrix has low environmental impact is growing significantly. Biopolymers are environmental friendly but they show some limitations in terms of performance like thermal resistance, barrier and mechanical properties, associated with the costs. Then, this kind of packaging materials needs more research, more added value like the introduction of smart and intelligent molecules which is the nanotechnology field and to be able to give information about the properties of the food inside the package like quality, shelf-

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life, microbiological safety and nutritional values. It is necessary to make researches on this kind of material to enhance barrier properties, to ensure food properties integrity, to incorporate intelligent labeling, to give to the consumer the possibility to have more detailed product information than the current system.

2. BIOPOLYMERS FOR FOOD PACKAGING Depending on the production process and on the source, biopolymers can have properties similar to traditional ones. PLA is becoming a growing alternative as a green food packaging materials because it was found that in many situations it performs better than synthetic ones, like oriented polystyrene (OPS) and PET materials [2]. There are several other types of biopolymers on the market and same coming from petrochemical monomer, like certain types of polyester, polyester amides and polyvinylalcohol, produced by different manufacturer, used principally as films or moulding. Other bio polymers are starch materials, cellulose materials and polyesters (polylactic acid, PLA; polyhydroxy acid, PHA). Until now, the PHA polymer is a very expensive polymer because it is commercially available in very limited quantities but different types of materials can be combined to form blend or compounds or semifinished products such as films [3]. Many bioplastics are mixes or blends containing synthetic components, such as polymers and additives, to improve the functional properties of the finished product and to expand the range of application. It is important to study the change that can occur on the characteristics of the bioplastics during the time of interaction with the food [4]. It is important to understand not only the physical and mechanical properties of such materials for the task but also the compatibility with the food, which has been recognized as a potential source of loss in food quality properties. Polyhydroxyalkonoates (PHAs) have attracted much attention as biocompatible and biodegradable and these biopolymers are polyesters of various hydrocarboxylic acids, which are accumulated as an energy/carbon storage or reducing power material by numerous microorganisms under unfavourable growth conditions in the presence of excess carbon source [5]. PHAs exhibit material properties similar to various synthetic thermoplastic and elastomers currently in use, from polypropylene to synthetic rubber. Besides, upon disposal, they are completely degraded to water and carbon dioxide (and methane under anaerobic conditions) by micro-organisms in various environments such as soil, sea lakes and sewage. However, widespread industrial use has been limited due to the high cost of these polyester polymers. Blends of two or more polymeric materials or copolymers represent a good alternative for reducing the final cost of industrial products and an effective alternative way to acquire a new material with desired properties [6]. Biopolymers like starch present some drawbacks, such as the strong hydrophilic behavior (poor moisture barrier) and poorer mechanical properties than the conventional nonbiodegradable plastic films used in the food packaging industries [7,8,9,4]. Nevertheless, nanotechnology research is entirely multidisciplinary and can be applied to improve current products. A new approach has been developed, which use hybrid materials consisting of polymers and layered silicates the nanocomposites. In the nanocomposites technology, a layered silicates, such as montmorillonite (MMT) clay mineral, result from the stacked arrangement of negatively charged silicate layers and contain a platelet thickness of about 1

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nm with a high aspect ratio (ratio of length to thickness) is filled in a polymer matrix and the final product after processing exhibit extraordinary enhancement of mechanical, thermal and physicochemical properties at a low level of filler concentration in comparison to pure polymer and conventional microcomposites. So, incorporation of nanoclay in biopolymers like starch can improve its properties such as barrier and mechanical properties, furthermore can improve optical properties. In particular, these nanocomposites have excellent barrier properties because the presence of clay layers delays the diffusing molecule pathway due to tortuosity [10-12]. However, most work done on polymer/clay nanocomposites has focused mainly on synthetic polymers [13, 14]. Biopolymer-based nanocomposites have been examined in few studies [15; 17]. Another interesting aspect of nanocomposite technology is the significant improvements of biodegradability of biodegradable polymers after nanocomposite preparation with organoclay. So, a comprehensive review of biopolymer nanocomposite film applications in food packaging industry is necessary. In particular, these nanocomposites have excellent barrier properties because the presence of clay layers delays the diffusing molecule pathway due to tortuosity [18-20].

3. ACTIVE AND ANTIMICROBIAL PACKAGING Active packaging is new concept of packaging that changes the condition inside of the packaging to extend shelf-life or improve safety or sensory properties while maintaining the quality of the food. Fresh foods just after harvest are still active biological systems then the atmosphere inside a package constantly changes during metabolic processes. Research have shown that each fresh food has its own optimal gas composition and humidity level for maximizing its shelf life and active packaging offers promise in this area; it is difficult with conventional packaging to optimize the composition of the headspace in a package. The great challenged in the active biodegradable films and to incorporate the active agent material in the biodegradable matrix and to produce active films in conventional industry processing. So, for this new innovative concept packaging is very important specific properties from the biopolymers and it is important study how the antimicrobials can be incorporated in the biopolymer matrix. The active agents that can be added is very diverse like organic acids, enzymes, bacteriocins, fungicides, natural extracts, ions, ethanol, etc. and the systems can be in contact only with the atmosphere surrounding the food, in contact with the food surface or placed inside the food itself (for liquid foods). Some of the food packaging applications are postulated, such as intelligence, sensing and signaling microbiological and/or biochemical change; or active. Edible coating is an excellent vehicle to enhance the nutritional value of fruits and vegetables by carrying basic nutrients and/or nutraceuticals that are lacking or are present in only low quantity in fruits and vegetables. The application of cassava starch film in the highest concentration (4%), provided to the cucumber a better aspect of conservation, turning the most attractive product [21]. The functional properties of biopolymer-based edible films or coatings have been shown to act as barrier to solute and gas and enhance food quality and

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shelf life [22-23]. However, these films do not display good mechanical and water vapor barrier properties due to their hydrophilic characteristics. Again the active packaging from biopolymer is an innovative concept but for this new innovative concept packaging is very important specific properties from the biopolymers and it is important study how the antimicrobials can be incorporated in the biopolymer matrix. The nanotechnology can be the potential solution for this because nanosctructures display a high surface-to-volume ratio and the nanofiller can be use for the incorporation of the active agents. It is necessary to study the incorporation of the active agent in the nanofiller and then the nanofiller in the biopolymer matrix. The nanobiocomposites are able to enhance desired properties or to introduce new additional effective functionalities with small amount of nanofillers [24] Recent studies also show that nano-particles can be tailored for both controlled release and/or specificity in the action of the active agent having moisture or temperature as triggering mechanisms (second generation of nano-structured materials). Recently, Rhim et al. [25] found that chitosan-based nanocomposite films blended with some organically modified MMT, such as Cloisite 30B, exhibited antimicrobial activity against Gram-positive bacteria. In another study they conclude that the PLA/Cloisite 30B composite film showed a bacteriostatic function against Listeria monocytogenes. Miyagawa et al [26] reported the preparation of novel biobased nanocomposites from functionalized vegetable oil and organically-modified layered silicate clay. Wibowo et al. [27] have investigated on cellulose acetate (CA) nanocomposites. Biobased nanocomposites based on cellulose have been extensively applied to delay loss of quality in fresh products such as tomatoes, cherries, fresh beans, strawberries, mangoes and bananas. Petersson & Oksman [28] compared the mechanical and barrier properties of two different types of biopolymer based nanocomposites. The two nanoreinforcements chosen for this study were bentonite a layered silicate and microcrystalline cellulose (MCC). The polymer matrix was poly lactic acid (PLA). PLA is linear aliphatic thermoplastic polyester. The PLA/bentonite nanocomposite showed a 53% increase in tensile modulus and a 47% increase in the yield strength compared to pure PLA. The PLA/S-MCC system on the other hand showed no increase in tensile modulus and only a 12% increase in yield strength compared to pure PLA. These results were lower than expected. Also, the bentonite nanocomposite is able to reduce the oxygen permeabilityof the PLA while the MCC nanocomposite drastically increased the oxygen permeability of the PLA. No et al. [29] in the study showed the applications of chitosan for improvement of quality and shelf life of various foods from agriculture, poultry, and seafood origin. The hydrophilic nature of such packaging materials, which produces a loss of barrier properties or even a solubilization into foods with high-water activities, prevents their industrial applications [30]. In the study of Villalobos et al. [31] the syntheses of films with HPMC were reported. The water vapor permeability of films incorporates by Sorbitan monostearate and sucrose palmitate was minimal with a hydrocolloid/surfactant ratio. Elsabee et al. [31] reported the syntheses of films formulated with polypropylene (PP) and Chitosan/pectin. They shown that chitosan forms complex compounds with pectin and this property was used to build up a stable multilayered structure on the PP film surface, to produce a much better antimicrobial film which can be used to fabricate excellent packaging materials for post-harvest crop protection. CS has the ability to gel spontaneously on contact

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with multivalent polyanions due to the formation of inter- and intramolecular cross-linkage mediated by these polyanions. The polyanions investigated tripolyphosphate (TPP) is the most popular because of its non-toxic property and quick gelling ability. The CS-TPP nano system exhibits some attractive features which render them promising carriers for the delivery of macromolecules [33]. The application of these nanoparticles in edible films is very promising due to the food-grade properties of both components. The aim of the present study was to investigate the effect of nanoparticles with chitosan on the HPMC films mechanical and water vapor permeability properties. In this paper, chitosan/tripolyphosphate nanoparticles were prepared and incorporated in hydroxypropyl methylcellulose films. Hydroxypropyl methylcellulose films with CS-TPP nanoparticles could then be a potential material for food packaging applications to extent the shelf life of foods and products. Research and development of bio-nanocomposite materials for food applications, such as packaging and other food contact surfaces is expected to grow in the next decade with the advent of new polymeric materials and composites with inorganic nanoparticles. The application of nanocomposites promises to expand the use of edible and biodegradable films [34-36]. Some works [37-38] has shown that, at relatively low nanoparticle loadings, polymer composites are reinforced because chains within these nanocomposites are restricted to confined domains between sheets of the films. For instance, the combination of chain confinement, alignment of nanoparticles (i.e., nanostructure) and strong surface interaction, results in improvements in mechanical properties, as well as in decreases in gas and liquid permeability. The incorporation of particles that carry antimicrobials into the polymer matrix may change the film’s mechanical, barrier and optical properties [39-42]. Plant extracts commonly impart color and opacity to polymers [39] and sorbates decrease transparency of LDPE films [40]. Oxygen and water vapor transmission rates increase in LDPE containing chitosan but decrease in LDPE containing benzoic acid. Other functional ingredients, such as antioxidants, antimicrobials, nutraceuticals, flavor, and color agents, can be carried by edible coatings and retained on the food surface for enhancing food quality, stability, and safety. Common antimicrobial agents used in food systems, such as benzoic acid, sodium benzoate, sorbic acid, potassium sorbate, and propionic acid, may be incorporated into coatings. Starch-based coatings containing potassium sorbate were applied on the surface of fresh strawberries for reducing microbial growth and extending storage life of the fruit . HPMC coating containing ethanol was effective in inactivating Salmonella montevideo on the surface of fresh tomatoes [43]. Lysozyme was incorporated into chitosan coatings for enhancing the antimicrobial activity of chitosan against Escherichia coli and Streptococcus faecalis [44]. In addition, chitosan coatings containing potassium sorbate were shown to increase antifungal activity against the growth of Cladosporium and Rhizopus on fresh strawberries [45]. A new patented edible film comprising organic acids, protein, and glycerol (for example, 0.9% glycerol, 10% soy protein; and 2.6% malic acid) can inhibit pathogen growth, including L. monocytogens, S. gaminara, and E. coli 0157:H7. Such film also provides a method for coatingcomestible products with edible films without masking the color but increasing the shelf life [46]. Antioxidants can be added into the coating matrix to protect against oxidative rancidity, degradation, and discoloration of certain foods. Nuts were coated with pectinate, pectate, and

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zein coatings containing BHA, BHT, and citric acid to prevent rancidity and maintain their texture [47]. Ascorbic acid was incorporated into edible coatings for reducing enzymatic browning in whole and sliced mushrooms [48]. Xanthan gum coatings mixed with α-tocopherol enhanced nutritional quality and improved the surface color of peeled baby carrots [49]. Carrageenan and whey protein coatings containing antibrowning agents such as ascorbic acid and citric acid effectively prolonged the shelf life of apple slices [50]. MC-based coatings containing ascorbic acid and sorbic acid were able to retard browning and to enhance texture of cut-pear wedges [51]. Chitosan-based coating containing α-tocopheryl acetate significantly delayed the color change of fresh and frozen strawberries [52,53]. Xanthan gum coating was utilized to contain a high concentration of calcium and vitamin E for not only preventing moisture loss and surface whitening, but also significantly increasing the calcium and vitamin E contents of the carrots [54]. The development of chitosan coatings containing high concentrations of calcium, zinc, or vitamin E also provided alternative ways to fortify fresh fruits and vegetables that otherwise could not be accomplished with common processing approaches [45]. This application has been successfully demonstrated on fresh and frozen strawberries [53]. Flavor and coloring agents may also be added to edible coatings to improve the sensory quality of coated products. However, very little has been reported regarding this application. Some antimicrobial coatings were developed as well by incorporation of nisin, lactoferrin (an antimicrobial peptide derived from bovine lactoferrin in cow’s milk), sodium diacetate, sorbic acid, and potassium sorbate into a coating material. Films containing sorbic acid were the most compatible with the resin solution and had the best physical appearance. The water vapor barrier of films containing sorbic acid was almost unchanged compared to the control film (no antimicrobial agent). Antioxidants are widely used as food additives to improve oxidation stability of lipids and to prolong shelf-life, mainly for dried products and O2-sensitive foods. Antioxidants can also be incorporated into plastic films for polymer stabilization in order to protect the films from degradation. It is well established that antioxidant concentrations in polymeric films decrease during storage due to oxidation but also because of diffusion through the bulk of the polymer towards its surface followed by evaporation [55]. In the US cerealindustry waxed paper has sometimes been used as a reservoir for antioxidant release [56].Recently, vitamins E and C have been suggested for integration in polymer films to exert their antioxidative effects. Vitamin E has proved to be very stable under processing conditions and has an excellent solubility in polyolefins. [57]. In Japan Ag-substituted zeolite is the most common antimicrobial agent incorporated into plastics. Ag-ions which inhibit a range of metabolic enzymes, have strong antimicrobial activity. The normal incorporation level varies from 1 to 3% [58]. Several other compounds have been proposed and/or tested for antimicrobial activity in food packaging including organic acids such as sorbate, propionate and benzoate or their respective acid anhydrides, bacteriocins e.g. nisin and pediocin , enzymes such as lysozyme, metals and fungicides such as benomyl and imazalil [58, 59-63]. The choice of the antimicrobial is often limited by the incompatibility of the component with the packaging material or by the heat lability of the component during extrusion. One per cent potassium sorbate in a LDPE film inhibited the growth of yeast on agar plates. The LDPE resin and potassium sorbate powder can be mixed, extruded and pelletized to produce a

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masterbatch. These pellets can be added to LDPE resin. The masterbatch should be produced at low temperature to prevent heat decomposition of the potassium sorbate [59]. The relatively polar sorbate, benzoate and propionatecan be incompatible with the apolar LDPE [65, 66]. Acid anhydrides were thought to be more compatible than free acids and their salts because of their lower polarity. Two commercial biocidal films are currently marketed. One is composed of a chlorinated phenoxy compound and the other consists of chlorine dioxide. A commercial antifungal coating containing chitosan is also sold as a shelf-life extender for fresh fruit [62].

4. FUTURE TRENDS AND NECESSITIES An industrial point of view, active films obtained by using classical polymer technological processes (such as extrusion) are generally preferred. Despite this great interest, in the literature very few works are reported dealing with the development of antimicrobial biodegradable films obtained by means of extrusion processes. This is mainly due to the fact that most antimicrobial activity deterioration can occur during the extrusion using high temperature, high shear rates, high screw velocities and thus, high pressure. So, during the process, in fact, the high temperature and pressure in the extruder barrel can affect the chemical stability of the embedded antimicrobial compounds (which generally are heat sensitive and thermally unstable) and reduce their efficacy on food-borne spoilage microorganisms. Research and development in the field of active and intelligent packaging materials should be developing and research for environment friendly packaging s packaging need a real challenge and will imply the use of reverse engineering approach based on food requirements and not anymore only on the availability of packaging materials. Nanotechnologies should be expected to play a major role here and can help in the development for active and biodegradable packaging for foods.

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[2]

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Soares, N.F.F. (1998) Bitterness reduction in citrus juice through naringinase immobilized into polymer film. Ph.D. Dissertation. Cornell University, New York, 130 p. Auras, R., Singh, S. P., & Singh, J. J. (2005). Evaluation of oriented poly(-lactide) polymers vs. existing PETand oriented PS for fresh food service containers. Packaging Technology and Science, 18, 207-216. Reis, K.C., Pereira, J., Smith, A.C., Carvalho, C.W.P., Wellner, N. & Yakimets, I. (2008) Characterization of polyhydroxybutyrate-hydroxyvalerate (PHB-HV)/maize starch blend films. Journal of Food Engineering, Volume 89, Issue 4, Pages 361-369 Scott, G. (2000). Green polymer. Polymer Degradation and Stability, 68, 1-7. Lai, S.-M., Don, T.-M., Huang, Y.-C. (2005) Preparation and properties of biodegradable thermoplastic starch/poly (hydroxybutyrate) blends. Journal of Applied Polymer Science, 100, 2371–2379.

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[24] Cava, D., Gime´nez, E., Gavara, R. and Lagaron, J.M. (2006). Comparative Performance and Barrier Properties of Biodegradable Thermoplastics and Nanobiocomposites versus PET for Food Packaging Applications, Journal Plastic. Film, 22(4): 265–274. [25] Rhim, J.W., Hong, S.I., Park, H.M., Ng, P.K.W., (2006) Preparation and characterization of chitosan-based nanocomposite films with antimicrobial activity. Journal of Agricultural and Food Chemistry, 54, 5814–5822. [26] Miyagawa, H., Misra, M., Drazal, L.T., Mohanty, A.K., (2005) Novel biobased nanocomposites from functionalized vegetable oil and organically modified layered silicate clay. Polymer, 46, 445–53. [27] Wibowo, A.C., Misra, M., Park, H.M., Drzal, L.T., Schalek, R., Mohanty, A.K., (2005) Biodegradable nanocomposites from cellulose acetate: Mechanical, morphological, and thermal properties. Composites, Part A, 37: 1428–1433 [28] Petersson, L., & Oksman, K., (2006) Biopolymer based nanocomposites: Comparing layered silicates and microcrystalline cellulose as nanoreinforcement. Composites Science and Technology, 66, 13: 2187-2196. [29] No, H.K., Meyers, S.P., Prinyawiwatkul, W., Xu, Z., (2007). Applications of chitosan for improvement of quality and shelf life of foods: a review. Journal Food Science, 72 (5), 87–100. [30] Bertuzzi, M.A., Armada, M., Gottifredi, J.C., (2007). Physicochemical characterization of starch based films. Journal of Food Engineering 82 (1), 17–25 [31] Villalobos, R., Hernández-Muñoz, P., Chiralt, A., (2006). Effect of surfactants on water sorption and barrier properties of hydroxypropyl methylcellulose films. Food Hydrocolloids, 20 (4), 502–509. [32] Elsabee, M.Z., Abdou, E.S., Nagy, K.S.A., Eweis, M., (2008). Surface modification of polypropylene films by chitosan and chitosan/pectin multilayer. Carbohydrate Polymers, 71 (2), 187–195. [33] Gan, Q., Wang, T., Cochrane, C., McCarron, P., (2005). Modulation of surface charge, particle size and morphological properties of chitosan–TPP nanoparticles intended for gene delivery. Colloids and Surfaces B: Biointerfaces 44 (2–3), 65–73. [34] Lagarón, J.M., Cabedo, L., Cava, D., Feijoo, J.L., Gavara, R., Gimenez, E., (2005). Improving packaged food quality and safety. Part 2: Nanocomposites. Food Additives and Contaminants, 22 (10), 994–998. [35] Sinha Ray, S., Bousmina, M., (2005). Biodegradable polymers and their layered silicate nanocomposites: in greening the 21st century materials world. Progress in Material Science, 50 (8), 962–1079. [36] Sorrentino, A., Gorrasi, G., Vittoria, V., (2007). Potential perspectives of bionanocomposites for food packaging applications. Trends in Food Science & Technology, 18 (2), 84–95. [37] Okada, A., Usuki, A., (1995). The chemistry of polymer–clay hybrids. Materials Science and Engineering: C 3 (2), 109–115. [38] Orts, W.J., Shey, J., Imam, S.H., Glenn, G.M., Guttman, M.E., Revo, J.-F.,(2005). Application of cellulose microfibrils in polymer nanocomposites. Journal of Polymers and the Environment, 13 (4), 301–306. [39] Han, J.H., (2000). Antimicrobial food packaging. Food Technology 54, 56–65.

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[40] Han, J., Castell-Perez, M.E., Moreira, R.G., (2007). The influence of electron beam irradiation of antimicrobial-coated LDPE/polyamide films on antimicrobial activity and film properties. LWT 40, 1545–1554. [41] Han, J. H. (2005). In: J. H. Han (Ed.), Innovations in food packaging. Elsevier Academic Press. [42] Han, J. H., Ho, C. H. L., & Rodrigues, E. T. (2005). Intelligent packaging. In J. H. Han (Ed.), Innovations in food packaging. Elsevier Academic Press. [43] Zhuang, R., Beuchat, L. R. Chinnan, M. S. Shewfelt, R. L. & Huang, Y. W. (1996) Inactivation of Salmonella montevideo on tomatoes by applying cellulose-based edible films, Journal of Food Protection. 59: 808 . [44] Park HJ. (2005). Edible coatings for fruit. In: Jongen W, editor. Fruit and vegetable processing. Boca Raton, Fla.: CRC Press LLC. [45] Park SI, Daeschel MA, Zhao Y. (2004). Functional properties of antimicrobial lysozymechitosan composite films. Journal of Food Science, 69: 215–21. [46] Hettiarachchy NS, Satchithanandam E. inventors; (2007) The Board of Trustees for the Univ. of Arkansas, assignee. Organic acids incorporated edible antimicrobial films. U.S. patent 7,160,580 [47] Swenson HA, Miers JC, Schultz TH, Owens HS. (1953). Pectinate and pectate coatings. II. Application to nut and fruit products. Food Technology, 7:232–5. [48] Nisperos-Carriedo MO, Baldwin EA, Shaw PE. (1992). Development of an edible coating for xtending postharvest life of selected fruits and vegetables. Florida State Hort Soc. 104:122– 5. [49] Mei Y, Zhao Y, Yang J, Furr HC. (2002). Using edible coating to enhance nutritional and sensory qualities of baby carrots. J Food Sci. 67:1964–8. [50] Lee JY, Park HJ, Lee CY, Choi WY. (2003). Extending shelf-life of minimally processed apples with edible coatings and antibrowning agents. Lebens Wissen Technol 36:323–9. [51] Guadalupe IO, Rodriguez JJ, Barbosa-C´anovas GV. (2003). Edible coatings composed of methylcellulose, stearic acid, and additives to preserve quality of pear wedges. J Food Proc Pres 27:299–320. [52] Han C, Lederer C, McDaniel M, Zhao Y. (2004a). Sensory evaluation of fresh strawberries (Fragaria ananassa) coated with chitosan-based edible coatings. J Food Sci. 70:S172–8. [53] Han C, Zhao Y, Leonard SW, Traber MG. (2004b). Edible coatings to improve storability and enhance nutritional value of fresh and frozen strawberries (Fragaria × ananassa) and raspberries (Rubus ideaus). Postharvest Biol Technol. 33:67–78. [54] Mei Y, Zhao Y, Yang J, Furr HC. (2002). Using edible coating to enhance nutritional and sensory qualities of baby carrots. Journal of Food Science, 67:1964–8. [55] Miltz, J., Hoojjat, P., Han, J., Giacin, J.R., Harte, B.R. and Gray, I.J. (1988) Loss of antioxidants from high density polyethylene-its effect on oatmeal cereal oxidation in Food and Packaging Interactions, ACS symposium, 365 (1989), 83-93. [56] Labuza, T.P. and Breene, W.M. (1989) `Applications of active packaging'' for improvement of shelf-life and nutritional quality of fresh and extended shelf-life foods' in Journal of Food Processing and Preservation, 13, 1-69

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[57] Wessling, C., Nielsen, T., Leufven, A. and Jagerstad, M. (1998) `Mobility of _tocopherol and BHT in LDPE in contact with fatty food simulants in Food Additives Contaminants, 15, 709-715. [58] Ishitani, T. (1995) `Active packaging for food quality preserva- tion in Japan' in Foods and Packaging Materials-Chemical Interactions, (Ackerman, P., JaÈ gerstad, M., Oglsson, T., eds), pp. 177-188, Cambridge, Royal Society of Chemistry. [59] Han, J.H. and Floros, J.D. (1997) `Casting antimicrobial packaging films and measuring their physical properties and antimicrobial activity in J. Plastic Film and Sheeting, 13, 287-298. [60] Weng, Y.M. and Chen, M.J. (1997) Sorbic anhydride as antimycotic additive in polyethylene food packaging films in Food Science and Technology. 30, 485-487. [61] Ming, X., Weber, G.H., Ayres, J.W. and Sandine, W.E. (1997) Bacteriocins applied to food packaging materials to inhibit Listeria monocytogenes on meats in J. Food Sci. 62, 413-415 [62] Padgett, T., Han, I.Y. and Dawson, P.L. (1998) Incorporation of food-grade antimicrobial compounds into biodegradable packaging films' in J. Food Prot. 61, 1130-1335. [63] Halek, W. and Garg, A. (1989) Fungal inhibition by a fungicide coupled to an ionomeric ®lm' in J. Food Safety 9, 215-222. [64] Weng, Y.M. and Hotchkiss, J.H. (1992) Inhibition of surface moulds on cheese by polyethylene ®lm containing the antimycotic imazalil' in J. Food Prot. 55, 367-369. [65] Katz, F. (1998) New research in packaging' in Food Technology, 52, 56. [66] Weng, Y.M. and Hotchkiss, J.H. (1993) Anhydrides as antimycotic agents added to polyethylene films for food packaging' in Packaging Technol. Sci. 6, 123-128.

In: New Topics in Food Engineering Editor: Mariann A. Comeau

ISBN: 978-1-61209-599-8 © 2011 Nova Science Publishers, Inc.

Chapter 11

PREDICTIVE MODELLING OF THERMAL PROPERTIES OF FOODS James K. Carson Department of Engineering, University of Waikato Private Bag 3015, Hamilton 3240, New Zealand

ABSTRACT Thermal properties of foods are vital inputs for many food process models. With the recent advances in mathematical modelling and significant reduction in the cost of computational power, uncertainties in model inputs are more and more becoming the limiting factor in model accuracy rather than the model formulation or solution process. In this chapter methods and models are presented for predicting thermal properties based solely on data for the composition of the food in terms of its basic components (liquid water, ice, protein, fat, carbohydrate, ash and air). This type of model provides genuine predictions of thermal properties since no thermal property measurements are required, as is necessary with some effective property models that may be found in the literature. Foods are divided into different classes, depending on the difficulties they pose for thermal properties prediction. Simple guidelines for thermal property prediction are presented along with worked examples to serve as illustrations.

Keywords: effective thermal conductivity, heat capacity, thermal diffusivity, density.

INTRODUCTION The World’s population is expected to pass the 7 billion mark within the next five years with about 85% of the population inhabiting developing countries [1]. Although the current rate of food production is sufficient, in theory, to feed every person adequately, the reality, according to the United Nations, is that worldwide 860 million people are undernourished. Thermal processes such as cooking, refrigeration and drying are capable of extending the shelf-life of foodstuffs significantly; in fact figures collated by the International Institute of

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Refrigeration show a correlation between rates of food wastage and malnutrition, and refrigeration storage capacity per capita [2]. For this reason much work has gone in to the development and implementation of mathematical modelling of thermal processes with the aim of improving their efficiency and reliability. Recently Datta suggested that the development of models for thermal processing of foods has reached the stage where accuracy is limited more by the availability of reliable input data than by an understanding of the physical mechanisms involved in the processes or computing power [3]. These input data include thermal properties such as specific heat capacity and/or specific enthalpy, and thermal conductivity and/or thermal diffusivity. In addition, related physical properties such as density and ice fraction data are required, depending on the thermal process in question. For primary, or minimally processed food products much of this data is available in the literature [4-7]; however, for more highly processed foods, it is probable that this data will need to be determined either by direct measurement or by predictive models such as the ones outlined in this chapter. The aim of this chapter, therefore, is to overview methods for predicting a food’s thermal properties based in its composition. The literature contains literally hundreds of effective thermal property models, many of which are purely empirical, and hence are limited in their range of applicability. Others have theoretical bases but contain empirical parameters whose values must be obtained by experimentation. Relatively few models solely require the foods’ composition data, and these are the focus of this chapter because they provide genuine predictions. Simple algebraic models for density, specific heat capacity, thermal conductivity and thermal diffusivity will be introduced, and worked examples illustrating their use is provided at the end of the chapter. Readers interested in surveys and reviews of thermal property models are directed to [4-6,12].

DENSITY AND POROSITY Density is defined as the mass of a substance divided by the volume it occupies:

ρ≡

m V

(1)

While density is, in principle, a relatively straightforward quantity to define and comprehend, in practice its measurement is not always convenient by a direct method. Hence when dealing with food, in particular when porosity is significant, several ‘types’ of density have been defined, depending on the actual quantities which are being measured. If a material contains no pores or air voids and both the mass and volume of the material may be measured directly, then the quantity measured by Eq. (1) would be referred to as its true density. However, in the case, for example, of a granular material where it is impractical to measure the mass and volume of an individual grain, it is common to determine its apparent or effective density by measuring the mass of the material within a container of known volume. The apparent density is related to the true density of the material by Eq. (2):

ρe = ρ aε + ρt (1 − ε ) ≈ ρt (1 − ε )

(2)

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where ε is the total porosity defined as the volume of the food which is occupied by air (or in some cases carbon dioxide) divided by the total volume (gas plus solids) of the food package or container. Observing that the product ρaε is negligible, rearrangement of Eq. (2) gives us a method for determining ε provided ρe and ρt are known:

ε ≈ 1−

ρe ρt

(3)

However often the true density of the food material is unknown and its porosity may be simpler to measure than to be inferred from Eq. (3). At this point it is instructive to consider different types of pores within a food. Figure 1 shows a blow-up schematic of a granular material. Carson [8,9] classified porosity as being either external (Figure 1a) or internal (Figure 1b), depending on whether the air forms a continuous or dispersed phase. This distinction is particularly useful for heat transfer calculations since for a given porosity and solid-phase thermal conductivity, the effective thermal conductivity will be higher if the air makes up the dispersed phase than it would if it made up the continuous phase [8]. Rahman [10] classifies pores as being open, closed or blind pores (Figure 1b). This classification is particularly useful for direct porosity measurement, since some techniques which rely on filling the pores with gas or mercury (e.g. pycnometry) will be not be suitable if a significant quantity of closed pores are present. It is worth noting that a food may contain all types of porosity described here (e.g. popped corn).

Open pore Closed pore

Blind pore a) External (interstitial) porosity

b) Internal porosity

Figure 1. Illustration of different types of porosity.

For all the equations outlined in this chapter it is assumed that ε includes the volumes of all pores present in the food (including closed pores). Clearly, therefore, it is important to have an awareness of the types of porosity that are contained within the food product of

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interest and how it has been (or might be) measured, since a significant number of closed pores will limit the types of suitable porosity measurement. Returning to the problem of density, we can extend and generalise Eq. 2 for i components:

ρ e = ∑ν i ρ i

(4)

i

An equivalent expression to Eq. (4) in terms of mass fractions (xi) is:

1

ρe

=∑ i

xi

(5)

ρi

In practice, mass fractional compositions are easier to determine for the condensed phase (i.e. solid and liquid) food components, whereas the volume fraction of air (i.e. ε) is easier to determine than its mass fraction, hence a hybrid of Eqs. (4) and (5) will often be the most useful:

1− ε

ρe

=∑ j

xj

(6)

ρj

where there are j components other than air (or carbon dioxide). Table 1 shows the densities of the major food components as a function of temperature between –40°C and 150°C. Table 1. Densities of major food components as a function of temperature (°C) between 40°C and 150°C [5,11] Food Component

Density (kg m-3)

Protein

ρ = 1.3299 x 103 – 5.1840 x 10-1T

Fat

ρ = 9.2559 x 102 – 4.1757 x 10-1T

Soluble carbohydrate

ρ = 1.5991 x 103 – 3.1046 x 10-1T

Fibre

ρ = 1.3115 x 103 – 3.6589 x 10-1T

Ash

ρ = 2.4238 x 103 – 2.8063 x 10-1T

Liquid Water

ρ = 9.9718 x 102 +3.1439 x 10-3T – 3.7574 x 10-1T2

Ice

ρ = 9.1689 x 102 +1.3071 x 10-1T

Air

ρ=

353 T + 273.15

In principle, the density of any food product may be determined by using Eq. (6) and the data in Table 1, as will be illustrated in the worked example later in the chapter.

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SHRINKAGE AND ICE FRACTION Shrinkage During drying processes liquid water leaves biological material and is largely replaced by air. However, the replacement is usually not exact with some shrinkage caused by the collapse of cellular structure. The literature contains data and some empirical models for predicting shrinking during drying [6,10], but these are beyond the scope of this chapter, suffice to say that it is important to bear this factor in mind.

Ice Fraction Prediction Below the initial freezing temperature of a food product (which will usually be less than 0°C) the conversion of liquid water to ice proceeds with a non-linear dependence on temperature. Due to the difference in thermal properties between liquid water and ice and the strong dependence of ice fraction on temperature, the dependence of a food’s thermal properties on temperature will be more significant in the freezing range of temperatures than it is for above-zero temperatures. There are several different models relating ice fraction to temperature, but perhaps the simplest reliable one is Pham’s model [14]:

⎛ Tf xice = ( x w − 0.4 x pr )⎜⎜1 − T ⎝

⎞ ⎟⎟ ⎠

(7)

where xw is the total water content, and Tf is the initial freezing point of the food, for which data may be found in food properties databases [5-7]. The use of Eq. (7) will be illustrated in the worked example at the end of the chapter.

SPECIFIC HEAT CAPACITY The literature contains a large number of experimental data for the heat capacity of foods, as well as a number of empirical models that have been derived from them [5,6]. For predictions based on composition data we use the same approach as for density with the application of a weighted arithmetic mean:

C p ,e = ∑ xi C p ,i

(8)

i

Unlike density we can ignore the heat capacity contribution of air (or carbon dioxide) since the mass fraction of gaseous components are always small (even for high porosities). Table 2 shows the density and heat capacity of the major food components as a function of temperature between –40°C and 150°C.

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Table 2. Specific heat capacities of major food components as a function of temperature (°C) between -40°C and 150°C [5,11] Food Component

Specific Heat Capacity (kJ kg-1 K-1)

Protein

Cp = 2.0082+1.2089 x 10-3T – 1.3129 x 10-6T2

Fat

Cp = 1.9842+1.4733 x 10-3T – 4.8008 x 10-6T2

Soluble carbohydrate

Cp = 1.5488+1.9625 x 10-3T – 5.9399 x 10-6T2

Fibre

Cp = 1.8459+1.8306 x 10-3T – 4.6509 x 10-6T2

Ash

Cp = 1.0926+1.8896 x 10-3T – 3.6817 x 10-6T2

Liquid Water below 0°C

Cp = 4.1289 – 5.3062 x 10-3T – 9.9516 x 10-4T2

Liquid Water above 0°C

Cp = 4.1289 – 9.0864 x 10-3T – 5.4731 x 10-6T2

Ice

Cp = 2.0623 + 6.0769 x 10-3T

Air

Cp = 1.0053 + 2.9799 x 10-5T+1.7095 x 10-7T2

THERMAL CONDUCTIVITY The thermal conductivities of the major food components are shown as a function of temperature in Table 3. Table 3. Thermal conductivities of the major food components a function of temperature (°C) between -40°C and 150°C [5,11] Food Component

Thermal Conductivity (W m-1 K-1)

Protein

k = 1.7881 x 10-1+1.1958 x 10-3T – 2.7178 x 10-6T2

Fat

k = 1.8071 x 10-1+2.7604 x 10-3T – 2.7178 x 10-6T2

Soluble carbohydrate

k = 2.0141 x 10-1+1.3874 x 10-3T – 4.3312 x 10-6T2

Fibre

k = 1.8331 x 10-1+1.2497 x 10-3T – 3.1683 x 10-6T2

Ash

k = 3.2962 x 10-1+1.4011 x 10-3T – 2.9069 x 10-6T2

Liquid Water

k = 5.7109 x 10-1+1.7625 x 10-3T – 6.7036 x 10-6T2

Ice

k = 2.2196 – 6.2489 x 10-3T + 1.0154 x 10-4T2

Air

k = 2.364 x 10-2+7.2822 x 10-5T

Thermal conductivity and thermal diffusivity differ from density and specific heat capacity in that they are volumetric properties and have path dependence. Consider a layered two-component material as depicted in Figure 2:

Predictive Modelling of Thermal Properties of Foods Heat Flow

k1

267

Heat Flow

k1

k2

k2 Figure 2. Heat flow in parallel and series with layers of material having differing thermal conductivities.

For this material the effective thermal conductivity will be equal to:

k e = ∑ vi k i = v1k1 + v2 k 2 (9) i

if the direction of heat flow is parallel with the layers, while it will be equal to:

ke =

1 1 = v v v ∑i ki k1 + k2 1 2 i

(10)

if the direction of the heat flow is perpendicular to the layers (i.e. if the heat flows through the layers in series). In fact these two models, known as the Parallel and Series Models respectively (for obvious reasons), represent the upper and lower bounds of effective thermal conductivity of a mixture, sometimes referred to as the Wiener Bounds. For isotropic materials Hashin and Sthrikman [17] derived even narrower bounds:

k e = k1

2 k 1 + k 2 − 2( k 1 − k 2 ) v 2 2 k1 + k 2 + ( k1 − k 2 )v 2

(11)

ke = k2

2k 2 + k1 − 2( k1 − k 2 )v1 2 k 2 + k1 + ( k1 − k 2 )v1

(12)

Figure 3 shows plots of the Wiener and Hashin-Shtrikman bounds for a range of component thermal conductivity ratios (i.e. k1/k2): From Figure 3 we can see that the envelope bounded by the Hashin-Shtrikman bounds increases as k1/k2 increases. Since the Hashin-Shtrikman bounds represent the maximum and minimum values of thermal conductivity for an isotropic mixture it follows that the uncertainty in predicting the effective thermal conductivity of a mixture increases as k1/k2 diverges from unity. On the basis of this observation Carson [9,12] divided thermal conductivity problems into four classes based on the ratio of the highest component thermal conductivity to the lowest. Class I: Unfrozen, non-porous foods (kw/ksolids ≈ 3) (c.f. Figure 3a) Class II: Frozen, non-porous foods (kice/ksolids ≈ 12) (c.f. Figure 3b) Class III: Unfrozen, porous foods (kw/kair ≈ 25) (c.f. Figure 3c) Class IV: Frozen, porous foods (kice/kair ≈ 100) (c.f. Figure 3d)

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k 1 /k 2 = 3

1

k 1 /k 2 = 10

1 Parallel

0.8

HS-Low er

HS-Upper HS-Low er

0.6

k e /k 1

0.4

Series

0.8

HS-Upper

0.6

k e /k 1

Parallel

Series

0.2

0.4 0.2

0

0 0

0.2

0.4

0.6

0.8

1

0

0.2

0.4

v2

1

(B)

k 1 /k 2 = 25

k 1 /k 2 = 100

1 Parallel

Parallel

Series

0.8

HS-Low er

0.2

HS-Upper HS-Low er

0.6

k e /k 1

0.4

Series

0.8

HS-Upper

0.6

k e /k 1

0.8

v2

(A) 1

0.6

0.4 0.2

0

0 0

0.2

0.4

0.6

v2

(C)

0.8

1

0

0.2

0.4

0.6

0.8

1

v2

(D)

Figure 3. Wiener and Hashin-Shtrikman thermal conductivity bounds for a range of component thermal conductivity ratios (k1/k2).

There are numerous models for predicting the thermal conductivity of heterogeneous materials, including many that have been applied to foods, and these are listed and reviewed elsewhere [4-6,9,12]. Some of these models are functions of the food’s composition data alone, while others contain one or more parameters whose value must be determined empirically. Although the latter category have the potential to provide greater accuracy, it is undesirable to have to perform any measurements which might be required to determine the value of the empirical parameter(s) since in many situations that will defeat the purpose of the prediction exercise in the first place. For this reason, as mentioned previously, only the former category are considered in this chapter.

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269

Although there is an abundance of effective thermal conductivity models in the literature there is much less in the way of guidelines for deciding which models are most suitable for a given food undergoing a particular process. Carson [12] recommended models for each of the four different classes of foods described above. Cogné et al. [13] used a sequential, phase-byphase approach to model the thermal conductivity of ice cream successfully. A similar, generalised approach was proposed by Wang et al. [15] and is the method that will be outlined here. The principle of this method is to determine the thermal conductivity of each phase of the food sequentially, starting with the non-porous, unfrozen phase (containing liquid water, protein, fat, carbohydrates and ash). The thermal conductivities and volume fractions of these components are used to predict the effective thermal conductivity of the non-porous, unfrozen phase using the Parallel model (Eq. 9). For Class I foods this is the desired result (knpuf) and the method stops here. For Class II foods containing ice, knpuf is combined with kice using Levy’s model [16] to yield knpf:

k npf = kice

2kice + k npuf − 2(kice − k npuf ) F 2kice + k npuf + (kice − k npuf ) F

(13)

where:

F=

G=

2 / G − 1 + 2vnpuf − (2 / G − 1 + 2vnpuf ) 2 − 8vnpuf / G 2 (k ice − k npuf ) 2 (k ice + k npuf ) 2 + k ice k npuf / 2

(14)

(15)

For Class II foods knpf is the required result. For Class IV foods containing both air and ice, kpf, is obtained by combining knpf with ka using Maxwell’s model either with air as the dispersed phase, i.e.:

k pf = k npf

2k npf + k a − 2(k npf − k a )ε 2k npf + k a + (k npf − k a )ε

(16)

or alternatively with air as the continuous phase (e.g. in the case of particulate materials). This approach is represented diagrammatically in Figure 4, and is illustrated in the worked example later in the chapter. For Class III foods which contain air but not ice the second step is missed and knpuf instead of knpf is used to determine kpuf (or alternatively the method recommended by Carson [9,12] may be used).

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James K. Carson

k fa , x fa , ρ fa k fi , x fi , ρ fi k sc , x sc , ρ sc k pr , x pr , ρ pr k w , x w, ρ w

k ash , x ash , ρ ash Parallel Model (Eq. 9) k npuf , v npuf

k ice , x ice , ρ ice

Levy's Model (Eq. 13) k ice , ε

k npf , v npf

Maxwell's Model (Eq. 16) k pf Figure 4. Illustration of sequential application of thermal conductivities – phase by phase.

Wang et al. [15] applied this method to a variety of frozen foods and found that on average it provided prediction accuracies of within ±10% of measured values from the literature, while almost all were within ±20%. It should be noted that most of the data used in the analyses that resulted in the development of this method were high-water content foods such as meat, fish fruit and vegetables. The method will probably not be as accurate for low water-content foods such as, for example, butter and cheese.

THERMAL DIFFUSIVITY Thermal diffusivity is defined by Eq. (17):

α≡

k ρC p

(17)

Because of its dependence on thermal conductivity it has similar characteristics, and is more difficult to model than density or specific heat capacity. Since thermal conductivity data is often required for heat transfer analyses in addition to thermal diffusivity data one might well ask why α is used at all. The answer is, most probably, that often thermal diffusivity data

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271

may be measured more directly than thermal conductivity data and would therefore be used preferentially, wherever possible, to thermal conductivity values derived from them via Eq. (17). Table 4 shows thermal diffusivity values for the major food components between –40°C and 150°C. Table 4. Thermal conductivities of the major food components as a function of temperature (°C) between -40°C and 150°C [5,11] Food Component

Thermal diffusivity (m2 s-1)

Protein

α = 6.8714 x 10-8 + 4.7578 x 10-10T – 1.4646 x 10-12T2

Fat

α = 9.8777 x 10-8 + 1.2569 x 10-11T – 3.8286 x 10-14T2

Soluble carbohydrate

α = 8.0842 x 10-8 + 5.3052 x 10-10T – 2.3218 x 10-12T2

Fibre

α = 7.3976 x 10-8 + 5.1902 x 10-10T – 2.2202 x 10-12T2

Ash

α = 1.2461 x 10-7 + 3.7321 x 10-10T – 1.2244 x 10-12T2

Liquid Water

α = 1.3168 x 10-7 + 6.2477 x 10-10T – 2.4022 x 10-12T2

Ice

α = 1.1756 x 10-6 + 6.0833 x 10-9T – 9.5037 x 10-11T2

Air

α = 1.8210 x 10-5 + 1.2491 x 10-7T – 1.6785 x 10-10T2

The literature contains relatively few models for the prediction of thermal diffusivity of foods, most of which are empirical [6]. However, for Class I foods (as defined in the previous section) the effective thermal diffusivity may reasonably be determined from a weighted arithmetic mean:

α e = ∑ α i vi

(18)

i

since thermal conductivity (for Class I foods only), density and specific heat capacity may also be determined from weighted arithmetic means. For the other Classes of food it is best to use Eq. (17).

WORKED EXAMPLES Problem To illustrate the use of the models described above, consider a carton of meat which is cooled from an initial temperature of 10°C to a final temperature of –20°C. We want to determine the effective thermal diffusivity of the meat at the start of the process and at the finish, based on the following dimensions and composition data (note that the meat expands when it freezes).

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James K. Carson Table 5. Problem specifications Variable

Value

xpr

0.19

xfa

0.16

xw

0.64

Tf

–0.8°C

M

27.2 kg

Initial width

0.36 m

Initial length

0.52 m

Initial height

0.173 m

Final height

0.18 m

Solution Part 1: At 10°C (Start of the Process) At the start of the process the carton of meat represents a Class III food since it is unfrozen but contains pores. In order to determine αpuf we first need to determine ρe, Cp,e and kpuf. To begin with, we can work out the effective density using Eq. (1) since we have the mass of the meat and the dimensions of the carton:

ρe =

27.2 = 841 kg m −3 0.36 × 0.52 × 0.173

Since this value is well below the densities of water, fat or protein we know that the carton contains air voids, which is to be expected since a carton contains cuts of meat having irregular shapes which do not pack together without leavening interstitial gaps. It will be necessary for us to know the porosity in order to calculate the thermal conductivity of the meat in bulk, so we use a rearranged form of Eq. (6):

ε = 1 − ρe ∑ i

xi

ρi

where the xi were given in Table 5 and ρi may be determined for 10°C using the correlations in Table 1. Hence;

0 .1 ⎞ ⎛ 0.19 0.16 0.64 + + + ⎟ = 0.19 ⎝ 1325 921 997 2421 ⎠

ε = 1 − 841 × ⎜

Note that the mass fractions in Table 5 sum to 0.99; we may assume that the balance is made up of an equal mix of ash and carbohydrates.

Predictive Modelling of Thermal Properties of Foods

273

Next we determine Cp,e using Eq. (8) and the correlations in Table 2:

C p ,e = 0.19 × 2020 + 0.16 × 1998 + 0.64 × 4219 + 0.01 × 2000 = 3424 J kg −1 K −1 Note that this value for the specific heat capacity compares well with literature data for beef [4-7]. The determination of the thermal conductivity is a little more involved. Firstly we need to determine the volume fractions of each of the components within the non-porous unfrozen phase, i.e. we need to determine what volume of the meat itself (without any air voids) is occupied by protein, by water etc. e.g.

v pr =

x pr / ρ pr

∑x

i

/ ρi

=

0.19 / 1325 = 0.149 0.19 / 1325 + 0.16 / 921 + 0.64 / 997 + 0.01 / 2421

i

Similarly, vfa = 0.180, vw = 0.666 with the ash/carbohydrate making up the remainder. Now we determine the thermal conductivity of the condensed phase (i.e. the meat itself) using Eq. (9):

knpuf = 0.149 × 0.190 + 0.180 × 0.178 + 0.666 × 0.588 + 0.005 × 0.343 = 0.454 W m −1 K −1 Note that this value too compares well with literature data for beef [4-7]. knpuf represents the thermal conductivity of the meat itself; however, in a freezing process we are interested in the thermal conductivity of the contents of the carton (kpuf), which includes the entrained air that makes up 19% of its volume. k puf = k npuf

2k npuf + k a − 2(k npuf − k a )ε 2k npuf + k a + (k npuf − k a )ε

= 0.454

2 × 0.454 + 0.025 − 2(0.454 − 0.025) × 0.19 2 × 0.454 + 0.025 + (0.454 − 0.025) × 0.19

= 0.344 W m −1 K −1

Finally we use Eq. (17) to determine αpuf:

α npuf =

0.344 = 1.2 × 10 −7 m 2 s −1 841 × 3424

Solution Part 2: At –20°C (End of the Process) At the end of the process the carton of meat is a Class IV food since it contains both air and ice, and the property we are trying to determine is αpf. We will need to determine how much of the liquid water would be converted to ice and we also recall that due to thermal

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expansion the carton has increased in height (although we will assume that the porosity does not change significantly). The effective density will be determined as in Part 1 of the solution based on the new carton height:

ρe =

27.2 = 807 kg m −3 0.36 × 0.52 × 0.18

The ice fraction will be determined using Eq. (7):

− 0 .8 ⎞ ⎛ xice = (64 − 0.4 × 0.19)⎜1 − ⎟ = 0.541 − 20 ⎠ ⎝ The remaining unfrozen water is the difference between the total water fraction and the ice fraction:

xufw = xw − xice = 0.64 − 0.541 = 0.099 We now calculate the volume fractions in the non-porous, unfrozen phase as we did for Part 1 of the solution:

e.g. v pr =

0.19 / 1325 = 0.34 0.19 / 1325 + 0.16 / 921 + 0.099 / 996 + 0.01 / 2421

Similarly, vfa = 0.41, vw = 0.098 with the ash/carbohydrate making up the remainder. To calculate the effective heat capacity we use the same procedure as for Part 1 of the solution, noting that we now include the ice fraction and have a reduced liquid water fraction:

C p,e = 0.19 × 1983 + 0.16 × 1952 + 0.099 × 3624 + 0.541×1940 + 0.01× 2000 = 2117 J kg−1 K −1 Next we calculate knpuf as in Part 1 of the solution:

k npuf = 0.34 × 0.154 + 0.41 × 0.186 + 0.098 × 0.533 + 0.004 × 0.3 = 0.259 W m −1 K −1 Note that because of the reduced liquid water content knpuf at –20°C is significantly lower than it was at 10°C. We now use Levy’s model (Eqs. 13-15) to calculate knpf based on vnpuf and knpuf:

Predictive Modelling of Thermal Properties of Foods

vnpuf = 1 − ε − vice = 1 − ε −

275

xice / ρ ice ∑ xi / ρi i

= 1 − 0.19 −

0.541/920 0.19/1340 + 0.16/934 + 0.099/996 + 0.541/920 + 0.01/2429

= 0.414 G=

(2.385 − 0.259) 2 = 0.619 ( 2.385 + 0.259) 2 + 2.385 × 0.259 / 2

F=

2 / 0.619 − 1 + 2 × 0.414 − ( 2 / 0.619 − 1 + 2 × 0.414) 2 − 8 × 0.414 / 0.619 = 0.529 2

k npf = 2.385 ×

2 × 2.385 + 0.259 − 2 × (2.385 − 0.259) × 0.529 = 0.964 W m −1 K −1 2 × 2.385 + 0.259 + (2.385 − 0.259) × 0.529

Once again the values for Cp,e and knpf fall within the range of values of experimental data for frozen meat that may be found in the literature [4-7]. To calculate kpf we use Eq. (16) based on knpf: k puf = k npf

2k npf + k a − 2(k npf − k a )ε 2k npf + k a + ( k npf − k a )ε

= 0.964

2 × 0.964 + 0.025 − 2(0.964 − 0.025) × 0.19 2 × 0.964 + 0.025 + (0.964 − 0.025) × 0.19

= 0.723 W m −1 K −1

Finally we calculate αpf from Eq. (17):

α pf =

0.723 = 4.2 × 10 −7 m 2 s −1 807 × 2117

Due to the ice content which has higher thermal conductivity and lower volumetric heat capacity αpf is significantly greater than αpuf.

CONCLUSION In this chapter simple models have been introduced that provide predictions of the density, specific heat capacity, thermal conductivity and thermal diffusivity of foodstuffs based solely their composition data. These models are particularly useful for highly processed foodstuffs (e.g. cooked/baked products, convenience meals, snack-foods and desserts) for which thermal property data is unlikely to be available in the literature. As was illustrated in

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the worked example, these models are also useful for determining the thermal properties of the food in its packaged state, which will often differ from the unpackaged state due to the presence of air voids. These models provide genuine predictions of thermal properties since no thermal property measurements are required, as is the case with some effective property models that may be found in the literature. In addition, it is worth noting that, particularly with minimally processed foods such as raw meat, fruit, vegetables and raw grains, biological variation due to species, cultivar, farming region and harvesting season will result in corresponding variations in physical properties which means that it is unlikely that a literature value of, for example an ‘apple’ or even a ‘granny smith apple’ will match experimental data for a different granny smith apple to within ± 10%. In this regard, the thermal properties predicted from the models in this chapter based on actual composition data for a given food might well provide better predictions than thermal property data found in the literature. However, it should also be recognised that, particularly in the case of thermal conductivity, these models are best suited for high-water-content foods, and will not be as accurate for high-fat foods such as, for example, butter and cheese. It should also be understood that these models really only provide first approximations and that it is possible to achieve greater prediction accuracy with models which contain some empirical data.

NOMENCLATURE Cp F G k m T v V x

specific heat capacity intermediate variable defined by (Eq. 14) intermediate variable defined by (Eq. 15) thermal conductivity mass temperature volume fraction volume mass fraction

α ε ρ

thermal diffusivity porosity density

Subscripts 1 2

component 1 component 2

a ash

property of air property of ash

(J kg-1 K-1) (W m-1 K-1) (kg) (°C) (m3)

(m2 s-1) (kg m-3)

Predictive Modelling of Thermal Properties of Foods e fa fi i ice j npf npuf pf pr puf sc t ufw w

277

effective property property of fat property of fibre ith component of food ice content of food jth component of food property of non-porous, frozen phase/food property of non-porous, unfrozen phase/food property of porous, frozen phase/food property of protein property of porous, unfrozen phase/food property of soluble carbohydrate true value unfrozen water content property of water

REFERENCES [1] [2]

United Nations. World Population Prospects: The 2008 Revision Population Database. 5th Informatory Note on Refrigeration and Food, International institute of Refrigeration (IIR), 2009 (http://www.iifiir.org/en/doc/1215.pdf) [3] Datta, A. K. Porous media approaches to studying simultaneous heat and mass transfer in food processes. II: Property data and representative results, Journal of Food Engineering, 2007, 80(1) 96-110. [4] Rao, M. A., Rizvi, S. S. H., Datta, A. K. Engineering Properties of Foods 3rd Ed. CRC Press, Boca Raton, Florida, 2005. [5] ASHRAE Handbook – Refrigeration, American Society of Heating, Refrigeration and Air-Conditioning Engineering, Atlanta, 2006. [6] Rahman, M. S. Food Properties Handbook 2nd Ed., CRC Press, Boca Raton, Florida, 2009 [7] www.nelfood.com [8] Carson, J. K., Lovatt, S. J., Tanner, D. J., Cleland, A.C. Thermal conductivity bounds for isotropic, porous materials. International Journal of Heat and Mass Transfer, 2005, 48(11), 2150-2158. [9] Carson, J. K., Lovatt, S. J., Tanner, D. J., Cleland, A.C. Predicting the effective thermal conductivity of unfrozen, porous foods. Journal of Food Engineering, 2006, 75(3), 297307. [10] Rahman M. S., Mass-Volume-Area-Related Properties of Foods, in : Rao, M. A., Rizvi, S. S. H., Datta, A. K. Engineering Properties of Foods 3rd Ed. CRC Press, Boca Raton, Florida, 2005. [11] Çengel, Y. A., Turner, R. H., Fundamentals of Thermal-Fluid Sciences, 2nd Ed., McGraw-Hill, New York, 2007. [12] Carson, J. K. Review of effective thermal conductivity models for foods, International Journal of Refrigeration, 2006, 29(6), 958-967.

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[13] Cogné, C., Andrieu J., Laurent P., Besson A., Nocquet J. Experimental data and modeling of thermal properties of ice creams, Journal of Food Engineering, 2003, 58 (4), 331-341 [14] Pham, Q.T., Calculation of bound water in frozen food, Journal of Food Science, 1987, 52(1), 210-212. [15] Wang, J. F., Carson, J. K., Willix, J., North, M. F., Cleland, D. J., Prediction of thermal conductivity for frozen foods with air voids, Proceedings of the 1st IIR Conference on Sustainability and the Cold Chain, Cambridge, UK, March 29-31, 2010. [16] Levy F. L., A modified Maxwell-Eucken equation for calculating the thermal conductivity of two-component solutions or mixtures, International Journal of Refrigeration, 1981, 4(4), 223-225. [17] Hashin, Z. Shtrikman, S., A variational approach to the theory of the effective magnetic permeability of multiphase materials, Journal of Applied Physics, 1962, 33, 3125-3131.

In: New Topics in Food Engineering Editor: Mariann A. Comeau

ISBN: 978-1-61209-599-8 © 2011 Nova Science Publishers, Inc.

Chapter 12

APPLICATIONS OF MEMBRANE CONTACTORS IN THE FOOD INDUSTRY Catherine Charcosset* Université de Lyon, F-69622, Lyon, France ; Université Lyon 1, Villeurbanne ; CNRS, UMR 5007, Laboratoire d’Automatique et de Génie des Procédés ; ESCPE-Lyon, Villeurbanne

1. INTRODUCTION Membrane contactors represent an emerging technology in which the membrane is used as a tool for inter phase mass transfer operations [Sirkar et al. 1999, Drioli et al. 2003]. The membrane does not act as a selective barrier, but the separation is based on the phase equilibrium. This review specifically addresses to two membrane contactor processes: membrane distillation and membrane emulsification and their applications in the food industry. Membrane distillation is a thermally driven membrane process in which a hydrophobic microporous membrane separates a hot and cold stream of water. The hydrophobic nature of the membrane prevents the passage of liquid water through the pores while allowing the passage of water vapour. The temperature difference produces a vapour pressure gradient which causes water vapour to pass through the membrane and condense on the colder surface. The result is a distillate of very high purity which, unlike in conventional distillation, does not suffer from the entrainment of species which are non-volatile. On the other hand, membrane emulsification involves using a low pressure to force the dispersed phase to permeate through a membrane into the continuous phase. The distinguishing feature is that the resulting droplet size is controlled primarily by the choice of the membrane and not by the generation of turbulent droplet break-up.

*

Corresponding author: Catherine Charcosset tel : 00 33 4 72 43 18 67 Fax : 00 33 4 72 43 16 99 E-mail : [email protected]

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Catherine Charcosset

This review illustrates the fundamental concepts of these two membrane contactors: membrane distillation and membrane emulsification. The most significant applications in the food industry of these two membrane processes are presented and discussed, including membrane distillation for partial dealcoholization of wine, production of vegetable oil in milk emulsions using membrane emulsification, and membrane distillation for the recovery of volatile aroma compounds from black currant juice.

2. MEMBRANE DISTILLATION 2.1. Principles of Membrane Distillation Methods Membrane distillation is a thermally driven membrane process in which a hydrophobic microporous membrane separates a hot and cold stream of water [Hogan et al. 1991, Lawson and Lloyd 1997]. The hydrophobic nature of the membrane prevents the passage of liquid water through the pores while allowing the passage of water vapour (Figure 1). The temperature difference produces a vapour pressure gradient which causes water vapour to pass through the membrane and condense on the colder surface. The result is a distillate of very high purity which, unlike in conventional distillation, does not suffer from the entrainment of species which are non-volatile.

Membrane

Condensation plate

Hot feed solution

Coolant

Condensation channel Figure 1: Principle of the membrane distillation process.

Applications of Membrane Contactors in the Food Industry

281

Configurations A variety of configurations are employed to impose a vapor pressure difference across the membrane [Lawson and Lloyd 1997]. The permeate side of the membrane may consist of a condensing fluid in direct contact with the membrane (direct contact membrane distillation), a condensing surface separated from the membrane by an air gap (air gap membrane distillation), a sweeping gas (sweeping gas membrane distillation), or a vacuum (vacuum membrane distillation). The configuration employed dependents upon the permeate composition, the flux, and the volatility of the solutions. The direct contact membrane distillation configuration requires the least equipment and is simplest to operate. It is well suited for applications such as desalination or the concentration of aqueous solutions (orange juice), in which water is the major permeate component. Weeping gas membrane distillation and vacuum membrane distillation are used when a volatile organic or a dissolved gas is being removed from an aqueous solution. Air gap membrane distillation, is the most versatile membrane distillation configuration. It is applied to almost any application. Membranes The most suitable materials for membrane distillation include polyvinyldifluoride (PVDF), polytetrafluoethylene (PTFE) and polypropylene (PP) [Jiao et al. 2004]. The size of micropores can range between 0.2 and 1.0 μm. The membrane porosity ranges from 60% to 80% of the volume and the overall thickness from 80 - 250 μm, depending on the absence or presence of a support. The membrane configurations include flat sheet, spiral wound and hollow fiber, the latter being the most investigated.

2.2. Applications of Membrane Distillation Concentration of Fruit Juices Because membrane distillation can be carried out at the atmospheric pressure and at a temperature which can be much lower than the boiling point of the solution, it can be used to concentrate solutes sensitive to high temperature, also at high osmotic pressure. Therefore, membrane distillation has received a great attention for concentrating fruit juices. Drioli et al. [1992] investigated the concentration of orange juice by membrane distillation by studying the effect of various parameters on the permeate flux, including membrane types, feed juice concentration, operating temperature, and ultrafiltration pretreatment. Membrane distillation was further applied to concentrate fruit juices [Alves and Coelhoso 2006, Cassano et al. 2004] based on the use of a salty solution as stripping liquid that selectively extract the water from aqueous solutions under atmospheric pressure and at room temperature. Laganà et al. [2000] used a direct contact membrane distillation process to produce a highly concentrated apple juice using hollow fiber modules. A high 64°Brix concentration was achieved with fluxes of 1 kg/m2 h. Dealcoholization Membrane distillation was also used to reduce the ethanol content in alcoholic beverages without altering the organoleptic properties of the product [Hogan et al. 1998]. In this process, an aqueous phase containing the volatile components was circulated through a

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Catherine Charcosset

hydrophobic hollow fiber membrane contactor while a second aqueous phase, used as stripping liquid, flew along the downstream side of the membrane. The stripping phase employed was degassed water that allowed the ethanol transfer but avoided the water transfer across the membrane. Diban et al. [2008] used a membrane contactor to partial dealcoholize wine (a 2% v/v reduction), without spoiling significantly its flavor characteristics.

Recovery of Volatile Aroma Compounds Aroma profiles of fruit juices usually comprise a mixture of a large number of volatile organic compounds. The individual aroma components differ according to their molecular structure, which in turn defines the solubility, the boiling point, and the volatility of each type of compound [Ramteke et al. 1990]. For example, the unique aroma profile of black currant (Ribes nigrum L.) juice comprises more than 60 constituents. Bagger-Jørgensen et al. [2004] evaluated the potential of vacuum membrane distillation to recover black currant juice aroma. They reported the influence of feed temperature and flow rate on the fluxes and concentration factors of seven characteristic black currant aroma compounds using a laboratory scale vacuum membrane distillation set up. Vacuum membrane distillation at feed flow from 100 to 500 l/h at 30 °C gave concentration factors from 4 to 15, calculated for each aroma compound as Cpermeate=Cfeed. The recovered levels of the highly volatile aroma compounds ranged from 68 to 83 vol.% with a feed volume reduction of 5 vol.% (10 °C, 400 l/h).

3. MEMBRANE EMULSIFICATION 3.1. Principles of Membrane Emulsification Methods Membrane emulsification has received increasing attention over the last 15 years [Peng and Williams 1998a and b, Joscelyne and Trägårdh 2000, Nakashima et al. 2000, Vladisavljević and Schubert 2005]. The membrane emulsification process is schematically shown in Figure 2. The dispersed phase is pressed through the pores of a microporous membrane, while the continuous phase flows along the membrane surface. Droplets grow at pore openings until they detach when having reached a certain size. Surfactant molecules in the continuous phase stabilize the newly formed interface, to prevent droplet coalescence immediately after formation. The distinguishing feature is that the resulting droplet size is controlled primarily by the choice of the membrane and not by the generation of turbulent droplet break-up. The apparent shear stress is lower than in classical emulsification systems, because small droplets are directly formed by permeation of the dispersed phase through the micropores, instead of disruption of large droplets in zones of high energy density. Besides the possibility of using shear-sensitive ingredients, emulsions with narrow droplet size distributions can be produced. Furthermore, membrane emulsification processes allow the production of emulsions at lower energy input (104-106 J/m3) compared to conventional mechanical methods (106-108 J/m3) [Altenbach-Rehm et al. 2002].

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Continuous phase Tangential flow

283

Droplets/or particles

Membrane Dispersed phase permeation under applied pressure

Figure 2: Principle of the membrane emulsification process.

Configurations In conventional membrane emulsification, fine droplets are formed at the membrane/continuous phase interface by pressing the disperse phase through the membrane. In order to ensure a regular droplet detachment from the pore outlets, shear stress is generated at the membrane/continuous phase interface by recirculating the continuous phase using a pump or by agitation in a stirring vessel [Vladisavljević and Williams 2005]. The rate of mixing should be high enough to provide the required tangential shear on the membrane surface, but not too excessive to induce further droplet break up. Other systems use a moving membrane, in which the droplet detachment from the pore outlets is obtained by rotation or vibration of the membrane within the stationary continuous phase. Droplets can be spontaneously detached from the pore outlets at small disperse phase fluxes, particularly in the presence of fast adsorbing emulsifiers in the continuous phase and for a pronounced noncircular cross section of the pores. Rotating membrane devices are also tested to increase the performances of the membrane emulsification process, especially to increase the flux of the dispersed phase through the membrane [Aryanti et al. 2006, Schadler and Windhab 2006]. Membranes The most commonly used membrane for the preparation of emulsions is the Shirasu porous glass (SPG) membrane (Ise Chemical Co., Japan) because of its narrow pores size distribution and tubular shape [Nakashima et al. 1991]. The SPG membrane is synthesized from CaO-Al2O3-B2O3-SiO2 type glass, which is made from “Shirasu”, a Japanese volcanic ash. The SPG membrane has uniform cylindrical interconnected micropores, a wide spectrum of available mean pore sizes from 0.1 to 20 μm and a high porosity from 50 to 60 %. In addition to the SPG membranes, o/w emulsions were successfully prepared using silicon and silicon nitride microsieves membranes (Aquamarijn Microfiltration BV, The Netherlands) [Zhu and Barrow 2005, Geerken et al. 2007]. These membranes are made by photolithographic treatment of a silicon wafer and subsequent etching, or electrochemical metal deposition on a skeleton in an electrolysis bath, respectively. They have interesting properties, such as a smooth and flat surface, a very low membrane resistance and narrow pore size distribution. Different pore geometries (circular, square, slit shaped), pore size, pore edges and membrane porosities are available. Other commercial microfiltration membranes are attractive because of their availability in very large surface area, and their high flux through the membrane pores: ceramic aluminium

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oxide (α-Al2O3) membranes [Schröder and Schubert 1999], α-alumina and zirconia coated membranes [Joscelyne and Trägårdh 1999], and polytetrafluoroethylene (PTFE) membranes [Suzuki al. 1998, Yamazaki et al. 2002]. W/o emulsions were successfully prepared using polytetrafluoroethylene (PTFE) membranes [Suzuki et al. 1998, Yamazaki et al. 2003], polyamide hollow fibers membrane [Giorno et al. 2003, 2005], and home made silica-based monolithic membrane [Hosoya et al. 2005].

3.2. Applications Simple Emulsions Various food emulsions were prepared by membrane emulsification. Katoh et al. [1996] prepared w/o food emulsions from a low fat spread with a fat content of 25 % (v/v). They showed that the dispersed phase flux was increased 100 times using a hydrophilic membrane pre-treated by immersion in the oil phase, and that the membrane emulsification process was suitable for preparation of large scale w/o food emulsions. Suzuki et al. [1998] prepared o/w and w/o food emulsions from vegetable oil by membrane emulsification combined with preliminary emulsification. Using hydrophilic (or hydrophobic) PTFE membranes, o/w (or w/o) emulsions were prepared with a narrow diameter distribution. The authors show that the higher the flux of the pre-emulsified emulsion via the membrane, the higher the monodispersity of the emulsion was. The preparation of emulsions with reconstituted milk is usually done by high-pressure homogenization. In such cases the molecular or ultrastructural status of the milk components (casein micelles, whey proteins, free milk fat globule membranes in buttermilk) may be changed. The structure and composition of the proteins at the fat surface play an important role in determining the functional properties of recombined milks. Using SPG membranes, Scherze et al. [1999] and Muschiolik et al. [1997] prepared o/w emulsions with liquid butter fat or sunflower oil as the dispersed phase and a continuous phase containing milk proteins. The emulsions so obtained were characterised by particle size distribution, creaming behaviour and protein adsorption at the dispersed phase. The advantage of membrane emulsification was pointed out to be the low shear forces on the physicochemical and molecular properties of the proteins. Loaded o/w emulsions were also prepared by membrane emulsification, e.g. astaxanthinloaded o/w emulsions by repeated premix membrane emulsification [Ribeiro et al. 2005]. Astaxanthin is a natural carotenoid product with exceptional antioxidant properties. In the emulsification process, a pre-emulsion is repeatedly pushed through a hydrophilic or hydrophobic membrane. Pre-emulsions were produced by dispersing palm oil containing dissolved astaxanthin in water [Ribeiro et al. 2005]. The oil droplets were stabilized with a combination of two emulsifiers. Each o/w passed the membrane 3 times under pressure of 5 to 15 bar and disperse phase fraction from 10 wt% to 40 wt%. Multiple Emulsions Double emulsions (e.g. w/o/w) can be produced by membrane emulsification [van der Graaf et al. 2005]. The primary emulsion may be produced by means of a conventional method or by membrane emulsification. The mild conditions of membrane emulsification are

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especially useful for the second emulsification step in order to prevent rupture of the double emulsion droplets, which might even lead to inversion into a single o/w emulsion. In contrary to conventional emulsification methods, it becomes possible to produce small and monodisperse droplets without using high-shear stresses that cause escape of the internal droplets. For example, Mine et al. [1999] prepared w/o/w emulsions using egg yolk phospholipids and soybean oil without any coalescence of oil drops or breakdown of the emulsions. The particle size distribution of the multiple emulsion depended on the pore size of the microporous glass membrane. Phospholipids occur widely in nature (e.g. soybean and egg yolk), and are widely used as a stabilizing agent in food emulsions. Shima et al. [2004] investigated w/o/w emulsions to protect fragile bioactive compounds from stomach acid and intestinal digestive fluids. These authors prepared w/o/w emulsion as a carrier system for the daily uptake of a hydrophilic model compound of a bioactive substance (1,3,6,8pyrenetetrasulfonic acid tetrasodium salt). Membrane filtration of a coarse w/o/w emulsion prepared with a rotor/stator homogenizer produced a fine emulsion with a mean oil-droplet diameter below 1 μm and an encapsulation efficiency higher than 90 %. However, the authors observed that the included water-phase disappeared during the membrane filtration of the coarse emulsion when preparing the fine emulsion.

Encapsulation There has been considerable recent interest in probiotics for promotion of human health. The probiotic bacteria most commonly studied include members of the genera Lactobacillus and Bifidobacterium. Microorganisms used as probiotic adjuncts are commonly delivered in the food system. However, when these microorganisms are injected, their activity and viability are reduced under highly acidic conditions. Hence, there is a need for lactic acid bacteria that are resistant to the stressful conditions of the stomach and the upper intestine, which both contain bile. Song et al. [2003] prepared microcapsules with a narrow particle size distribution with the SPG membrane. For artificial gastric acid and bile, the viable count of encapsulated cells was constant through the incubation time, while the count of nonencapsulated cells was significantly decreased. A storage stability test at different temperatures resulted in a viability of encapsulated cells 3 to 5 log cycles higher than the viability of monoencapsulated cells. Aerated Food Gels Food gels are soft solids containing a high amount of an aqueous phase (i.e. >80 %) that have received much attention [i.e. Zúñiga and Aguilera 2008]. Aerated food gels were produced recently by membrane foaming [Bals and Kulozik 2003a and b]. The method is based on pressing the dispersed phase (gas) through the pores of a tubular membrane into the continuous phase. The bubbles formed are covered with surface-active substances of the continuous phase and they are detached from the membrane surface by the shear forces exerted by the phase flowing along the membrane surface. Bals and Kulozik [2003a] investigated the influence of pore size, foaming temperature and viscosity of the continuous phase on the properties of foams produced by membrane foaming. An important factor is that the added amount of gas must be stabilised as completely as possible in the foam. Raising the foaming temperature increased the quantity of stabilised gas. The whey proteins then diffused

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faster to the bubble surfaces and stabilise these by unfolding and networking reactions to prevent the coalescence of the bubbles. Bals and Kulozik [2003b] also studied the effect of pre-heating on the foaming properties of whey protein isolate using a membrane foaming apparatus. These authors concluded that further improvement of the process conditions were needed to obtain stable foams with narrow bubble size distributions.

4. CONCLUSION This review article aims to present some aspects of the large range of membrane applications in the food industry, by focusing on two membrane processes: membrane distillation and membrane emulsification. Significant applications are presented including membrane distillation for partial dealcoholization of wine, production of vegetable oil in milk emulsions using membrane emulsification, and membrane distillation for the recovery of volatile aroma compounds from black currant juice. Limitations of membrane processes may be associated to the low fluxes due to fouling phenomena. These disadvantages may be solved by recent improvements in the devices, as premix membrane emulsification, and rotating or vibrating membrane devices. In order to gain a foothold in the food industry, studies on developments of highly selective, permeable membranes, robust and stable in long-term application for food processing, need to be carried out in detail. Improvements of process engineering including module design and process design and optimization are also highly needed.

REFERENCES Altenbach-Rehm J., Suzuki K., Schubert H., Production of O/W-emulsions with narrow droplet size distribution by repeated premix membrane emulsification. 3ième Congrès Mondial de l’Emulsion, 24-27 September 2002, Lyon, France. Alves V.D., Coelhoso I.M., Orange juice concentration by osmotic evaporation and membrane distillation: a comparative study, J. Food Eng. 74 (2006) 125-133. Aryanti N., Williams R.A., Hou R., Vladisavljevic G.T., Performance of rotating membrane emulsification for o/w production, Desalination, 200 (2006) 572-574. Bagger-Jørgensen R., Meyer A. S., Varming C., Jonsson G., Recovery of volatile aroma compounds from black currant juice by vacuum membrane distillation, J. Food Eng. 64 (2004) 23–31 Bals A., Kulozik U., The influence of pore size, the foaming temperature and the viscosity of the continous phase on the properties of foams produced by membrane foaming, J. Membr. Sci. 220 (2003a) 5-11. Bals A., Kulozik U., Effect of pre-heating on the foaming properties of whey protein isolate using a membrane foaming apparatus, Int. Dairy J. 13 (2003b) 903-908. Cassano A., Jiao B., Drioli E., Production of concentrated kiwifruit juice by integrated membrane process, Food Res. Int. 37 (2004) 139-148.

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Diban N., Athes V., Bes M., Souchon I., Ethanol and aroma compounds transfer study for partial dealcoholization of wine using membrane contactor, J. Membr. Sci. 311 (2008) 136-146. Drioli E., Criscuoli A., Curcio E., Membrane contactors and catalytic membrane reactors in process intensification, Chem. Eng. Technol. 26 (2003) 975-981. Drioli E., Jiao B., Calabrò V., The preliminary study on the concentration of orange juice by membrane distillation. In Proceedings of VII International Citrus Congress, Acireale, Italy, 1992. Geerken M.J., Lammertink R.G.H., Wessling M., Tailoring surface properties for controlling droplet formation at microsieve membranes, Colloids Surf. A 292 (2007) 224-235. Giorno L., Li N., Drioli E., Preparation of oil-in-water emulsions using polyamide 10 kDa hollow fiber membrane, J. Membrane Sci. 217 (2003) 173-180. Giorno L., Mazzei R., Oriolo M., De Luca G., Davoli M., Drioli E., Effects of organic solvents on ultrafiltration polyamide membranes for the preparation of oil-in-water emulsions, J. Colloid Interface Sci. 287 (2005) 612-623. Gryta M., Osmotic MD and other membrane distillation variants, J. Membr. Sci., 246 (2005) 145-156. Hogan P. A., Sudjito, Fane A.G., Morrison G.L., Desalination by solar heated membrane distillation, Desalination, 81 (1991) 81-90. Hogan P.A., Canning R.P., Peterson P.A., Johnson R.A., Michaels A.S., A new option: osmotic distillation, Chem. Eng. Prog. 94 (1998) 49-61. Hosoya K., Bendo M., Tanaka N., Watabe Y., Ikegami T., Minakuchi H., Nakanishi K., An application of silica-based monolithic membrane emulsification technique for easy and efficient preparation of uniformly sized polymer particles, Macromol. Mater. Eng. 290 (2005) 753-758. Jiao B., Cassano A., Drioli E., Recent advances on membrane processes for the concentration of fruit juices: a review, J. Food Eng. 63 (2004) 303–324. Joscelyne S. M., Trägårdh G., Food emulsions using membrane emulsification: conditions for producing small droplets, J. Food Eng. 39 (1999) 59-64. Joscelyne S.M., Trägårdh G., Membrane emulsification - a literature review. J. Membr. Sci., 169 (2000) 107-117. Katoh R., Asano Y., Furuya A., Sotoyama K., Tomita M., Preparation of food emulsions using a membrane emulsification system, J. Membr. Sci. 113 (1996) 131-135. Laganà F., Barbieri G., Drioli E., Direct contact membrane distillation: modelling and concentration experiments, J. Membr. Sci. 166 (2000) 1–11 Lawson K.W., Lloyd D.R., Membrane distillation, J. Membr. Sci., 124 (1997) 1-25. Mine Y., Shimizu M., Nakashima T., Application of size-controlled microporous glass membranes for designing simple and multiple emulsions, Recent Res. Devel. Agricultural Food Chem. 3 (1999) 131-137. Muschiolik G., Dräger S., Scherze I., Rawel H. M., Stang M., Protein-stabilized emulsions prepared by the micro-porous glass method, in Food Colloids: Proteins, lipids and polysaccharides, ed by Dickinson. Royal Society of Chemistry, Cambridge, pp 393-400 (1997). Nakashima T., Shimizu M., Kukizaki M., Membrane emulsification by microporous glass, Key Eng. Mat. 61-62 (1991) 513-516.

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Nakashima T., Shimizu M., Kukizaki M., Particle control of emulsion by membrane emulsification and its application, Adv. Drug Deliv. Rev. 45 (2000) 47-56. Peng S. J., Williams R. A. Controlled production of emulsions using a crossflow membrane, Chem. Eng. Res. Des. 76 (1998a) 894-901. Peng S. J., Williams R. A., Controlled production of emulsions using a crossflow membrane, Part. Part. Syst. Charact., 15 (1998b) 21-25. Ramteke R. S., Eipeson W. E., Patwardhan M. V., Behaviour of aroma volatiles during the evaporative concentration of some tropical fruit juices and pulps, J. Sci. Food Agri. 50 (1990) 399–405. Ribeiro H. S., Rico L. G., Badolato G. G., Schubert H., Production of O/W emulsions containing astaxanthin by repeated premix membrane emulsification, J. Food Sci. 70, 2 (2005) 117-123. Schadler V., Windhab E.J., Continuous membrane emulsification by using a membrane system with controlled pore distance, Desalination, 189 (2006) 130-135. Scherze I., Marzilger K., Muschiolik G., Emulsification using micro porous glass (MPG): surface behaviour of milk proteins, Colloids Surf. B 12 (1999) 213-221. Schröder V., Schubert H., Influence of emulsifier and pore size on membrane emulsification, Spec. Publ. –R. Soc. Chem. 227 (1999) 70-80. Shima M., Kobayashi Y., Fujii T., Tanaka M., Kimura Y., Adachi S., Matsuno R., Preparation of fine W/O/W emulsions through membrane filtration of coarse W/O/W emulsion and disappearance of the inclusion of outer solution, Food Hydrocolloids, 18 (2004) 61-70. Sirkar K. K., Shanbhag P. V., Kovvali A. S., Membrane in a reactor: a functional perspective, Ind. Eng. Chem. Res. 38 (1999) 3715-3737. Song S.H., Cho Y.H., Park J., Microencapsulation of Lactobacillus casei YIT 9018 using a microporous glass membrane emulsification system, J. Food Sci. 68 (2003) 195-200. Suzuki K., Fujiki I., Hagura Y., Preparation of corn oil/water and water/corn oil emulsions using PTFE membranes, Food Sci. Technol. 4 (1998) 164-167. van der Graaf S., Schroën C. G. P. H., Boom R. M., Preparation of double emulsions by membrane emulsification- a review, J. Membr. Sci. 251 (2005) 7-15. Vladisavljević G. T., Williams R. A., Recent developments in manufacturing emulsions and particulate products using membranes, Adv. Colloid Interface Sci. 113 (2005) 1-20. Vladisavljević G. T., Schubert H. Preparation and analysis of oil-in-water emulsions with a narrow droplet size distribution using Shirasu-porous-glass (SPG) membranes. Desalination, , 144 (2002) 167-172. Yamazaki N., Naganuma K., Nagai M., Ma G.-H., Omi S., Preparation of w/o (water-in-oil) emulsions using a PTFE (polytetrafluoroethylene) membrane- A new emulsification device, J. Dispersion Sci. Technol. 24 (2003) 249-257. Yamazaki N., Yuyama H., Nagai M., Ma G.H., Omi S., A comparison of membrane emulsification obtained using SPG (Shirasu Porous Glass) and PTFE [poly(tetrafluoroethylene)] membranes, J. Dispersion Sci. Technol. 23 (2002) 279-292. Zhu J., Barrow D., Analysis of droplet size during crossflow membrane emulsification using stationary and vibrating micromachined silicon nitride membranes, J. Membr. Sci. 261 (2005) 136-144. Zúñiga R.N., Aguilera J. M., Aerated food gels: fabrication and potential applications, Trends Food Sci. Technol. 19 (2008) 176-187.

In: New Topics in Food Engineering Editor: Mariann A. Comeau

ISBN: 978-1-61209-599-8 © 2011 Nova Science Publishers, Inc.

Chapter 13

POSSIBILITIES FOR REMOVAL OF GLUCOSE FROM VARIOUS FOODSTUFFS AND FOOD BIOPROCESSES K. Bélafi-Bakó* University of Pannonia Egyetem u. 10., 8200 Veszprem, Hungary

ABSTRACT In many areas of food industry removal of glucose is considered as an important step in the processing line. In some raw materials – like eggs – glucose concentration should be reduced to avoid undesired co-reaction during drying process. In the beverage industry glucose level of certain fruit juices needs to be controlled/lowered either to produce lowcaloric beverages or to get low-alcohol wine after fermentation (e.g. grape must). In addition glucose removal is a significant step in some enzymatic processes like polysaccharide hydrolysis or fructo-oligosaccharide synthesis, where glucose is an inhibitory by-product. Separation methods applicable for glucose removal are discussed and compared in this chapter.

INTRODUCTION Glucose (C6H12O6) belongs to the group of monosaccharides and is considered as one of the key platform compounds in industrial biotechnology, since it is a raw material for numerous fermentation processes. Lactic acid, malic acid, citric acid and other acidic compounds, moreover many low molecular weight food industrial components can be fermented from glucose [Smith, 2004]. It is the monomer of starch and cellulose, which are – due to their high glucose content – the basic substances of biorefineries [Kamm, 2007]: the biomass based alternatives of fine chemical industry. *

Email: [email protected]

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Removal and/or recovery of glucose are therefore becoming highly important processes. Its separation is generally based on the size difference, e.g. in case of polysaccharide – glucose mixture. For this purpose membrane separation techniques seem attractive, due to their advantageous features (mild reaction conditions, no hazardous waste formed, low energy consumption and environmental-safe processes). Among membrane processes pressure driven techniques – where size of the molecules plays an important role – should be selected. The most promising technique is ultrafiltration, where porous membranes with a proper pore size can be applied. These ultrafiltration membranes are able to retain the polysaccharides, while glucose pass through the membrane [Drioli, 1999]. Glucose is usually difficult to separate from a mixture containing molecules with similar size and chemical character. In these cases special methods are needed. Among them one of the most successful methods is the enzymatic conversion, since the active centre of the enzymes is highly specific for the substrate, thus glucose can be selectively bound and converted by the given enzyme. Glucose oxidase (GOD, E.C. 1.1.3.4.) is the enzyme [Dixon, 1964] mostly used for the particular purpose. In the process glucose is converted into glucono-1,5-lactone, which spontaneously hydrolyses non-enzymatically to gluconic acid using molecular oxygen and releasing hydrogen peroxide. GOD β-D-glucose + O2

D-gluconic acid + H2O2.

Hydrogen peroxide, the by-product of the reaction is an effective bacteriocide [Tucker, 1995] on one hand, but has an inactivation effect on the GOD enzyme, on the other hand. Therefore it should be degraded or removed from the system. Another enzyme, catalase (E.C. 1.11.1.6.) is able to convert it to water and molecular oxygen: catalase H2O2

H2O + 1/2 O2.

For most applications the two enzymatic activities are not separated. Glucose oxidase and catalase may be used together when net hydrogen peroxide production is to be avoided. The enzyme mixture can be produced by various moulds (e.g. Aspergillus) and applied without separation. The method is useful for oxygen removal, as well, e.g. from the head-space above bottled and canned drinks and reducing non-enzymatic browning in wines and mayonnaises [Tucker, 1995].

REMOVAL OF GLUCOSE FROM FOODSTUFFS Eggs In food industry eggs are usually processed to manufacture powdered whole eggs by spray drying, or – after separation of the constituents – powdered egg whites and yolk. In all processes the problem is the Maillard browning caused by the reaction of glucose and egg

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protein (mainly mucin) [Nagodawithana, 1993]. Removal of glucose (desugaring) can be used to prevent the undesirable reaction. It is quite difficult to find a suitable separation process for glucose removal from a common, but natural substance like an egg, having special texture. Its conversion into gluconic acid seemed a promising method. Since gluconic acid has no reducing end group it can not take part in the Maillard reaction and browning can be avoided. In our laboratory glucose oxidase and catalase enzyme system was immobilized in an anion exchange resin and used in a three-unit, packed column (fluid bed) reactor, where egg white was flown through. Volume of the reactor was 46 l. Since the reaction needs oxygen, proper aeration had to be ensured in the system. However, the traditional aeration system caused severe foaming. To avoid it a special method was developed: a perforated silicon tube was placed inside the reactor in spiral wound and this provided the air (oxygen) for the enzymatic reaction (Figure 1).

Figure 1. Scheme of the bioreactor for glucose elimination from egg white. 1) bottom part of the reactor unit, 2) upper part with window 3) bottom closing part, 4) upper closing part, 5) stirrer motor, 6-7) inlet and outlet for the egg white, 8) outlet for recirculation of white egg, 9) inlet for addition of catalyst, 10) unload, 11) manometer, 12) oxygen electrode.

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The immobilized enzyme system in the reactor was able to reduce the initial 4 g/l glucose concentration down to 0.02 g/l, with 910 l/day capacity and the volumetric productivity of the reactor was 19.8 l white egg /day/l reactor volume.

Beverages Low-caloric, non-alcoholic valuable beverages – having valuable constituents to contribute to the healthier life and low-caloric diet– can be manufactured from fruit juices. The high-caloric monosaccharides (sugars) of these juices can be removed resulting in reduced caloric value. One of the most promising techniques is application of the glucose oxidise-catalase system for glucose removal – similar to grape must (see below). Low alcoholic beverages are becoming more and more popular recently. Alcohol-free bier and low-alcohol wine are two relevant examples among them. In both manufacturing process ethanol can be removed by some selective separation process. In case of wine, however, there is an alternative method: reduction of fermentable carbohydrate content of the grape must before fermentation, instead of wine treatment. The fermentable sugar content of wine consists of approximately 50 % glucose and 50 % fructose, depending on the maturation, climate, grape species and local conditions. To reduce sugar content harvesting of the grape prior to maturation can be used, but it results in higher acid level and weak aroma profile in the wine obtained after fermentation. Another process is the enzymatic treatment of the fermentable sugars. Glucose oxidase enzyme is able to convert glucose into gluconic acid, which can not be metabolised into ethanol by yeasts, thus less amount of ethanol is formed during fermentation [Heresztyn, 1987]. During the process of glucose conversion by glucose oxidase in grape must the most important factor is the low pH value, which is optimal for the enzymatic reaction [Pickering, 1999a]. In the experiments aimed to manufacture low-alcohol white wine 40 % reduction of ethanol content was achieved, but the ratio of non-volatile compound was a bit altered: higher amount of ester and oleic acid was present. Moreover, the colour of the wine obtained from treated must was deeper, its SO2 content increased compared to the reference wine [Pickering, 1999 b and c] The acid level of the wine was raising, as well, due to the gluconic acid formed. To compensate it, grape must of must concentrate can be added to the wine in order to enhance the organoleptic features [Pickering, 1999d and 2000]. Utilisation of glucose oxidise is not permitted in numerous countries, though its source, Aspergillus niger produces many types of enzymes for food industrial processes. This glucose removal technique nowadays are only applied for low-alcohol white wine production according to the literature, however it is possible to use is for red wines, as well, by slightly modifying the operational parameters. Comparing the quality of tradition and low-alcohol wines, experts found splitting of aroma-equilibrium and lack of “fullness” in low-alcohol wines [Howley, 1992]. Concerning organoleptic properties, the wines having less than 6 % alcohol are placed between the traditional wine and the grape must [Duerr, 1988]. These altered organoleptic properties are directly connected with the lower alcohol content. When alcohol is removed from the wine, especially by using thermo-processes, a characteristic boiled taste is appearing in the wine, moreover undesirable compounds may be formed due to the heat effect. Therefore the

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enzymatic glucose removal from the grape must (before fermentation) seems a promising and mild technique for low-alcohol wine production, which may result in higher quality.

RECOVERY OF GLUCOSE FROM ENZYMATIC FOOD PROCESSES Polysaccharide Hydrolysis Hydrolysis of polysaccharides like starch and cellulose results in glucose which is an important raw material for various food products. One of them is high-fructose corn syrup (HFCS), an alternative of sweetener and mainly applied in production of biscuits, cereal based food wares….. Moreover many different fermentation products (ethanol, organic acids…etc.) can be manufactured based on glucose. Degradation of polysaccharides can be carried out either by using strong acids or by enzymes. Nowadays the enzymatic way seems more attractive due to its environmental and economical advantages. The kinetics of the enzymatic starch hydrolysis by amylases has been studied for long and it was found that strong product inhibition occurred [Nagy, 1992; Law, 1993]. The mechanism of the inhibition caused by the glucose formed during the process by e.g., glucoamylase enzyme (1,4-α-D-glucan glucohydrolase, E.C. 3.2.1.3.) was described as a competitive inhibition and the inhibition constant (KI) was determined as 0.82 g/l [Koutinas, 2001]. Due to the strong inhibition effect – and since starch hydrolysis is carried out in continuous mode of operation – glucose should be removed during the hydrolytic reaction to enhance productivity. Separation is based on the different size of starch, enzyme and glucose. Since glucose is much smaller than the others, membrane separation seems suitable for it. Among pressure driven membrane processes ultrafiltration – as mentioned earlier – has a proper retention for the substrate and enzyme, while the membrane pores are able to let glucose pass through. Moreover – due to the desirable continuous operation – it is sensible to integrate the product separation step into the bioprocess, and ultrafiltration membrane modules can be easily inserted to these systems [Drioli, 1999]. Hollow fiber ultrafiltration membrane module for cassava starch hydrolysis by Aspergillus niger glucoamylase has been used by Spanish researchers [Lopez-Ulibarri, 1997], where the enzyme was in soluble form. Also cassava starch was the substrate in another work with membrane bioreactor [Paolucci-Jeanjean, 2000], but the enzyme used was an α-amylase from Bacillus licheniformis and Carbosep (50 kDa) membrane was applied. Glucoamylase from Aspergillus niger was immobilized onto a ceramic membrane in the hydrolytic process developed by Ida et al. (2000). Wheat starch and glucoamylase from Aspergillus awamori was used in the work of Frater (2005), where a flat ultrafiltration membrane module was applied as a membrane bioreactor and continuous hydrolysis was realised (Figure 2). During the starch hydrolysis by glucoamylase glucose was continuously removed and collected, thus pure glucose solution was obtained as a permeate and product inhibition was completely avoided, resulting in higher productivity.

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feeding vessel

overflow pipe

condenser membrane module 

equalizer container 

reactor

magnetic stirrer

Figure 2. Scheme of the membrane bioreactor for starch hydrolysis.

Hydrolysis of cellulose – the other highly important, abundant polysaccharide – is more difficult to achieve than that of the other polysaccharides (starch). The difficulties involve slow reaction rate, lack of an ideal reactor system, complexity of interfacial heterogeneous hydrolysis influenced by various factors, e.g. structure and composition of cellulosic materials, cellulase adsorption and desorption, enzyme inhibition by cellobiose and glucose [Wald, 1984; Lee, 1996; Mansfield, 1999]. The product inhibition – like in stach hydrolysis – can be solved by ultrafiltration membrane which are built into the system and operated as membrane bioreactors. Experimental data on cellulose hydrolysis in membrane bioreactor are summarized in Table 1. It is clear from these data that glucose/cellobiose inhibition could be reduced significantly by the use of suitable membranes, which are able to allow glucose through the membrane while retaining the macromolecules. All of the membranes applied were ultrafiltration membranes (polymeric or inorganic) having a cut off between 10 and 50 kDa. In most cases the solution of substrate and enzyme was recirculated in the primary side of the membrane reactor. It was found that the cellulose particles present in the substrate solution caused severe fouling in the membrane bioreactor resulting in remarkable flux decline. Therefore, in most papers in this area the main purpose was to reduce the fouling either by vigorous stirring in the primary side of the membrane (flat sheet modules) or by increasing the transmembrane pressure for higher permeate flux. Enzymatic saccharification of cellulose in a special tubular membrane reactor was studied [Belafi-Bako, 2006], where a hairy textile layer was wounded around the stainless steel tube membrane. It had a positive effect on the process, since simultaneous immobilisation of the biocatalyst and the substrate onto the surface (prior to the hydrolysis) was possible. Thus the special membrane reactor was able not only to retain the biocatalysts

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and to separate the inhibitory products (glucose and cellobiose), but to enhance the efficiency of the enzymatic reaction (conversion, productivity), as well. Table 1. Experimental data on enzymatic cellulose hydrolysis in membrane bioreactors Substrate Type Oven dried Solka Floc BW200 Solka Floc SW40, BW200, Sugarcane bagasse, Sorghum stubble, Peanut shell Pretreated sallow Salix Q 082

KC Floc (SanyoKokusaku Pulp. Co., Japan) α-cellulose Pretreated, milled olive mill solid residue (OMSR) α-cellulose (Sigma) Pretreated Mavicell

Initial Converconc. sion 1% 70 %

Membrane

Cut off Temp. o C kDa

Amicon PM 10

10

50

0.45 g/l

91 %

Amicon XM50 50 Romicon XM50

50

10 %

50-80 %

Polyamide BM100, Berghof Germany UP 20 (ToyoRoshi Co. Japan) Amicon Carbosep M5 (ZrO2 mineral)

10 %

55-60 %

4%

45%

2.5 % 53 % 1% 70 %

Amicon PM 10 Textile covered stainless steel tubular

40

References Howell, 1975 Henley, 1980

Ohlson, 1984

20

37

Kinoshita, 1986

10 10

50

Lee, 1993 Maneri, 2000

10 45

40 40

Gan, 2002 BelafiBako, 2006

As a summary, glucose removal during polysaccharide hydrolysis can be successfully carried out in membrane bioreactors, where ultrafiltration membranes are applied. The small molecular weight products, e.g. glucose is able to pass through the membrane, while the high molecular weight substrates and enzymes are retained. Moreover an important advantage of the membrane bioreactors is that they make recovery and reuse of biocatalysts possible and in such a system continuous uptake of substrate and release of product without loss of biocatalysts can be achieved.

Synthesis of Fructo-Oligosaccharides Nowadays the so-called functional foods are considered as important part of our diet and contain useful components that have beneficial effects on health conditions [Blandino, 2001]. Typical representatives of functional foods are fructo-oligosaccharides (FOSs). Their significance has raised recently in human and animal nutrition, mainly because of the

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advantageous effects on the intestinal bacterial population and general health conditions in the body [Bornet, 2002]. FOSs are able to reach the colon where they are fermented by the microbial flora (e.g. Bifidobacteria sp., Lactobacillus sp.) to lactate and short chain fatty acids, like acetate, propionate, butyrate. Consequently, FOSs stimulate the growth and vitality of these microbes and prevent spreading of the harmful pathogens. In addition, they have low sweetness intensity, their caloric value is low, approximately 8-9 kJg-1 and cause no caries. So they can be applied as alternative sweeteners, as well [Losada, 2002; Fooks, 1999]. Short chain FOSs are mainly composed of 1-kestose (GF2), nystose (GF3) and fructosylnystose (GF4), where two, three and four fructose units are bound to one unit of glucose, respectively (Figure 3). They can be found in plants and vegetables, including onion, asparagus, rice, sugar beet, wheat, etc. but generally in low concentration. The industrial scale recovery from these plants is not economical since their low concentration, for this reason, FOSs are produced commercially via biosynthetic as well as hydrolytic methods using fructosyl-transferase (FTF) enzyme. The raw material of the synthetic reaction is sucrose and the product mixture contains unconverted sucrose besides GF2, GF3 and GF4 and glucose as a by-product [Yun, 1996]. The latter component is a strong competitive inhibitor of the synthesis [Sheu, 2001]. Elimination of the formed by-product component can result an increase in the product yield. For this purpose special separation methods can be applied: e.g. chromatographic separation or enzymatic method like elimination by glucose oxidase.  

Figure 3. Structure of short chain FOSs: 1-kestose (GF2), nystose (GF3) and fructosyl-nystose (GF4).

To realise the complex system, an immobilised FTF enzyme preparation for the synthesis of fructo-oligosaccharides (15.6 U g-1) was developed, on one hand; and for the elimination of glucose a co-immobilized glucose oxidase-catalase solid-phase biocatalyst was manufactured, on the other hand [Sisak, 2006]. Hydrogen peroxide formed in the reaction is an inhibitor for the enzyme, so it should be removed from the reaction mixture. In this system catalase was applied for the decomposition of H2O2. Based on the results of the shaken flask experiments a complex reactor system was constructed for the integrated fructo-oligosaccharides production and glucose elimination [Csanadi, 2008]. The reactor unit for fructo-oligosacharides production was tempered to 53°C, the other was operated at 25°C. In the system the two reactor units were connected. One of them was

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filled with 10 g immobilized Pectinex Ultra SP-L, the other was filled with 5 g coimmobilized glucose oxidase-catalase, and 130-130 cm3 sucrose solution (2 M, pH=5.6, 0.05 M acetate buffer) was added. The reaction mixtures were changed between the two reactor units in every second hour, so the reaction and glucose elimination were carried out consequently in numerous cycles. In the synthesis of fructo-oligosaccharides in shaken flask experiments with immobilized fructosyl-transferase enzyme ~60 % product yield was reached, while in the integrated reactor system for the simultaneous FOS synthesis and elimination of glucose higher (~74 %) product yield was achieved.

SUMMARY Removal possibilities of glucose from foodstuffs and food bioprocesses were discussed in this chapter. One of the separation processes presented was a membrane technique: ultrafiltration, which can be applied in case of significant size difference between the compounds of the mixture to be separated (e.g. glucose – polysaccharides). The other separation method is the application of glucose oxidase – catalase enzyme system, which is able to convert glucose selectively, thus it is possible to remove it from various mixtures.

ACKNOWLEDGMENTS The author would like to thank Ms Eva Lövitusz and Dr. Zsofia Csanadi for the technical help in the measurements and preparing the manuscript.

REFERENCES Alfani, F., Cantarella, M. & Scardi, V. (1983). Use of a membrane reactor for studying enzymatic hydrolysis of cellulose. J. Membr. Sci. 16, 407-416. Belafi-Bako, K., Koutinas, A., Nemestothy, N., Gubicza, L. & Webb, C. (2006). Continuous enzymatic cellulose hydrolysis in a tubular membrane bioreactor, Enz Microb Technol. 38, 155-161. Belafi-Bako, K. (2007). Enzymatic Processes and Fermentation. in Handbook of Waste Management and Co-product Recovery in Food Processing, ed. by Waldron, K., Woodhead Publishing Ltd, Cambridge. 198-216. Belafi-Bako, K. (2008). Simultaneous application of enzymes and membranes in food processing, in Food Engineering Research Trends, Ed. by Jerrod, M Cantor, Nova Science Publishers, New York. 263-279. Blandino, A., Macias, M. & Cantero, D. (2001). Immobilization of glucose-oxidase within calcium alginate gel capsules. Process Biochem. 36, 601-606. Bornet, F.R.J., Brouns, F., Tashiro, Y. & Duvillier, V. (2002). Nutritional aspects of shortchain fructooligosaccharides: natural occurence, chemistry, physiology and health implications. Dig. Liver Dis. 34, 111-120.

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Csanadi, Z. & Sisak, C. (2008). Production of short chain fructooligosaccharides. Hung. J. Ind. Chem. 36, 23-26. Dixon, M. & Webb, E.C. (1964). Enzymes. Longmans, London. Drioli, E. & Giorno, L. (1999). Biocatalytic membrane reactors. Taylor and Francis Group, London. Duerr, P. & Cuenat, P. (1988):.Production of dealcoholised wine In: Smart, R., Thornton, R. Rodriguez, S. & Young, J. (Eds.). Proc. 2nd Int. Symp. Cool Climate Viticulture and Oenology, Auckland, New Zealand, 363-364. Fooks, L., Fuller, R. & Gibson, G.R. (1999). Prebiotics, Probiotics and human gut microbiology. Int. Dairy J. 9, 53-61. Frater, T., Nemestothy, N., Gubicza, L. & Belafi-Bako, K. (2005). Enhancement of operation and storage stability of glucoamylase from Aspergillus awamori by a protease inhibitor preparation, Biocat. Biotrans. 23, 281-284. Frater, T., Gubicza, L., Szöllősy, A. & Bakos, J. (2006). Enantioselective hydrogenation in ionic liquids: Recyclability of the [Rh(COD)(DIPAMP)]BF4 catalyst in [bmim][BF4]. Inorg Chim Acta, 359, 2756-2759. Gan, Q., Allen, S.J. & Taylor G. (2002). Design and operation of an integrated membrane reactor for enzymatic cellulose hydrolysis. Biochem. Eng. J. 12, 223-229. Henley, R.G., Yang, R.Y.K. & Greenfield, P.F. (1980). Enzymatic saccharification of cellulose in membrane reactors. Enzyme Microb. Technol. 2, 206-208. Heresztyn, T. (1987). Conversion of glucose to gluconic acid by glucose oxidase enzyme in Muscat Gordo juice. The Australian Grapegrower and Winemaker, 4, 25–27. Howell, J.A. & Stuck, J.D. (1975). Kinetics of Solka Floc cellulose hydrolysis by Trichoderma viride cellulose. Biotechol. Bioeng. 17, 873-893. Howley, M. & Young, N. (1992). Low alcohol wines: the consumers choice? Int. J. Wine Marketing, 4, 45–46. Kinoshita, S., Wei Chua, J., Kato, N., Toshiomi, Y. & Taguchi, H. (1986). Hydrolysis of cellulose by cellulases of Sporotrichum cellulophilum in an ultrafilter membrane reactor. Enzyme Microb. Technol. 8, 691-695. Koutinas, A., Belafi-Bako, K., Kabiri-Badr, A., Toth, A., Gubicza, L. & Webb, C. (2001) Enzymatic hydrolysis of polysaccharides. Food Bioprod. Process 79(C1), 41-45. Law, C.S.R., Webb, C. and Williams, R.A. (1993) Kinetic studies on glucoamylase with maltodextrin as substrate, Chem. Eng. Res. Des. 71(A3), 296-298. Lee, X.G. & Kim, H.S. (1993). Optimal operating policy of the ultrafiltration membrane bioreactor for enzymatic hydrolysis of cellulose. Biotechnol. Bioeng. 42, 737-746. Lee, I., Evans, B.R., Lane, L.M. & Woodward, J. (1996). Substrate-enzyme interactions in cellulase systems. Bioresource Technol. 58, 163-169. Lopez-Ulibarri, R. and Hall, G.M. (1997) Saccharification of cassava flour strach in a hollow fiber membrane reactor. Enzyme Microbial Technol. 21, 398-404. Losada, M.A. & Olleros, T. (2002). Towards a healthier diet for the colon: the influence of fructooligosaccharides and lactobacilli on intestinal health. Nutrit. Res. 22, 71-84 . Maneri, N., Hamdache, F., Abdi, N., Belhocine, D., Grib, H., Lounici, H. & Piron, D.L. (2000). Enzymatic saccharification of olive mill solid residue in a membrane reactor. J. Membr. Sci. 178, 121-130. Mansfield, S.D., Mooney, C. & Saddler, J.N. (1999). Substrate and enzyme characteristics that limit cellulose hydrolysis. Biotechnol. Prog. 15, 804-16.

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Nagodawithana, T. & Reed, G. (eds) (1993). Enzymes in food processing, Academic Press, Harcourt Brace and Company, San Diego. Nagy, E., Bélafi-Bakó, K. & Szabó, P. (1992). A kinetic study of the hydrolysis of maltodextrin by soluble glucoamylase enzyme. Starch/Stärke, 44, 145-148. Ohlson, I., Tragardh, G. & Hahn-Hagerdal, B. (1984). Enzymatic hydrolysis of sodiumhydroxide pretreated sallow in an ultrafiltration membrane reactor. Biotechnol. Bioeng. 26, 647-653. Paolucci-Jeanjean, D., Belleville, M.P., Rios, G.M., Zakhia, N. (2000) Kinetics of continuous starch hydrolysis in a membrane reactor. Biochem. Eng. J. 6, 233-238. Pickering, G.J., Heatherbell, D.A. & Barnes, M.F. (1999A). Optimising glucose conversion in the production of reduced alcohol wines from glucose oxidase treated musts,.Food Res. Int. 31, 685–692. Pickering, G.J., Heatherbell, D.A. & Barnes, M.F. (1999B). The production of reduced alcohol wine using glucose oxidase-treated juice-composition, Am. J. Enol. Viticult. 50, 291–298. Pickering, G.J., Heatherbell, D.A. & Barnes, M.F. (1999C). The production of reducedalcohol wine using glucose oxidase-treated juice II., SO2-binding and stability, Am. J. Enol. Viticult. 50, 299–306. Pickering, G.J., Heatherbell, D.A. & Barnes, M.F. (1999D). GC-MS analysis of reducedalcohol Müller-Thurgau wine produced using glucose oxidase-treated juice, Trends in Food Sci. Technol. 34, 89-94. Pickering, G. J. (2000). Low- and Reduced-alcohol wine: A Review. J. Wine Res. 11, 129– 144. Sheu, D.C., Lio, P.J., Chen, S.T., Lin, C.T. & Duan, K.J. (2001). Production of fructooligosaccharides in high yield using a mixed enzyme system of betafructofuranosidase and glucose oxidase. Biotechnol. Lett. 23, 1499-1503. Sisak, C., Csanádi, Z., Rónay, E., Szajáni, B. (2006). : Elimination of glucose in egg white using immobilized glucose oxidase. Enz. Microbial Technol. 39, 1002-1007. Smith, J. S. & Hui, Y.H. (eds) (2004). Food processing: Principles and Applications, WileyBlackwell, New York. Tucker, G.A. & Woods, L.F.J. (eds.) (1995). Enzymes in food processing, Blackie Academic and Professional, London Wald, S., Wilke, C.R. & Blanch, H.W. (1984), Kinetics of the enzymatic hydrolysis of cellulose, Biotechnol. Bioeng. 26, 221-230. Yun, J.W. (1996). Fructooligosaccharides - Occurrence, preparation and application. Enz. Microbial Technol. 19, 107-117.

In: New Topics in Food Engineering Editor: Mariann A. Comeau

ISBN: 978-1-61209-599-8 © 2011 Nova Science Publishers, Inc.

Chapter 14

INSTANT RICE PHYSICOCHEMICAL PROPERTIES AND EATING QUALITY Prisana Suwannaporn Kasetsart University, Bangkok, Thailand

ABSTRACT Instant, or quick-cooking, rice is becoming more popular nowadays. However, it still poses problems with respect to rehydration time and quality. The effects of processing factors which are: moisture content, pressure and drying temperature has a significant effect on its physicochemical properties and eating quality. The hardness and chewiness of rice decreased as moisture content and pressure increased. Higher drying temperatures caused increases in hardness and chewiness. Only pressure and moisture content affected density, rehydration ratio, and increase in the volume of instant rice, which was due to the porosity of the kernels. Rehydration ratio had a negative correlation with density (r= 0.886) but a positive correlation with volume increase (r = 0.637). Pressure was the main factor influencing the pasting properties of instant rice. All pasting properties of instant rice were far lower than those of milled rice, but instant rice had higher cold paste viscosity, which is typical of pregelatinized flour. This indicated rapid water absorption and shorter cooking time. Instant rice processing also caused development of amyloselipid complexes observed as the V-type pattern in an X-ray diffractometer

INTRODUCTION In modern lifestyles, instant food is becoming more popular. However, instant rice is still beset by the problems of long rehydration time and inferior quality compared to cooked milled rice . The texture of cooked rice is related to its amylose content and the fine structure of amylopectin. The intra- and/or intermolecular interactions of starch with other components in rice such as protein, lipid and non-starch polysaccharides results in a harder texture [1] (Ong and Blanshard, 1994). Moreover, processing conditions also affect the texture of cooked rice in a way similar to the parboiling process. The basic processes in preparing instant rice

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and parboiled rice are similar, consisting of soaking, steaming and drying. These processes have marked impacts on the organoleptic properties of cooked rice. Derycke et al. 2005a found that the heat-moisture conditions during parboiling, cooling and drying had impact on cooked parboiled rice [2]. They observed that the texture of cooked parboiled rice was usually firmer and less sticky than that of non-parboiled rice. This firmer texture was related to the level of crystalline amylose–lipid complexes formed during parboiling which were stable during the cooking process. According to Robert et al. (1972), dry grains of instant rice should be separate and should resemble milled rice in shape. Its bulk density should be 0.4-0.42 g/cm3 with a low percentage of broken kernels. After rehydration, the volume of instant rice should increase to 1.5-3 times that of dry grains, and its color, flavor and texture should be similar to cooked rice (Smith et al. 1985) with no hard core or ungelatinized center (Luh et al., 1980). Previous investigators tried to propose instant rice processes which were mainly concerned with three main factors: 1) the initial moisture content 2) the degree of gelatinization and 3) the drying or puffing method. The initial moisture content could be manipulated by the temperature of the water used in soaking and/or time. The initial moisture content has been reported to affect the product’s homogeneity (Baz et al., 1992), degree of gelatinization, percentage of broken kernels (Ahromit et al., 2006) and degree of starch leaching (Bello et al. 2004). Degree of gelatinization was related to cooking method, cooking time and/or temperature. Partial gelatinization (around 80%) (Smith et al., 1985) or complete gelatinization either by boiling or steaming have been proposed as necessary in the instant rice preparation process. High pressure cooking process resulted in more homogeneous gelatinization and reduced the percentage of broken kernels (Bhattacharya, 1985; Baz et al., 1992). Drying processes varied from single step drying at low temperature (70 ºC) for a long time (2-3 hours) to multi-step drying at high temperature for a short time to induce case hardening followed by low temperature drying for a long time to reduce moisture content (Robert et.al., 1972; OzaiDurrani, 1948). Other drying methods included the use of tray dryers or centrifugal fluidized bed dryers (Baz et al., 1992, Ramesh and Rao, 1996; Carlson et al., 1979), drum dryers (Robert et al., 1972; Lewis et al., 1991; Ando et al., 1980), a freeze-thaw process (Robert et al., 1972) and high pressure cooking (Leelayuthsoontorn and Thipayarat, 2006; Bhattacharya, 1985; Baz et al., 1992). Drying is a complex process involving simultaneous heat and mass transfer. Its results in significant changes in chemical composition, structure, and physical properties of foods. Heating process and loss of water cause stresses in the cellular structure that lead to changes in microstructure such as the formation of pores and shrinkage. (Banu et al., 2008) Instant food mostly invoved in freeze dried process which provide porous structure with little shrinkage, superior taste and aroma retention, and better rehydration. However, freeze dried process is uneconomical due to its large capital outlays, high operating cost and relatively long drying time. Several attempts have been made to reduce freeze-dried cost by applyng the combined drying process altogether with low atmospheric pressure drying or vacuum drying. Vacuum drying is an alternative method which is suitable for products that are sensitive to heat. However, heat transfer becomes difficult as convection is ineffective at low pressure (Giri and Prasad, 2007). Litvin et al. (1998) studied the combination of drying conditions by partial freeze drying of carrot slices followed by a short (50 s) microwave treatment and air or vacuum drying. The total process time was shortened considerably. The development of pores and shrinkage depended upon the variation in moisture transport mechanisms and the external

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pressure. The strength of the solid matrix can also be affected by ice formation, case hardening, permeability of crust, and matrix reinforcement (Rahman, 2003). Thus, the drying method and conditions applied has a significant effect on product characteristics such as porosity, shrinkage, and bulk density. The % rehydration of dehydrated foods depended on its water absorption capability and water holding capacity (Lewicki, 1998). Vacuum drying mostly used in fruit / vegetable accounted for its porous structure and high water holding capacity that ease heat transfer and promote water absorption during rehydration. ArévaloPinedo and Murr (2006) found that water penetration was highest in pumpkin slide which was frozen at -20 oC for 3 hrs prior to low pressure drying at 0.7 lb/in2 for 70 oC.

PROCESS OPTIMIZATION A five-level, three-variable central composite design (CCD) was applied to estimate the relationship between variables concerning texture and the whiteness index (WI) of instant rice. CCD consisted of eight factorial points, six axial points (two axial points on the axis of each design variable at a distance of 1.68 from the design center) and six center points, leading to 20 sets of experiments. The experiments were run in random order to minimize the effects of unexpected variability in the observed responses due to extraneous factors. Milled rice was soaked in water for various soaking times to obtain moisture contents of 35 – 60 % wet basis (X1; -1.68 to +1.68 level). After soaking, the rice grains were cooked under pressures of from 11.6 to 28.4 lb/in2 (X2; -1.68 to +1.68 level) for 5 min. Then, the cooked rice was dried using a tray dryer at temperatures from 166.4 to 233.6°C (X3; -1.68 to +1.68 level) to obtain a product with a moisture content of less than 12%. The variables and their process levels are shown in Tables 1. Table 1. Coded levels for independent variables used in developing experimental data

Factor Moisture Content (% wb) Pressure (lb/in2) Drying Temperature (°C)

Code X1 X2 X3

Level - α (-1.68) 34.90 11.60 166.36

-1 40 15 180

0 47.5 20 200

+1 55 25 220

+α (1.68) 60.10 28.40 233.64

The responses modeled as linear, quadratic and cubic functions of the three independent variables were tested for adequacy and model fitness using ANOVA. The selections of adequate models (Table 2) were determined using model analysis, lack-of fit test and Rsquare analysis (Chen et al., 2005). The ‘‘lack of fit test’’ compared the residual error to the pure error from replicated design points. The model with no significant lack-of-fit and high R2 was selected (Table 2). The results showed that hardness, chewiness, and whiteness index had high coefficient of determination (R2) which equaled 0.927 0.633 and 0.836, respectively, and no significant lack of fit (Table 3). Park et al. (2001) reported a high correlation between the instrumental texture parameters hardness and chewiness and the sensory attributes of cooked rice. Likewise, Prakash et al. (2005) found positive correlation between instrumental hardness and sensory

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hardness and chewiness in thermal processed rice. As a consequence, hardness and chewiness were used as the main responses in RSM for the texture of instant rice. Table 2. Regression coefficients of the polynomial function and the coefficients of determination (R2) Coefficient Hardness AdhesiveSpringiness Cohesive Gumminess Chewiness WI .4100 2,355.99* 78.24* 5,804.59* -943.58 .970 B0 2,439.98 .0219 -.013 0.17* -61.67* -378.58* 38.71 B1 -32.55 .0018 0.32* -82.90* B2 -287.67* -30.80 .005 -97.81 -.0029 -0.12* 97.82* 305.06* -38.35 .002 94.64 B3 .0024 -1.01* 157.06* 351.13* -41.34 .002 155.07 B12 .0133 .004 -0.34* -46.41* -276.76* 53.95 B13 -35.74 .0029 0.25* -32.79 .008 7.86* -18.85* -11.29 B23 -.0050 -.001 0.06* -106.56* -171.02* 52.59 -108.86 B11 -.0013 -.0006 -0.75* 9.10 -8.76* 4.24* -7.49 B22 -.0016 0.33* -26.86 .00005 19.00* 82.94* 17.15 B33 0.927 0.377 0.274 0.462 0.553 0.633 0.836 R2 0.425 0.113 0.043 0.032 0.062 0.054 Sig Lack-of0.072 Fit

INSTANT RICE TEXTURE Thirty grams of instant Jasmine rice was rehydrated with 110 ml. water and microwaved for 6 minutes. TPA was performed using a texture analyzer (TA-XT.plus, Stable Micro System, UK). Following Park et al. (2001). It was compressed to 60% with a rod-type probe (2.5 diameters) at a speed of 1.7 mm/sec. Hardness, adhesiveness, springiness, cohesiveness, gumminess and chewiness were determined.

1. Hardness Estimation of texture in terms of hardness and chewiness over independent variables X1, X2 and X3 is shown in Figure 1. Hardness of the rice decreased as the moisture content and pressure increased. But an increase in drying temperature caused a harder texture. The statistical analysis in Table 2 indicated that all variables had a significant effect on hardness, especially moisture content in linear, quadratic and interaction terms. At higher moisture content, thinner case hardening and bigger pore size were noticeable in the SEM images. High pressure induced high gelatinization, which also resulted in a softer texture in the rehydrated rice. The hardness of cooked instant rice had positive correlation with density (Table 3). Density was related to the porosity of the structure of processed rice seen in the SEM images. The cracks and pores in instant rice permitted rapid entry of water and heat transfer during cooking, resulting in a softer texture rehydrated rice.

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Figure 1. Contour plot of hardness of rehydrated instant rice as a function of instant rice process conditions

Table 3. Correlation coefficients between texture and physical properties of instant rice Texture Hardness Adhesiveness Springiness Cohesiveness Gumminess Chewwiness

Density .564 * -.123 .169 -.758** .102 .152

Rehydration Ratio -.291 .133 -.284 .756** .194 .125

Volumn Increase -.127 -.020 -.249 .481 .202 .144

*, ** significant at p < 0.05 and 0.01, respectively.

Figure 2. Contour plot of chewiness of rehydrated instant rice as a function of instant rice process conditions

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2. Chewiness Chewiness refers to number of chews required to masticate cooked rice before it was suitable for swallowing or the amount of work required to chew the sample for sensory evaluation. It was observed that pressure and drying temperature had main effects on chewiness. Initial moisture content affected chewiness in the interaction term and with pressure in the quadratic term (P < 0.05) (Table 2). Chewiness was decreased with increased pressure or drying temperature (Figure 2).

WHITENESS INDEX (WI) The whiteness of rehydrated rice was measured using a colorimeter (Chromameter model CR-300, Japan). Measurement was based on the Hunter system with color values of L, a and b. The measurements were performed in two replications and were repeated three times per replicate. The whiteness index (WI) was calculated as follows: WI = 100 – [(100 –L) 2 + a2 +b2]0.5 All factors influenced the whiteness index, especially pressure. At high moisture content, the whiteness index decreased when pressure increased (Figure 3). This result is in accord with the study on parboiled rice by Bhattacharya (1996), who found that pressure and steaming time had marked effects on product’s Hunter color. Yellowish color was prominent when paddy was parboiled for longer times. Islam et al. (2002) showed that brightness of parboiled rice decreased with the increase in steaming temperature. The deterioration of the whiteness of parboiled rice was more pronounced at higher temperatures. Leelayuthsoontorn and Thipayarat (2006) found that the WI of cooked rice decreased as the cooking temperature increased. The cooking temperature was clearly an important factor influencing WI. However, at low moisture content, the whiteness index also decreased with increased in pressure.

Figure 3. Contour plot of whiteness index of rehydrated instant rice as a function of instant rice process conditions

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INSTANT RICE GRAIN STRUCTURE drying temperature 220 °C

Pressure 25 lb/in

2

Pressure15 lb/in

2

drying temperature 180°C

Pressure 25 lb/in

2

Pressure 15 lb/in

2

(a) moisture content 40%

(b) moisture content 55%

Figure 4. Scanning electron micrographs transverse of instant rice (dry grain) at different pressure and drying temperature treatment at (a) 40% moisture content (b) 55% moisture content

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SEM of instant rice samples were taken with a Hitachi TableTop/Tischmikroskop model TM-1000 at magnitude x 80. The samples were prepared by breaking a rice kernel at the center and sticking it on the stub without a gold film coating. The SEM images showed that instant rice grains had a hollow structure and were case hardened (Figure 4). In the soaking step, the increase in grain dimensions has been attributed to the swelling of starch granules and subsequent widening of cracks in the grain by water diffusion (Ahromrit et al., 2006). Puffed kernels, in which could be seen larger hollow centers, occurred noticeably when both pressure and drying temperature increased altogether. The puffing phenomenon resulted from the sudden expansion of water vapor in the granule. In the drying step, moisture was removed from the surface of the rice grains faster than from the interior (Baz, 1992). The surface of rice became slightly harder than the center during drying (Lin, 2002). This caused case hardening, which blocked water vapor leaching during the drying process and resulted in puffing of the grain, giving it a larger, hollow structure in the center that allows easy rehydration.

DENSITY, REHYDRATION RATIO AND VOLUME INCREASE The results in Table 4 show that only pressure and moisture content affected density, rehydration ratio and volume increase. Leelayuthsoontorn and Thipayarat (2006) also found that high pressure caused larger pore size and thickness with a sponge-like texture. As a consequence, low density rice was obtained under conditions of high moisture and high pressure. The rehydration ratio had a negative correlation with density (r = -0.546) and a positive correlation with volume increase (r = 0.542). Rice was rehydrated more rapidly because of the increase in its surface area as its volume increased. Table 4. Analysis of variance showing the effect of variable as a linear terms and interaction (cross product) on response parameters. Source X1 X2 X3 X1*x2 X1*x3 X2*x3 X1*x2*x3

Density 18.590* 4.573* 0.444 0.022 2.319 3.641 3.572

Rehydration ratio 8.047* 0.619 1.457 2.322 0.160 0.017 0.906

Volume Increase 1.990 2.778 2.707 11.185* 0.953 1.509 1.124

* significantly different at the 95% confidence level

INSTANT RICE PASTING PROPERTIES Pasting properties of instant rice were determined using a Rapid Visco Analyzer (Rapid Visco Analyzer model RVA3D Newport Scientific Instruments and Engineering, Australia) according to AACC standard method no.61-02 (AACC, 1995). Paste viscosity plotted in arbitrary RVA units (RVU) versus time was used to determine the peak viscosity (PV),

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309

trough viscosity, final viscosity (FV), breakdown viscosity (BKD = PV- trough), and setback viscosity (SB = FV - trough). Result showed that the integrity of the starch granule and its hydration properties can be investigated easily by measuring the pasting behavior of rice flour before and after treatment. All pasting properties of instant rice were far lower than those of milled rice (Figure 5), but instant rice showed higher cold paste viscosity, which is the typical hydration property of pregelatinized flour (Lai, 2001).

Figure 5. Rapid Visco Amylograph showed pasting properties of milled rice flour and instant rice flour

The disruption of molecular order within starch granules during steaming caused loss of starch granule integrity and the destruction of crystallinity, resulting in cold soluble starch (Lai, 2001) and a decrease in the magnitude of all pasting properties. A similar observation was previously reported (Hagenimana et al., 2006). Unlike pregelatinized starch, peak viscosity was still observed in instant rice flour, which indicated the presence of partially ungelatinized starch polymers. Increase in pressure caused degradation and gelatinization of starch. The RVA profile of instant rice indicated that instant rice could absorb water more rapidly and required a shorter cooking time than milled rice. It indicated granule rigidity and molecular re-association, which were significantly enhanced by the hydrothermal treatment. The change in pasting properties depended mainly on the combined effects of moisture, pressure and drying temperature. However, the formation of disulphide bonds in the protein fraction and the complexation of lipid with amylose also had effects. Derycke et al. (2005b) suggested that the formation of disulphide bonds during the parboiling process restricted starch granule swelling capacity. Moreover, the formation of starch lipid complexes also restricted the swelling capacity, hence lowering the pasting properties.

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AMYLOSE - LIPID COMPLEXES X-ray diffraction patterns of instant rice flour were obtained with a diffractometer (JEOL, model JDX-3530, Japan) using monochromatic Cu-K α radiation 1.542 A˚. The diffractometer was operated at 40 kV, 45 mA and the spectra scanned over a diffraction angle (2θ) range of 5-40˚ at a step size of 0.02˚ 2θ per sec. Percentage of crystallinity was calculated as the percentage of peak area to the total diffraction area using this equation (Cheetam et al., 1998):

% Relative crystallinity

=

Area above the smooth curve × 100 Total diffraction area above the baseline

Amylose-lipid complex formation depended on both heat and moisture applied during the instant rice preparation process. A-type crystallinity was either greatly reduced or completely destroyed. The effect of process conditions on the formation of crystalline amylose–lipid complexes was investigated using XRD as shown in Figure 6.

Figure 6. X-ray diffraction of milled rice flour (A) and instant rice flour (B-E)

The X-ray patterns of milled rice showed the A pattern which disappeared in instant rice. Instant rice showed the V-type pattern, or the intensity of the reflections at 2θ =13 and 20º. This result indicated that the instant rice preparation process destroyed the crystalline structure of the starch granules. This result agrees well with Miyoshi (2002) and Shih et al. (2007) found that the formation of amylose-lipid complexes occurred during heat-moisture treatment of starch, as indicated by the occurrence of the V-type pattern. Before

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gelatinization, starch had limited binding capacity with lipid because most of the lipid in the system was unable to come into contacted with starch (Pilli et al., 2008). After gelatinization, the V-type pattern was detected either because more complexes were formed during heating or the crystalline regions had increased in size. The V-type crystal pattern was greatest when the highest pressure was used in the process. A similar result was also observed in the parboiling process, in which amylose-lipid complexes were created during the heating step. The diffraction lines of amylose-lipid complexes increased progressively with the degree of parboiling. (Priestley, 1976; Biliaderis et al., 1993; Derycke, 2005a).

CONCLUSIONS Processing conditions affected the physical and physicochemical properties of instant rice. The hardness and chewiness of rehydrated rice was decreased as moisture content and pressure increased. At high moisture content, the whiteness index decreased when pressure increased. Only pressure and moisture content affected density, rehydration ratio, and volume increase because they increased the kernel’s porosity, as demonstated by the SEM image. The cracking and puffing in the structure of instant rice were important to the texture of the rehydrated rice. Pressure was the main factor influencing the pasting properties of instant rice. All pasting properties of instant rice were far lower than those of milled rice. But instant rice showed higher cold paste viscosity, which is the typical hydration property of pregelatinized flour. The instant rice preparation process caused development of amylose-lipid complexes observed as the V-type pattern in the X-ray diffractometer.

REFERENCES American Association of Cereal Chemists. Approved Methods of the AACC (10th ed.) Method 61-02, (2000). A. Ahromrit, D.A., Ledward, and K. Niranjan, Journal of Food Engineering. 72, 225 (2006). M. Ando, J. Minami, M. Takata, F. Ohinishi, and S. Kawamoto. United State Patent No. 4,233,327, (1980). A.A.. Baz, J.Y. Hsu, and E. Scoville. United State Patent No. 5,089,281. (1992). M. Bello, R. Baeza, and M.P. Tolaba. Journal of Food Engineering. 72, 124 (2006). S. Bhattacharya. Journal of Food Engineering. 29, 99 (1996). C.G. Biliaderis, J.R. Tonogai, C.M. Perez, and B.O. Juliano. Cereal Chemistry. 70, 512 (1993). R.A. Carlson, R.L. Roberts, and D.F. Farkas. United State Patent No. 4,133,898 (1979). N.W.H. Cheetam, and L. Tao. Carbohydrate Polymers. 36, 277(1998). M.J. Chen, K.N. Chen, and C.W. Lin. Journal of Food Engineering. 68, 471(2005). V. Derycke, G.E. Vandeputte, R. Vermeylen, W. De Man, B. Goderis, M.H.J. Koch, and J.A. Delcour. Journal of Cereal Science. 42, 334 (2005a). V. Derycke, G.E. Vandeputte, R. Vermeylen, W. De Man, B. Goderis, M.H.J. Koch, and J.A. Delcour. Journal of Cereal Chemistry. 82, 468 (2005b). A. Hagenimana, X. Ding, and T. Fang. Journal of Cereal Science. 43, 38 (2006).

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M.R. IslamN. Shimizu, N., and T. Kimura. Food Scence and Technoogy Research. 8, 131 (2002). H.M. Lai. Food Chemistry. 72, 455 (2001). P. Leelayuthsoontorn, and A. Thipayarat. Food Chemistry. 96, 606 (2006). V.M., Lewis, and D.A. Lewis. United State Patent No. 5,045,328 (1991). Y.H.E. Lin, and L. Jacobs. United State Patent No. 6,416,802 (2002). B.S. Luh, R.L. Robert, and C.F. Li. Rice: Production and Utilization. The AVI Publishing company, Inc., Connecticut (1980). E. Miyoshi. Cereal Chemistry. 79, 72 (2002). R.H. Myers, and D.C. Montgomery. Response surface methodology: Process and product optimization using designed experiments (2nd ed.). New York: John Wiley and Sons, Inc. (2002). M.H. Ong and J.M.V. Blanshard. Journal of Cereal Science. 21, 261(1995). A.K. Ozai-Durrani. United State Patent No. 2,438,939 (1948). J.K. Park, S.S. Kim, and K.O. Kim. Cereal chemistry. 78, 151(2001). T.D. Pilli, K. Jouppila, J. Ikonen, J. Kansikas, A. Derossi, and C. Severini. Journal of Food Engineering. 87, 495–504. (2008). M. Prakash, R. Ravi, H.S. Sathish, J.C. Shyamala, M.A. Shwetha, and G.C.P. Rangarao. Journal of sensory studies. 20, 410 (2005). R.J. Priestley. Food Chemistry. 1, 5 (1976). M.N. Ramesh, and P.N.S. Rao. Journal of Food Engineering. 27, 389 (1996). R.L. Robert. Rice: Chemistry and Technology. Association of American Cereal Chemist, St. Pual, Minn, USA. (1972). F. Shih, J. King, K. Daigle, H.J. An, and R. Ali. Cereal Chemistry. 84, 527 (2007). D.A. Smith, R.M. Rao, J.A. Liuzzo, and E. Champagne. Journal of Food science. 50, 926 (1985).

INDEX A  absorption, xii, 49, 104, 196, 204, 301, 303  accommodation, 155  accounting, 143  accuracy, xi, 36, 107, 261, 262, 268, 276  acetone, 61  acid, x, 15, 36, 67, 79, 125, 128, 129, 130, 143, 145,  146, 147, 152, 171, 175, 176, 177, 178, 179, 206,  211, 212, 213, 214, 215, 216, 217, 218, 219, 220,  221, 222, 223, 224, 225, 226, 250, 252, 253, 254,  258, 285, 289, 290, 291, 292, 298  acidity, 212  acoustics, 120  activated carbon, 158, 168  active site, 165  additives, 31, 33, 153, 174, 212, 250, 254, 258  ADH, 230  adhesion, 179, 200, 207, 208, 210, 225, 245  adjustment, 14, 107, 112, 186, 234  adsorption, 152, 153, 155, 156, 158, 159, 160, 161,  163, 165, 166, 167, 168, 169, 170, 196, 284, 294  adsorption isotherms, 168, 170  advantages, viii, 36, 81, 121, 183, 293  aflatoxin, 82, 84, 97  agar, 213, 216, 217, 254  agriculture, 252  alcohols, 235  algorithm, viii, 17, 48, 81, 82, 83, 84, 85, 86, 87, 88,  89, 91, 92, 93, 95, 96, 97, 98  allergy, 172, 206, 230  allocating, 235  almonds, viii, 81, 82, 84, 88, 92, 96, 97  aluminium, 283  ambient air, 204  amino acids, 58 

amplitude, 103, 104, 105, 106, 108  amylase, 293  ANOVA, 238, 240, 303  anthocyanin, 134, 147, 148  antioxidant, 58, 78, 79, 122, 130, 140, 142, 143, 144,  145, 146, 147, 148, 254, 284  apples, 82, 97, 143, 147, 258  aqueous solutions, 62, 117, 281  arithmetic, 265, 271  ascorbic acid, 125, 130, 143, 145, 146, 147, 152, 254  assessment, 36, 80, 97, 101, 103, 104, 169  atmospheric pressure, 185, 187, 209, 210, 235, 236,  281, 302  atomization, 180, 186  authors, 14, 18, 30, 54, 97, 100, 112, 247, 284, 285,  286  automation, vii  avoidance, 29 

B  background, 225  bacteria, x, 171, 175, 176, 177, 178, 179, 211, 212,  213, 214, 215, 216, 217, 218, 219, 220, 221, 222,  223, 224, 225, 226, 227, 228, 230, 252, 285  bacteriocins, 251, 254  bacteriostatic, 252  bacterium, 213  bandwidth, ix, 99, 104  barriers, 164  basic research, 209  beef, vii, 35, 36, 39, 42, 43, 44, 45, 48, 49, 50, 51, 52,  53, 54, 130, 146, 247, 273  beneficial effect, 134, 135, 295  beverages, viii, xi, 57, 58, 59, 60, 64, 72, 73, 74, 77,  79, 281, 289, 292 

314

Index

bias, 47  bile, 285  binding energy, 196  biocatalysts, 294, 295  bioconversion, 60  biodegradability, 251  biological systems, 251  biomass, 289  biopolymer, 59, 64, 72, 251, 252, 256  biotechnology, 228, 289  biotic, 223, 224, 226  blend films, 255  blends, 31, 60, 250, 255, 256  bonds, 64, 125, 160, 166, 309  bone, 143  bounds, 115, 267, 268, 277  brain, 47  branching, 58, 85  Brazil, 249  breakdown, 285, 309  breast cancer, 143  bridges, 28, 29  Bulgaria, 211  bulk density, 302, 303 

C  cabbage, 125, 126, 148  cabinets, 237  caffeine, 58  calcium, 146, 254, 297  calibration, 37, 42, 45, 46, 47, 49, 50, 52, 53, 61, 62,  63, 64, 65, 67, 72, 74, 75, 77, 115, 119  calorie, 36, 42, 46, 53  calorimetry, 14, 23  cancer, 175  capillary, 166, 192, 196  carbohydrate, xi, 235, 261, 264, 266, 271, 273, 274,  277, 292  carbohydrates, 219, 269, 272  carbon, 158, 168, 170, 236, 250, 263, 264, 265  carbon dioxide, 236, 250, 263, 264, 265  carbon materials, 170  caries, 296  carotene, 130, 134, 136, 137, 138, 143  carotenoids, 122, 130, 134, 135, 143, 145, 147, 148  case study, 245  casein, 284  catalyst, 291, 298  category a, 153, 268 

cellulose, 61, 250, 252, 257, 258, 289, 293, 294, 295,  297, 298, 299  ceramic, 109, 283, 293  challenges, vii, 147  character, 21, 29, 290  cheese, 148, 227, 259, 270, 276  chemical industry, 289  chemical properties, 145, 148, 188, 199, 209, 210,  225  chemical reactions, 58  chemical stability, 255  chicken, 36, 82, 145, 146  chlorination, 197  chlorine, 255  cholesterol, viii, 35, 37, 38, 42, 43, 45, 46, 47, 49, 50,  51, 52, 53, 54, 143  chromatograms, 67, 74  chromatography, 60, 62, 64, 77  circulation, 209  clarity, 115, 167  class, 64, 82, 87, 256  classification, viii, 81, 82, 84, 88, 89, 90, 91, 92, 94,  95, 96, 263  climate, 292  CMC, 229  coatings, 32, 33, 236, 237, 245, 247, 251, 253, 254,  256, 258  cobalamin, 130, 144  cocoa, vii, 13, 14, 15, 16, 17, 18, 20, 23, 26, 30, 31,  32, 33, 58  cocoa butter, vii, 13, 14, 15, 16, 17, 18, 20, 23, 30,  31, 32, 33  coefficient of variation, 42  coffee, 58, 60, 79, 160, 168, 205, 206  collagen, 39  colon, 296, 298  color, 33, 39, 42, 44, 69, 79, 253, 254, 302, 306  colostrum, 135  combined effect, 309  commodity, 130  compatibility, 250  compensation, 152, 158, 163, 166, 168, 169, 170  competitiveness, 30  compilation, 226  complexity, 87, 294  composites, 253  composition, xi, 15, 24, 30, 31, 32, 33, 58, 63, 64, 65,  66, 67, 69, 72, 77, 79, 114, 163, 168, 236, 237,  245, 251, 261, 262, 265, 268, 271, 275, 276, 281,  284, 294, 299, 302 

315

Index compounds, 43, 58, 63, 67, 69, 74, 78, 79, 125, 134,  135, 143, 144, 148, 173, 234, 235, 250, 252, 254,  255, 259, 280, 282, 285, 286, 287, 289, 292, 297  compression, 235, 256  computing, 87, 90, 152, 262  condensation, 58, 63, 135, 155, 156, 186, 188, 191,  196  conditioning, 22, 26, 204, 236, 237  conduction, 199  conductivity, 261, 262, 263, 266, 267, 268, 269, 270,  271, 272, 273, 275, 276, 277, 278  configuration, 109, 214, 215, 281  confinement, 253  conformity, 194  conjugation, 67  consciousness, x, 171, 175, 224  conservation, 102, 251  constipation, 172  consumer demand, xi, 36, 249  consumption, 16, 20, 25, 122, 143, 148, 172, 224,  290  contamination, ix, xi, 151, 152, 153, 249  control condition, 178  cooking, xi, 42, 44, 76, 80, 81, 139, 149, 229, 261,  301, 302, 304, 306, 309  cooling, 14, 17, 18, 19, 20, 26, 29, 31, 237, 302  copolymers, viii, 57, 65, 77, 250  correlation, xii, 58, 107, 128, 262, 301, 303, 304, 308  correlations, 272, 273  corruption, 196  cost, xi, 17, 30, 82, 119, 172, 174, 176, 222, 225,  250, 261, 302  cost saving, 30  covalent bond, 125  CPU, 237  cross‐validation, 37, 42, 46, 50, 51, 53  crystal growth, 14, 17  crystal structure, 15, 28  crystalline, ix, 15, 24, 26, 28, 29, 151, 302, 310  crystallinity, vii, 13, 24, 29, 309, 310  crystallization, vii, 13, 14, 15, 16, 18, 19, 20, 21, 29,  31, 32, 33, 154, 160, 247  crystallization kinetics, 31, 32, 33  crystals, 14, 15, 17, 18, 19, 20, 22, 24, 26, 29, 32, 33,  115  cultivation, 175  culture, 173, 175, 176, 178, 212, 213, 217, 227  cycles, 135, 285, 297  cytochrome, 50  Czech Republic, 63 

D  damping, 101  danger, 114  data analysis, 49  data collection, 44  data distribution, 49  data set, 42, 44, 46, 49, 83, 198  database, 82, 83, 84, 87, 89, 91, 92, 94, 95, 96, 97  decomposition, 46, 255, 296  deduction, 107  defects, vii, 13, 20, 29, 30, 82, 97  deformability, 21  deformation, 100, 237, 239  degradation, 59, 61, 65, 67, 76, 135, 144, 152, 154,  169, 172, 253, 254, 309  degradation rate, 154  degree of crystallinity, 24  dehydration, vii, 58, 235, 247  denaturation, 130, 135, 172  depolymerization, 58, 77  deposition, 283  desorption, 153, 159, 170, 294  destruction, 309  detachment, 283  detection, 42, 61, 64, 77, 82, 84, 97, 98  detection system, 77  developed countries, 36  developing countries, 261  deviation, viii, 35, 42, 47, 53, 241  dew, 237  dialysis, 59  diarrhea, 172  diet, 227, 229, 230, 292, 295, 298  differential scanning, 14, 23  differential scanning calorimetry (DSC), 14, 23, 24,  25, 31, 114, 115  diffraction, 25, 104, 310, 311  diffuse reflectance, 21, 33  diffusion, x, 29, 146, 156, 164, 165, 196, 233, 234,  244, 254, 308  diffusivity, 261, 262, 266, 270, 271, 275, 276  digestibility, 58, 131, 143, 145, 148  dimensionality, 82  direct measure, 262  direct observation, 110  disadvantages, viii, 81, 286  discriminant analysis, 38, 95  discrimination, 92  disorder, 155, 160 

316

Index

dispersion, 22  displacement, 111, 118, 190  distillation, xi, 279, 280, 281, 282, 286, 287  distilled water, 213  distribution function, 194  DNA, 183  double bonds, 64  drugs, 230  dry matter, 36  drying, vii, xi, 82, 83, 97, 171, 172, 173, 174, 175,  176, 177, 178, 179, 183, 184, 185, 186, 188, 189,  190, 191, 193, 194, 196, 198, 199, 200, 201, 202,  203, 204, 205, 206, 207, 208, 209, 210, 211, 214,  216, 217, 218, 219, 220, 221, 222, 223, 224, 225,  226, 227, 228, 229, 230, 231, 247, 261, 265, 289,  290, 301, 302, 304, 306, 307, 308, 309  durability, 33  dynamic viscosity, 109  dynamics, 168, 234, 248 

E  editors, 32  efficiency, 17, 176, 202, 226, 262, 285, 295  egg, 285, 290, 291, 292, 299  elastomers, 250  electric field, 148  electrolysis, 283  electromagnetic, 183  electron, 26, 28, 258, 307  electron microscopy, 26  emission, 229, 235  emulsions, x, xi, 34, 234, 236, 237, 245, 247, 280,  282, 283, 284, 285, 286, 287, 288  encapsulation, 285  endothermic, 24, 160  energy consumption, 122, 290  energy density, 282  energy technology, 228  engineering, vii, viii, 99, 100, 227, 228, 230, 255, 286  England, 146, 237  entanglement network, 113  entropy, 152, 153, 154, 155, 156, 157, 158, 160,  162, 163, 164, 165, 166, 167, 168, 169, 170  environmental factors, x, 171, 224  environmental impact, 249  enzyme inhibitors, 78  enzyme interaction, 298 

enzymes, ix, 58, 121, 122, 130, 131, 143, 144, 146,  179, 223, 224, 226, 251, 254, 290, 292, 293, 295,  297  epithelial cells, 148  equilibrium, ix, x, xi, 18, 20, 29, 60, 101, 151, 152,  153, 156, 157, 159, 165, 168, 195, 204, 219, 233,  234, 235, 279, 292  equipment, 14, 17, 20, 159, 172, 174, 175, 180, 183,  209, 210, 224, 226, 229, 281  ESR, 78  essential fatty acids, 144  ester, 292  etching, 283  ethanol, 60, 107, 251, 253, 281, 292, 293  ethylene, 249  evaporation, 183, 184, 198, 199, 200, 201, 202, 204,  209, 254, 286  excitation, 108  exclusion, 64  execution, 87  exercise, 268  experiences, 174  experimental condition, 61, 202, 214, 222  experts, 292  exposure, 67, 122, 143, 144, 178, 243  extinction, 179, 225  extraction, 45, 60, 82, 95, 96, 135, 138, 143, 144,  147  extrusion, 254, 255 

F  fabrication, viii, 99, 119, 288  false negative, 88  false positive, 88, 93  fat, vii, xi, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24,  25, 26, 29, 30, 31, 32, 33, 34, 35, 36, 37, 42, 43,  44, 45, 46, 47, 49, 50, 51, 52, 53, 54, 130, 144,  172, 196, 212, 261, 269, 272, 276, 277, 284  fat soluble, 130, 144  fatty acids, 32, 36, 130, 134, 135, 144, 296  feature selection, viii, 81, 82, 83, 84, 88, 89, 90, 91,  92, 93, 95, 96, 97, 98  feature subset selection, 98  fermentation, xi, 60, 114, 289, 292, 293  FFT, 104  fiber, 83, 281, 282, 287, 293, 298  fibers, 118, 284  films, vii, xi, 146, 234, 236, 245, 247, 248, 250, 251,  252, 253, 254, 255, 256, 257, 258, 259 

317

Index filters, 82  filtration, 60, 77, 285, 288  financial support, 247  fish, 36, 121, 122, 134, 135, 144, 153, 270  fitness, 303  flavonoids, 135, 143  flavor, x, 58, 69, 79, 171, 173, 174, 184, 206, 224,  236, 249, 253, 282, 302  flora, 135, 296  flour, xii, 298, 301, 309, 310, 311  fluid, 109, 113, 116, 117, 173, 176, 178, 180, 186,  205, 210, 225, 281, 291  fluidized bed, 302  fluorescence, 97  foams, 285, 286  folate, 125, 130  food additives, 153, 254  food industry, vii, viii, x, xi, 99, 100, 146, 171, 172,  224, 229, 279, 280, 286, 289, 290  food production, 261  food products, ix, 36, 37, 59, 61, 63, 67, 121, 122,  143, 163, 188, 246, 262, 293  food safety, 249  formula, x, 234, 246  fouling, 286, 294  fractional composition, 264  France, 99, 279, 286  free energy, 155, 158, 160, 161  free volume, 115  freezing, 174, 178, 216, 222, 224, 226, 228, 230,  265, 273  frequencies, 100, 101, 103, 104, 112, 113, 114, 115,  117  fructose, 60, 67, 125, 127, 292, 293, 296  fruits, ix, 119, 121, 122, 125, 134, 135, 143, 144,  164, 166, 251, 254, 258  FTIR, 39  functional changes, 80  furan, 64  fusion, 207, 208, 210, 225 

G  gel, 60, 62, 252, 297  gel permeation chromatography, 62  Germany, 32, 56, 147, 237, 295  germination, 129  glass transition, ix, 100, 113, 114, 115, 116, 117,  151, 153, 169, 212, 219 

glass transition temperature, 115, 154, 169, 212,  219  global markets, 122  glucoamylase, 293, 298, 299  glucose, xi, 60, 67, 79, 125, 127, 146, 289, 290, 291,  292, 293, 294, 295, 296, 297, 298, 299  glucose oxidase, 291, 292, 296, 297, 298, 299  glutathione, 131, 148  glycerin, 112, 113, 116  glycerol, x, 233, 234, 235, 236, 237, 239, 242, 243,  245, 246, 253  glycol, 235  glycoproteins, 58  granules, 308, 309, 310  graph, 169, 196  gravity, 210  grouping, 59  guidelines, xi, 36, 44, 261, 269 

H  hair, 213  halogen, 45, 83  hardness, vii, xi, 13, 14, 16, 20, 21, 25, 29, 33, 122,  234, 246, 301, 303, 304, 305, 311  harvesting, 276, 292  hazards, x, 171, 224  haze, 21  health effects, x, 171  heat capacity, 58, 59, 63, 77, 261, 262, 265, 266,  270, 271, 273, 274, 275, 276  heat release, 18, 20  heat transfer, 263, 270, 302, 304  height, 24, 186, 188, 210, 272, 274  hemoglobin, 49  heterogeneity, viii, 57, 63, 64, 77, 118  high density polyethylene, 258  homogeneity, 302  honey bees, 60, 69  hue, 21, 160, 163, 184  human brain, 47  human milk, 135, 148  Hungary, 289  Hunter, 170, 306  hybrid, 250, 264  hydrogen, 113, 115, 290  hydrogen peroxide, 290  hydrogenation, 298  hydrolysis, xi, 78, 146, 289, 293, 294, 295, 297, 298,  299 

318

Index

hydroxide, 299  hypothesis, 244, 245  hysteresis, 153, 169 

isotherms, 152, 153, 154, 158, 163, 167, 168, 169,  170  Italy, 57, 61, 287  Ivory Coast, 118 

I  ice, xi, 234, 237, 261, 262, 265, 269, 273, 274, 275,  277, 278, 301, 303  ideal, 117, 133, 155, 196, 294  illusion, 113  image, 311  images, 22, 23, 97, 304, 308  IMF, 36, 37, 38, 39, 40, 41  immersion, 109, 235, 284  immunity, 172  immunoglobulin, 135  impacts, 302  impedances, 105  impregnation, x, 233, 234, 235, 239, 246, 247  incidence, 105, 108, 144  inclusion, 153, 288  incompatibility, 254  incubation time, 285  independent variable, 46, 303, 304  infrared spectroscopy, 32, 97  ingestion, 135  inhibition, 58, 259, 293, 294  inhibitor, 296, 298  insertion, 17, 109, 196  integration, 65, 67, 69, 75, 254  intelligence, 251  interface, 100, 105, 106, 107, 118, 155, 156, 282,  283  intermolecular interactions, 301  intestinal flora, 135  intestine, 285  intrinsic viscosity, 62, 63  inventors, 258  inversion, 285  iodine, 152, 158, 168  ionic strength, 212  ions, 58, 134, 251, 254  iron, 130, 147  irradiation, 183, 258  IRS, 36  Islam, 306, 312  isoflavone, 135, 139  isolation, 230  isothermal crystallization, 18 

J  Japan, x, 32, 61, 171, 224, 226, 227, 228, 229, 230,  231, 254, 259, 283, 295, 306, 310  Java, 56 

K  keyword, 14  kinetics, ix, 17, 29, 31, 32, 33, 152, 153, 154, 158,  159, 165, 170, 293 

L  labeling, 250  lactic acid, x, 171, 175, 176, 177, 178, 179, 211, 212,  213, 214, 215, 216, 217, 218, 219, 220, 221, 222,  223, 224, 225, 226, 227, 228, 230, 252, 285  lactobacillus, 230  lactoferrin, 135, 147, 254  lactose, 205, 206  lakes, 250  lattice formation, 18  leaching, 302, 308  learning, 47, 48  lecithin, 21  lens, 213  light scattering, 21, 22  limitations, ix, 144, 151, 154, 249  linear dependence, 265  linear model, vii, 35, 45, 51, 53, 54  linear polymers, 62  linearity, 51, 52, 53  lipid oxidation, ix, 125, 130, 132, 134, 144, 145, 146,  148, 151, 152  lipids, 33, 134, 172, 173, 205, 207, 219, 254, 287  liquid phase, 235, 242  liquids, 59, 103, 104, 109, 116, 235, 298  Listeria monocytogenes, 252, 259  lithium, 197  liver, 38  living environment, x, 171, 212, 224  low temperatures, 178, 179, 183, 222, 225  LTD, 226 

319

Index lutein, 134  lycopene, 134, 143  lysine, 58  lysozyme, 254 

M  macromolecules, 58, 61, 69, 72, 253, 294  macronutrients, 125, 126  magnesium, 197  magnetic resonance, 14, 23  Maillard reaction, 67, 78, 79, 291  majority, ix, 53, 125, 151  malnutrition, 262  management, ix, 16, 18, 151  manipulation, 244  manufacture, vii, 13, 14, 16, 19, 30, 114, 147, 227,  290, 292  manufacturing, 14, 205, 288, 292  markers, 143, 148  marketability, 22  masking, 253  material surface, 203  matrix, 29, 65, 87, 91, 152, 153, 154, 160, 165, 223,  249, 251, 252, 253, 303  meat, vii, 35, 36, 38, 42, 43, 44, 49, 53, 122, 130,  134, 144, 145, 270, 271, 272, 273, 275, 276  mechanical properties, 16, 20, 21, 25, 31, 32, 33, 63,  100, 235, 236, 239, 247, 249, 250, 251, 253  media, 59, 100, 109, 119, 176, 213, 277  medium composition, 58  melon, 134  melt, 15, 17, 18, 24, 25, 26, 58, 59, 63, 77, 115  melting, vii, 13, 14, 15, 16, 17, 18, 21, 24, 25, 26, 29,  30, 31, 32  melting temperature, vii, 13, 15, 25  membranes, 196, 283, 284, 286, 287, 288, 290, 294,  295, 297  mercury, 263  mesoporous materials, 170  metabolism, 172  metals, 58, 254  meter, 61  methodology, 30, 247, 312  methylcellulose, 253, 257, 258  micelles, 284  microcrystalline, 252, 257  microcrystalline cellulose, 252, 257  microemulsion, 245  micrometer, 118 

micronutrients, 128, 130, 144  microorganism, 152, 219  microscopy, 26  microstructure, viii, x, 14, 16, 21, 29, 31, 65, 99, 152,  153, 158, 163, 166, 167, 168, 302  microstructures, 28  migration, x, 30, 31, 32, 233, 234, 235, 236, 247, 248  mixing, 44, 173, 213, 283  model specification, 43  model system, 31, 32, 59, 125  modeling, vii, 35, 36, 37, 42, 43, 46, 53, 54, 278  modification, 257  modules, 281, 293, 294  modulus, 33, 100, 102, 104, 105, 106, 111, 112, 252  moisture, viii, ix, x, xi, 30, 35, 36, 82, 100, 114, 115,  116, 119, 151, 152, 153, 154, 157, 159, 160, 161,  163, 164, 166, 167, 193, 194, 204, 210, 233, 234,  235, 236, 238, 247, 248, 250, 252, 254, 301, 302,  303, 304, 306, 307, 308, 309, 310, 311  moisture content, viii, ix, xi, 35, 36, 100, 114, 115,  116, 119, 151, 153, 154, 157, 160, 161, 163, 164,  166, 167, 193, 194, 204, 247, 301, 302, 303, 304,  306, 307, 308, 311  moisture sorption, 152, 154  molecular oxygen, 290  molecular structure, 64, 282  molecular weight, 58, 59, 62, 63, 64, 65, 67, 69, 71,  72, 75, 76, 78, 79, 143, 157, 235, 242, 289, 295  molecules, viii, 15, 18, 57, 59, 63, 67, 72, 77, 115,  135, 152, 153, 154, 155, 159, 160, 163, 164, 165,  169, 173, 235, 249, 282, 290  momentum, 48  monitoring, 18, 36, 130  monolayer, 154, 155, 196  morphology, ix, 22, 99  motivation, 87  moulding, 250  mucin, 291  multilayered structure, 252  multiphase materials, 278  muscles, 36, 38  mutant, 143  MWD, 62, 63, 65, 67, 68, 69, 70, 71, 72, 73, 75, 76,  78  myoglobin, 36, 134 

N  nanocomposites, 250, 251, 252, 253, 256, 257  nanoparticles, 253, 257 

320

Index

nanotechnology, 249, 250, 252  National Bureau of Standards, 170  Netherlands, 145, 169, 283  networking, 286  neural network, vii, 35, 44, 47, 48, 54  Neural Network Model, 52  neurons, 47, 48, 54  New Zealand, 261, 298  next generation, 249  niacin, 130, 144  NIR, v, 35, 37, 38, 39, 40, 41, 42, 45  nitrate, 61, 197  nodes, 85, 86, 87, 88  Norway, 56  nuclear magnetic resonance (NMR), 14, 23, 31  nucleation, 14, 17, 20, 26, 29  nuclei, 14  nutrients, ix, 36, 125, 144, 151, 251  nutrition, 130, 146, 295 

O  obesity, 36  objective criteria, 152  oil, 18, 30, 32, 33, 82, 154, 160, 168, 185, 196, 252,  257, 280, 284, 285, 286, 287, 288  oligosaccharide, xi, 289  omega‐3, 135  opacity, 253  opportunities, 147  optical properties, 33, 251, 253  optimization, 48, 49, 63, 286, 312  optimization method, 48  organ, 251  organic compounds, 282  organic solvents, 287  oscillations, 108  osmotic pressure, 178, 212, 281  Ostwald ripening, 29  oxidation, ix, 82, 125, 130, 132, 134, 144, 145, 146,  148, 151, 152, 153, 154, 165, 168, 172, 196, 254,  258  oxidation products, 134  oxidative damage, 135  oxygen, xi, 178, 236, 249, 252, 290, 291  ozone, vii 

P  packaging, xi, 18, 168, 237, 249, 250, 251, 252, 253,  254, 255, 256, 257, 258, 259  palm oil, 18, 33, 284  parallel, 17, 103, 267  parallelism, 234  parameter, 21, 51, 63, 88, 92, 109, 114, 116, 118,  153, 163, 190, 194, 207, 208, 242, 268  particle size distribution, 21, 29, 30, 32, 33, 206,  284, 285  pasteurization, 60, 69  patents, 145  pathogens, 121, 249, 296  pattern recognition, 84, 97  peptides, 134, 143, 148  performance, viii, 35, 37, 38, 40, 42, 43, 46, 47, 48,  49, 50, 51, 52, 53, 54, 83, 84, 87, 89, 90, 91, 92,  95, 249  performance indicator, 54  permeability, 135, 144, 248, 252, 253, 278, 303  permeable membrane, 286  permeation, 62, 282  peroxide, 160, 162, 290, 296  PET, 250, 257  phase transformation, 15  phase transitions, 32  PHB, 255  phenol, 137  phospholipids, 285  physical and mechanical properties, 250  physical mechanisms, 262  physical properties, 14, 21, 29, 59, 72, 77, 82, 100,  259, 262, 276, 302, 305  physicochemical properties, xi, 251, 301, 311  physics, 152  physiology, 297  pigments, 122, 125, 134, 135  plants, viii, 81, 82, 84, 296  plasticization, 204  plasticizer, ix, x, xi, 151, 234, 236, 247  plastics, 254  platform, 289  PLS, 38, 39, 43, 46, 56  polarity, 255  pollution, 184  polydimethylsiloxane, 106  polydispersity, 58, 62, 72  polyesters, 250  polyhydroxybutyrate, 255 

321

Index polymer, 59, 60, 62, 63, 64, 67, 113, 249, 250, 251,  252, 253, 254, 255, 256, 257, 287  polymer composites, 253  polymer films, 254  polymer matrix, 65, 249, 251, 252, 253  polymer nanocomposites, 257  polymeric films, 254  polymeric materials, 250, 253  polymerization, 58, 63, 64, 69, 72, 76, 77  polymers, 58, 59, 61, 62, 63, 64, 106, 108, 173, 227,  249, 250, 251, 253, 255, 257, 309  polymorphism, vii, 14, 31, 32, 33  polyolefins, 254  polypropylene, 194, 250, 252, 257, 281  polystyrene, 250  polyunsaturated fat, 134  polyunsaturated fatty acids, 134  polyvinylalcohol, 250  poor performance, 42, 43  porosity, xii, 262, 263, 264, 272, 274, 276, 281, 283,  301, 303, 304, 311  porous materials, 277  positive correlation, xii, 301, 303, 304, 308  postmenopausal women, 143  potassium, 197, 253, 254  potato, 97  poultry, 53, 97, 252  prevention, x, 79, 171, 175, 224  principal component analysis, 44  probability, viii, 57, 77, 82  probability density function, viii, 57, 77  probe, 17, 109, 118, 237, 304  probiotic, x, 171, 227, 230, 285  process control, vii, 36  producers, 60, 77  productivity, 292, 293, 295  project, 247  propagation, 44, 47, 101, 103, 111  propylene, 235  proteins, 36, 122, 130, 131, 135, 143, 146, 223, 224,  226, 284, 285, 288  proteolysis, 211  proteolytic enzyme, 146, 148  PTFE, 281, 284, 288  pulp, 118, 168  pure water, 116, 155, 156, 191, 195  purity, 279, 280  PVC, 44 

Q  quadriceps, 44  qualitative differences, 69  quality control, viii, 16, 21, 22, 25, 36, 53, 99, 100,  116, 119  quartz, 83 

R  radiation, 67, 310  radical polymerization, 63  radiometer, 37, 44  rape, 292  raw materials, viii, xi, 58, 59, 60, 64, 172, 174, 178,  186, 210, 224, 289  RDP, 54  reactants, ix, 58, 151  reaction rate, 294  reactions, ix, 58, 59, 61, 67, 69, 72, 76, 77, 78, 130,  135, 151, 152, 160, 165, 168, 196, 286  reactivity, 58  reading, 45  reagents, 197, 198  real time, 36  reality, 261  recall, 92, 104, 273  recognition, 84, 97  recrystallization, 26  reducing sugars, viii, 57, 58, 59, 60, 61, 67, 77, 97  reflection, 100, 103, 104, 105, 106, 108, 210  reflectivity, 116  refractive index, 61, 63, 66, 67, 77, 78, 114  regression, vii, 35, 36, 38, 46, 54, 96  regression model, 54  rehydration, xi, 301, 302, 308, 311  reinforcement, 303  rejection, 235  reliability, 36, 262  relief, 175  reparation, 256, 298  replacement, 265  replication, 46  requirements, 122, 172, 224, 255  residual error, 303  residuals, 43, 51, 52  resilience, 245  resistance, 15, 152, 158, 165, 167, 203, 242, 243,  244, 245, 249, 283 

322

Index

resolution, 24, 44, 61, 118  resonator, 100, 108  respect, viii, xi, 22, 51, 58, 59, 60, 61, 64, 67, 69, 72,  77, 130, 216, 222, 301  restaurants, 36  retardation, 125, 126  retention volume, 62  retinol, 130  rheology, ix, 31, 99, 100, 113  riboflavin, 125  room temperature, viii, 44, 57, 58, 60, 100, 101,  114, 117, 134, 141, 143, 237, 281  rotations, 237  roughness, 21  Royal Society, 30, 33, 259, 287  rubber, 219, 250 

S  salmon, 131, 146, 149  salts, 255  saturation, 21  savings, 30, 82  scaling, 82  scanning calorimetry, 14, 23  scanning electron microscopy, 26  scattering, 21, 22  scavengers, 78  schema, 180, 181  screening, 42, 50, 52, 53, 54, 194  seafood, 134, 252  SEC, viii, 47, 49, 51, 52, 53, 57, 59, 63, 64, 65, 77, 78  second generation, 252  SECV, 42  seed, 14, 17, 18, 26  sensing, 36, 251  sensitivity, 61, 125, 176  sensors, 44, 45, 104, 112, 113  sewage, 250  SFS, 85, 91, 92, 93, 94  shape, 17, 18, 21, 84, 194, 213, 244, 283, 302  shear, viii, 14, 17, 32, 39, 99, 100, 101, 102, 103,  104, 105, 106, 107, 109, 110, 111, 112, 115, 116,  186, 223, 237, 255, 282, 283, 284, 285  shear rates, 255  shear strength, 39  shock, 244  shrinkage, 265, 302  signal processing, 104, 107  signals, 61, 107 

silica, 106, 108, 284, 287  silicon, 83, 187, 210, 283, 288, 291  skeleton, 283  skin, xi, 82, 84, 97, 168, 234, 239, 241, 242, 245, 246  sodium, 197, 253, 254, 299  software, 48, 63  solid matrix, 303  solidification, 18, 19  solubility, 254, 282  solvents, 287  sorption, ix, 151, 152, 153, 154, 155, 156, 158, 159,  160, 163, 164, 165, 166, 167, 168, 169, 170, 257  sorption isotherms, 153, 154, 163, 167, 168, 169  sorption kinetics, 152, 153, 154, 158, 159, 165  sorption process, 152, 153, 159, 166, 169  soybeans, 143, 146  soymilk, 135, 146  space, 82, 84, 222, 235, 290  Spain, 233, 236  species, 60, 63, 76, 176, 177, 223, 224, 226, 230,  276, 279, 280, 292  specific heat, 58, 59, 63, 77, 262, 266, 270, 271, 273,  275, 276  specifications, 174, 188, 209, 210, 225, 226, 272  spectrophotometer, 66  spectroscopy, 32, 36, 42, 52, 53, 97  sponge, 308  stabilization, 254  stabilizers, 153  standard deviation, 42, 47, 49, 53, 241  standard error, 42, 47, 50, 53  starch, x, 163, 172, 173, 234, 236, 237, 245, 250,  251, 255, 256, 257, 289, 293, 294, 299, 301, 302,  308, 309, 310  starch blends, 256  starch granules, 308, 309, 310  starch polysaccharides, 301  statistics, 63  steel, 108, 109, 202, 210, 294, 295  sterile, 217  stomach, 285  storage, viii, ix, 14, 15, 18, 20, 29, 30, 31, 32, 58, 59,  76, 105, 106, 112, 118, 119, 123, 124, 127, 128,  129, 131, 132, 133, 134, 135, 137, 138, 140, 141,  142, 143, 144, 145, 146, 147, 151, 152, 153, 154,  160, 167, 168, 169, 172, 175, 176, 195, 197, 209,  214, 215, 221, 222, 223, 224, 225, 226, 227, 237,  244, 250, 253, 254, 262, 285, 298  strategy, 17, 88  strategy use, 88 

323

Index strontium, 197  structural changes, 122, 135, 144  substrates, 176, 295  sucrose, 33, 60, 125, 127, 252, 296, 297  sugar beet, 169, 296  Sun, 119, 122, 133, 146, 149, 236  supply chain, 16, 18, 30  surface area, 173, 186, 244, 283, 308  surface properties, 287  surfactant, 252  survival, 173, 174, 176, 218, 220, 221, 222, 224, 225,  227  survival rate, 173, 174, 176, 218, 220, 221, 222, 224,  225  swelling, 160, 308, 309  symptoms, 143, 175  synthesis, xi, 289, 296, 297  synthetic polymers, 58, 173, 251 

T  tanks, 119  tannins, 58  temperature dependence, 155, 192  test data, 49, 95  testing, x, 44, 45, 46, 152, 234, 246  textural character, 235  texture, vii, x, xi, 13, 14, 18, 20, 21, 29, 31, 118, 121,  122, 130, 147, 152, 160, 164, 233, 234, 235, 239,  242, 243, 246, 247, 254, 291, 301, 302, 303, 304,  305, 308, 311  Thailand, 81, 301  thermal analysis, 152  thermal expansion, 274  thermal properties, xi, 257, 261, 262, 265, 276, 278  thermal resistance, 249  thermal treatment, viii, 58, 67, 79, 141, 142, 146  thermodynamic equilibrium, 18, 152, 153  thermodynamic parameters, ix, 151  thermodynamic properties, 152, 154, 160  thermodynamics, 152, 154, 234  thermograms, 24, 25  tics, 180, 181  time periods, 143, 153  tissue, 118  topology, 48  toxicity, 143  TPA, 304  training, 47, 48, 49, 82, 83, 84, 87, 88, 89, 90, 91, 92,  93, 95 

transducer, 103, 104, 106, 107, 115  transformation, 29, 32, 51  transformations, 15, 17, 18, 20, 25, 46, 51, 60  transition temperature, 115, 154, 169, 212, 219  translation, 229  transmission, 44, 82, 83, 84, 103, 104, 253  transmittance spectra, 84  transparency, 253  transport, 302  trial, 46, 47, 48, 188  triglycerides, 15, 32  trimmings, 44  trypsin, 148  tryptophan, 58  tumor, 97  tumors, 82  tungsten, 45, 83 

U  ultrasound, 61, 100, 101  uniform, 45, 122, 209, 283  United Nations, 261, 277  universal gas constant, 244 

V  vacuum, x, 132, 134, 175, 178, 179, 183, 185, 186,  188, 190, 191, 210, 225, 228, 233, 234, 235, 236,  237, 239, 242, 246, 247, 281, 282, 286, 302  Valencia, 145, 233, 236  validation, viii, 35, 37, 42, 46, 49, 50, 51, 52, 53, 84,  88, 92  valuation, 118  vapor, 155, 156, 157, 168, 169, 174, 195, 248, 252,  253, 254, 281, 308  variations, vii, 14, 24, 26, 84, 109, 115, 209, 231, 276  vector, 44, 88, 92, 101, 111  vegetable oil, 31, 252, 257, 280, 284, 286  vegetables, ix, 121, 122, 125, 134, 135, 143, 145,  146, 251, 254, 258, 270, 276, 296  velocity, 101, 102, 103, 104, 106, 111, 114, 116, 117,  166  vibration, 108, 194, 283  viscoelastic liquids, 103, 104  viscoelastic properties, 63, 109, 119  viscosity, viii, xii, 18, 58, 59, 61, 62, 63, 76, 77, 99,  109, 112, 186, 192, 193, 214, 219, 242, 285, 286,  301, 308, 309, 311 

324

Index

vision, 97  vitamin C, 134, 135, 143, 145, 148  vitamin E, 254  vitamins, 125, 130, 134, 144, 148, 179, 223, 224,  226, 254  volatility, 281, 282  VSD, 178, 179, 180, 181, 182, 183, 185, 186, 187,  188, 190, 193, 198, 199, 200, 201, 202, 204, 205,  206, 209, 210, 211, 212, 213, 214, 216, 217, 218,  219, 220, 221, 222, 223, 224, 225, 226 

wave vector, 101  wavelengths, 46, 61, 89  weight changes, x, 233, 237  weight control, 18  weight gain, 239  weight loss, 190, 243, 244  weight ratio, 236, 239  wholesale, 118  wood, 60 

X 

W  waste, 184, 290  water absorption, xii, 301, 303  water activity, ix, x, 58, 151, 152, 153, 154, 162, 163,  164, 165, 168, 169, 195, 196, 197, 198, 199, 201,  202, 206, 208, 210, 211, 214, 218, 219, 220, 224,  225, 233, 234, 235, 236, 237, 239, 240, 243, 244,  246  water diffusion, 308  water evaporation, 183, 184, 199, 200, 202  water sorption, ix, 151, 163, 164, 165, 166, 168, 169,  170, 257  water vapor, 169, 195, 248, 252, 253, 254, 308  wave number, 102  wave propagation, 101 

x‐ray, xii, 25, 301, 310, 311  x‐ray diffraction (XRD), 25, 310 

Y  yeast, 114, 254  yolk, 285, 290 

Z  zinc, 254  zippers, 214, 216  zirconia, 284