Nondestructive Testing of Food Quality (Institute of Food Technologists Series)

Nondestructive Testing of Food Quality EDITORS Joseph Irudayaraj r Christoph Reh Nondestructive Testing of Food Qual...

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Nondestructive Testing of Food Quality

EDITORS

Joseph Irudayaraj r Christoph Reh



The IFT Press series reflects the mission of the Institute of Food Technologists – advancing the science and technology of food through the exchange of knowledge. Developed in partnership with Wiley-Blackwell, IFT Press books serve as leading edge handbooks for industrial application and reference and as essential texts for academic programs. Crafted through rigorous peer review and meticulous research, IFT Press publications represent the latest, most significant resources available to food scientists and related agriculture professionals worldwide.

IFT Book Communications Committee Dennis R. Heldman Joseph H. Hotchkiss Ruth M. Patrick Terri D. Boylston Marianne H. Gillette William C. Haines Mark Barrett Jasmine Kuan Karen Nachay

IFT Press Editorial Advisory Board Malcolm C. Bourne Fergus M. Clydesdale Dietrich Knorr Theodore P. Labuza Thomas J. Montville S. Suzanne Nielsen Martin R. Okos Michael W. Pariza Barbara J. Petersen David S. Reid Sam Saguy Herbert Stone Kenneth R. Swartzel


EDITORS

Joseph Irudayaraj r Christoph Reh

Joseph Irudayaraj, PhD, is an associate professor of Agricultural and Biological Engineering at Purdue University, West Lafayette, IN. With over 15 years of research and teaching experience in biological and food engineering, Dr. Irudayaraj has been a faculty member at the University of Saskatchewan, Utah State University, and Penn State. His current role at Purdue is to develop micro and nanosensors for food, health, and environmental applications. Christoph Reh, PhD, is a research scientist at Nestlé Research Center, Lausanne, Switzerland working on scientific projects for innovative beverage concepts. Prior to his appointment he was involved for more than 10 years in process analytics including non-destructive testing for factory application and physico-chemical characterization of foods. C 2008 Blackwell Publishing and the Institute of Food Technologists All rights reserved Chapter 7 copyright is held by Malvern Instruments, Ltd.

Blackwell Publishing Professional 2121 State Avenue, Ames, Iowa 50014, USA Orders: Office: Fax: Web site:

1-800-862-6657 1-515-292-0140 1-515-292-3348 www.blackwellprofessional.com

Blackwell Publishing Ltd 9600 Garsington Road, Oxford OX4 2DQ, UK Tel.: +44 (0)1865 776868 Blackwell Publishing Asia 550 Swanston Street, Carlton, Victoria 3053, Australia Tel.: +61 (0)3 8359 1011 Authorization to photocopy items for internal or personal use, or the internal or personal use of specific clients, is granted by Blackwell Publishing, provided that the base fee is paid directly to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923. For those organizations that have been granted a photocopy license by CCC, a separate system of payments has been arranged. The fee codes for users of the Transactional Reporting Service are ISBN-13: 978-0-8138-2885-5/2008. First edition, 2008 Library of Congress Cataloging-in-Publication Data Nondestructive testing of food quality / edited by Joseph Irudayaraj and Christoph Reh. – 1st ed. p. cm. – (IFT Press series) Includes bibliographical references. ISBN-13: 978-0-8138-2885-5 (alk. paper) ISBN-10: 0-8138-2885-6 (alk. paper) 1. Food–Quality. 2. Food industry and trade–Quality control. 3. Food adulteration and inspection. I. Irudayaraj, Joseph, 1961– II. Reh, Christoph. III. Series. TP372.5.N66 2008 664 .117–dc22 2007023792 The last digit is the print number: 9 8 7 6 5 4 3 2 1

Titles in the IFT Press series r Accelerating New Food Product Design and Development (Jacqueline H.P. Beckley, Elizabeth J. Topp, M. Michele Foley, J.C. Huang and Witoon Prinyawiwatkul) r Biofilms in the Food Environment (Hans P. Blaschek, Hua Wang, and Meredith E. Agle) r Calorimetry and Food Process Design (Gönül Kaletun¸c) r Food Ingredients for the Global Market (Yao-Wen Huang and Claire L. Kruger) r Food Irradiation Research and Technology (Christopher H. Sommers and Xuetong Fan) r Food Risk and Crisis Communication (Anthony O. Flood and Christine M. Bruhn) r Foodborne Pathogens in the Food Processing Environment: Sources, Detection and Control (Sadhana Ravishankar and Vijay K. Juneja) r High Pressure Processing of Foods (Christopher J. Doona, C. Patrick Dunne, and Florence E. Feeherry) r Hydrocolloids in Food Processing (Thomas R. Laaman) r Microbiology and Technology of Fermented Foods (Robert W. Hutkins) r Multivariate and Probabilistic Analyses of Sensory Science Problems (Jean-Francois Meullenet, Rui Xiong, and Chris Findlay r Nonthermal Processing Technologies for Food (Howard Q. Zhang, Gustavo V. Barbosa-Canovas, V.M. Balasubramaniam, Editors; C. Patrick Dunne, Daniel F. Farkas, James T.C. Yuan, Associate Editors) r Nutraceuticals, Glycemic Health and Diabetes (Vijai K. Pasupuleti and James W. Anderson) r Packaging for Nonthermal Processing of Food (J. H. Han) r Preharvest and Postharvest Food Safety: Contemporary Issues and Future Directions (Ross C. Beier, Suresh D. Pillai, and Timothy D. Phillips, Editors; Richard L. Ziprin, Associate Editor) r Processing and Nutrition of Fats and Oils (Ernesto M. Hernandez, Monjur Hossen, and Afaf Kamal-Eldin) r Regulation of Functional Foods and Nutraceuticals: A Global Perspective (Clare M. Hasler) r Sensory and Consumer Research in Food Product Design and Development (Howard R. Moskowitz, Jacqueline H. Beckley, and Anna V.A. Resurreccion) r Thermal Processing of Foods: Control and Automation (K.P. Sandeep) r Water Activity in Foods: Fundamentals and Applications (Gustavo V. BarbosaCanovas, Anthony J. Fontana Jr., Shelly J. Schmidt, and Theodore P. Labuza) r Whey Processing, Functionality and Health Benefits (Charles I. Onwulata and Peter J. Huth)

Contents

Contributors Preface

ix xiii

Chapter 1. An Overview of Nondestructive Sensor Technology in Practice: The User’s View Christoph Reh Chapter 2. The Influence of Reference Methods on the Calibration of Indirect Methods Heinz-Dieter Isengard Chapter 3. Ultrasound: New Tools for Product Improvement ˙ Ibrahim Gülseren and John N. Coupland Chapter 4. Use of Near Infrared Spectroscopy in the Food Industry Andreas Niemöller and Dagmar Behmer

1

33

45

67

Chapter 5. Application of Mid-infrared Spectroscopy to Food Processing Systems 119 Colette C. Fagan and Colm P. O’Donnell Chapter 6. Applications of Raman Spectroscopy for Food Quality Measurement Ramazan Kizil and Joseph Irudayaraj

143

Chapter 7. Particle Sizing in the Food and Beverage Industry 165 Darrell Bancarz, Deborah Huck, Michael Kaszuba, David Pugh, and Stephen Ward-Smith vii

viii

Contents

Chapter 8. Online Image Analysis of Particulate Materials Peter Schirg Chapter 9. Recent Advances in Nondestructive Testing with Nuclear Magnetic Resonance Michael J. McCarthy and Young Jin Choi

197

211

Chapter 10. Electronic Nose Applications in the Food Industry 237 Parameswarakumar Mallikarjunan Chapter 11. Biosensors: A Theoretical Approach to Understanding Practical Systems Yegermal Atalay, Pieter Verboven, Steven Vermeir, and Jeroen Lammertyn

283

Chapter 12. Techniques Based on the Measurement of Electrical Permittivity Malcolm Byars

321

Index

339

Contributors

Yegermal Atalay (11) Division Mechatronics, Biostatistics and Sensors, Department of Biosystems, Katholieke Universiteit Leuven, Willem de Croylaan 42, B-3001 Leuven, Belgium Darrell Bancarz (7) Malvern Instruments Ltd., Grovewood Road, Enigma Business Park, Malvern, Worcestershire, WR14 1XZ, United Kingdom Dagmar Behmer (4) Bruker Optik GmbH, Rudolf-Plank-Str. 27, 76275 Ettlingen, Germany Malcolm Byars (12) Process Tomography Ltd., 86, Water Lane, Wilmslow, Cheshire, SK9 5BB, United Kingdom Young Jin Choi (9) Department of Food Science and Biotechnology, San 56-1, Sillim-dong, Gwanak-gu, Seoul 151-742, Republic of Korea John Coupland (3) Department of Food Science, 103 Borland Lab, The Pennsylvania State University, University Park, PA 16802, USA Colette Fagan (5) Biosystems Engineering, UCD School of Agriculture, Food Science and Veterinary Medicine, Earlsfort Terrace, Dublin 2, Ireland

ix

x

Contributors

˙ Ibrahim Gulseren ¨ (3) Department of Food Science, 103 Borland Lab, The Pennsylvania State University, University Park, PA 16802, USA Deborah Huck (7) Malvern Instruments Ltd., Grovewood Road, Enigma Business Park, Malvern, Worcestershire, WR14 1XZ, United Kingdom Joseph Irudayaraj (6) 225 S. University Street, Purdue University, West Lafayette, IN 47907, USA Heinz–Dieter Isengard (2) University of Hohenheim, Institute of Food Science and Biotechnology, Garbenstr. 25, D-Stuttgart, Germany Michael Kaszuba (7) Malvern Instruments Ltd., Grovewood Road, Enigma Business Park, Malvern, Worcestershire, WR14 1XZ, United Kingdom Ramazan Kizil (6) Istanbul Technical University, Chemical Engineering Department, Maslak, 34469 Istanbul, Turkey Jeroen Lammertyn (11) Division Mechatronics, Biostatistics and Sensors, Department of Biosystems, Katholieke Universiteit Leuven, Willem de Croylaan 42, B-3001 Leuven, Belgium Parameswarakumar Mallikarjunan (10) 312 Seitz Hall, Virginia Tech, Blacksburg, VA 24061, USA Michael J. McCarthy (9) Department of Food Science and Technology, University of California– Davis, Davis, CA 95616-8598, USA Andreas Niemoeller (4) Bruker Optik GmbH, Rudolf-Plank-Str. 27, 76275 Ettlingen, Germany

Contributors

xi

Colm O’Donnel (5) Biosystems Engineering, UCD School of Agriculture, Food Science and Veterinary Medicine, Earlsfort Terrace, Dublin 2, Ireland David Pugh (7) Malvern Instruments Ltd., Grovewood Road, Enigma Business Park, Malvern, Worcestershire, WR14 1XZ, United Kingdom Christoph Reh (1) Nestle research Center, Vers-Chez-les-Blanc, CH-1000 Lausanne, Switzerland Peter Schirg (8) PS Prozesstechnik GmbH, Novartis Areal, K-970.1, CH-4002 Basel, Switzerland Pieter Verboven (11) Division Mechatronics, Biostatistics and Sensors, Department of Biosystems, Katholieke Universiteit Leuven, Willem de Croylaan 42, B-3001 Leuven, Belgium Steven Vermeir (11) Division Mechatronics, Biostatistics and Sensors, Department of Biosystems, Katholieke Universiteit Leuven, Willem de Croylaan 42, B-3001 Leuven, Belgium Stephen Ward-Smith (7) Malvern Instruments Ltd., Grovewood Road, Enigma Business Park, Malvern, Worcestershire, WR14 1XZ, United Kingdom

Preface

During the last few years, nondestructive testing of food quality has drawn increasing attention by the food industry and research institutions. Based on the overwhelming need and the motivation provided by the success of the past Institute of Food Technologists (IFT) symposia on food quality testing and measurements, we brought together scientists and engineers from academia and industry to provide their perspectives on nondestructive testing methods. When preparing the book we realized the opportunity that nondestructive testing has provided to food science and food technology. On one hand, the food industry is now able to automate a large number of production control analyses, allowing the reduction of analytical costs, improving processes, and increasing product quality to meet the quality standards and regulations as well as customer satisfaction. Because of nondestructive testing methods, it is now possible to follow food products during processing without disturbing the product as a result of sampling requirements. The improvements were made possible by developments in related technology areas such as computing, optical devices, and miniaturization. The rapid development of CCD optical chips combined with a huge drop in price is a simple example that will attest to this fact. We hope that this book will help people become aware of the different technologies available and increase the impact of nondestructive testing of food in production and research. We leave the readers with the advice that a holistic approach considering process, product, people, and method will always give the best application for nondestructive testing. We are very thankful to all of our authors from academia and industry for giving us their precious time and providing us several interesting perspectives and valuable insights. xiii


Chapter 1 An Overview of Nondestructive Sensor Technology in Practice: The User’s View Christoph Reh

Introduction This introductory chapter describes the area where nondestructive food testing is relevant and why it is considered to be an area of increased interest. This chapter should give an idea of the main drivers of this area of analytics and illustrate the limitations users will face when they will develop new applications. The requirements of a factory application are different from those of the use of nondestructive instrumentation in the field, on the farm, in a warehouse, in the supermarket, in central laboratories, or even for specific research purposes. The underlying argument of this chapter is that the understanding of the operation of the applied sensor is important to validate the application. Often the simple use of nondestructive instruments lets the user believe that the analysis performed is relevant and valid. However, in reality, it might not be so.

Why Do We Need Nondestructive Testing to Increase Food Quality? The success of nondestructive instrumentation in the food industry is driven by several considerations. Despite the often significant investment, more and more installations are beneficial to the operator because of their good implementation. This can only be achieved when the target 1

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environment is well analyzed to fit the desired equipment in the optimum manner into plant operations. The planning phase is probably most important since the implications of people, instrumentation, methodology, required material, environment, and management are identified and translated into specifications. Success can be planned, and many difficulties can be avoided if the final installation is well understood from the start. The underlying drivers for using nondestructive instruments are either cost reduction or improved operations. During the evaluation phase, often only direct cost reductions and investments are considered. Because investments are often significant, the benefits are not always completely seen at the start of the project. The principal advantages of online applications are reduction of the analysis time, reduction of the cost of analysis, shortening of the release time, and, as a consequence, lowering of production costs. Additionally, operators can improve their process understanding, control of the process, and, as a consequence, the first time quality as a result of improved product consistency. Nondestructive testing equipment can be widely used throughout the food industry. The following areas are the most relevant: 1. 2. 3. 4. 5.

Raw material control in the field or at the factory reception Process control either online or off-line after sampling Rapid analysis of intermediate or final products in the laboratory Product development and storage testing Research

Raw Material Raw material is of great importance for the food industry. To keep the stock in the warehouse, ingredients are often delivered just-in-time. This requires very rapid release procedures forcing companies to apply rapid nondestructive testing widely. Other drivers of this trend are the increased consumer demand for fresh products. This results in much of the industry shortening the chain between the farm and the consumer. Wherever time can be cut out of the supply chain, the consumer will benefit. Another aspect is the relatively narrow specifications of raw materials required for more and more products. An integral part of nondestructive testing at raw material reception often ensures compliance

An Overview of Nondestructive Sensor Technology in Practice

3

Figure 1.1. Online near infrared analyzer Corona to perform compositional analysis in food production (Carl Zeiss GmbH, Jena).

with specifications set. The procedure leads in consequence to reduced product losses that are a result of more narrowly controlled specifications. In the longer term, an improvement of the consumer-perceived product quality is observed. Raw material control is therefore even extended into agricultural production. Ingredients can be oriented for their optimum use based on their on-site quality assessment. Process Control Process control can be done either online or off-line. Under online analysis, we normally understand that no human sampling is involved in the measurement process. We further differentiate direct and bypass solutions. In a direct measurement, the instrument does not affect the process, and the product is directly placed in the process line, a storage tank, or a mixing operation. Figure 1.1 is a typical installation of an online analyzer in direct measurement showing a diode-array near infrared spectrometer type Corona from Carl Zeiss GmbH (Jena, Germany). A bypass instrument is placed in a bypass loop to which the product is diverted in order to perform the measurement. The product is then returned to the line after measurement. This is applied

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when the measurement instrument requires much defined measurement conditions, which cannot be achieved directly in the process. Examples are requirements for a specific distance between a transmitter and receiver, the collection of sufficient product, or the compaction of the product. In some cases, the product is discarded, which is uncharacteristic of nondestructive testing. Nevertheless, these applications might still be very beneficial because they have significant advantages in terms of reduction of the use of chemicals, reduction of the influence of humans, and very short measurement times. The reduction or elimination of the use of chemicals is a strong driver for using indirect nondestructive techniques. To avoid any risk, chemicals are often banned in areas where they come into contact with the food. The alternative to online analysis is the measurement either off-line, at-line, or near-line. In all of these applications, a sampling procedure from the process line toward the instrument is required. The instrument can be either located next to the line, in a production laboratory, or in a central laboratory. The sampling procedure is in all cases a risk for the quality of the analytical result. Operator influence is considered to be a major source of error. Another influence can be the physical modifications a product undergoes before it is measured. On the other hand, it is often easier to install an off-line nondestructive installation because normally a standard instrument setup can be used. Final Products The rapid analysis of intermediate or final products in the laboratory is from a measurement point of view very similar to off-line analysis. The motivation for this type of application is to increase the efficiency of the analysis required for the release of these products. This is especially advantageous if a large number of samples need to be screened. Typical drivers are also cost reductions because of reduced cost of analysis and the reduction of chemicals used in the laboratory. Product Development For product development and storage testing, nondestructive testing can be an advantage because of the ability to follow the properties of one single product over time. The majority of traditional analyses in the food


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industry are based on destructive procedures, and it is not always certain that all products are exactly the same. By following a single product, the evolution can often be established more precisely. Research The interest of research groups to apply nondestructive analysis is similar to the one mentioned for product development. The ability to follow one single product during a process or during its shelf life gives a huge advantage compared to traditional testing procedures. Even more, it gives the ability to follow changes, which could not be detected using traditional approaches. The development of the area of nondestructive analysis for food research is principally driven by the improved resolution of sensors and the mathematical capabilities of today’s computers. In the case of food, this allows researchers to follow processes such as drying, cooking, baking, crystallization, homogenization, gellification, or agglomeration.

Changes in the Food Industry and Consequences for the Use of Sensors The food industry is undergoing a significant process of consolidation. This typically results in an increase in the size of the production facility allowing the operator better use of the installation. It is obvious that real-time online analysis or rapid near-line analysis based on nondestructive techniques leads to clear benefits for the operator. One of the observed trends is the increased automation of production. This allows an increased use of online instrumentation and especially of nondestructive instruments. The following list gives some of the advantages: r r r r r r r

Improved product quality Less downtime between production cycles Reduction of waste Increase of capacity Improved operational security Better use of energy and resources Shorter holding time of raw materials and finished products

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A large number of production facilities are still labor intensive with a low level of automation. There is a strong tendency to reduce the human influence on the product by having more continuous or automated batch processes. This is only possible if the lot sizes are sufficiently big to generate an economical advantage. Another driver is the increased demand for traceability of the production. With an automated process and integrated sensors for measuring key attributes, the product quality at any point in time can be mapped. More and more, products receive a time coding, which often can be traced back to the data collected during production. On the other hand, not all product parameters can be measured automatically. Whereas the principal chemical composition and some physical product aspects can be measured by nondestructive instruments, the sensory characterization of the product still requires human testing. Despite massive efforts to develope electronic noses or electronic tongues, only very few applications are used industrially for product release. It is more common to use the available measurement capabilities to optimize the process and to keep the process conditions in an operating range where the required sensory parameters are delivered. The products are controlled for their key sensory aspects by a panel of experienced people for release purposes. This procedure is unlikely to be changed in the coming years because minor components or modifications can lead to significant changes in the product. It should be pointed out that contrary to the chemical and pharmaceutical industries, food products are generally less defined regarding their chemical composition. The majority of the raw agricultural products have quite a wide specification. Additionally, not all processes in the food industry are understood in detail. This is especially true for all aspects related to aroma and taste of food because of the very complex chemistry occurring during processing. Additionally, if the product at the time of production is not yet in physical and chemical equilibrium, it does not provide the characteristics the consumer perceives.

What Are the Central Elements of Successful Use? The successful use of nondestructive instrumentation relies on a complete understanding of the environment. The nondestructive technology


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and its technical capabilities are only one piece. The following elements are of central importance: 1. 2. 3. 4. 5. 6.

Staff Instrumentation Method Consumables Place of installation Management

Involving the Right Staff Staff with adequate training is often key to installing a nondestructive testing instrumentation. This is normally not an issue for an academic application where the instrument is often a central part of the research. When used in an industrial environment, this issue becomes more critical. Often management considers nondestructive testing equipment easier to use and, therefore, assumes that normal factory staff should be easily able to install and operate the application. This assumption is especially incorrect for the period between the selection of the equipment and its installation. It might be true for the operational phase as long as the staff is well trained to perform the maintenance of the instrument. To develop correct specifications, to define adequate methods, and to find the correct location, a global understanding of the task is required. The most difficult applications are normally online applications because the process defines quite a number of parameters affecting the measurement. Often it is best to bring together a team covering production, engineering, quality assurance, and product development to correctly plan and install the equipment. After training of the operational staff, outside help will be required on an occasional basis.

Specifying the Instrumentation The specification process is crucial for the success of any large investment in analytical equipment, and this is especially true for nondestructive testing installations. As already mentioned, it would be

8


preferable to run this process with a team of people with different expertise. The specification process is very well described by Bedson (1996). This paper gives guidance on how to perform equipment validation for any analytical instrument. In practice, it is very difficult to give general advice for the installation process covering a large number of different technologies because the critical points can vary from one technology to another. It is therefore extremely useful to consider the introduction of a nondestructive application as a process with a generic structure. Bedson (1996) developed guidance for the equipment qualification process, including the following four stages: 1. 2. 3. 4.

Design qualification Installation qualification Operational qualification Performance qualification

Design Qualification Design qualification covers all tasks related to planning and selecting the application, including development of the specifications leading to selection of the supplier. The choice to develop an application should start from a clearly defined need for a certain measurement. The design qualification should lead to the development of instrument specifications, which will be the basis of the relationship with the instrument supplier. These specifications strongly depend on where the instrument will be deployed. For process control, an optimum performance within a relatively small range of variation of the targeted parameter might be targeted. For a research application, one will choose an instrument with high flexibility regarding its range of application. Apart from purely instrument-related specifications, one should also define the requirements related to staff, methods, installation, and consumables. Installation Qualification The next step of the process is installation qualification covering all of the procedures related to installation of the equipment in its place of use. One of the most time-consuming exercises can be calibration, especially when process parameters are measured indirectly with techniques such


9

as near infrared spectroscopy, refractometry, microwave absorption, or similar techniques. To calibrate correctly the choice of a well-adapted reference method is often critical. Other issues in this context are reference laboratory performance, sampling procedures, and generation of a sample set covering the calibration range required. In a production environment, the variation in concentration of one naturally occurring ingredient can be quite small. Sometimes the range needs to be extended to develop a stable calibration. In other cases, physicochemical changes originating from the production process can affect either the reference analysis or the nondestructive technique. These are some of the problems which should be explored by the team during either installation or operational qualification. Operational Qualification Operational qualification is required for the instrument to operate under defined conditions. This step is usually less critical when the two earlier steps have been performed well. It is obvious that a better specification of an application will lead to fewer surprises at this stage. It is important to accurately document all actions that have been performed. The better the documentation, the easier required actions can be identified. Performance Qualification The final step of the described process is performance qualification where one has to demonstrate that the installation performs according to the specifications set at the beginning of the process. For process equipment, the focus of this validation process is assessment of the precision, accuracy, and robustness of the instrumental setup. It should be pointed out that the qualification process does not stop after the four qualification steps have been completed. Instruments will have to undergo requalification after any significant change such as: r r r r r

Change of place of installation Modification of the instrument or the operating software Replacement of parts Maintenance Modification of product or process

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The degree of this requalification is strongly dependent on the type of equipment and the gravity of the change. Based on experience, during the qualification process, a protocol should be established for the most common changes. Changes related to the equipment often can be estimated based on validation studies. This can be best illustrated for the place of installation. Presentation of the food product to the equipment is often the most critical parameter for the overall performance of the equipment. During installation and calibration, aspects such as the distance of the sensor to the product, aperture of a measurement window, or angle of measurement are often assessed and documented. These data are very helpful to define the critical parameters for requalification. In the case of maintenance, repair, or upgrade, requalification is mainly targeted toward assessing the equal functioning of the new component. For most of these changes, the equipment supplier normally provides a testing procedure. It is important to ensure the presence of these tests during design qualification because it limits the time required for requalification. Because measurement equipment is normally maintained by the quality assurance department, changes in the product or process are often overlooked or their influence on the measurement is underestimated. Compositional changes due to recipe adaptations or even natural variations of raw materials can cause differences in the results. Other sources of difference can be changing operating conditions of ovens, mixers, homogenizers, and other processing devices. The effect of homogenization of milk on the measurement of fat and solid-non-fat (SNF) by mid-infrared spectroscopy is widely known and studied. This example will be discussed in more detail later in this chapter. For the reliable composition analysis of powders by near infrared spectroscopy, particle size is a critical parameter. Particle size distributions of powders often vary as a result of the operating conditions of mills, spray dryers, or agglomerators. This influence can be limited either by including the variation of the particle size in the calibration model or by better controlling the operating conditions of the process unit. In reality, an approach taking both factors into account will probably be chosen. This illustrates that due to the introduction of nondestructive measurement, variation of the process and, as a consequence, of the product, can be detected and fixed. This leads then to an improved definition of the product and production with higher consistency.

An Overview of Nondestructive Sensor Technology in Practice 11 Defining the Method Definition of the method is complementary to instrument specification and focuses more on the operational aspects of the application. This work should be done just after a first decision has been determined on what technology will be applied for the testing procedure. It might be that the required method procedures are part of the final decision of what technology will be applied. Defining the method is the outcome of the equipment qualification process mentioned under instrument specification. It is very important to translate all of the knowledge collected into an actionable method. The individual steps need to be well defined and documented to ensure long-term application by the operator. Operator training must include generating alertness to the critical points of any nondestructive testing application.

Ensuring the Supply of Consumables The permanent availability of consumables is probably the easiest point to achieve. The importance of this point is the operational availability of nondestructive testing instrumentation. Especially in the case of online applications, an outage can lead to significant losses in production. This point is especially relevant in countries or regions where after-sales support from the supplier is limited or slow. Apart from a service guarantee from the equipment supplier, it is often advantageous to perform some of the maintenance in-house with your own staff. In this context, the availability of all consumables required for the normal operation of the application should be kept in stock. This could even include equipment parts in order to be able to perform any repairs that have a higher risk of happening. Training of the operator for these tasks needs to be additionally considered.

Identifying the Place of Installation The place of installation refers to technical and operational parameters. The technical parameters are driven by the technology used for the

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nondestructive testing procedure. Almost all technologies have technical limitations in order to achieve an optimal result. These limitations will be covered in more detail in the chapters on major technologies used in the food industry. At this point it should be mentioned that in the case of nondestructive testing, normally the measurement setup should be adapted to the product and, in the case of production control, to the process line. Therefore, nondestructive instruments are often used after sample preparation. Sample preparation is applied when the outcome of the testing procedure can be improved by optimizing sample presentation to the equipment. It might be, therefore, important to decide if sample preparation might lead to a better result. To clarify the technical impact of the place of installation on the quality of the measurement, thorough knowledge of both aspects is required. Sample presentation of the measurement technology needs to be adapted to produce reliable and actionable results. The instrument setup needs to base its result on a representative sample. Parameters such as sample volume or measurement time are very important in this context. The operational aspects are most often more important than the technical aspects because the economical advantage is principally driven by optimization of the operation. It is of key importance to measure or control in order to be able to correct. But it is of limited use to measure a parameter that can no longer be changed when the result of the testing procedure comes available. There might be an advantage to automating a testing procedure used for product release. Nevertheless the gain in determining a process parameter, which can be used for process control, will lead to important savings for the operator.

Getting Management Support Management support is the ultimate requirement for successful installation of nondestructive instruments. Automated analysis of product quality control often requires a change in mind-set. It goes along with significant changes on the production floor and in the factory laboratory. This affects the day-to-day work of the staff involved in the specific area and can completely change the way product quality is approached. As a consequence of improved product quality monitoring, more proactive intervention in the production process will be required,

An Overview of Nondestructive Sensor Technology in Practice 13 which needs management commitment toward continuous improvement. Often automation is solely seen as a cost reduction exercise, and neither management nor the involved staff is prepared to provide the necessary input to further improve. Cost is the principal driver of any management support. Sometimes management is confronted with an investment decision for equipment without having a good idea of the overall cost of ownership. Nondestructive testing instrumentation is often expensive, therefore, it can be easily overlooked that other savings compensate for the investment. Automated or semiautomated food testing tends to be less labor intensive in routine use and, despite an expensive introduction phase, major savings can be made on salaries. One aspect often overlooked is gain through improved product quality and process reliability. Because these aspects can be related to consumer satisfaction and subsequently to increased sales, the final gain is difficult to evaluate. Practice has shown that quality improvement via process optimization becomes increasingly the driver for using nondestructive testing. Modern sensors provide permanent availability, excellent measurement precision, and short frequency of measurement. As more and more food plants are operated 24 hours a day, this argument becomes increasingly important. Costs, which can be better calculated, are the investment cost, cost of infrastructure, operation cost, and cost of running the project. All critical factors resulting out of the mentioned elements should be translated into specifications to define the requirements to properly operate nondestructive food control. Here is a summary of 10 points that should be well defined before investment: 1. The overall business environment to achieve a significant advantage for the operation. 2. Clear documentation related to the use of the instrument, including easy-to-use operating manuals, identification of versions, protocols for equipment qualification, etc. 3. Well-defined level of skill to operate the instrument and the details for required training. 4. The sample throughput and the sampling details. 5. The requirements for data acquisition, data processing, and transformation of the data into actionable information. 6. The requirement for services, utilities, and consumables, such as electricity, special gases, supports, etc.

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7. The location and its environmental conditions affecting the operation of the instrument. 8. Maintenance and installation of the instrument including aspects, such as calibration, validation, and servicing of the instrument (service contract defining procedures and intervals). 9. Support of the instrument by the supplier or a third party, clarifying points like delay of intervention or the availability of a replacement unit. 10. Health and safety requirements related to the installation, which could either affect the staff or the product.

PAT Initiative—What Can the Food Industry Learn from the Pharmaceutical Industry? The Process Analytical Technology (PAT) initiative (Guidance for Industry 2004) is another interesting development, which should have a longer-term impact on the way the food industry operates. This will be especially relevant for the segment of the food industry covering health food or functional food. This product area has been significantly growing over the last years with segments such as infant products, baby food, sports nutrition, or clinical nutrition. Additionally, other products are fortified with vitamins or minerals, for example, beverages, juices, breakfast cereals, or dairy products. For many products the concentration of these ingredients needs to be ensured by the producer within limits given by legislation. Traditionally, this had been done as in the pharmaceutical industry by very frequent analysis for product release. This practice can be very expensive in the long run and is highly reactive. The food industry has, therefore, moved increasingly toward nondestructive online or at-line applications. This gains time and avoids out-of-norm products. The PAT initiative (Guidance for Industry 2004) states that product quality and performance are ensured through the design of effective and efficient manufacturing processes. As a consequence, product quality and compliance are produced, and not measured, into the product. Specifications need to be based on the understanding of the process and the variability occurring due to the influence of ingredients and process parameters. Continuous real-time quality assurance was, therefore, identified as a key element to help reduce the variability of release

An Overview of Nondestructive Sensor Technology in Practice 15 parameters. The combination of the latest scientific understanding of formulation and manufacturing process and an adequate process control strategy should lead to a continuous improvement of the manufacturing process. Kueppers and Haider (2003) have summarized very well the advantages of process analytical chemistry for industry, which also applies to the food industry. Accordingly on-line process analysis and especially nondestructive food testing allows the following: r r r r

Collection of constant information on process status Identification of problems Validation of the process Improvement of the applied analytical measurement by error reduction

Nondestructive Sensors for Production Control In this and the next part of the chapter, some recently published applications of nondestructive food testing are described, which are or could be used to control food production or could be applied for research purposes. The border between both fields of applications is quite open, and the techniques are often used for both. The examples are chosen to illustrate some of the issues arising during the use of these technologies. It is not the aim to elaborate these techniques in detail or to give a complete overview of all possible applications. These techniques and applications will be discussed in more specific detail later in this book. For production, the way nondestructive food testing is performed is very important and influences the outcome that can be expected. As mentioned earlier, there are three main approaches: r r r

Off-line analytics At-line analytics Online analytics

Off-line Analytics Off-line analytics uses standard laboratory equipment and requires sampling and transport of the food product to the instrument. Placement of the nondestructive equipment in a central laboratory allows the use of

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standard equipment without special adaptation to factory floor use. The biggest disadvantage is the slow response time and, as a consequence, the absence of direct process adaptation. Another disadvantage is often modification of the sample during the sampling, which can be overcome by a good definition of sampling procedure. At-line Analysis At-line analysis requires equipment well defined for the application. An easy, well-defined sampling procedure is essential because the environment near the line and the qualifications of the staff limit complex procedures. Equipment needs to be robust. In contrast to off-line instruments, at-line instruments are always available because of their dedication to a specific application and clear ownership of the operation. At-line analysis allows adaptation of the process conditions dependent on the frequency of sampling. It does not allow real process control. Online Analytics Online analytics is the only way of controlling processes in real time because it allows correlating measurement results, the processing parameters, and product characteristics. Preferably any measurement is done directly, but in some cases, automated sampling is necessary to apply a technology. Online analytics has its highest value for applications targeted to properties that can be easily influenced by the process. Examples are water content of a dryer, particle size distribution after a homogenizer, or composition after a mixer. Probably the most important process parameter in the food industry is the water content. Water has a great influence on the stability of food products during storage, which could be either related to microbiology or be physicochemical. Additionally, narrow regulatory limits are in place because of economical aspects or food safety. A wide range of techniques, such as near infrared (NIR) spectroscopy, mid-infrared (MIR) spectroscopy, refractometry, capacitance sensors, microwave absorption, and nuclear magnetic resonance, has been employed for the nondestructive determination of water during production. NIR is probably the most successful at this time because the water absorption band of 1,940 nanometers (nm) is not as greatly influenced by other variations of a given product. Benson and others (2001) describe very well the specific

An Overview of Nondestructive Sensor Technology in Practice 17

Figure 1.2. Near infrared analyzer MM710 to determine the water content in food (Infrared Engineering).

use of filter-based process analyzers for water determination or moisture determination. Figure 1.2 illustrates a process analyzer, MM710 from Infrared Engineering, Maldon, United Kingdom, used for the online determination of the moisture content in food. Most of the analyzers today are based on the application of a filter-wheel, but in the future we can expect an increase in new technologies like Fourier transform near infrared (FT-NIR) or diode-array-based detectors. The advantage of filter-based technology for process analyzers is their proven robustness and low maintenance. One major issue for the use of near infrared spectroscopy is the need to calibrate the sensor via a reference method. As shown by Reh (2004), the analytical results from the reference method need to link to the signal measured by the sensor, which is, in the case of near infrared spectroscopy, the absorption of the water molecule. Ideally this absorption should not be influenced by the structure of the product on the production line. The quality of the reference method is the most important element for good calibration. We have therefore dedicated a specific chapter in this book to the relation of reference method and calibration of the sensor. In Dionisi and others (1998), reference methods for the

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Nondestructive Testing of Food Quality 5.00

Gauge output

4.50

4.00

3.50

3.00

2.50

2.00 2.00

2.50

3.00

3.50

4.00

4.50

5.00

Reference moisture value (%)

Graph 1.1. Determination of moisture in skimmed milk powder using a near infrared process analyzer.

determination of total fat are reviewed. The total fat content comes second in importance for nondestructive compositional analysis. Because fat is defined via the definition of a reference method, it is sometimes difficult to relate it to an indirect signal. In consequence, variation in the origin of the fat or oil can impact the performance of the measurement. Graph 1.1 presents a typical calibration for moisture determination in skimmed milk powder with an industrial filter based on near infrared process analyzer. Other industrial examples, besides dairy products, are soluble coffee, potato chips, sugar confectionary, tobacco, cereals, grains, pasta, and cookies. The most critical parameters for this application are distance of the sensor from the product, measurement time, sample presentation, and finally, as already mentioned, choice of the reference methodology including sampling. The composition of milk products has been determined for quite some time using milk analyzers using mid-infrared absorption. In the past these instruments were based on filter technology (Lefier et al. 1996), whereas today more and more Fourier transform infrared (FT-IR) technology is applied (Lanher 1996). Filter-based analyzers principally only determine the SNF and fat content of fresh milk. FT-IR analyzers today are used in the production of a variety of milk products, ice cream,


Figure 1.3. Mid-infrared analyzer to control the composition of liquid milk online (Foss Electric).

beverages, and wine. Additional parameters such as protein, several carbohydrates, or certain additives can be quantified. FT-IR analyzers have been used in the dairy industry as process analyzers for some time (Reh 2001). Figure 1.3 shows a ProcesScan FT process analyzer from Foss Electric, Hillerod, Denmark. This instrument quantifies fat, protein, lactose, and water content continuously during production. The instrument is operated in bypass because the FT-IR technology requires a very well controlled sample preparation to provide accurate results. The frequency of 1 measurement per 30 seconds allows optimizing the standardization process and achieving very narrow targets for the final product. Curda and Kukackova (2004) have used NIR to follow processed cheese manufacturing via the assessment of dry matter, fat content, and crude protein content. NIR can be used in combination with fiber optics without any significant sample presentation. On the other hand, variations of the process conditions should be included in the calibration model to avoid false predictions in the case of too large variations of parameters such as temperature or particle size of the fat globules.

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Computer vision is a technology with rapid growth in the food industry. Brosnan and Sun (2004) have reviewed this technology recently and have identified the analysis of meat, fish, pizza, cheese, and bread as major applications. Tan (2004) reviewed specifically the application of computer vision in meat quality evaluation. Based on several results, Tan concluded that color image processing is a useful technique for meat quality evaluation allowing efficient characterization of muscle color, marbling, maturity, and muscle texture. Up to a certain extent, parameters such as textural attributes, sensory scores, and cooked-meat tenderness could be calibrated. One major advantage of the technology compared to human grading is the elimination of inconsistencies between different testers and thereby an increase in consistent quality. Lamb tenderness was predicted by Chandraratne and others (2006) using image surface texture features. Results of texture analysis were correlated with the data from a 3-CCD color digital camera. Results were best using artificial neural networks as expected for complex biological systems with numerous influencing parameters. Other applications are the sorting of agricultural products such as fruits, vegetables, or grains. Leemans and Daestain (2004) reported a sorting method for apples based on image analysis. It illustrates the complexity of the grading process and how difficult it is to fix the border between classes. Definition of the borders between classes becomes even more important for food products, for example, for the automated elimination of cookies from the production line. Hatcher and others (2004) described one application in this area. They applied image analysis to sort oriental noodles with black spots from the production lot. In this case, eliminated product leads most of the time to an operative loss because of the very limited possibilities to validate them differently. In another agricultural work, Kilic and others (2007) developed a classification system for beans using a computer vision system and artificial neural networks based on size and color quantification of the samples. The system was calibrated with a training set of 69 samples and validated with another set of 71 samples. The final system was then tested with another group of samples achieving an overall performance of the system for the classification of beans of about 91%. Vision systems are replacing human inspection and provide in most of the cases better performance at even higher throughput of the lines. Commercially these systems are very useful for products where either the visual aspect of the product is very important or where the safety of

An Overview of Nondestructive Sensor Technology in Practice 21 the products is assured. A typical application is the control of the completeness of food products like pizza. Du and Sun (2005a) studied the spread of pizza sauce and the correct presence of the toppings on a pizza using color vision. Munkevik and others (2007) extended the application of a computer vision system for appearance-based descriptive sensory evaluation of meals. This application illustrates the potential of these systems in replacing or reducing sensorial analysis. Food safety aspects can be the detection of foreign bodies or spoilage by either chemical or microbiological nature. Monitoring should only be seen as an additional assurance because, in general, the design of the production facility has the largest impact on limiting the presence of foreign bodies or avoiding any type of spoilage. The increasing number of applications can be explained by the cost reduction for cameras and computer equipment and the further development of adapted software running these systems. Additionally, cameras able to operate in the infrared region (Wen and Tao 2000) have become available, providing additional information and analytical opportunities. Park and others (2006) illustrated the use of hyperspectral imaging or imaging spectroscopy for the detection of surface fecal contamination of chicken carcasses. This application illustrates the possibilities of having imaging capabilities combined with spectroscopical information, which could be applied even at quite high processing speeds. Kim and others (2005) studied the automated detection of fecal contamination of apples based on multispectral fluorescence image fusion. The application has been able to detect 100% of the spots on apples artificially contaminated with cow feces. Fluorescence tools are very sensitive and able to detect contaminations not visible to the naked eye. In another application, Katsumata and others (2007) used photoluminescence to evaluate cereals for quality purposes allowing, for example, to differentiate glutinous rice from nonglutinous rice. Noda and others (2006) have used energy dispersive X-ray fluorescence (ED-XRF) to determine the phosphorous content of potato starch. This is of interest because potato starch is rich in starch phosphate, which additionally can be related to textural attributes. The work showed that the starch phosphate content predicted by ED-XRF can be related to peak viscosity of a potato starch paste. The validity of the calibration including 20 samples has been checked with a validation set including 15 samples. In a similar approach, Perring and others (2005) have shown the potential of the use of wavelength dispersive X-ray fluorescence to

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Figure 1.4. ED-XRF analyzer MiniPal 4 to determine minerals in food products (PANalytical).

determine several minerals in infant cereal matrices. They were able to determine sodium, magnesium, phosphorous, potassium, and calcium as macroelements, and magnesium, iron, and zinc as trace elements. It is obvious that the limit of quantification, limit of detection, and the observed repeatability will depend on the levels of the measured mineral and matrice of the product it is in. Figure 1.4 shows an ED-XRF analyzer MiniPal 4 from PANalytical, Almelo, Netherlands, that can be used for the applications described above. Physical parameters are another area increasingly tested and applied in industry, especially because textural attributes are heavily related to consumer perception of food products. In every application, a model has to be developed linking measurable parameters to textural attributes. A number of these applications are described throughout this book. The reliability of these models is often weaker than models for chemical applications for several reasons. In some cases, the instrument measures a signal related to the chemistry of the product, which is then related to a physical observation causing variation of texture. In other applications, the precision of the textural method or physical test might limit the prediction capability of the nondestructive technique. In other cases, in-line measurement and off-line results of reference methodology might not correlate. Singh and others (1997) evaluated a refractometer for total solids, a pycnometer for density, and a pH meter and a viscometer in continuous food processing of fruit preparation and yogurt-based beverages. The viscometer was based on the dampening of oscillation due to


Figure 1.5. Pilot plant installation with in-line particle sizer (see left side of picture) for a fermentation process (Messtechnik Schwartz GmbH, Düsseldorf, Germany).

the changing viscosity. All sensors gave satisfactory results applicable to process control. The first three methods related well to the calibration method. Only the viscosity measurement could not be related because the measured viscosity depends on instrumental setup and measurement conditions. In quite a few cases, the viscosity of food materials changes during the sampling period prior to the reference measurement. Allais and others (2006) used fluorescence spectroscopy to study the relationship among density, color, and texture of ladyfinger batters and biscuits. They were able to relate fluorescence spectra with macroscopic properties, such as density, hardness, and springiness, and the authors concluded the applicability of the method to online industrial application after further validation of the system. It seems that some biological (nicotinamide adenine dinucleotide [NADH] content) or chemical (egg content) parameters are linked to structural attributes, which consequently allows the prediction of texture by spectroscopical methods. Particle sizing is very often overlooked when nondestructive food testing is discussed. We therefore included two chapters tackling this subject. Figure 1.5 illustrates an on-line image analyzer from Messtechnik Schwartz GmbH, Düsseldorf, Germany, able to characterize

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particles in a process. Automated image analysis allows processes, such as crystallization, homogenization, agglomeration, sieving, or dissolution to be followed. Apart from particle size distributions, other aspects such as elongation relationship, sphericity, presence or absence of unexpected particles, abrasion of crystals, or particle shape can be studied. In another example, Hepworth and others (2004) determined the bubble size distributions in beer by applying computer vision. The aim of the research was to develop a system able to follow bubble nucleation, bubble growth, and bubble velocities in beer after it was poured into a glass. Using this method one can optimize beer processing in order to better match consumer expectations. Sometimes more simple instrumental approaches can be used to study food nondestructively. Nunes and others (2006) performed a study of milk using microwave spectroscopy with frequencies between 1 and 20 gigahertz (GHz). The technology allowed milk composition to be roughly determined. Nevertheless, some doubt was raised as a result of the significant variation of the spectra in the case of spoilage or physicochemical variations. Therefore, the equipment can be used very well to study physicochemical modifications caused by microbiological spoilage. In other studies, Tanaka and others (2005) analyzed the dielectric properties of soy sauce and Everard and others (2006) the dielectric properties of processed cheese and could relate them to the composition of the product. In the second study, 16 cheeses were characterized at temperatures from 0.3 to 3 GHz for their dielectric properties at temperatures between 5◦ C and 85◦ C. The results of the statistical model suggests that dielectric measurements can be used as a quality control tool to measure the moisture content and the inorganic salt content of processed cheese. To complete this section on production control, three additional examples should illustrate technologies we have not included in specific chapters in this book. This does not judge their value, and for their use, the same rules apply as for the technologies we have covered in more detail. Bairi and others (2007) described a simple method for the determination of the thermal diffusivity of foods based on an analytical solution of the 1D Fourier equation applied to a cylinder. It is obvious that in the food industry, temperature control during production, storage, and transport is essential. Castillo and others (2005) used a simple-to-use optical sensor technology to measure the whey fat concentration in cheese making. The method is based on the determination of responses

An Overview of Nondestructive Sensor Technology in Practice 25 of light sidescattering and transmission using a fiber optic spectrometer. Specific wavelength ratios then can be used to predict parameters, such as the whey fat concentration. This method can be easily adapted to an industrial process where instrumentation with specific filters could also be applied. In the last example, Chen and others (2004) measured the electric resistance of tubing to detect the fouling of milk production lines and the effect of the cleaning process. This is one application where the placement of the sensor is very important. The sensor should be placed where fouling occurs first and then could be used as an indicator for initiation of the cleaning process.

Nondestructive Sensors for Product Development and Food Research Nondestructive measurement of products during a treatment has been a huge step forward in understanding food processing. It allows following a single product through the process without any sample procedure stopping the process or modifying the measured parameter as a result of the sample preparation. Magnetic resonance imaging (MRI), ultrasound spectroscopy, and dielectric imaging are technologies that are now available to study product changes during cooking, drying, freezing, or thawing. Because of the calculation speed of computers, higher resolution of the equipment, and shorter response time of the sensor a great deal of information can be collected. This often allows the development of models for the analyzed process. MRI could be used for process control requirements but faces the difficulty that it can only be operated through a limited number of materials. Therefore, it has been more successfully applied in studying nondestructive food processes. Lucas and others (2005) characterized the ice gradients in a dough stick during freezing and thawing. This type of application allows the influence of process parameters on water distribution in the dough to be followed and, as a consequence, optimization of the process conditions. Measuring water distribution in a product is almost only possible by nondestructive testing, and MRI is one of the key technologies. Thybo and others (2004) could predict sensory texture attributes of cooked potatoes with nuclear magnetic resonance (NMR) imaging of raw potatoes. The group suggests that MRI relates to the water distribution and some anatomic structures within the raw

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potatoes, which are of importance for the perceived textural properties of the cooked potatoes. Veliyulin and others (2006) used nondestructive NMR imaging to study the bursting of the belly of herrings. It relates to the decomposition of the feed the fish has consumed prior to capture during heavy feeding season. In the case of belly bursts, the fish often is no longer consumable. Xing and others (2007) used NMR imaging to study the drying of pasta by measuring the moisture distribution in pasta. The investigators were able to achieve interesting results on the diffusion of water under different drying conditions, which can be used to understand and optimize industrial drying. Nondestructive texture analysis of porous cereal products was performed by Juodeikiene and Bawsinskiene (2004) using a low frequency acoustic spectrometer. In the study, structural and mechanical properties of cereal products such as wafer sheets, crisp bread, crackers, and ring-shaped rolls were estimated according to amplitude of a penetrating acoustic signal. This type of application is of interest for product development and industrial process control because the measured parameters are of high relevance for quality as perceived by the consumer. Additionally, these texture parameters are easily modified by the processing parameters. In a similar approach, Gan and others (2006) looked at noncontact ultrasonic quality measurements of food products during processing, giving the example of monitoring a coagulation process. Noncontact is a very important innovation for ultrasound applications because in the past the instrument had to be coupled to a process line, limiting these applications to liquid matrices. Resa and others (2007) studied the monitoring of lactic acid fermentation in culture broth using ultrasonic velocity. He demonstrated the use of ultrasound for process control purposes by relating ultrasound velocity and bacterial catabolism. This technique is especially adapted to biotechnological processes because they are often operated as batch processes. Ultrasound velocity has its strength in determining process changes because physical parameters, such as temperature, pressure, or particle size influence the accuracy of the measurement. Other studies looked into monitoring of the milk gelation process (Nassar et al. 2004), the freezing process of food such as gelatin, chicken, or beef (Sigfusson et al. 2004), monitoring of the specific gravity of food batters (Fox et al. 2004), and evaluation of the turgidity and hydration of orange peel (Camarena and Martinez-Mora 2006). These examples show that ultrasound is most useful in the determination of physical changes during processing.

An Overview of Nondestructive Sensor Technology in Practice 27 The quality and authenticity of food products become more and more important due to free trade between countries. Karoui and others (2006) have reviewed the use of nondestructive techniques for the rapid authentication of dairy products. Dairy products are traded in high volumes and relatively high prices and are, therefore, sometimes vulnerable to adulteration. Standard chemical or physical analysis is slow, expensive, and does not always provide all the elements required for proper authentication. Alternative techniques, such as near infrared spectroscopy, midinfrared spectroscopy, front face fluorescence spectroscopy, or nuclear magnetic resonance coupled with chemometrics might not provide all the required information either, but could be a more rapid way of identifying products with significant differences versus a defined standard. Standard analytics can then be used for validation purposes. In another review, Karoui and others (2006) looked at methods to evaluate egg freshness in research and industry. Besides destructive analysis, they identified near infrared, mid-infrared, and fluorescence spectroscopy as potential techniques to screen large numbers of eggs for freshness. For suspicious products, according Karoui and De Baerdemaeker (2006), further physicochemical analysis is required to establish the cause of an out-of-norm classification. In a similar way, these technologies can be used to monitor raw material quality. Another area for application of spectroscopic techniques is the assessment of fruit or vegetable quality. The quality of tomatoes during storage and maturation was studied by van Dijk and others (2006) using kinetic and near infrared models to describe firmness, loss of firmness, loss of moisture, and pectin degrading enzymes. Zude and others (2006) predicted apple fruit flesh firmness and soluble solids content by measurement on the tree and during shelf life. This shows that with the help of miniaturized visible/near infrared (VIS/NIR) spectrometer the analysis can be brought to the field allowing the time of harvest to be defined in real time. Measurement of color changes using video image analysis was used by Lana and others (2006) to describe the effects of temperature during storage and ripening. In a similar approach, Jha and others (2007) used color measurement for the nondestructive evaluation of mango maturity. The quality of the prediction will depend on the information collected by the instrument and if this information describes the predicted parameter, for example, maturity. In the case of mango, the color information seems to be sufficient whereas in other cases information related to the chemical composition collected by near infrared will significantly improve the prediction. FT-Raman spectroscopy was used

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by Kimbaris and others (2006) to quantify unsaturated acyclic components in garlic oil. FT-Raman spectroscopy together with the already mentioned mid-infrared and near infrared spectroscopy is part of the spectroscopic methods mainly focused on chemical composition. Near infrared spectroscopy has its strength in the quantification of major ingredients and can be easily applied to heterogeneous materials. The other two techniques are often used to determine ingredients at lower concentrations because of specificity of the signals. Conclusions This chapter shows the potential of nondestructive food testing for industry and research. It illustrates the huge advancements that have been, and will be, made as a result of advances in instrumentation and automation, and the improved performance of modern sensors. The second argument is especially valid for all imaging technologies where the technological development has been especially strong. It can be foreseen that decreasing prices and the resulting increased use of nondestructive testing equipment will lead to increased applications, and a better understanding of the process, which will result in food products that are safe and improved in quality. Acknowledgement The author would like to express his gratitude to Elizabeth Prior at Nestlé Research Center for her help in finalizing this chapter. References Allais I, R-B Edoura-Gaena, and E Dufour. 2006. Characterisation of lady finger batters and biscuits by flourescence spectroscopy: relation with density, colour and texture, Journal of Food Engineering, 77, pp. 896–909. Bairi A, N Laraqi, and JM Garcia de Maria. 2007. Determination of thermal diffusivity of foods using 1D Fourier cylindrical solution, Journal of Food Engineering, 78, pp. 669–675. Bedson P and M Sargent. 1996. The development and application of guidance on equipment qualification of analytical instruments. Accred Qual Assur 1, 265–274.

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Juodeikiene G and L Bawsinskiene. 2004. Non-destructive texture analysis of cereal products, Journal of Food Engineering, 37, pp. 603–610. Karoui R and J De Baerdemaeker. 2006. A review of analytical methods coupled with chemometric tools for the determination of the quality and identity of dairy products, Food Chemistry, in press. Karoui R, B Kemps, F Bamelis, B De Ketelaere, E Decuypere, and J De Baerdemaeker. 2006. Methods to evaluate egg freshness in research and industry: A review, Eur Food Res Technol, 22, pp. 727–732. Katsumata T, T Suzuki, H Aizawa, and E Matashige. 2007. Photoluminescence evaluation of cereals for a quality control application, Journal of Food Engineering, 78, pp. 588–590. Kilic K, IH Boyaci, H Koeksel, and I Kuesmenoglu. 2007. A classification system for beans using computer vision system and artificial neural networks, Journal of Food Engineering, 78, pp. 897–904. Kim MS, AM Lefcourt, Y-R Chen, and Y Tao. 2005. Automated detection of fecal contamination of apples based on multispectral fluorescence image fusion, Journal of Food Engineering, 71, pp. 85–91. Kimbaris AC, NG Siatis, CS Pappas, PA Tarantilis, DJ Daferera, and MG Polissiou. 2006. Quantitative analysis of garlic (Allium sativum) oil unsaturated acrylic components using FT-Raman spectroscopy, Food Chemistry, 94, pp. 287–295. Kueppers S and M Haider. 2003. Process analytical chemistry—future trends in industry, Anal Bioanal Chem, 376, pp. 313–315. Lana MM, LMM Tijskens, and O van Kooten. 2006. Effects of storage temperature and stage of ripening on RGB colour aspects of fresh-cut tomato pericarp using video image analysis, Journal of Food Engineering, 77, pp. 871–879. Lanher BS. 1996. Evaluation of Aegys MI 600 Fourier Transform Infrared milk analyzer for analysis of fat, protein, lactose and solid non-fat: a compilation of eight independent studies, J. of AOAC Int., 79 (6), pp. 1388–1399. Leemans V and M-F Daestain. 2004. A real time grading method of apples based on features extracted from defects, Journal of Food Engineering, 61, pp. 83–89. Lefier D, R Grappin, and S Pochet. 1996. Determination of fat, protein and lactose in raw milk by Fourier Transform Infrared Spectroscopy and by analysis with a conventional filter-based milk analyzer, J. of AOAC Int., 79 (3), pp. 711– 717. Lucas T, A Grenier, S Quellec, A Le Bail, and A Davenel. 2005. MRI quantification of ice gradients in dough during freezing of thawing processes, Journal of Food Engineering, 71, pp. 98–108. Munkevik P, G Hall, and T Duckett. 2007. A computer vision system for appearancebased descriptive sensory evaluation of meals, Journal of Food Engineering, 78, pp. 246–256. Nassar G, B Nongaillard, and Y Noel. 2004. Study by ultrasound of the impact of technological parameters changes in the milk gelation process, Journal of Food Engineering, 63, pp. 229–236. Noda T, S Tsuda, M Mori, S Takigawa, C Matsuura-Endo, S-J Kim, N Hashimoto, and H Yamauchi. 2006. Determination of the phosphorus content in potato starch

An Overview of Nondestructive Sensor Technology in Practice 31 using an energy-dispersive X-Ray fluorescence method, Food Chemistry, 95, pp. 632–637. Nunes AC, X Bohigas, and J Tejada. 2006. Dielectric study of milk for frequencies between 1 and 20 GHz, Journal of Food Engineering, 76, pp. 250–255. Park B, KC Lawrence, WR Windham, and DP Smith. 2006. Performance of hyperspectral imaging system for poultry surface fecal contaminant detection, Journal of Food Engineering, 75, pp. 340–348. Perring L, D Andrey, M Basic-Dvorzak, and J Blanc. 2005. Rapid multimineral determination in infant cereal matrices using wavelength dispersive X-ray fluorescence, Journal of Agricultural and Food Chemistry, 53 (12), pp 4696–4700. Reh C. 2001. In-line and off-line FTIR measurements, pp. 213–232, In: Kress-Rogers E and CJB Brimelow, Instrumentation and sensors for the food industry, 2nd Ed., CRC Press, Cambridge, England. Reh C, SN Bhat, and S Berrut. 2004. Determination of water content in powdered milk, Food Chem., 86, pp.457–464. Resa P, T Bolumar, L Elvira, G Perez, and F Montero de Espinosa. 2007. Monitoring of lactic acid fermentation in culture broth using ultrasonic velocity, Journal of Food Engineering, 78, pp. 1083–1091. Sigfusson H, GR Ziegler, and JN Coupland. 2004. Ultrasonic monitoring of food freezing, Journal of Food Engineering, 62, pp. 263–269. Singh PC, RK Singh, RS Smith, and PE Nelson. 1997. Evaluation of in-line sensors for selected properties measurements in continuous food processing, Food Control, 8 (1), pp. 45–50. Tan J. 2004. Meat quality evaluation by computer vision, Journal of Food Engineering, 61, pp. 27–35. Tanaka F, K Morita, P Mallikarjunan, Y-C Hung, and GOI Ezeike. 2005. Analysis of dielectric properties of soy sauce, Journal of Food Engineering, 71, pp. 92–97. Thybo AK, PM Szcypinski, AH Karlsson, S Donstrup, HS Stodkilde-Jorgensen, and HJ Andersen. 2004. Prediction of sensory texture quality attributes of cooked potatoes by NMR-imaging (MRI) of raw potatoes in combination with different image analysis methods, Journal of Food Engineering, 61, pp. 91–100. Van Dijk C, C Boeriu, F Peter, T Stolle-Smits, and LMM Tijskens. 2006. The firmness of stored tomatoes (cv. Tradiro). 1. Kinetic and near infrared models to describe firmness and moisture loss, Journal of Food Engineering, 77, pp. 575–584. Veliyulin E, HS Felberg, H Digre, and I Martinez. 2006. Non-destructive nuclear magnetic resonance image study of belly bursting in herring (Clupea harengus), Food Chemistry, in press. Wen Z and Y Tao. 2000. Dual-camera NIR/MIR imaging for stemend/calyx identification in apple defect sorting, Transactions of the ASAE, 43 (2), pp. 449–452. Xing H, PS Takhar, G Helms, and B He. 2007. NMR imaging of continuous and intermittent drying of pasta, Journal of Food Engineering, 78, pp. 61–68. Zude M, B Herold, J-M Roger, V Bellon-Maurel, and S Landahl. 2006. Non-destructive tests on the prediction of apple fruit flesh firmness and soluble solids content on the tree and in shelf life, Journal of Food Engineering, 77, pp. 254–260.

Chapter 2 The Influence of Reference Methods on the Calibration of Indirect Methods Heinz-Dieter Isengard

Introduction Direct methods, also called primary methods, measure the property of the sample or an analyte as such. Such direct or primary methods often serve as reference methods. Primary methods may be relatively complicated and time-consuming, and they may require sophisticated instruments and experienced personnel or be very expensive. The use of other methods may therefore be advantageous to avoid these drawbacks. These other methods, however, do not necessarily measure the analyte or the property of the sample itself, but may measure something that depends on the extent of the property or the concentration or amount of the analyte. They are therefore called indirect or secondary methods. Secondary methods may work with simpler and cheaper equipment and may be easy to carry out. In other situations, the main advantage is a gain of time for one analysis. The secondary method may even be applicable in-line or the taking of samples may be avoided. On the contrary, an indirect method does not measure the sample property or the analyte itself. It needs a relation or correlation to a direct method to which it is referred (and which is therefore the reference method). The value measured must allow a conclusion to the property of the sample or the concentration or amount of analyte.

33

34


Calibration The property or analyte concentration of a sample must be measured with a direct method, which serves in this sense as the reference method. The values found by this technique are often called true values or laboratory values, because they are usually measured in an analytical laboratory in a more elaborate and time-consuming way rather than near a production line. Thus, samples with known properties are then measured by a secondary method, and the values obtained are related to the true values received by the direct technique. This is done with several samples having different properties or analyte concentrations. In this way, value pairs are obtained. The indirect values are plotted versus the direct values, and a regression curve (usually a straight line) is erected. This is the calibration line. A sample with unknown property or analyte concentration can now be measured by the indirect method. From the value measured, the property or analyte concentration can be read off from the calibration line. When chemometric methods are applied, the analytical data points are calculated from many measurements. These values are often called predicted values. The calibration line is therefore the graph of the predicted values versus the true values obtained by the reference method. This calibration line should ideally be a straight line with the gradient running through the origin of the coordinate system. The scattering of data points around this line, expressed as the regression coefficient, is a measure of the precision of the method.

Correctness of Results It is always possible to construct a regression line through a number of data points. For every secondary measurement, a corresponding value for the analyte concentration will then be found. In the case of chemometric methods like near infrared (NIR) spectroscopy where the calibration is based on a large number of measurements, the regression coefficient may even be high, indicating a high precision of the measurements. This, however, is no proof for the high accuracy of predicted values. If the reference method used is not really adequate, a good (in terms of mathematics) calibration may be established, based,

The Influence of Reference Methods on Calibration

35

however, on erroneous values. These erroneous reference values will then be translated into predicted values with high precision and repeatability. Consequently, they contain the same errors as the reference method. A secondary method cannot yield more accurate results than the reference method used. It is therefore a vital condition for correct results obtained by the indirect method that the reference values be correct. The reference values must be true.

Examples: Water Content Determination Water content—and thus dry matter—is often determined by drying techniques, particularly by drying the product at a certain temperature for a certain time in a drying oven. Drying techniques, such as the classical oven drying, vacuum drying, freeze drying, infrared drying, or microwave drying, do not distinguish between water and other volatile substances. The result of all of these methods is not water content but the mass loss the product undergoes under the conditions applied. These conditions (sample size, temperature, pressure, time, energy input, criteria to stop the analysis) can principally be freely chosen. The result depends very much on these conditions but may be very reproducible. This alone shows that this technique, leading to different results when the parameters are changed, cannot be the correct one, because water content is a sample property that has a certain, though unknown value. From the scientific point of view, the results of drying methods should therefore not be called water content but rather mass loss on drying with indication of the drying conditions. In the past years, the term moisture content was introduced as a compromise. It means the relative mass loss by evaporation of water (though possibly not all of the water) and other volatile compounds under the drying conditions. The problem with all drying techniques is that they do not measure water specifically. All of the volatile compounds under the analytical conditions contribute to the mass loss, even compounds that are not originally contained in the sample but are formed by chemical reactions during the analysis, particularly by decomposition reactions at higher temperatures. On the other hand, strongly bound water may escape detection.

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These conflicting errors, inclusion of other volatiles on the one hand and water not detected on the other hand, may account for each other when the drying parameters are chosen in an appropriate way. The appropriate choice of the parameters necessitates, of course, that the true water content has been analyzed before with a method selective for water as a primary method. The parameters of the secondary method must then be chosen in a way in which the result corresponds to the water content determined with the primary method. When the secondary method is calibrated in this way, it can be applied for this particular type of product. The calibration is product-specific, and the same parameters cannot be applied for other types of samples. The most important primary method to determine water content is the Karl Fischer titration, which is based on a chemical reaction selective for water: (2.1a) ROH + SO2 + Z → ZH+ + ROSO− 2 − − + + − ZH +ROSO2 +I2 + H2 O + 2Z → 3ZH + ROSO3 + 2I (2.1b) Overall reaction: − 3Z + ROH + SO2 + I2 + H2 O → 3ZH+ + ROSO− 3 + 2I

(2.2)

Z is a base (very often imidazole), and ROH is an alcohol, usually methanol, recently also ethanol in special reagents. In the first step, the alcohol is esterified with sulphur dioxide to form alkyl sulphite. The base provides for a practically complete reaction, as shown in Equation 2.1a. In the second step, this alkyl sulphite is oxidized by iodine to form alkyl sulphate; this reaction requires water, as shown in Equation 2.1b. The overall reaction (Equation 2.2) shows that the consumption of iodine is stoichiometrically equivalent to water present in the sample. In many situations, water content is determined by drying techniques, most often in a classical drying oven. The results are regarded as water content, although they represent only the mass loss under the applied conditions. This has consequences if this technique is used to calibrate a secondary method like the NIR spectroscopy. For a number of products, the drying results correspond quite well to the water content. In other cases, the difference may be enormous.


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Table 2.1. True values (by Karl Fischer titration [KFT] and oven drying [OD]) and predicted values for six samples of wheat semolina. Reference values (true values) in g/100 g

Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6

NIR values (predicted values) in g/100 g

KFT

OD

Calibrated by KFT

Calibrated by OD

12.49 12.88 12.98 13.01 13.47 13.97

12.49 12.99 12.99 13.16 13.52 14.23

12.48 12.79 12.88 12.89 13.46 13.97

12.60 12.96 13.13 12.99 13.61 14.23

An example for the first situation is the water content determination in wheat semolina. Figure 2.1 shows the NIR calibration line based on Karl Fischer titration as the primary method. Figure 2.2 depicts the NIR calibration line based on oven drying (2 hours at 130◦ C). Both figures contain the values used for calibration (calibration set [C-set]) and the values found for validation (validation set [V-set]). The lines for the Cset and the V-set are in both cases nearly identical. This is an indication of a successful calibration. A comparison of Figures 2.1 and 2.2 shows also that they are nearly identical. Table 2.1 gives examples for determinations by the primary methods (Karl Fischer titration [KFT] and drying) and the values that were predicted by NIR measurements calibrated with the corresponding primary method. The juxtaposition shows that both techniques yield practically the same results. The statistical values are, however, slightly better for the KFT-based measurements. For this product, water content can be measured by drying, and NIR determinations can be calibrated on this basis, not only on KFT. For another product, a lactoserum, things are completely different. By KFT, water content can be determined easily. Drying of the sample at 145◦ C leads to a continuous mass loss, far beyond the Karl Fischer value. Figure 2.3 shows the drying curve. The interesting phenomenon is, however, that the dried product still contains water, which can be found by KFT. This is due to the lactose content. The α-modification of lactose contains one mole of water per mole. This water of crystallisation

38 .

Figure 2.1. Calibration line and sample determination based on Karl Fischer titration.

39 Figure 2.2. Calibration line and sample determination based on oven drying.

40

Nondestructive Testing of Food Quality Lactoserum Euvoserum

16.0 14.0 12.0 10.0 % 8.0 6.0 4.0 2.0 0.0 0

50

100

150 Drying time in minutes

200

250

Water content [%] of the original sample determined by Karl Fischer titration

Gravimetric loss of mass [%] of the sample in a drying closet at 145 °C Residuary water content [%], by Karl Fischer titration, of the sample in a drying closet at 145 °C

Figure 2.3. Drying curve of Lactoserum Euvoserum at 145◦ C, water content of the original product determined by Karl Fischer titration and water content of the dried product after various drying times.

cannot be detected completely by drying at this temperature. The mass loss is caused by decomposition reactions in the product, which occur already at lower temperatures. An NIR calibration on the basis of the drying method is possible. This is shown in Figure 2.4. The calibration was established using the samples dried for various times in the oven. The calibration is successful and the mass loss can be predicted. It is, however, obviously not a useful method to determine the water content. Because the original sample contains only 4.5 grams (g) of water per 100 g, values above this value are not possible. The relevant area is highlighted in Figure 2.4. A correct calibration within the relevant water content values could be established using KFT as the reference method. The calibration line is shown in Figure 2.5.

41 Figure 2.4. Calibration line for mass loss determination at 145◦ C of Lactoserum Euvoserum with indication of the highest possible water content in the sample.

42 Figure 2.5. Calibration line for water content determination of samples predried at 145◦ C of Lactoserum Euvoserum based on Karl Fischer titration.


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Conclusion Secondary methods must be calibrated against a primary or direct method. This direct method must determine the entity to be analyzed. The transfer of the values measured by the secondary method is purely a mathematical process, particularly when chemometric or other analytical tools are involved. The relevance or chemical logic of the secondary values and of the calibration is not checked. The secondary method may measure a different property of the sample. Particularly when the calibration line is good, it may lead to the false conclusion that the sample property to be measured is well analyzed. A spectacular error is possible when water content is to be measured. Often drying techniques are used for this purpose. They yield, however, a mass loss as a result. This is caused because all compounds are volatile under the drying conditions, including those that are formed during drying by chemical reactions. For the samples that contain other volatile substances than water or undergo decomposition reactions, a calibration on the basis of a drying technique may be possible. This calibration can then however only predict a mass loss, but not the water content of the product.

Chapter 3 Ultrasound: New Tools for Product Improvement ˙ Ibrahim Gülseren and John N. Coupland

Introduction Low-intensity ultrasound is used as a nondestructive evaluation (NDE) technology in fields as diverse as oceanography, geological studies, medicine, material science, and also in foods (Blitz 1963, Hill et al. 2004, Rose 2004, Coupland 2004). The term ultrasound here refers to inaudible, high frequency (that is, >20 kilohertz [kHz]), low energy sound waves that propagate as deformations in the media of interest. Due to their low energy content, ultrasonic propagation for sensing applications does not influence the physical properties of the materials, but is itself influenced by them. In general, ultrasonic sensors are appropriate for online use because they are relatively cheap and robust compared to most other sensing technologies and are applicable for many food applications. In recent years, a number of reviews were published regarding ultrasound and foods (Coupland 2004, Coupland and Saggin 2003, Povey 1998, McClements 1995), and this work focuses mostly on recently published work. The two most common types of ultrasonic waves employed in ultrasonic studies are bulk longitudinal (L) and shear/transverse (T) waves. L-waves propagate in the same direction as the direction of oscillation of material elements (Figure 3.1), whereas for T-waves, propagation is perpendicular to the motion of the direction of oscillation (Figure 3.1). Liquids and gases do not support T-wave propagation over useful distances. Shear waves travel more slowly than longitudinal waves.

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(a)

(b)

(c)

Figure 3.1. Diagram showing the displacement of volume elements from (a) their equilibrium position due to the propagation of (b) L-waves and (c) T-waves. Propagation is left to right in both cases.

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Ultrasound: New Tools for Product Improvement

47

Wave propagation is described in terms of the ultrasonic velocity (c, that is, distance traveled per unit time) and attenuation coefficient (α, that is, logarithmic loss of wave-energy per unit distance). These terms can be expressed concisely as a complex wave number, (k) (Coupland and Saggin 2003): ω k = + iα (3.1) c where ω (= 2π f ) is the angular frequency. Ultrasonic parameters are only useful for sensing because they can be measured and related to the physicochemical properties of the food: ρ k = (3.2) ω E where E is the appropriate modulus and ρ the density. Ultrasonic propagation is an adiabatic process rather than an isothermal one and so the moduli may be different than those made in traditional measurements where heat has time to dissipate. Equation 3.3 is often written for L-wave propagation in low attenuation fluids as: 1 c= (3.3) ρκ where κ is the adiabatic compressibility (that is, reciprocal bulk modulus).

Measurement Methods The most commonly used ultrasonic measurement techniques (that is, pulsed and resonance methods), have all been reviewed in detail elsewhere (for example, Coupland 2004, Buckin and Smyth 1999, Sheen et al. 1995, 1996). Briefly, pulsed methods measure the time taken and energy lost for a pulse of ultrasound to travel through a fixed distance in the sample. Pulsed methods can either be implemented with a single transducer acting as a transmitter and receiver in pulse-echo mode (Figure 3.2a) or with separate transmitter and receiver transducers in through transmission mode (Figure 3.2b). Resonance methods measure the frequencies at which a fixed path length of sample resonates under a continuous single frequency of ultrasound (Figure 3.2c). Although

Transducer Container wall Sample container

Container wall

(a)

Transducer 1 Container wall

Sample container

Container wall Transducer 2 (b)

Transducer 1 Container wall

Sample container

Container wall Transducer 2 (c)

Figure 3.2. Diagram showing the alignment of transducers and ultrasonic path for (a) pulse-echo, (b) through transmission, (c) resonator, (d) reflectance, (e) guided wave, and (f) ultrasonic Doppler velocimetry measurement systems.

48


49

Transducer Transducer Container wall

Sample container Container wall (d)

Transducer Container wall Sample container

Ultrasonic path

Container wall

(e)

Transducer 1 Container wall Sample container

(f)

Container wall

Figure 3.2. (cont.)

resonance methods tend to be the most precise in a lab setting, it may not be possible to achieve their advantages in an online setting. Pulsed measurements are often more rapid. Precise ultrasonic measurements require a fixed and well-defined path length for the sound to propagate across. The path length must be sufficient to cause measurable changes in the ultrasonic signal, yet not too

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great so that diffraction of the wave and attenuation by the food will completely absorb the sound energy. This can also be a limitation for online applications where it is often challenging to find a suitable path for an ultrasonic signal in process equipment. Finally, ultrasonic properties are highly dependent on temperature, and temperature control can often limit the precision of both online and laboratory ultrasonic measurements. Solid foods that cannot be contained in a pipe or sample cuvette are particularly difficult to analyze because it is hard to couple the ultrasonic transducer to the sample (even a thin layer of air will attenuate ultrasound completely), and it is hard to control the temperature adequately. In addition, because the transducers must be held against the food and manually supported a fixed distance apart (usually on a pair of calipers or via a stepper-motor), it is not possible to adequately precisely calibrate the path length. Two recent developments in measurement technology have increased the range of options available for ultrasonic measurements, particularly online. Noncontact Measurements Unlike most ultrasonic sensors that must be in direct contact with the food (either directly or more likely via a delay line), noncontact ultrasonic sensors transmit ultrasonic waves through ambient air, which makes them more practical in the monitoring of online processes. However, the acoustic signal will be affected by instability in the air coupling the transducer to the food, and some compensation is required (Cho and Irudayaraj 2003a). Furthermore, any nonparallelism between the food surface and transducer will massively increase the signal losses and limit the value of the readings. Reflectance Measurements In a reflectance measurement, the properties of the food are calculated from the proportion of an ultrasonic pulse reflected at the sample surface. The main ultrasonic parameter that influences the reflection/ transmission of ultrasound from one material to another is the acoustic impedance (Z): Z = ρc

(3.4)


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If an ultrasonic wave passes across a boundary between two materials with different impedances (for example, between a food and the container wall), it will be partially reflected and partially transmitted (Figure 3.2d). The amplitude (a) of the signal reflected at the interface between material 1 and 2 can be formulated as follows: Z2 − Z1 ar = Ra = (3.5) ai Z1 + Z2 where subscripts r and i represent the reflected and incident echoes, respectively. The reflectance coefficient can be readily measured and related to the impedance and hence physical properties of the material under investigation. A reflectance measurement is particularly useful when the material is too attenuating to allow a measurement of transmitted sound (that is, shear waves in fluids or otherwise highly attenuating foods). Some recent applications of reflectance methods include the determination of solution viscosity by shear wave reflectance (Saggin and Coupland 2001c), the estimation of foam bubble size by longitudinal wave reflectance (Kulmyrzaev et al. 2000). In all of these cases, the wave would be too highly attenuated by the food to allow transmission measurements. In other cases, reflectance measurements may be preferred over transmission measurements because the instrumental setup is frequently simpler and easier to implement online, particularly because a stable path length is no longer required. For example, reflectance measurements have been used to estimate the concentration of simple solutions (Saggin and Coupland 2001c), the crystallization of lipids (Saggin and Coupland 2002a), and the dissolution of powders (Saggin and Coupland 2002b). Reflectance measurements may be limited because they are only sensitive to the surface of the food and because the transducers may vary in output, they must be regularly calibrated. It is possible to generate combinations of shear and longitudinal waves as a guided wave that can travel long distances along a pipe (or other solid) by frequent reflections with the pipe wall (Figure 3.2e). Each reflection at the wall-food interface will affect the wave properties and so the guided wave signal will depend on the food material properties in a manner similar to reflectance measurements. Guided waves are particularly useful for the analysis of equipment surfaces (such as commercial steel pipes) to detect fouling for example (Hay and Rose 2003).

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The ultrasonic transducer is located at a fixed location on the pipe, while the wave propagates through the flowing medium (Figure 3.2f).

Applications An important paradigm in the study of food quality is that composition (ingredients) is modified by processing to produce structures that are responsible for the functional properties of foods, particularly texture. Ultrasound has been used to measure composition, structure, and texture of foods, and in this section, we will review some of the major recent developments in these fields. Ultrasonic Measurement of Food Composition The measurement of the composition of simple binary mixtures by ultrasonic velocity measurements is the simplest and often most successful group of applications. Examples include solid fat in liquid oil (McClements and Povey 1987, 1988a), sugar solutions and fruit juices (Contreras et al. 1992), and changes in emulsion composition (Dickinson et al. 1994). Bamberger and Greenwood (2004) used this approach to measure the density of a liquid flowing in a pipe. For simple liquids, attenuation is typically low and relatively hard to measure and velocity gives large and measurable differences. Measured velocity can be related to composition either via empirical calibration curve or by using theoretical approaches. For example, in the analysis of a two-component system, Equation 3.6 can be rewritten as: 1 φ 1−φ 2 c = + · (3.6) [(1 − φ)ρ1 + φρ2 )] ρ1 c12 ρ2 c22 where ø is the volume fraction. For a two-component system with known volume fraction and component velocities, a convenient definition of mixture velocity becomes: c = c1 φ + c2 (1 − φ)

(3.7)

It is important to recognize that any changes in structure may overwhelm differences due to changes in composition. One good example is the apparent variation of ultrasonic velocity in salmon muscle due to the alignment of myosepta. It is necessary to account for this effect to allow


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the estimation of fat content in salmon muscle by ultrasonic velocity measurement (Shannon et al. 2004). Although ultrasonic velocimetry is useful to characterize simple binary mixtures, most foods contain many more ingredients, and their ultrasonic quantification is more complex. The ultrasonic properties of food vary with frequency, but rarely enough for spectral resolution of multiple components (as is often used in infrared [IR] methods). Therefore, to measure multiple components, it is usually necessary to combine the ultrasonic method with another technique (for example, ultrasonic velocity and density measurements for ethanol determination in wine) (Resa et al. 2004) or to make measurements at different temperatures to exploit the different temperature dependency of the ultrasonic properties of different food components. For example, the speed of sound in the aqueous portion of chicken increases with temperature and solids content while the speed of sound in the fatty portion decreases with temperature. Using the method of mixtures, Chanamai and McClements (1999) were able to make measurements at two temperatures and calculate the fat, water, and solids content of a chicken sample. Similarly, the oil content in waste water from an olive oil extraction process was measured (Benedito et al. 2004) and the composition of processed foods (Simal et al. 2003). In some cases, reflectance (impedance) measurements are preferable to velocity measurements. For example, foams scatter ultrasound strongly and traditionally through transmission measurements cannot usually be made. However, Fox and others (2004) were able to use ultrasonic reflectance measurements to measure overrun in cake batter. This work is particularly interesting because they also considered bubble size distribution in their theoretical analysis. Elmehdi and others (2003a, 2003b, 2004) were able to reach similar conclusions based on attenuation measurements made in through transmission by using thin (1 to 5 centimeter [cm]) bread samples that were freeze-dried and by using bread dough. Phase transitions in lipids, sugars, salts, and water are important in food quality and can typically be readily detected and monitored by ultrasound, because the physical properties of the solid and liquid phases are very different. In some cases, this can be considered as a simple change in composition and approached using similar methods as described above (for example, McClements and Povey 1987, 1988). Martini and others (2005a, 2005b, 2005c) simultaneously measured the

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temperature dependence and ultrasonic properties of six fat blends. The ultrasonic attenuation and velocity increased with increasing solid fat content (SFC) values, because velocity values are more sensitive to crystallization (Martini et al. 2005a). Furthermore, SFC data obtained from ultrasonics agreed well with the SFC data from low-resolution pulsed nuclear magnetic resonance (NMR) (Martini et al. 2005b). Ultrasound is typically more sensitive to small changes in solids than the more commonly used NMR methods, and this is particularly important in the study of droplet crystallization in emulsions. For example, the extent of supercooling in emulsified lipids (Kloek et al. 2000) and the effects of additives on droplet nucleation rate (Awad 2004) were measured ultrasonically. More recent work has examined cases where the structures formed can influence the ultrasonic properties and must also be considered. Lipid crystallization is complex because the solid can exist in a variety of different polymorphic forms and microstructures. Recently, several workers have considered whether the ultrasonic properties of a partially crystalline fat system depend simply on the SFC or also on other microstructural properties. Singh and others (2002, 2004) showed that for cocoa butter and anhydrous milk fat, ultrasonic velocity was not a simple function of SFC and argued that more stable (that is, more dense) polymorphic forms had higher ultrasonic velocities. Martini and others (2005c) showed that ultrasonic attenuation depends on crystal size and lipid microstructure. In another study by Hindle and others (2002), ultrasound was suggested to be sensitive to polymorphic changes in a cocoa butter in an oil-in-water emulsion. Beyond approximately 10% solids, many semicrystalline lipids become highly attenuating and it is difficult to make ultrasonic measurements. In their recent work on highly crystalline fats, Martini and others (2005a, 2005b, 2005c) used a novel chirp-wave signal generator and special transducers, which together had exceptional penetrating power, and they were able to make measurements across a substantial (∼8 cm) thickness of various fats with up to ∼20% solids. Saggin and Coupland (2002a) took an alternative approach and measured the impedance of a sample of semicrystalline fats by a reflectance technique and measured the solid fat content. Ultrasound has been applied less often to the study of phase transitions in water in foods (for example, freezing), but some recent work shows


55

the technique has some possibilities. Measurement of freezing should be a relatively straightforward application, particularly because ice has (under most conditions) none of the issues around polymorphism that are controversial in the lipids work. However, most frozen foods have a very large volume fraction of ice, and the assumptions in the simple formulations often used to relate solids content to ultrasonic velocity (as shown in Equations 3.6 and 3.7) break down. Furthermore, air bubbles are usually formed during freezing, which scatter sound strongly and can dominate the signal. Lee and others (2004) measured the ultrasonic velocity and ice content (by NMR) in frozen orange juice samples (0 to −50◦ C). Although an increase in ice content corresponded to an increase in ultrasonic velocity, they were unable to provide a simple relationship between these variables akin to those shown in Equation 3.7. Recently we made similar measurements in sucrose solutions (Figure 3.3) and showed (1) the onset of freezing corresponded to an increase in ultrasonic velocity, (2) the temperature at which the increase initiated decreased with increased sugar content, and (3) the ultimate speed of sound decreased with increased sugar content (presumably because of the reduced ice content). Taking an alternative approach, Sigfusson and others (2004) measured the ultrasonic properties of a block of food parallel to the direction of heat flux. They were able to reflect part of an ultrasonic pulse from the moving ice front and thereby position it and calculate the proportion of the food frozen. Ultrasonic Measurement of Food Structure Ultrasound can be used to characterize various scales of structure in food. Macroscale Structure Ultrasound can be used to measure millimeter scale structures in foods using the techniques of imaging. The time of flight of an ultrasonic pulse reflecting from a food surface can measure its position and hence shape by scanning the position of the transducer across a two-dimensional grid. Similarly reflectance from internal structures can visualize internal defects. Most imaging is done with L-wave sound. In most imaging operations, both the sample and transducers are immersed in a tank of water to allow good acoustic coupling as the

56

Nondestructive Testing of Food Quality 3500

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1000 −12

−10

−8

−6

−4

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Figure 3.3. Speed of sound in sucrose solutions as a function of temperature (●: 5%, h: 10%, ▼: 20%, : 30%, ■: 40%, : 50%, ◆: 60%, ♦: 70% sucrose). The formation of ice corresponded to the discontinuity seen.

transducers move. This is obviously impractical with most nonpackaged foods; however, the recent development of air-coupled ultrasound has opened up a range of new applications. For example, Saggin and Coupland (2001b) and Cho and Irudayaraj (2003b) used noncontact ultrasound to measure the thickness of a variety of sliced meat and cheese products. One useful application of one-dimensional imaging is to position the surface of a liquid during a filling operation. Griffin and others (2001) described how ultrasonic techniques (time of flight and Doppler shift measurements) could be used to monitor and control a bottle-filling operation. Jeffries and others (2002) implemented ultrasonic sensors as part of a control system for liquid level measurements, while other workers used noncontact ultrasonic sensors to measure liquid level in containers (Gan et al. 2002). Foreign bodies (for example, fragments of glass, wood, metal, or plastic) typically have impedances significantly different from that of


57

the food so they can be ultrasonically imaged as part of a quality control process. This approach was used to detect foreign bodies in bottled beverages, fruit juices, and pie filling (Zhao et al. 2003, Zhao et al. 2004). Internal defects in foods can also be imaged acoustically if there is an impedance mismatch. Benedito and others (2001) were able to detect defects and air cells in cheese. Some of the difficulties that must be overcome include nonparallelism (that is, oblique incidence) and the location of the foreign body and variation in the acoustic properties of the food (Hæggström and Luukkala 2001). Microscale Structure Ultrasonic imaging is limited in resolution by the wavelength of the sound (∼mm) and the finite size of the acoustic beam. However, smaller structures can be characterized if they scatter sound (that is, are acoustically dissimilar to the material in which they are imbedded). Many colloidal scale objects scatter sound in a frequency-dependent manner, and the spectra can be readily measured. If a theoretical prediction of the ultrasonic properties can be made as a function of structural properties of interest (for example, particle size and concentration), it is possible to calculate details of the structure. This technology has been widely implemented in the commercial development of ultrasonic particle sizers capable of measuring a wider range of size (from 0.01 up to 1,000 micrometer [µm]) and concentration (up to 20 to 30%) than other devices (McClements and Coupland 1996). The principles of scattering theory (McClements and Coupland 1996) and ultrasonic particle sizing (Coupland and McClements 2001) have been described in detail elsewhere. Important recent developments include the extension of scattering theory to higher volume fractions and include flocculated systems (McClements 1991, Dukhin and Goetz 1996). These theoretical developments have allowed characterization of complex instability mechanisms in food emulsions. For example, the flocculation of an emulsion leads to a reduction of the scattering efficiency of the individual droplets that can be detected as changes in attenuation coefficient (Herrmann et al. 2001, Chanamai et al. 2000). Biopolymer structure can also be characterized ultrasonically. Some workers have used ultrasonic scattering to measure the apparent size of protein aggregates (Griffin and Griffin 1990), but it is important to remember that proteins can also absorb ultrasonic energy through chemical resonance of protonation-deprotonation reactions as well as

58


scattering from aggregates and changes in molecular hydration (Bryant and McClements 1999). Ultrasonic attenuation and velocity were used to detect thermal denaturation in concentrated bovine serum albumin solutions (Apenten et al. 2000). However, Corredig and others (2004) showed that the ultrasonic measurements (for whey protein) were not identical to those from differential scanning calorimetry, suggesting that the ultrasonic method is responding to distinct molecular processes. Measurement of Food Texture Rheology involves the study of the deformation of matter in response to applied forces. Because ultrasonic propagation involves small deformations of the material in response to the sound wave and their elastic recovery, it is reasonable to consider an ultrasonic measurement a microrheological technique and for the readings to relate to macrorheological properties. However, ultrasound operates at higher frequencies (∼103 to 106 times) than those commonly seen in most real-world material deformations and used in small deformation rheological measurements. Furthermore, ultrasonic measurements do not depend purely on the elastic modulus but also on density differences and scattering from homogeneities. Despite these difficulties, there have been considerable successes in using ultrasound to measure the rheological and flow properties of foods. The various approaches to this problem can be categorized as (1) the use of T-waves to directly probe (or infer) shear modulus, (2) the measurement of flow rate (or flow profile) under known conditions and back calculation of viscosity, and (3) the measurement of L-wave properties and correlation with texture. Shear Wave Methods Because we are typically more concerned with the shear modulus than the bulk modulus of foods, shear ultrasonic waves (T-waves) provide a more direct method to access the appropriate modulus. However, as noted above, shear waves do not propagate well in fluids. One way around this measurement difficulty is to use reflectance (impedance) measurements at the interface between the food and a solid delay line of known properties. The proportion of ultrasonic energy reflected from the interface of two materials depends on the impedance mismatch (Equation 3.5).


59

The normalized shear reflectance for food and hydrocarbon oils was shown to be proportional to the viscosity of oils (Saggin and Coupland 2001a) and diluted honey (Kulmyrzaev and McClements 2000). However, mechanical relaxations that occur in the fluid between the high-frequency ultrasonic measurements and the low-frequency conventional measurements mean the relationship is complex (Kulmyrzaev and McClements 2000, Saggin and Coupland 2004). For example, concentrated sugar syrups have a glass transition frequency below the ultrasonic measurement frequency used (that is, 10 megahertz [MHz]) and therefore, had a very low ultrasonically measured viscosity while their conventionally measured viscosity remained high (Kulmyrzaev and McClements 2000, Saggin and Coupland 2004). The presence of a biopolymer reduced the glass transition frequency by binding water (Saggin and Coupland 2004). Other workers studied the mechanical properties of a casein gel using a shear resonance method and conventional low-oscillatory rheological techniques (Buckin and Kudryashov 2001). Again, time-scale and wavelength differences prevented extrapolation of physical properties such as viscosity, storage modulus, and loss tangent in between the two techniques, but shear resonance was able to reveal complementary information about submicron structure. In other cases, a more empirical approach has been taken to the shearwave characterization of food texture. For example, a number of studies focused on the ultrasonic evaluation of structural and rheological properties of dough and bakery products (Lee et al. 2004a, Elmehdi et al. 2003, Elmehdi et al. 2004, Ross et al. 2004). Fermentation of the dough was characterized by pronounced velocity dispersion at low frequencies (that is, 10 microns). Over time, samples of the solution were collected and measured. As can be seen, a mode at very fine particle sizes was observed as the larger powder mode decreased in volume. This fine mode relates to protein micelle formation during the rehydration of the powder. Hydration is initially rapid but then slows down dramatically, with the process taking several hours to complete. In this case, skimmed-milk powder was used, thus no fat was detected.

Particle Sizing in the Food and Beverage Industry 1000 min

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Figure 7.15. Following milk powder reconstitution using Mastersizer 2000.

Flavor Emulsions Flavor emulsions are oil and water emulsions that are normally prepared as a concentrate and can be diluted to form a final product. Two types of flavor emulsion are used in the food industry. One is a high concentration oil emulsion that is essential oil stabilized with emulsifiers, stabilizers, and other additives. The other is a flavored oil emulsion with added vegetable oil that is formulated to give a cloudy appearance. The emulsions must be stable over time (both in their concentrated and diluted forms). Sedimentation of flocculated material at the bottom of the container, or creaming of oil to the top of the container, are both undesirable. The system is often stabilized by the addition of hydrocolloids such as xanthan gum, gum Arabic, alginates, and carageenans. These will absorb to the oil water interface and can impart both electrostatic and steric (electrosteric) stability to the emulsion. The optimal particle size is often considered to be

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