Mathematical and Statistical Applications in Food Engineering PDF
Preview Mathematical and Statistical Applications in Food Engineering
Mathematical and Statistical Applications in Food Engineering Editors Surajbhan Sevda Department of Biosciences and Bioengineering Indian Institute of Technology Guwahati Guwahati-781039, India Department of Biotechnology National Institute of Technology Warangal Warangal-506004, India Anoop Singh Department of Scientific and Industrial Research (DSIR) Ministry of Science and Technology Government of India, Technology Bhawan New Delhi-110016, India p, p, A SCIENCE PUBLISHERS BOOK A SCIENCE PUBLISHERS BOOK CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2020 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Version Date: 20191219 International Standard Book Number-13: 978-1-138-34767-0 (Hardback) This book contains information obtained from authentic and highly regarded sources. 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For permission to photocopy or use material electronically from this work, please access www.copyright.com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com Foreword Mathematical and Statistical techniques have a very broad applications in a vast number of fields including food technology. The food industry is growing very fast with increasing food demand and facing challenge to supply safe, nutritious and palatable products. The development of new food products/processes is complex, expensive and multistage risky process. The utilization and applications of mathematical and statistical modelling have increased in food science and technology to overcome the risks involved in the developmental processes of various food products. Several Food Engineering and Food Science students face problems in understanding the mathematical application, especially in their research. This is the latest and updated book that having 25 different chapters with details of various new mathematical and statistical applications that will be used in food engineering to optimize process parameter. This book explained the use of the design of the experiment, full factorial design, cluster analysis, multi-way statistical methods, partial least squares regression, principal component regression, response surface methodology, CFD simulation, 3 paradigm method, neural networks in experiments in food engineering and technology. The use of correlation, association, and regression to analyse food processes and products are also included. All these above methods are explained with their basic principle and used case study so that Food engineering researchers can understand these methods well and can use these in the domain of food engineering. I congratulate the editors, Dr Surajbhan Sevda and Dr Anoop Singh, for seeing the need for such a book and producing it. This is a well-edited book and its material will be helpful for food engineering and food process students and food industry professionals and at a level appropriate to their backgrounds. This will also enhance student’s maths skills with respect to the process parameter optimization and food quality measurement. Overall, the information provided in this book is highly scientific, up-dated and would be beneficial for the researchers and practitioners equally; this will also be useful for those entering into this area. I strongly recommend this excellent book to food engineering scientists, Food process engineers and research students who are interested in Food Engineering in general and Food Process optimization in particular. Poonam Singh Nigam PhD CBiol PGCUT FRSB SFHEA FCHERP FBRSI FIFIBiop FAMSc DYEd DNSc Programme Director Biotechnology Research, School of Biomedical Sciences Ulster University Northern Ireland, UK Food is a very complex matrix having different components such as carbohydrates, proteins, fats, vitamins, minerals besides moisture. This makes Food Processing also very complex and difficult proposition for more than one reason. Unlike inert chemical material, food cannot be subjected to harsh processing conditions because food is meant for human consumption. Hence apart from sensory attributes of the processed foods, care should be taken to minimize the loss in color, texture, nutrients, etc. As working woman is sign of times, premium on time is increasing. Accordingly, the demand for processed foods as convenience foods is increasing. So the challenge for food processing engineer is to give the consumer the processed food which is very close to raw material in sensory quality and nutrition. iv Mathematical and Statistical Applications in Food Engineering To achieve this, the food needs to be processed under optimal conditions. It requires optimization of process parameters of different unit operations. Another complexity in food processing, unlike that of chemical processing, the physical (density, viscosity, porosity, texture, etc.), chemical (protein denaturation, enzyme inactivation, etc.) and thermal (thermal conductivity, thermal diffusivity, etc.) properties of food change during processing, making it difficult to apply mathematical models for prior prediction of performance output. In spite of these challenges, in order to arrive at the best possible process conditions, it is essential to employ different forms of modelling. The present book is an important contribution in this direction. It is aimed at bridging the gap between statistical science and engineering science and also at ensuring that food process engineers and food technologists are better equipped to serve the food processing industry at large. The book presents application of various mathematical and statistical methods for food processing by renowned researchers from different countries. It covers application of the following in general for food processing: Optimization techniques, Role of design of experiments in process optimization, Application of correlation, regression and cluster analysis, Application of partial least square and principal component regression methods and Surface response methodology. It also covers mathematical modelling for specific unit operations/processes such as high pressure processing, drying, baking, microwave heating, microwave drying, deep fat frying. In addition, it presents application of neural networks and computational fluid dynamics simulation for food processing. This book will be of immense use to the Food Technologists and Food Process Engineers working in design and development of new products/process besides those involved in production. This is mainly because it offers a choice of suitable statistical techniques/methods which can be helpful in prior prediction of sensory quality and also in addressing variations/problems during large scale production before releasing the product into the market. This book is invaluable for Researchers, Academicians and Students. (KSMS Raghavarao) FNAE, FNAAS, FASc Director CSIR-Central Food Technological Research Institute (CSIR-CFTRI), Mysuru, India Preface Mathematical/statistical modelling is a prerequisite operation in food engineering as it is a complex process due to the presence of various scalable throughputs, rheological properties of food molecules, different process and environmental parameters, quality and stability concerns. The complexity lies in raw and processed food commodities as variability, which impacts the composition and their processability in further operations. The influencing parameters also impacted the other parameters due to mutual dependencies. Modelling facilitates the easy understanding of the proposed system by taking care of interaction and square terms along with the individual effects of the process. The application of mathematical and statistical tools is an essential component of a food-engineering program in both teaching and research. The importance of reaping the benefits of mathematical techniques has been used in the process analysis, design and optimization from an empirical to a scientific and model-based approach. This book is a cornucopia of information on mathematical and statistical methods that can be applied in food engineering. The use of these techniques is also included as a case study, which will make things easier for the researcher in terms of the development of alternative processes and their optimization in food engineering and technology. The book on ‘‘Mathematical and Statistical Applications in Food Engineering’ provides state-of- the-art information on the different mathematical and statistical methods application in food engineering. We have put together a host of highly relevant topics, ranging from the importance of the artificial neural network, design of experiments, full factorial design, correlation, association, regression, cluster analysis, multiway statistical methods for food processes, product development for food engineering and technology. Various applications such as multivariate statistical analysis, partial least squares regression, principal component regression, CFD simulations, reaction engineering approach are discussed in detail for optimizing different food processes, sensory evaluation, baking behaviour dough biscuits and analysis for food safety and quality assurance. Modelling facilitates the easy understanding of the proposed system by taking care of interaction and square terms along with the individual effects of the process. These aspects have been dealt with by their peers. Mathematical and statistical application in food engineering is a very powerful tool that can be used in different areas in order to reduce the number of experiments needed to decide which factors influence dependent variables. All these have been achieved in the book by describing the specialty processes and pioneering works. The editors have brought together a pool of expertise to present the state-of-art information, which has presented an in-depth analysis of the knowledge on various aspects. This book provides a complete solution for all the latest mathematical methods used in food engineering and fermentation technology. This book provides the basic knowledge of how statistical methods can be used to solve the typical problem associated with food engineering and fermentation technology. Combining theory with a practical, hands-on approach, this book covers the key aspects of food engineering. A complement to Mathematical and statistical applications in food engineering. Presenting cutting-edge information, this book is an essential reference for the fundamental concepts associated with food engineering today. With contributions from a broad range of leading professors and scientists from all over the globe, this book focuses on new areas of mathematical and statistical methods for food engineering to help meet the increasing food demand of the rapidly growing populations of the world. This is a friendly book on the mathematical method for food engineering, it will help researchers, and students to overcome this vi Mathematical and Statistical Applications in Food Engineering apathy, appreciate the beauty of analytical tools, sharpen their mathematical skills through examples, and exercise problems. This book offers an accessible guide to applying statistical and mathematical technologies in the food-engineering field whilst also addressing the statistical methods and theoretical foundations. Using clear examples and case studies by way of practical illustration, the book is more than just a theoretical guide for non-statisticians is, and may, therefore, be used by scientists, students and food industry professionals at different levels and with varying degrees of statistical skill. This book also provides all different mathematical methods used in food engineering, which will be helpful to the researchers in this area. Guwahati, India Surajbhan Sevda New Delhi, India Anoop Singh Contents Foreword iii Preface v 1. Role of Mathematical and Statistical Modelling in Food Engineering 1 Surajbhan Sevda, Vijay Kumar Garlapati and Anoop Singh 2. Evolutionary Optimization Techniques as Effective Tools for Process Modelling 5 in Food Processing Lakshmishri Roy, Debabrata Bera and Vijay Kumar Garlapati 3. Optimization of Food Processes Using Mixture Experiments: Some Applications 21 Daniel Granato, Verônica Calado and Edmilson Rodrigues Pinto 4. Microorganisms and Food Products in Food Processing Using Full Factorial Design 36 Davor Valinger, Jasna Gajdoš Kljusurić, Danijela Bursać Kovačević, Predrag Putnik and Anet Režek Jambrak 5. The Use of Correlation, Association and Regression Techniques for Analyzing 51 Processes and Food Products Jimy Oblitas, Miguel De-la-Torre, Himer Avila-George and Wilson Castro 6. Application of Cluster Analysis in Food Science and Technology 68 Chapman, J, Power, A, Chandra, S, Roberts, J and Cozzolino, D 7. Multiway Statistical Methods for Food Engineering and Technology 74 Smita S Lele and Snehasis Chakraborty 8. Application of Multivariate Statistical Analysis for Quality Control of Food Products 98 Soumen Ghosh and Jayeeta Mitra 9. Importance of Normality Testing, Parametric and Non-Parametric Approach, 112 Association, Correlation and Linear Regression (Multiple & Multivariate) of Data in Food & Bio-Process Engineering Soumen Ghosh and Jayeeta Mitra 10. Regression Analysis Methods for Agri-Food Quality and Safety Evaluations Using 127 Near-Infrared (NIR) Hyperspectral Imaging Chandra B Singh and Digvir S Jayas 11. Partial Least Square Regression for Food Analysis: Basis and Example 141 Wilson Castro, Jimy Oblitas, Edward E Rojas and Himer Avila-George 12. Mathematical Modelling of High Pressure Processing in Food Engineering 161 Deepak Kadam, Surajbhan Sevda, Namrata Tyagi and Chetan Joshi 13. Food Process Modeling and Optimization by Response Surface Methodology (RSM) 181 Narjes Malekjani and Seid Mahdi Jafari viii Mathematical and Statistical Applications in Food Engineering 14. A Mathematical Approach to the Modelling of the Rheological Properties 204 of Solid Foods Ryszard Myhan and Marek Markowski 15. Mathematical Models for Analyzing the Microbial Growth in Food 224 Jyoti Singh and Vishal Mishra 16. Computational Fluid Dynamics (CFD) Simulations in Food Processing 243 Abhishek Dutta, Ferruh Erdoğdu and Fabrizio Sarghini 17. Application of Multivariate Statistical Analysis for Food Safety and 263 Quality Assurance S Jancy and R Preetha 18. Mathematical Modelling in Food Science through the Paradigm of Eggplant Drying 276 Alessandra Adrover and Antonio Brasiello 19. Use of Mathematical Modelling of Dough Biscuits Baking Behaviour 294 Noemi Baldino, Francesca R Lupi, Domenico Gabriele and Bruno de Cindio 20. Applications of Principal Component Analysis (PCA) for Fruit Juice Recovery 307 and Quality Analysis Debabrata Bera, Lakshmishri Roy and Tanmoy Bhattacharya 21. Use of Artificial Neural Networks in Optimizing Food Processes 321 RA Conde-Gutiérrez, U Cruz-Jacobo and JA Hernández 22. Application of Neural Networks in Optimizing Different Food Processes: 346 Case Study KK Dash, GVS Bhagya Raj and MA Gayary 23. Mathematical Modelling for Predicting the Temperatures During Microwave 363 Heating of Solid Foods: A Case Study Coskan Ilicali, Filiz Icier and Ömer Faruk Cokgezme 24. Microwave Drying of Food Materials Modelled by the Reaction Engineering 389 Approach (REA)—Framework Aditya Putranto and Xiao Dong Chen 25. Modelling of Heat Transfer During Deep Fat Frying of Food 398 KK Dash, Maanas Sharma and MA Bareen Index 423 Color Section 425 1 CHAPTER Role of Mathematical and Statistical Modelling in Food Engineering Surajbhan Sevda,1,* Vijay Kumar Garlapati 2 and Anoop Singh 3 1. Importance of Mathematical Modelling in Food Engineering The food engineering domain is considered to be a complex process due to the presence of various scalable throughputs, rheological properties of food molecules, different processes and environmental parameters, quality and stability concerns. Moreover, the influencing parameters impact the other parameters through the mutual dependencies. The complexity also lies in the raw and processed food commodities in the form of variability, which impacts the composition and their processability in further operations. Various food engineering problems, such as heat and mass transfer operations, are heterogeneous and variable. Therefore, it is not possible to handle them with only basic disciplines, such as mathematics, physics and science (Barnabé et al., 2018). The heterogeneity and variability encountered in the food engineering domain can be made manageable by utilizing the modelling approaches which put forth the non-linear relationship of the different process parameters on the performance of the process or on the final yield of the food product (Shenoy et al., 2015). Mathematical modelling plays a vital role in understanding the food process related to thermal (heat and mass transfer) and non-thermal processes (high-pressure processing, pulsed electric field) and in assessing the possible microbial and biochemical changes during the shelf-life of the food products (Farid, 2010). The modelling approaches also tackle the scale-up associated problem and are useful in simulations, since they take care of impossible process parameters, such as high temperatures (Dutta, 2016). The machine-learning/simulation-based optimization approaches help in attaining/predicting the optimum output by considering the individual, square and interaction parameters of the process. The machine-learning based optimization approaches produce a huge set of data for processing with the intuitive knowledge of the nature-based phenomenon. Moreover, the modelling and optimization 1 Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, India. 2 Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Wakhanaghat-173234, Himachal Pradesh, India. 3 Government of India, Ministry of Science and Technology, Department of Scientific and Industrial Research (DSIR), Technology Bhawan, New Mehrauli Road, New Delhi-110016, India. * Corresponding author: [email protected]
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