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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
NONTHERMAL FOOD ENGINEERING OPERATIONS Presenting cutting-edge information on new and emerging food engineering processes, Nonthermal Food Engineering Operations, the latest volume in the series, "Bioprocessing in Food Science," is an essential reference on the modeling, quality, safety, and technologies associated with food processing operations today. "Bioprocessing in Food Science" is a series of volumes covering the entirety of unit operations in food processing. This latest volume covers nonthermal food engineering operations, focusing on packaging techniques, artificial intelligence and other emerging technologies and their use and relevance within food engineering, fluid extraction, nanotechnology, and many other topics. As the demand for healthy food is increasing in the current global scenario, manufacturers are searching for new possibilities for occupying a greater share in the rapidly changing food market. Compiled reports and updated knowledge on thermal processing of food products are imperative for commercial enterprises and manufacturing units. In the current scenario, academia, researchers, and food industries are working in a scattered manner and different technologies developed at each level are not compiled to implement for the benefits of different stakeholders. However, advancements in bioprocesses are required at all levels for the betterment of food industries and consumers. This series of groundbreaking edited volumes will be a comprehensive compilation of all the research that has been carried out so far, their practical applications, and the future scope of research and development in the food bioprocessing industry. During the last decade, there have been major developments in novel technologies for food processing. This series will cover all the novel technologies employed for processing different types of foods, encompassing the background, principles, classification, applications, equipment, effect on foods, legislative issue, technology implementation, constraints, and food and human safety concerns.
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Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 Artificial Intelligence (AI) in Food Processing
1.1 Introduction
1.2 Evolution of Artificial Intelligence
1.3 Artificial Intelligence in Food Processing
1.4 Artificial Neural Network (ANN)
1.5 Fuzzy Logic System
1.6 Knowledge-Based Expert System (ES)
1.7 Machine Learning System (ML)
1.8 Conclusion
References
2 Advances in Ultrasound in Food Industry
2.1 Introduction
2.2 Background of Ultrasound
2.3 Ultrasonic Waves
2.4 Applications of Ultrasonics in the Food Industry
2.5 Detection of Fruit Quality
2.6 Ultrasound in Dairy Sector
2.7 Conclusion
References
3 Biosensors in Food Quality and Safety
3.1 Introduction
3.2 What is a Biosensor?
3.3 Categorization of Biosensors
3.4 Application of Biosensors
3.5 Future Prospects
References
4 Cold Plasma: Principles and Applications
4.1 Introduction
4.2 Physics of Plasma
4.3 Methods of Generation
4.4 Principles of Cold Plasma Decontamination
4.5 Plasma Species’ Role in Microbial Inactivation
4.6 Cold Plasma Affecting Microbial Cells
4.7 Limitations
4.8 Conclusion and Future Prospects
References
5 Food Extrusion: An Approach to Wholesome Product
5.1 Introduction
5.2 Principle and Components of Extrusion Equipment
5.3 Types of Extruders
5.4 Food Product Based on Extrusion Technology
5.5 Effect of Extrusion Cooking on Nutritional Aspects of Food
5.6 New Research Area of Byproduct Waste Utilization
5.7 Conclusion
References
6 Image Processing Technology, Imaging Techniques, and Their Application in the Food Processing Sector
6.1 Introduction
6.2 Image Processing Technology
6.3 Machine Learning Algorithms
6.4 Industrial Applications
6.5 Novel Imaging Techniques and Their Applications
6.6 Challenges and Opportunities
References
7 Active and Passive Modified Atmosphere Packaging: Recent Advances
7.1 Introduction
7.2 Modified Atmosphere Packaging
7.3 Final Remarks
References
8 Membrane Processing Techniques in Food Engineering
8.1 Introduction
8.2 Overview of Membranes
8.3 Types of Membrane Separation Processes
8.4 Filtration Modes
8.5 Membrane Structure
8.6 Important Terms Related to Membrane Processes
8.7 Operational Requirements of Membranes
8.8 Theoretical Models for Membrane Processes
8.9 Factors Affecting the Separation Processes
8.10 Major Advantages of Membranes
8.11 Microfiltration
8.12 Ultrafiltration
8.13 Nanofiltration
8.14 Application of Membrane Separation in Food Industry
8.15 Conclusion
References
9 Nano Technology in Food Packaging
9.1 Introduction
9.2 Nanomaterials
9.3 Use of Nanotechnology in Improved Packaging
9.4 Use of Nanotechnology in Active Packaging
9.5 Use of Nanotechnology in Smart Packaging
9.6 Toxicological Aspects, Safety Consideration, and Migration of Nanoparticles
9.7 Future Outlook and Conclusion
References
10 Polysaccharide-Based Bionanocomposites for Food Packaging
10.1 Introduction
10.2 Classification of Polysaccharides
10.3 Extraction and Purification of Polysaccharides
10.4 Polysaccharide-Based Bionanocomposite Fabrication Techniques
10.5 Polysaccharide-Based Nanocomposites: Classification and Food Applications
10.6 Conclusions
References
11 Smart, Intelligent, and Active Packaging Systems for Shelf-Life Extension of Foods
11.1 Introduction
11.2 Novel Types of Food Packaging
11.3 Regulatory Framework
11.4 Novel Smart Packaging Proposals
11.5 Considerations
11.6 Conclusions
References
12 Supercritical and Subcritical Fluid Extraction Systems
12.1 Introduction
12.2 Supercritical Fluids
12.3 Super Critical Fluid Extraction
12.4 Factors Affecting Supercritical Fluid Extraction
12.5 Applications of Supercritical Fluid Extraction
12.6 Sub-Critical Fluid Extraction
12.7 Factors Affecting Subcritical Fluid Extraction
12.8 Application of Subcritical Fluid Extraction
12.9 Conclusion and Future Trends
References
13 Ultraviolet Rays in Food Processing
13.1 Introduction
13.2 Types of UV Radiation
13.3 Principles of Ultraviolet Radiation
13.4 Types of Ultraviolet Sources
13.5 Types of Influencing Factors of UV Processing
13.6 Effect of UV Processing on Vegetable Crops
13.7 Effect of UV Processing on Fruits Crops
13.8 Effect of UV Processing on Miscellaneous Foods
13.9 Conclusion
References
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Effect of ultrasound on microbial inactivation.
Table 2.2 Effect of ultrasound on enzyme system.
Table 2.3 Applications of ultrasonication on different processing used in the ...
Table 2.4 Application of ultrasound in the detection of fruit quality.
Table 2.5 Application of ultrasound in the dairy industry.
Chapter 3
Table 3.1 Advantages and disadvantages of biosensors.
Table 3.2 Evolution of biosensors.
Table 3.3 Summary of different biosensing techniques.
Chapter 5
Table 5.1 Structural classification of extruders.
Table 5.2 Functional classification of extruders.
Table 5.3 Thermodynamic classification of extruders.
Table 5.4 Moisture classification of extruders.
Table 5.5 Variables used in extrusion yechnology.
Table 5.6 List of materials that can be utilized as good sources of starch.
Table 5.7 Effect of extrusion cooking on different components of food.
Chapter 6
Table 6.1 Research work on different food products based on imaging techniques...
Chapter 7
Table 7.1 Suitable O
2
and CO
2
concentrations in MAP for different fruits and v...
Table 7.2 Commercially available carbon dioxide scavengers.
Table 7.3 Manufacturer and scavenger mechanism of different ethylene scavenger...
Table 7.4 O
2
Consumption and CO
2
production rates (cm
3
kg
-1
d
-1
) and respirato...
Table 7.5 Polymers, film types, and permeability available for packaging of MA...
Chapter 8
Table 8.1 Membrane separation processes.
Table 8.2 Pressure-driven membrane processes and their characteristics.
Table 8.3 Configurations of the membrane module.
Table 8.4 Applications of membranes in food processing.
Chapter 9
Table 9.1 Use of nanomaterials for food packaging applications.
Table 9.2 Nanomaterial used for antimicrobial packaging.
Chapter 10
Table 10.1 Different extraction Techniques for extraction of polysaccharides.
Table 10.2 Applications of polysaccharide-based nanocomposites.
Chapter 11
Table 11.1 Synopsis of main regulatory framework on food packaging systems.
Chapter 12
Table 12.1 Various feed matrix/material characteristics.
Table 12.2 Effect of Supercritical fluid extraction on target compounds of var...
Table 12.3 Recent studies of bioactive compounds extracted from natural produc...
Chapter 13
Table 13.1 General parameters and composition of UV radiation [49].
Table 13.2 Different parameters influencing ultraviolet radiation processing.
Table 13.3 Effects of UV radiation on vegetable cultivars.
Table 13.4 Effects of UV radiation on fruit.
List of Illustrations
Chapter 1
Figure 1.1 Components of artificial intelligence.
Figure 1.2 Artificial neural network.
Figure 1.3 Fuzzy logic system.
Figure 1.4 Knowledge-based expert system.
Figure 1.5 Machine learning process.
Chapter 2
Figure 2.1 Ultrasonic machining system.
Figure 2.2 Principle of ultrasonic cavitation.
Figure 2.3 Circuit diagram of magnetostrictive transducer.
Figure 2.4 Magnetostrictive transducer.
Figure 2.5 Circuit diagram of piezo-electric transducer.
Figure 2.6 Piezo-electric transducer.
Chapter 3
Figure 3.1 Schematic representation of biosensor components.
Figure 3.2 Classification of biosensors.
Figure 3.3 (a) calorimetric biosensors (source: Zhou
et al.
, 2012), (b) potent...
Chapter 4
Figure 4.1 Cold plasma effect on microbial cells [37].
Chapter 5
Figure 5.1 Piston extruder.
Figure 5.2 Roller extruder.
Figure 5.3 Single screw extruder.
Figure 5.4 Various types of twin screw configurations.
Figure 5.5 (a) Snack products (b) Texturized vegetable protein.
Figure 5.6 Different shapes of pasta products.
Figure 5.7 Meat products.
Figure 5.8 (a) Co-extruded products (b) Confectionary products.
Chapter 6
Figure 6.1 Near infrared imaging system and its working [57].
Figure 6.2 Multi/Hyperspectral imaging system [63].
Figure 6.3 Raman imaging system [68].
Figure 6.4 Laser light backscattering imaging system [75].
Figure 6.5 Structured-illumination reflectance imaging system [80].
Figure 6.6 Optical coherence tomography system [86].
Chapter 7
Figure 7.1 Optimum MAP conditions for fruits (Adapted from Mannapperuma, J. D....
Figure 7.2 Optimum MAP conditions for vegetables (Adapted from Mannapperuma, J...
Chapter 8
Figure 8.1 Diagrammatic representation of membrane separation process.
Figure 8.2 Components of pressure-driven membrane separation system.
Figure 8.3 Filtration spectrum.
Figure 8.4 Dead-end & cross-flow filtration modes.
Chapter 9
Figure 9.1 Application of nanotechnology in different sectors of packaging.
Chapter 10
Figure 10.1 Classification of composites.
Figure 10.2 Schematic diagram for polysaccharide-based bionanocomposites.
Chapter 12
Figure 12.1 Historic developments and chronological Eras of supercritical and ...
Figure 12.2 Critical properties of selected solvents (York
et al
., 2004).
Figure 12.3 Phase diagram representing supercritical fluid extraction.
Chapter 13
Figure 13.1 Electromagnetic spectrum and UV spectrum classification.
Figure 13.2 Schematics of (a) LPM, (b) LPA, and (c) MPM lamps (Source: [65]).
Figure 13.3 Schematics of excimer lamp (Source: [132]).
Figure 13.4 Microwave UV lamp assembly [136].
Figure 13.5 General electric PXA-80 pulsed Xenon-Arc helical flash lamp.
Figure 13.6 Conventional UV-LED.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Begin Reading
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Bioprocessing in Food Science
Series Editor: Anil Panghal, PhD
Scope: Bioprocessing in Food Science a series of volumes covering the entirety of food science, unit operations in food processing, nutrition, food chemistry, microbiology, biotechnology, physics and engineering during harvesting, processing, packaging, food safety, and storage and supply chain of food. The main objectives of this series are to disseminate knowledge pertaining to recent technologies developed in the field of food science and food process engineering to students, researchers and industry people. This will enable them to make crucial decisions regarding adoption, implementation, economics and constraints of the different technologies. Bioprocessing has revolutionised the food industry by allowing for more efficient and sustainable production methods. This comprehensive series focused on microbial fermentation, enzyme technology, genetic engineering, microbial transformations, and bioreactor design. As we continue to face challenges such as population growth and climate change, bioprocessing will play an increasingly important role in ensuring a sustainable food supply for future generations.
Manufacturers are looking for new opportunities to take a significant position in a food market that is continually changing as demand for healthy food rises in the current global environment. In the current scenario, academia, researchers and food industries are working in a scattered manner and different technologies developed at each level are not implemented for the benefits of different stake holders. Compiled reports and knowledge on bioprocessing and food products is a must for industry people. However, the advancements in bioprocesses are required at all levels for betterment of food industries and consumers.
The volumes in this series are comprehensive compilations of all the research that has been carried out so far, their practical applications and the future scope of research and development in the food bioprocessing industry. The novel technologies employed for processing different types of foods, encompassing the background, principles, classification, applications, equipment, effect on foods, legislative issue, technology implementation, constraints, and food and human safety concerns are covered in this series in an orderly fashion. These volumes are comprehensively meeting the knowledge requirements for the curriculum of undergraduate, postgraduate and research students for learning the concepts of bioprocessing in food engineering. Undergraduate, post graduate students and academicians, researchers in academics and in the industry, large- and small-scale manufacturers, national research laboratories, all working in the field of food science, agri-processing and food biotechnology will be benefitted.
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Nonthermal Food Engineering Operations
Edited by
Nitin Kumar
Anil Panghal
and
M. K. Garg
This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-77560-7
Cover image: Food-processing industry equipment: Feodor Vasilevich Korolevsky | Dreamstime.com, Salad Leaf: Yurok | Dreamstime.com, Food sterilization manufacturing process, Surasak Petchang | Dreamstime.comCover design by Kris Hackerott
Preface
The book, Nonthermal Food Engineering Operations, in the “Bioprocessing in Food Science” series is an up-to-date and comprehensive overview of this area. Written and edited by a team of experts in the field, this book covers topics such as artificial intelligence usage in food industry, ultrasound, biosensors, cold plasma, extrusion process, image processing, active packaging, membrane processing, nanotechnology, supercritical and subcritical fluid extraction, utilization of ultraviolet rays in technological processes, and food safety concerns. The engineering and science aspects of these technologies are significant component of the undergraduate and postgraduate degree programs in Biological and Agricultural Engineering, Food Engineering, Food Science & Technology, and Nutrition & Health Science throughout the world. These novel technology operations in food processing are considered as one of the core competencies for these programs and in industries as well. Researchers around the globe will be able to use the information as a guide in establishing the direction of future research on nutritional properties during food processing. The main reason for writing this book now is to disseminate the wealth of knowledge on novel processing and its effects on food products. It is envisioned for scientists; technologists/engineers working in the area of food processing, process equipment design, and product development; and students of food science, technology, nutrition, health science, and engineering. It will enable them to make informed decisions regarding adopting appropriate processing technology, implementation, economics, and constraints of different technologies. As the demand for healthy, nutritious, and safe food increases, manufacturers are looking for new possibilities to occupy a growing share of the rapidly changing food market. The book covers a range of emerging topics, from concepts to applications of different nonthermal processes in food engineering operations and their implications in nutritional sciences, nanotechnology in food systems, applications of nano-materials in the food system, emerging techniques in food preservation, and food safety and quality assurance in the food chain. The book also provides insights on advances in nonthermal technology for healthy and safe nutrition with illustrations of how to ensure public health safety. The authors and editors discussed the need for innovative food products, contamination in the food chain, risk assessment, regulatory frameworks, and their challenges within the context of the food engineering in the global food market.
Thanks are due to all authors for contributing their knowledgeable chapters in this volume and helping us to complete the book. We also thank the authorities of Chaudhary Charan Singh Haryana Agricultural University, Hisar (India) for their help and support. Finally, we also express indebtedness and thankfulness to Scrivener Publishing and Wiley team for their unfailing guidance and helpful assistance.
Editors
Nitin Kumar
Anil Panghal
M. K. Garg
1Artificial Intelligence (AI) in Food Processing
S. Abinaya1, Anil Panghal2, Sunil Kumar2, Anju Kumari1, Nitin Kumar2 and Navnidhi Chhikara3*
1Centre of Food Science and Technology, Chaudhary Charan Singh Haryana Agricultural University, Hisar, India
2Department of Processing and Food Engineering, AICRP-PHET, Chaudhary Charan Singh Haryana Agricultural University, Hisar, India
3Department of Food Technology, Guru Jambheshwar University of Science and Technology, Hisar, India
Abstract
The food processing sector holds a significant place among other business sectors globally that support high employability. The efficient production and packing of food products depend greatly on the human workforce. Owing to the involvement of the human workforce, the food industries are not only unable to maintain food safety but also the demand-supply chain. Food is a basic human need. Reducing food waste, streamlining the supply chain, and improving food delivery, logistics, and safety are imperative. The most efficient approach to address these problems in the food industry is through industrial automation. Artificial Intelligence (AI) plays a significant role in achieving these goals. AI is defined as a branch of study that mimics human thought processes, learning abilities, and knowledge storage systems. AI has become an integral part of technological advancements in the food industry in recent years as a result of growing food demands spurred on by a growing world population. The food industry is becoming more and more in need of these intelligent systems due to their versatility for performing tasks like food quality assessment, quality control, classification of foods, food processing, and forecasting. The numerous applications of artificial intelligence in the food sector will grow as a result of ongoing technological advancements and a wider range of application scenarios. This chapter helps to shed light on cutting-edge AI and its technologies in the food processing sector. The first part of this chapter explains what is AI, its components, techniques, and the ways forward its popularity in various sectors. The second part of this chapter provides insight into the various food processing applications of different AI technologies including Machine learning, Expert systems, Fuzzy logic systems, and machine vision, etc., It also discusses their benefits, drawbacks, and approaches to provide guidance for choosing the best approaches to advance future developments related to AI and the food industry. Furthermore, it also explains the efficiency of the use of combinations of two or more AI techniques to make tedious process and applications simple. In dayto-day life, the application of AI continues to grow because of its ability to improve waste management and maintain food quality, hygiene, and safety. In the future, AI significantly changes the food processing sector by producing more reasonable and healthier productivity for the growing population.
Keywords: AI, emerging technologies, expert system, neural networks, fuzzy logic, food industry, quality assessment, food safety
1.1 Introduction
With the advancement of mechanization, the processing sector and current industry have reached productivity peaks in a matter of decades. The processing sector was the first to be transformed by technology developments and many other industries followed (Volter, 2013). In the early 1900s, the thought of automation performing jobs with more precision and eliminating human labor in all disciplines was a vision of hope for a better future. Artificial Intelligence, popularly known as AI, has risen to prominence in recent times, surpassing humans in activities such as object identification and data analysis (Cohen & Feigenbaum, 2014). While learning systems and processing capacity improve, this scenario appears to make a significant step forward. The origins of automation may be traced back to the early 1800s when it enabled the manufacturing sector, which eventually led to current technical advancements (MacLeod, 2002). Automation has now infiltrated nearly all fields and is outperforming market trades by a wide margin (Frohm et al., 2008). The majority of equipment in the 18th century was designed to do simple operations like welding, spinning, and repetitive activities, allowing human workers to focus on more sophisticated activities (Mantoux, 2013). From the early 1900s until the present, various forms of automation have appeared in a few instances, ultimately turning their attention to a wide range of sectors. Nevertheless, recent advances in AI have caused humanity to reconsider the potential of learning and ask, “What might be the depths of AI when machines can learn?”. AI is a collection of various approaches and phenomena, amid which two fundamental principles, Neural Networks (NN) and Deep Learning (DL), are credited for AI’s remarkable progress (Norvig, 2002). AI is a term used to describe computer-generated intelligence that can develop to analyze, plan, comprehend, and interpret human language (Wang, 2008). It is the study and creation of digital systems capable of doing activities that would ordinarily need human intellect, such as vision, speech identification, strategic planning, and language processing (Kumar, 2018). The pioneer of AI, John McCarthy, described it as the science and engineering of creating intelligent devices, particularly intelligent computer programs. Artificial intelligence can be divided into two categories: strong AI and weak AI. The weak AI principle states that the computer should be built to serve as an intelligent element that mimics human decisions, but the strong AI concept states that the machine should be able to reflect the human brain (Borana & Jodhpur, 2016). AI has a range of algorithms to pick from including reinforcement learning, Expert Systems (ES), Fuzzy Logic (FL), Swarm Intelligence, Turing Test, Cognitive Science, Artificial Neural Networks (ANN), and logic programming (Borana & Jodhpur, 2016). AI’s seductive potential has earned it the most popular tool to use in fields such as decision-making and process optimization, intending to lower total costs, improve quality, and increase profitability (Ge et al., 2017; Mahadevappa et al., 2017). Food demand is expected to increase from 59 to 98% by 2050, as the world’s population grows (Elferink et al., 2016). Consequently, AI was used to meet this food demand in areas such as supply chain management, food sorting, production development, food quality enhancement, and adequate food hygiene (Funes et al., 2015). ANN was used to assist complicated problem-solving in the food industry (Funes et al., 2015), and the classification and prediction of variables are simple and easier when using ANN (Correa et al., 2018), which has resulted in a growing demand for ANN over the past year. In addition, FL and ANN were performed as controllers in the areas of food safety, quality management, yield increase, and reducing costs (Kondakci & Zhou, 2017; Wang et al., 2017).
1.2 Evolution of Artificial Intelligence
For scientists, Artificial Intelligence is not a new term or technique. This technique is much older. In Ancient Greek and Egyptian mythologies, there are even tales of mechanical men. The achievements in the history of AI that outline the route from AI formation to current development are listed below (McCorduck & Cfe, 2004):
Warren McCulloch and Walter Pits published the first study on artificial intelligence in 1943, which is today known as AI. They presented an artificial neuron approach.
In the year 1949, Donald Hebb developed an updated rule for altering the intensity of neuron connections. He named the rule Hebbian Learning.
In the year 1950, Alan Turing, an English mathematician, invented the machine learning system. In his paper “Computing Machinery and Intelligence,” Alan Turing proposes a test. A Turing test can be used to determine whether or not a machine can demonstrate intelligent behavior comparable to human intelligence.
In the year 1955, Allen Newell and Herbert A. Simon built “Logic Theorist,” the “first artificial intelligence program.” This program verified 38 of 52 mathematical theorems, as well as discovered new and more concise solutions for several of them.
At the Dartmouth Conference in 1956, John McCarthy, an American computer scientist, coined the term “Artificial Intelligence.” AI became a recognized academic discipline for the first time. High-level computer languages such as FORTRAN, LISP, and COBOL were created during the period. There was a lot of interest in AI during this period.
In the year 1966, the focus of researchers was on inventing algorithms that could solve mathematic problems.
In the year 1972, Japan produced WABOT-1, the world’s first intelligent humanoid robot.
The first AI winter took place between 1974 and 1980. The AI winter represents a duration when computer scientists faced a severe lack of government support (funds) for AI research. Throughout AI winters, there was a drop in public interest in AI.
After a brief hiatus, AI returned with Expert System. Expert systems have been built to mimic the abilities of a human expert to make decisions.
Between 1987 to 1993, the AI Winter lasted for the second time. Investors and the government have once again halted funding for AI research, citing excessive costs and ineffective results. XCON, for example, was a very cost-effective expert model.
In the year 1997, IBM Deep Blue defeats Gary Kasparov, the global chess champion, and becomes the first computer to defeat a world chess champion.
In the year 2002, AI made its first appearance in the house in the Roomba vacuum cleaner.
In the year 2006, artificial intelligence (AI) was introduced into the business world.
In the year 2011, IBM’s Watson won Jeopardy, a game show in which it had to tackle difficult questions and puzzles. Watson demonstrated that it could comprehend plain language and solve complex problems fast.
Sophia is a robot that resembles a human. It is a humanoid robot that was designed in Hong Kong by American company Hanson Robotics and has been operational since April 19, 2015. Even though she does not yet demonstrate the most remarkable features that are anticipated of AI, she has already been accepted as the First World Citizen Robot by Saudi Arabia in the year 2017, based on her language performance, conversational abilities, and expressive externalizations in her brief existence. Sophia’s key technological feature is her ability to understand human behavior through her interactions with others. Because of this, it has been granted as the latest breakthrough in AI due to its mastery of a complicated sequence of predictive algorithms based on computational statistics, a fluid artificial phonation, quick interpretation of the data she absorbs, and a wider ability to detect faces and voices (Retto, 2017).
Now, AI has progressed to a stunning level. The conception of deep learning, neural networks, expert systems, machine learning, computer vision, big data, and data science all have a major impact in all fields (McCorduck & Cfe, 2004).
1.3 Artificial Intelligence in Food Processing
Over the years, AI has been used in the food sector for a variety of reasons, including food sorting, selection, categorization, quality parameter forecasting, quality control, and food safety. Common approaches in the food industry include:
Artificial Neural Networks (ANN)
Fuzzy logic model
Expert Systems (ES)
Adaptive Neuro-Fuzzy Inference Systems (ANFIS)
Machine Learning (ML)
Before the introduction of AI, food research was conducted for many years to raise awareness in the community about food knowledge and to optimize the end outputs connected to food attributes and production (Rahman et al., 2012). The AI approach can provide numerous advantages and its use in the food business has been popular for decades and continues to grow today (Corney, 2002).
1.4 Artificial Neural Network (ANN)
There has been significant growth in interest in ANNs over the last 15 years. ANNs have been extensively used to solve complicated issues in a variety of domains, including the ones listed below (Sozen & Akcayol, 2004; Sozen & Arcaklıoglu, 2007):
Pattern correlation and pattern recognition
Associative recollections
Development of new emerging patterns
Function approximation
Some tasks are well-suited to ANNs, whereas others aren’t. They are particularly well-suited to jobs involving inadequate data sets, ambiguous or missing information, and very complicated and ill-defined situations where humans typically make decisions based on perception. They are capable of learning from investigations and dealing with nonlinear challenges. They also have a high level of reliability and are fault tolerant. The activities that ANNs cannot do well are those that need a lot of precision and efficiency, such as logic and arithmetic (Menlik et al., 2009). By identifying the needed information from the input data, ANNs circumvent the shortcomings of traditional methods. A very precise equation form is not required for an ANN. Instead, it requires enough input-output data. It can also be trained regularly so that it can easily respond to fresh data (Sozen & Ozalp, 2003). An ANN is made up of several layers: an input layer, an output layer, and several hidden layers. The training process of a neural network occurs when an input and target output are given to the network and the weights are modified so that the network tries to create the required outcome. The weights include useful information after the training phase, whereas before they were unpredictable and had no value. Once a suitable level is attained, the network ends training and utilizes the weights to make judgments, discover patterns, and describe correlations in test data (Satish & Setty, 2005). There are a variety of learning algorithms to choose from. The back-propagation algorithm, which has various versions, is a prominent algorithm. Furthermore, the algorithms’ performance is dependent on user-dependent variables, learning rate, and momentum constant.
1.4.1 Fats & Oils Quality Evaluation
Vegetable oils come in a wide range of flavors and trademarks. Vegetable oils have a lot of similarities in terms of color, odor, and flavor and it’s difficult to tell them just by looking at them. Methods for classifying these oils are frequently expensive and time-consuming and they frequently rely on analytical and mathematical methods to enhance their effectiveness. Because of the diverse range of products, more advanced techniques for qualifying, characterizing, and classifying these products are required, as the final cost would represent the quality of the product that reaches the purchaser.
Da Silva et al., 2015 proposed a model for identifying canola, sunflower, corn, and soybean oils from different manufacturers. To acquire the spectra of induced fluorescence in diluted oil samples, a simplified math approach, a light-emitting diode, and a CCD array sensor were used. An ANN with 3 layers, each of which has four neurons, performs spectrum categorization. They observed that this model enables quick network training while only requiring a few mathematical operations on the spectrum data. From the aspect of vegetable oil quality management, this is an efficient technique that can be utilized to conduct future research concentrating on the resolution of genuine blends of oils from various cultivars, namely for corn and soybean oils. More research on sunflower and canola oils is needed to increase categorization precision. This novel technique emphasizes the requirement for careful analysis of the characteristics that characterize the regions of interest over the fluorescence spectrum by employing the most related variables of the fluorescence spectrum as input data for ANN. As a result, it was concluded that this approach was capable of identifying vegetable oil and enabling rapid network training with a 72% success rate, utilizing only a few statistical manipulations in the spectral data (Da Silva et al., 2015).
Groselj et al., 2008 used the Counter-propagation Artificial Neural Networks (CP-ANN) approach to detect the presence of refined hazelnut oil in refined olive oil. FT-MIR spectroscopy was used to examine oil samples. They were divided into three categories: pure olive oil (Class 1), pure hazelnut oil (Class 2), and two types of adulterated olive oil samples: one with more than (or equal to) 10% hazelnut oil (Class 3) and the other with less than 10% hazelnut oil (Class 4). Furthermore, FT-MIR was used to assess an external set of blind samples. Five CP-ANN models were created and tested for classification performance using varied numbers of specified infrared spectral areas. The best models were chosen and utilized for blind sample estimation using the leave-one-out cross-validation approach. The results revealed that the ANN model has successfully classified pure olive oil and hazelnut oil and divided it into various groups. Furthermore, an acceptable distinction was established between mixes and pure oils (Groselj et al., 2008).
Due to the high expense of manufacturing compared to other oils on the market, Extra Virgin Olive Oil (EVOO) has always been vulnerable to deception and complexity. Spectral and Volatile Organic Compound (VOC) parameters are crucial in determining the authenticity of an EVOO. Violino et al., 2021 used an open-source UV VIS (Ultraviolet-Visible)-Near-infrared spectrophotometric observation, as well as a VOCs analyzer to determine different levels of sophistication. A total of 96 samples were examined. Pure EVOO, complex olive oil, pure seed oils, and olive oil samples adulterated with 7 different seed oils at different ratios were among the samples. To detect adulterations in the spectrum and VOC data, an artificial intelligence algorithm was used. Both the models, that is the models based on spectral and VOC data, exhibited faultless categorization (100%) of pure EVOO samples in comparison to complex ones. The most relevant spectral values and Volatile Organic Compounds (VOCs) were extracted. ANN models were created with the goal of not only identifying complex samples but also understanding the most relevant spectra and VOCs to design specific anti-fraud systems (Violino et al., 2021).
Fish oil deteriorates mostly due to oxidation, which results in significant quality and nutritive value losses. Chemical analysis is currently the only tool available for monitoring lipid oxidation in foods (Bhagya Raj & Dash, 2020). FTIR (Fourier Transform Infrared Spectroscopy) is a tool for studying the molecular structure and compositional alterations in a variety of foods. To estimate the oxidative qualities of Menhaden fish oil, Klaypradit et al., 2011 employed attenuated total reflectance-FTIR to evaluate the oxidative quality and applied the ANN model. The oil was kept at ambient temperature under the light. Each day during the three weeks of storage, the oxidation was assessed for primary and secondary oxidative modifications; Peroxide Value (PV) and Anisidine Value (AnV) were calculated using FTIR and matched to chemical analysis. ANN was used to estimate the oxidative values of the oil based on the wavenumber and absorbance readings of FTIR spectra. Wave number was used as an input and PV and AnV were used as absorbance outputs. Modifications in the zone between 3,500 and 1,700 cm-1, as well as absorbance, were shown to be connected to the chemical analysis of PV and AnV. FTIR spectroscopy with ANN reveals it is promising as an alternate and faster way for predicting food lipid oxidation than a traditional method (Klaypradit et al., 2011).
1.4.2 Fruits Quality Evaluation
A classifier for apples based on ANN was developed by Bhatt et al., (2014). The entire system is split into two parts. In the first section, the software’s built-in visual basic collects input (surface-level quality characteristics of the apple) from distinct sources via various input devices such as a web camera, a weight machine, and so on. The input data is then used by the ANN simulator in the second section to categorize the apples based on their quality. The classifier achieves excellent results in the grade A and B categories (100 and 98% classifications, respectively). Conversely, the classification efficiency was a bit lower in grade D (92%), whereas for grade C the efficiency was very low (75.5%). The overall efficiency was found to be 91.5%, with an error rate of only 8.5%. The modelling results revealed that the experimental data and predicted values are extremely well aligned. Hence, it was proved that the ANN was an effective tool for detecting apple quality, as evidenced by the low degree of error during prediction.
Lan et al., (2020) merged three ANN and several prediction models to predict the soluble solid content (SSC) of Korla fragrant pears during the maturity stage. The ANN models chosen were a General Regression Neural Network (GRNN), Back-Propagation Neural Network (BPNN), and Adaptive Network Fuzzy Inference System (ANFIS). When the prediction values of GRNN, BPNN, and ANFIS were examined, it was discovered that GRNN outperformed BPNN and ANFIS in determining the variations of the SSC of pears, followed by ANFIS. The findings of this study show that by integrating electrical attributes and ANN, it is possible to forecast the SSC of Korla fragrant pears during the maturity period in a non-destructive way.
Gabriels et al., (2020) used Visible Near-Infrared Spectroscopy (VNIR) to predict internal browning in Keitt mango halves. Individual mangoes were sliced into halves for the reference analysis and the level of internal browning was measured using a standardized color imaging (CI) method known as a browning index (BI). The CI assigned a value to each mango’s “browning index,” which confirmed the presence and intensity of interior browning. Both regression and classification analysis were used in the data modelling. To correlate the VNIR spectra with the BI data reported from the internal color analysis, regression was used. The classification analysis was used to categorize mangoes as either healthy or brown. Artificial Neural Networks (ANN) and Partial Least Squares (PLS) were used as analytical techniques. The study revealed that VNIRS paired with ANN can accurately categorize mangoes as healthy or having interior brown with an accuracy of above 80%. A reliable classification system could help upgrade quality selections throughout the mango production chain and also reduces post-harvest losses (Gabriels et al., 2020).
In the realm of agriculture research, determining the stage of ripeness of fruits is critical (Devgan et al., 2019; Devgan et al., 2020). That is because ripeness is linked to quality and can influence the product’s commercialization. A system for classifying the maturity condition of guavas is proposed by Lara-Espinoza et al., 2016. Guavas come in three different stages: green, ripe, and overripe. Color features are used as input in the classification system, which is based on an ANN. The proposed characteristics are derived from three different color spaces: RGB (Red, Green, Blue), CIELab, and CIELuv. The study employed the RGB color values because they provided the best separability between classes. The algorithm was put to the test with genuine guava photos, and it came out with a score of 97.44 % accuracy.
Chilling injury can occur in peaches stored in the refrigerator, resulting in a worsened texture and a loss of juice. Pan et al., 2016 built a hyper-spectral imaging system to identify chilling injury and an ANN model with eight ideal wavelengths was designed to analyze fruit quality. Major changes in fruit quality attributes and the spectral response to corresponding designed wavelengths were identified between normal and chill-damaged peaches. The correlation coefficients between quality factors and the relevant spectrum response of eight wavelengths ranged from 0.587 to 0.700, 0.393 to 0.552, 0.510 to 0.751, and 0.574 to 0.773. The mean accuracy rate of chill damage for all cold-stored samples was 95.8% when appropriate representative wavelengths were used as inputs for the ANN model (Pan et al., 2016).
1.4.3 Dairy Products Quality Evaluation
The rising popularity of low- and reduced-fat dairy products necessitates frequent health agency inspections to ensure their legitimacy. The typical laboratory procedures for analyzing fat in dairy products are extremely straightforward, but they need processing and the use of corrosive chemicals, like sulfuric acid, to disrupt the chemical bonds between fat and proteins. They also produce chemical residues, which must be disposed of properly. For the categorization of commercial yoghurts in the low- and reduced-fat groups, an ANN based on simple instrumental measurements such as pH, color, and hardness was proposed by Da Cruz et al., (2009). In this study, a total of 108 strawberry-flavored yoghurts (48 probiotic low-fat, 36 low-fat, and 24 full-fat yoghurts) from various commercial brands and lots were used. The results observed that full-fat yoghurts exhibited substantial differences when compared to probiotic low-fat and low-fat yoghurts, according to the mentioned instrumental analysis. Probiotic yoghurts have higher hardness ratings than non-probiotic yoghurts, regardless of their fat content. A database was developed, and a neural model was built using the outcomes of pH, hardness, and color analysis of samples from the three categories of yoghurt. The suggested model was 100% effective in predicting the yoghurt category after validation using unseen data sets (Da Cruz et al., 2009). The proposed model proved to be a rapid and effective approach to evaluating the legitimacy of these items because instrumental assessments do not need any sample processing and do not generate any chemical residues (Da Cruz et al., 2009).
The storage and shelf life of yoghurt is determined by physical, chemical, and microbial modifications. Sofu & Ekinci, (2007) conducted a study to determine the storage period of yoghurt using the ANN approach. In this study, microbial counts and pH values of yoghurt were measured at 1-, 7-, and 14-days during storage. Concurrently, image processing of yoghurt was digitized using a Machine Vision System (MVS) to assess color changes during storage and the resulting data was modelled using an ANN for shelflife forecasting of set-type whole-fat and low-fat yoghurts. BPNN with a single hidden layer and sigmoid activation functions were used to create the ANN system including pH, total aerobic, yeast, mold, coliform counts, and color analysis values measured by the MVS were taken as input parameters, whereas the storage period of yoghurt was taken as the output parameter of the ANN. The modelling findings revealed that the experimental results and values obtained were in reasonable agreement with a high coefficient of determination (R2 = 0.9996), indicating that the proposed model could analyze nonlinear multivariant data with outstanding performance, fewer parameters, and a quicker computation time. The approach could be a new way to control the expiration date of yoghurt on the label as well as provide a safer food source for consumers (Sofu & Ekinci, 2007).
Singh et al., (2009) conducted a comparative study to predict the shelf life of Ultrahigh Temperature (UHT) milk using chemical kinetics and an ANN approach. Changes linked with proteolytic, lipolytic, oxidative, and Maillard reactions were described as variables in this study, while sensory quality was assessed using taste score and total sensory score as dependent variables. Multiple regression calculations for the five physicochemical parameters as independent variables with their Arrhenius values were used to create kinetic models. The same five parameters were used as input data for the ANN and flavor and total sensory scores were taken as output data. The Bayesian regularization approach produced the most consistent results of the several ANN models investigated and was thus utilized to create the ANN models. The ANN-based models outperformed the kinetic models in terms of prediction accuracy, as measured by per cent root mean square error (Singh et al., 2009).
Vasquez et al., (2018) compared ANN and Partial Least Squares Regression (PLSR) approaches for the prediction of the hardness of Swiss-style cheese during the ripening process using hyperspectral images. In this study, 40 Swiss-style cheese samples were produced and allowed to ripen. Hyper-Spectral Images (HSI) in the range of 400–1000 nm was acquired throughout this technique in reflectance mode. The texture profile analysis method was used to determine the degree of hardness of Swiss-style cheese. PLSR and ANN were used to model the link between spectral patterns and hardness levels of swiss style cheese. Two models have been proposed for PLSR and ANN, the first of which uses all wavelengths and the second of which selects the appropriate wavelengths. The ANN model outperformed the PLSR model by a small margin. As a result, the suggested method (HSI + ANN) can be used to forecast the textural characteristics of Swiss-style cheeses throughout the ripening process.
Stangierski et al., (2019) conducted a study to compare the efficiency of Multiple Linear Regression (MLR) and ANN to determine the overall quality characteristics of spreadable Gouda cheese throughout storage at 8°C, 20°C, and 30°C, respectively. The ANN model employed five components that were chosen using principal component analysis as input information for the calculation. Figure 1.1 represents the components of artificial intelligence system. A training set, a validation set, and a test set were created out of the datasets. The multiple regression approach had high determination coefficients of 0.99, 0.87, and 0.87 for 8, 20, and 30°C, respectively, making them a viable tool for predicting quality degradation. Likewise, ANN models with determination coefficient values of 0.99, 0.96, and 0.96 for temperatures of 8, 20, and 30 °C were created. An ANN-based model with greater determination coefficients and lesser root mean square error (RMSE) values were shown to be more precise. Processed cheese held at 8°C provided the best model fit for the experimental observations (Stangierski et al., 2019).
Figure 1.1 Components of artificial intelligence.
The issue of whey adulteration in milk is one that both national and international officials are concerned about. Conde et al., (2020) used ANN and routine assessments of dairy samples to measure the whey concentration in tainted milk samples. Samples were tested for fat, non-fat solids (SNF), density, protein, lactose, minerals, and freezing point, totaling 164 tests, from which 60% have been used for network training, 20% for network validation, and 20% for neural network testing. Figure 1.2 represents the model of artificial neural network. The relevance of the parameters was determined using the Garson approach. The adulteration of whey to milk reduces the concentration of various constituents, with fat and density being the most altered, but constituent levels remain below legal limits. A neural network with 15 hidden layer neurons was developed to detect milk fraud, indicating potential fraud in the adulteration of whey into milk. The evaluation of the relative significance of input variables by the Garson approach revealed that the tested input variables with maximum relative relevance were found to be fat and density for the prediction of adulterated milk (Conde et al., 2020).
Figure 1.2 Artificial neural network.
1.4.4 Solvent Extraction
The desired component (analyte) was extracted from its original site in the solvent extraction procedure by combining it with a solvent with a polarity similar to the desired component. Cheok et al., 2012 designed and optimized a model for the solvent extraction of mangosteen hull using ANN and RMS approaches, taking total phenolic content (TPC) as a dependent parameter, whereas extraction time, solid to solvent ratio, and methanol concentration were the independent parameters taken for the ANN model. The model was created with a single hidden layer with 9 neurons and an MSE of 0.020. For the prediction of a continual nonlinear function, one hidden layer was appropriate because many hidden layers could result in overfitting (Madadlou et al., 2009). The results showed that the solid-to-solvent ratio and methanol concentration had substantial deleterious effects on TPC yield, whereas extraction duration had no major impact on TPC yield. With the addition of water to methanol, the polarity of the extraction solvent was changed, resulting in increased TPC yield. The experimental results acquired from the RSM (Response Surface Methodology) design were compared to the ANN and RSM estimated data. The RSM model’s r2 and Average Absolute Deviation (AAD) were found to be 0.897 and 5.37%, respectively. The ANN design was found to have a greater r2 value of 0.945 and a lesser AAD value of 4.01%. However, both the ANN and RSM estimated data agreed well with experimental results, but the results demonstrated that the ANN design had higher r2 and lower AAD values for nonlinear data, and hence superior response prediction capabilities than the RSM design (Bas & Boyaci, 2007).
1.4.5 Microwave Assisted Extraction (MAE)
To improve the extraction process variables, an ANN was used to simulate MAE of Stevia rebaudiana (Bertoni) leaves, Bixa orellana (Annatto) seeds, Punica granatum (pomegranate) rind, and Aronia melanocarpa (chokeberries) fruit (Bhagya Raj & Dash, 2020). The MAE processing of Stevia rebaudiana (Bertoni) leaves was simulated using ANN to achieve total extract yield (Y1), stevioside yield (Y2), and rebaudioside–A (Reb-A) yield (Y3) (Ameer et al., 2017). The ANN approach used three input parameters: extraction time (1–5 min), ethanol concentration (0–100%), and microwave power (40–200 W). ANN’s prediction performances were compared to those of RSM in respect to r2, RMSE (root mean square error), and AAD (average absolute deviation) values. When comparing the ANN approach to the RSM approach, the extraction procedure reported greater r2 values (0.9678, 0.9443, and 0.9818 for Y1, Y2, and Y3, respectively), lesser RMSE (1.87, 1.72, and 1.48 for Y1, Y2, and Y3, respectively), and AAD (average absolute deviation) values (0.416, 0.577, and 0.6382 for Y1, Y2, and Y3, respectively). As a result, by comparing two methods for the evaluation of extract yield, stevioside yield, and rebaudioside–A yield, the ANN approach performed better than RSM, concerning prediction performance (Ameer et al., 2017).
Sinha et al., (2012) used ANN and RSM approaches for simulating extraction procedures from pomegranate rind using MAE. The independent parameters such as extraction duration of 25-90 sec, pH value of the solution (3.5-8), and quantity of pomegranate rind were 0.5-1.5g, used as input data for the ANN approach. To design a model, a feed-forward backpropagation neural network with three layers was chosen, namely input, output, and hidden layers, each containing three, one, and ten neurons. The findings showed dye extraction values in the range of 1077–9715 mg/L, whilst RSM and ANN estimated values in the ranges of 1059.66–9643.99 mg/L and 1497–9422 mg/L, respectively. The highest yield was attained with the best combination of independent parameters as an extraction period of 90 sec and a quantity of pomegranate rind of 1.48 g at a pH value of 3.5. As a result, the estimated response generated by both RSM and ANN approaches was in a similar range to the measured responses for dye extraction from pomegranate rind using MAE.
1.4.6 Ultrasound-Assisted Extraction (UAE)
Bhagya Raj & Dash (2020b) used the ANN approach for simulating the extraction process of Phytocompounds from dragon fruit peel (DFP) using the ultrasound-assisted extraction (UAE) method. An ultrasonic homogenizer with a probe was utilized to extract phytocompounds from DFP powder at a steady power of 100W. The independent parameters were ultrasonic temperature (30–70 °C), solvent-to-solid ratio (10:1–30:1 mL/g), solvent concentration (30–60%), and ultrasonic exposure time (5–25 min). The three dependent parameters, total polyphenolic content, antioxidant activity, and betacyanin content, were investigated using various combinations of independent parameters for ANN output data. For the experimental setup, the central composite model was utilized with 30 trials including the varying pattern of independent variables. The input, hidden, and output layers of the ANN each had 4, 11, and 3 neurons. The results showed total polyphenolic content, antioxidant activity, and betacyanin content values ranging from 4.509 to 8.536 mg GAE/g, 41.404 to 74.811%, and 1.018 to 1.602 mg/g, respectively. As a result, the ANN approach estimated the response with a corresponding variation of less than 2.05% in the optimal condition for the UAE procedure (Bhagya Raj & Dash, 2020b).
1.4.7 Microwave Drying
Murthy et al., (2012) used the ANN approach for the determination of drying mechanisms during the drying process of mango and ginger using varying microwave power values. The drying studies were carried out for a certain period at four distinct microwave power levels of 315, 455, 595, and 800 W, which were then regarded as input data for ANN. For the determination of moisture content, a feed-forward ANN with the backpropagation algorithm was used and regarded as the output data. The system was designed with a single hidden layer comprised of 3 neural networks. Out of 4 microwave power values used, two power values, such as 315 and 800W, were utilized for training, whereas the other two values, 415 and 595W, were utilized for validation. As a consequence of the findings, it was discovered that increasing the microwave power values results in an increased rate of drying, as well as reduced time consumed for drying. As the microwave power level was increased, the moisture diffusivity of the sample was also increased, and the values ranged from 3.5 ×10-10 to 9.2 ×10-10 m2 /s. In the case of validation and training datasets, they ARE (average relative error) was found to be 17.6 and 9.2%, respectively. As a result, the ANN method proved to be effective in predicting moisture content during microwave drying of a variety of biological substances (Murthy et al., 2012).
1.4.8 Tray Drying
The efficiency of the tray drying method depends on various factors such as temperature, relative humidity (RH) and incoming airspeed, drying time, original moisture content of the sample, and the physical composition of the food matrix. Movagharnejad & Nikzad, (2007) investigated the drying patterns of tomatoes using a tray dryer and the study was simulated using ANNs and empirical mathematical formulations. The process of drying was conducted using four independent parameters: heater power of 50% to 100%, air velocity of 1 to 1.9 m/s, dry bulb temperature of 43, 51.5, 56, and 72°C, and wet bulb temperature of 30, 34, 37.5, and 48°C to suit the mathematical equations. A feed-forward approach with backpropagation was used to create ANN, which comprised one input layer with three neural networks including heating power, airspeed, and drying time, one output layer with one neural network including moisture ratio, and one hidden layer with four neural networks. For measuring data, the average percentage error of all empirical approaches was found to be around 5%, but the average percentage error of the ANN approach was found to be 1.18%. When both approaches were compared, the ANN system forecasted drying behavior better than the empirical approach (Movagharnejad & Nikzad, 2007).
1.4.9 Osmotic Dehydration
Osmotic dehydration is the process of removal of a portion of water from the food components by removal of water from food components, generally fruits and vegetables, by submerging them in a hypertonic solution (Shi & Le Maguer, 2002). Fathi et al.,
