Practical Applications of Machine Learning and Artificial Intelligence in the Food Industry

Auteur

Dr. Amit K. Jaiswal is a lecturer at Technological University Dublin (TU Dublin)City Campus, Ireland, where he teaches undergraduate and postgraduate courses in food engineering, food processing technology, and industrial biotechnology in the School of Food Science and Environmental Health. He also serves as the chair of the MSc program in Sustainable Food Safety Management at TU Dublin-City Campus. Dr. Jaiswal earned an MSc degree from Jiwaji University in India in 2005, a PhD in Food Technology from TU Dublin in Ireland in 2012, and an MBA in 2015.

His research focuses on valorisation of lignocellulosic biomass into biofuels, biomaterials, and biochemicals through innovative process development and techno-economic analysis. He also studies novel food processing technologies for extracting functional and nutritional ingredients from natural resources and is involved in the development of biodegradable and compostable materials for packaging.

Dr. Abhishek Kaushik works as an Assistant Lecturer at Dundalk Institute of Technology (DkIT). He was also a Postdoctoral Researcher in Ceadar UCD in 2022. Dr. Kaushik obtained his Ph.D. in Artificial Intelligence (AI) and Information Retrieval (IR) from Adapt Centre, Dublin City University (DCU) in 2021. Dr. Kaushik completed his master's degree in Information Technology from Fachhochschule Kiel, Germany in 2016. During his time in Germany, he was actively involved in developing AI algorithms for a multinational company Siemens. Dr. Kaushik holds bachelor's degree in Computer Science Engineering from Kurukshetra University (2012), India. He is a subject matter expert on Human Computer Interaction, Machine Learning, AI, Natural Language Processing, Block chain and Smart farming. He has published more than 40 papers (journal and conference) in the areas of Conversational Search, Human Computer Interaction, Sentiment Analysis and Smart Technologies.

Dr Swarna Jaiswal is a Lecturer in Applied Microbiology and Packaging Technology at TU Dublin-City Campus, Ireland. She obtained a Biotechnology (Hon's) from B. R. Ambedkar Bihar University, India in 2005 and received a master's in biotechnology from Bangalore University, India in 2007 and was awarded PhD in 2012 by Technological University Dublin, Ireland. Upon completion of PhD, she worked as a research scientist at Enterprise Ireland Technology Gateway Centre, CREST, TU Dublin, for 5 years. Dr Swarna finished a Postgraduate Certificate in Third Level Learning and Teaching from TU Dublin in 2021. As a research professional, she has focused on applied microbiological coatings/film projects with industry partners and providing research support to industries. Dr Swarna has published more than 50 peer-reviewed articles and 25 book chapters. She is also a frequent reviewer for more than 15 journals. Her research work was presented at numerous national and international conferences/symposiums.

Texte du rabat
Practical Applications of Machine Learning and Artificial Intelligence in the Food Industry provides a comprehensive overview of the applications of machine learning and artificial intelligence in the food industry. The book covers a wide range of topics including food safety, quality control, sustainability, personalized nutrition, and supply chain optimization. It also discusses various machine-learning techniques and provides examples of successful implementations in the industry.

The book is divided into six sections, the introductory section sets the stage by detailing key machine learning and artificial intelligence concepts, and their relevancy to the food industry. The second section addresses data collection and predictive analysis, featuring chapters on predictive maintenance, yield optimization, and consumer behaviour analytics. The third section focuses on food safety and quality control, detailing how machine learning can be deployed for tasks such as food fraud detection, predictive microbiology, and quality control enhancements. Section four further explores how these technologies can help in food texture, flavour, and shelf-life predictions. The fifth section presents an in-depth exploration of supply chain optimization and traceability, discussing the role of AI and machine learning in areas like real-time food safety monitoring, product authenticity assurance, and big data for food traceability. The final section highlights the application of these technologies in creating personalized nutrition strategies and promoting sustainable agricultural practices. Each section includes real-world examples of successful implementations, reinforcing the practicality of these applications.

Practical Applications of Machine Learning and Artificial Intelligence in the Food Industry serves as an ultimate practical guide for implementing machine learning and artificial intelligence in the food industry and provides readers with valuable insights into emerging technologies in the field.

Contenu

Section 1: Introduction

1. Introduction to Artificial Intelligence and Machine Learning in Food Industry

Section 2: Data Collection and Predictive Analysis

2. Data Collection and Management for ML in Food Industry
3. Predictive Maintenance for Food Processing Equipment
4. Yield Optimization in Food Production with Machine Learning
5. Predictive Analytics for Consumer Behaviour

Section 3: Food Safety and Quality Control

6. Improving Quality Control with Machine Learning
7. Machine Learning for Food Safety
8. Machine Learning for Food Fraud Detection
9. Predictive Microbiology using Machine Learning

Section 4: Food Texture, Flavour and Shelf-life Prediction

10. Texture Analysis with Machine Learning
11. Flavour Prediction using Machine Learning
12. Shelf-Life Prediction with Machine Learning

Section 5: Supply Chain Optimization and Traceability

13. Optimizing the Food Supply Chain with Machine Learning
14. IoT for Real-Time Food Safety Monitoring
15. Ensuring Product Authenticity using Machine Learning and AI
16. Big Data and AI for Food Traceability

Section 6: Personalized Nutrition and Sustainability

17. Personalized Nutrition with Machine Learning
18. Sustainable Agriculture through Machine Learning
19. Machine Learning for Allergen Detection in Food
20. Machine Learning for Food Preservation
21. Recipe Generation with Machine Learning