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Elsevier

Next Generation Food Safety

AI-Driven Mycotoxin Detection, Risk Analytics and Quality Assurance in Ready-to-Eat Foods

  • 1st Edition - February 1, 2027
  • Latest edition
  • Editors: C. Kishor Kumar Reddy, Hemant Kumar Saini, Inam Ullah Khan, S. P. Muthukumar
  • Language: English

Next Generation Food Safety: AI-Driven Mycotoxin Detection, Risk Analytics, and Quality Assurance in Ready-to-Eat Foods is an essential resource for food scientists, techno… Read more

Description

Next Generation Food Safety: AI-Driven Mycotoxin Detection, Risk Analytics, and Quality Assurance in Ready-to-Eat Foods is an essential resource for food scientists, technologists, and supply chain professionals seeking to harness AI for safer, higher-quality RTE foods. As contamination and quality control challenges grow, this book addresses the need for advanced detection methods and predictive analytics. It showcases how machine learning, deep learning, computer vision, and IoT-based sensors are transforming food safety across diverse categories, from cereals and dairy to plant-based and fermented products. The volume covers foundational AI concepts, model validation, and real-world applications such as hyperspectral imaging for mycotoxin detection and supply chain risk assessment. It discusses current limitations, including data quality and regulatory hurdles, offering practical strategies for validation and deployment. Ethical considerations and cybersecurity are also addressed, ensuring comprehensive guidance for industry adoption.

Next Generation Food Safety: AI-Driven Mycotoxin Detection, Risk Analytics, and Quality Assurance in Ready-to-Eat Foods empowers stakeholders to develop innovative, cost-effective solutions that enhance safety, ensure compliance, and support sustainable food systems, meeting the evolving demands of modern food safety management.

Key features

  • Explains advanced AI techniques for detecting contaminants in ready-to-eat foods
  • Provides practical strategies for validating and deploying AI-based food safety systems
  • Highlights real-world applications like hyperspectral imaging and supply chain risk management
  • Addresses regulatory, ethical, and cybersecurity challenges in implementing AI for food safety
  • Supports industry adoption of cost-effective, reliable, and sustainable food quality assurance solutions

Readership

Academicians, Researchers and Graduate Students working in AI driven food chemistry, food science, bioinformatics, agricultural engineering and toxicology, Graduate and doctoral students, early-career researchers, regulatory bodies and industry professionals, Professionals developing next-generation food technology systems in R&D labs, Microbiologists , universities and technical institutes, food Industries and Food Safety Auditors

Table of contents

Part I – Foundations and Emerging Paradigms

1. Introduction to Artificial Intelligence for Food Safety and Quality Assurance

2. Machine Learning and Deep Learning based Mycotoxin Detection

3. Computer Vision and Image-Based Mycotoxin Detection

4. Data Quality, Sampling Techniques, and Big Data / IoT in Food Quality Monitoring

5. AI Model Validation, Protocols, TRLs, and Interpretability Frameworks

6. Limitations/ Challenges of detection and quantification, instrumental methods, and the number of sampling.

Part II – AI Across Ready-to-Eat Food Categories

7. Cereal and Grain-Based Foods (Bread, Snacks, Infant Food) Contamination

8. Spectroscopic Detection in Dairy and Milk-Based Ready Foods

9. AI-driven biosensor in Meat and Poultry Convenience Foods

10. Deep learning based quality checking in Plant-Based Ready Meals and Alternatives

11. AI-spectroscopy in Snack Foods and Bakery Products

12. Identifying AI patterns in Fermented Foods and Quality Assurance

Part III – Risk, Regulation, and Supply Chains

13. Supply Chain Risk Assessment for Ready-to-Eat Foods

14. Mapping AI-driven detection to Regulatory Compliance and Standards

15. Economic Feasibility and Operational Deployment of AI Safety Systems

16. Cybersecurity and Data Integrity in AI-Driven Food Systems

17. Ethics, Transparency, and Responsible AI in Food Safety

Part IV – Case Studies and Future Directions

18. Case Studies: Global Perspectives on AI in Mycotoxin Detection

19. Eco-Friendly Smart Packaging and Embedded AI Sensing Technologies

20. Future Trends and Research Directions, next decade Ready-to-use Eatables

Product details

  • Edition: 1
  • Latest edition
  • Published: February 1, 2027
  • Language: English

About the editors

CR

C. Kishor Kumar Reddy

Dr. C. Kishor Kumar Reddy is a seasoned academician and researcher with over 12 years of experience in computer science and engineering. He holds a Ph.D. in Computer Science and Engineering and a Postdoctoral Fellowship from the University Kebangsaan Malaysia, Malaysia. Dr. Reddy has made significant contributions in areas such as Artificial Intelligence, Machine Learning, Deep Learning, Federated Learning, Cybersecurity, Healthcare 6.0, and Disaster Management. He has authored and co-authored 170+ research articles in reputed SCI/Scopus-indexed journals, presented at international conferences. He also holds several published patents and serves as an editor for multiple scholarly books on emerging technologies. Dr. Reddy is an active member of professional bodies such as the Indian Society for Technical Education, the Computer Society of India, and the International Association of Engineers, among others.

Affiliations and expertise
Professor, Department of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Hyderabad, India

HS

Hemant Kumar Saini

Hemant Kumar Saini is an academic at the School of Computer Science & Engineering, Bennett University (The Times Group), Greater Noida, India, and at Lincoln University, Malaysia. His research interests include digital transformation technologies such as artificial intelligence (AI), quantum computing, the Internet of Things (IoT), blockchain, aerial vehicles, ad-hoc networks, digital twins, and edge and serverless computing, including cloud-native computing.

Affiliations and expertise
Academic, School of Computer Science and Engineering, Bennett University, Greater Noida, India

IK

Inam Ullah Khan

Dr. Inam Ullah Khan is a Postdoctoral Research Fellow at Multimedia University, Malaysia, specializing in Artificial Intelligence, UAV networks, Machine Learning, and Intrusion Detection Systems. He is the founder of AI-Explain Your Science (AI-EYS) and a Senior Member of IEEE. Dr. Khan holds a Ph.D. and M.S. in Electronics Engineering, along with a Bachelor's degree in Computer Science.

With over 100 publications and approximately 25 edited books, he has made significant contributions to his fields. He actively participates in technical committees for international conferences and has delivered lectures worldwide. Additionally, Dr. Khan serves as a mentor at Impact Xcelerator in Spain.

His research focuses on integrating AI with cybersecurity and wireless communication, advancing knowledge at the intersection of these critical areas.

Affiliations and expertise
Postdoctoral Research Fellow, Multimedia University, Malaysia

SM

S. P. Muthukumar

Prof. (Dr.) S. P. Muthukumar is basically a veterinarian, BVSc from Tamil Nadu, MVSc from Kerala and PhD from Indian Veterinary Research Institute (IVRI), Izatnagar, Bareilly UP. His specialization is Biochemistry, Nutrition, Genetics and Breeding. He has more than 90 research papers in peer reviewed SCI-indexed journals, 60 invited lectures and presented more than 100 papers in national and international conferences.
Affiliations and expertise
Chief Scientist and Professor, Department of Biochemistry, CSIR - Central Food Technological Research Institute MYSURU, Karnataka, India