Description
Artificial Intelligence in Food Science: Transforming Food and Bioprocess Development covers the AI and machine learning techniques that are reshaping the food science landscape, introducing innovative solutions to improve food processing, safety, and sustainability. This book delves into the transformative potential of these cutting-edge technologies, exploring how they optimize food production, enhance bioprocess development, and tailor products to meet specific consumer needs. By integrating AI, researchers and industry professionals can address challenges such as resource efficiency and quality assurance, paving the way for a more sustainable and technologically advanced food system.Beyond optimization, the book examines AI applications in predicting food trends, analyzing complex datasets, and developing personalized nutrition plans. It provides insights into how AI enhances food storage, packaging design, and even consumer engagement through predictive models. With detailed case studies and forward-thinking perspectives, this book serves as a comprehensive guide for harnessing AI's power to revolutionize food science and bioprocess innovations.- Explores how AI and machine learning intersect with food for enhanced nutritional outcomes- Discusses incorporating robotics, automation and IoT into AI and ML driven food processes- Highlights the use of AI and ML for flavor profiling and sensory analysis- Leverages AI and ML for food waste analytics- Addresses the challenges and benefits of AI and ML in the food industry
Table of Contents
Section 1: Learning Approaches and Applications1. Data Collection and Preprocessing for AI and ML Applications in Food Science2. Supervised Learning Techniques in Food Science: Predictive Modeling and Classification3. Unsupervised Learning Techniques in Food Science: Clustering and Dimensionality Reduction4. Deep Learning Approaches for Food Science and Bioprocess Optimization 5.Reinforcement Learning in Food Industry ApplicationsSection 2: Ingredient discovery, Recipe and New Product Development6. Virtual Product Testing and Simulation: Reducing Time and Costs in New Product Development7. Computational intelligence for Plant-Based Alternatives: Transforming Ingredients and Developing Innovative Meat and Dairy Substitutes8. AI and ML for Ingredient Discovery and Formulation Optimization9. Technology-Enabled Smart Kitchen: AI Assistance for Recipe Development and Cooking Techniques10. Flavor Profiling and Sensory Analysis using AI and MLSection 3 Nutrition11. Blockchain, IoT, fuzzy systems in Food Science and Bioprocess Development12. Bioinspired optimization techniques in Food Industry13. AI mediated modelling approach for nutritional aspects of food and bioproducts14. Digital image analysis in Food and bioprocess industries15. Advancement in Computational fluid dynamics in food processing16. Shelf-life prediction through AI and ML17. Personalized Nutrition: AI-driven Approaches for Tailoring Functional Foods to Individual Needs18. Smart Packaging and Traceability: Ensuring Quality and Safety of Functional Food ProductsSection 4 Quality Control, Food Safety and Processing19. Quality Control and Inspection Techniques with AI and ML20. Sensor Technologies and AI Integration for Real-time Monitoring of Food Quality Parameters21. AI and ML in Food Safety Assessment: Rapid Detection of Contaminants and Pathogens22. Chemometrics and Multivariate Analysis for Quality Control of Food Products23. Machine Learning for Spectroscopic Analysis and Quality Evaluation of Food24. Robotic Systems and Automation for Quality Inspection in Food Production25. Traceability and Blockchain Technology: Ensuring Transparency and Authenticity of Food Quality26. Case Studies: Successful Applications of AI and ML in Food Quality Control27. AI and ML for Process Optimization in Food Manufacturing28. AI and ML for Food Safety and Traceability29. Robotics and Automation in Food Processing using AI and ML30. IoT Integration and Smart Technologies in Food Systems31. Blockchain Technology for Transparent Food Supply Chains: Enhancing Traceability and Reducing WasteSection 5 Food Waste32. AI and ML in Food Waste Analytics: Leveraging Data for Waste Identification and Quantification33. Predictive Modeling for Demand Forecasting and Inventory Management to Minimize Food Waste34. Dynamic Pricing Strategies: AI-Driven Approaches for Optimizing Sales and Reducing Food Waste35. AI and ML for Supply Chain Optimization: Minimizing Losses and Maximizing Efficiency36. Waste Utilization and Valorization: AI-Driven Approaches for Creating Value from Food Byproducts37. AI and Robotics in Food Processing: Efficient Sorting and Handling to Minimize WasteSection 6: Ethics, Compliance and future trends38. Ethical Considerations and Data Privacy in AI and ML Applications39. Case Studies and Success Stories: Real-world Applications of AI and ML in Food Science and Bioprocess Development40. Challenges and Limitations of AI and ML in the Food Industry41. Future Trends and Directions in AI and ML for Food Science and Bioprocess Development
