Artificial Intelligence in Food Science

Name: Artificial Intelligence in Food Science
Price: 250 USD
Availability: InStock
ISBN: 9780443264689

Transforming Food and Bioprocess Development

by Tanmay Sarkar, Anandakumar Haldorai

1st Edition

Publisher: Academic Press

(0 reviews)

Compare Prices

VitalSourceLifetime Access$250.00 AmazonPaperback$284.73 Best PriceeTextShelfPDF$38.00

Book Details

Print ISBN	9780443264689
eText ISBN	9780443264696
Publisher	Academic Press
Publishing Year	2025
Edition	1st Edition
Language	English
Pages	626

Artificial Intelligence in Food Science, 1st Edition, provides a textbook examination of computational methods applied to modern food manufacturing and bioprocess engineering. Edited by Tanmay Sarkar and Anandakumar Haldorai and published by Academic Press, this 626-page volume presents artificial intelligence and machine learning techniques designed to support food processing efficiency, safety protocols, and resource sustainability.

The content synthesizes computational strategies into distinct operational domains. Initial sections cover machine learning algorithms applied to flavor profiling and sensory evaluation. Subsequent chapters address personalized nutrition approaches, exploring how data-driven models help tailor functional food formulations to meet specific individual consumer requirements. Additional sections detail computational intelligence techniques used for ingredient discovery and formulation optimization in plant-based food alternatives.

By connecting predictive modeling directly with physical processing requirements, the volume supports data-driven decision-making across modern food technology and bioprocess engineering.

Chapter 1: Data Collection and Preprocessing for AI and ML Applications in Food Science
Chapter 2: Supervised Learning Techniques in Food Science: Predictive Modeling and Classification
Chapter 3: Unsupervised Learning Techniques in Food Science: Clustering and Dimensionality Reduction
Chapter 4: Deep Learning Approaches for Food Science and Bioprocess Optimization
Chapter 5: Reinforcement Learning in Food Industry Applications
Chapter 6: Virtual Product Testing and Simulation: Reducing Time and Costs in New Product Development
Chapter 7: Computational intelligence for Plant-Based Alternatives: Transforming Ingredients and Developing Innovative Meat and Dairy Substitutes
Chapter 8: AI and ML for Ingredient Discovery and Formulation Optimization
Chapter 9: Technology-Enabled Smart Kitchen: AI Assistance for Recipe Development and Cooking Techniques
Chapter 10: Flavor Profiling and Sensory Analysis using AI and ML
Chapter 11: Blockchain, IoT, fuzzy systems in Food Science and Bioprocess Development
Chapter 12: Bioinspired optimization techniques in Food Industry
Chapter 13: AI mediated modelling approach for nutritional aspects of food and bioproducts
Chapter 14: Digital image analysis in Food and bioprocess industries
Chapter 15: Advancement in Computational fluid dynamics in food processing
Chapter 16: Shelf-life prediction through AI and ML
Chapter 17: Personalized Nutrition: AI-driven Approaches for Tailoring Functional Foods to Individual Needs
Chapter 18: Smart Packaging and Traceability: Ensuring Quality and Safety of Functional Food Products
Chapter 19: Quality Control and Inspection Techniques with AI and ML
Chapter 20: Sensor Technologies and AI Integration for Real-time Monitoring of Food Quality Parameters
Chapter 21: AI and ML in Food Safety Assessment: Rapid Detection of Contaminants and Pathogens
Chapter 22: Chemometrics and Multivariate Analysis for Quality Control of Food Products
Chapter 23: Machine Learning for Spectroscopic Analysis and Quality Evaluation of Food
Chapter 24: Robotic Systems and Automation for Quality Inspection in Food Production
Chapter 25: Traceability and Blockchain Technology: Ensuring Transparency and Authenticity of Food Quality
Chapter 26: Case Studies: Successful Applications of AI and ML in Food Quality Control
Chapter 27: AI and ML for Process Optimization in Food Manufacturing
Chapter 28: AI and ML for Food Safety and Traceability
Chapter 29: Robotics and Automation in Food Processing using AI and ML
Chapter 30: IoT Integration and Smart Technologies in Food Systems
Chapter 31: Blockchain Technology for Transparent Food Supply Chains: Enhancing Traceability and Reducing Waste
Chapter 32: AI and ML in Food Waste Analytics: Leveraging Data for Waste Identification and Quantification
Chapter 33: Predictive Modeling for Demand Forecasting and Inventory Management to Minimize Food Waste
Chapter 34: Dynamic Pricing Strategies: AI-Driven Approaches for Optimizing Sales and Reducing Food Waste
Chapter 35: AI and ML for Supply Chain Optimization: Minimizing Losses and Maximizing Efficiency
Chapter 36: Waste Utilization and Valorization: AI-Driven Approaches for Creating Value from Food Byproducts
Chapter 37: AI and Robotics in Food Processing: Efficient Sorting and Handling to Minimize Waste
Chapter 38: Ethical Considerations and Data Privacy in AI and ML Applications
Chapter 39: Case Studies and Success Stories: Real-world Applications of AI and ML in Food Science and Bioprocess Development
Chapter 40: Challenges and Limitations of AI and ML in the Food Industry
Chapter 41: Future Trends and Directions in AI and ML for Food Science and Bioprocess Development