Artificial Intelligence in Food Science : Transforming Food and Bioprocess Development.

Format:

Book

Author/Creator:

Sarkar, Tanmay.

Contributor:

Haldorai, Anandakumar.

Language:

English

Physical Description:

1 online resource (1862 pages)

Edition:

1st ed.

Place of Publication:

Chantilly : Elsevier Science & Technology, 2025.

Summary:

Artificial Intelligence in Food Science Transforming Food and Bioprocess Development, looks at the advancements in both AI and Machine Learning (ML) and the potential for leveraging these latest technologies to optimize and elevate various aspects of the food sciences.

Contents:

Front Cover

Artificial Intelligence in Food Science

Copyright

Dedication

Contents

Contributors

About the editors

Preface

1 - Introduction to AI and ML in food science and bioprocess development

1. Introduction

2. Foundations of AI and ML in food science

2.1 Definition and basic concepts

2.2 Types of machine learning

2.3 Relevance to food science and bioprocessing

3. Applications in food science and bioprocess development

3.1 Quality control and assurance

3.2 Process optimization

3.3 Product development

3.4 Supply chain management

3.5 Sustainability and waste reduction

4. Challenges in Implementing AI and ML in food industry

4.1 Data availability and quality

4.2 Cost and expertise

4.3 Integration with existing processes

4.4 Adaptability and scalability of AI solutions

4.5 Resistance to change in traditional practices

5. Conclusion

References

2 - The basis, progress, and future of food and bioprocess with artificial intelligence (AI) and machine learning ( ...

2. Overview of food science and bioprocess sectors

2.1 The food science sector

2.2 Bioprocesses

3. What's AI?

4. The relationship between AI and ML

4.1 Applying knowledge-based expert systems in the food industry

4.2 Fuzzy logic technique in the food industry

4.3 ANN and deep learning techniques in the food industry

5. AI and ML applications in food science

5.1 Product sorting and packaging

5.2 Decision-making system for customers

5.3 Equipment cleaning and maintenance

5.4 Health and sanitation

5.5 Launching new products

5.6 Food flavor development and predicting consumer preferences

5.7 Demand-supply chain management

5.8 Data analytics

5.9 Robotics for the food industry.

5.10 Artificial intelligence in food safety

5.11 Postharvest loss mitigation and quality management of fruits and vegetables

5.12 Drying process control

6. AI applications in the bioprocess industry

7. Limitations of AI and ML

8. What is the futuristic view of AI and ML in food science and bioprocess development?

I - Learning approaches and applications

3 - Data collection and preprocessing for AI and ML applications in food science

2. Data gathering in food science

2.1 Importance of data collection

2.2 Conventional approaches in data collection

2.2.1 Sensors and IoT devices

2.2.2 Government and institutional studies

2.2.3 Surveys and consumer feedback

2.3 Adventitious data collection approaches

2.3.1 Collection of data using AI-powered automation

2.3.2 Crowd-sourcing for food data

2.3.3 Web crawling and information available online

3. Data preprocessing techniques

3.1 Overview of data preprocessing

3.2 Common data challenges

3.3 Key preprocessing techniques

3.3.1 Outlier detection and treatment

3.3.2 Imputing missing values

3.3.3 Feature selection and dimensionality reduction

3.3.4 Data scaling and transformation

4. Case studies in data collection and preprocessing

4.1 Possible uses of AI and ML in food science

4.1.1 Case study 1: Quality evaluation of extra virgin olive oil

4.1.2 Case study 2: Quality control in dairy products

4.1.3 Case study 3: Predicting yield outcomes while maximizing crop yields

4.2 Challenges faced in data collection and data preprocessing

4.2.1 Case study 4: Real-time food safety monitoring in supply chains

4.3 Lessons learned and best practices

5. Innovative approaches to data collection

5.1 IoT terminals for collecting data on the ground

5.2 Crowdsourcing for consumer insights.

5.3 Advanced data augmentation techniques

5.4 Future trends in data collection

6. Practical solutions for data challenges

6.1 Deconstruction of issues related to data quality

6.2 Methods of improving the use of data collection tools

6.3 Innovative data augmentation strategies

7. Future directions in data solutions

7.1 Possibility of including quantum computing

7.2 Formation of universal data protocols

7.3 Ethical data restructuring

7.4 Cross-disciplinary collaboration

8. Conclusion

4 - Supervised learning techniques in food science: predictive modeling and classification

2. Principle of supervised machine learning

2.1 Random forest

2.2 Support vector machines

2.3 Neural networks

2.4 Gradient boosting machines

2.5 Logistic regression

3. Process flow of supervised machine learning

3.1 Dealing with dataset

3.2 Selection of algorithm

3.3 Training and testing dataset

3.4 Prediction of results and classification

4. Demand forecasting and planning

5. Applications of supervised machine learning

5.1 Drying of foods

5.2 Flavor prediction

5.3 Food categorization

5.4 Food quality and safety

6. Conclusion

5 - Unsupervised learning techniques in food science: Clustering and dimensionality reduction

2. Review of literature

3. Machine learning

4. Working principle and algorithm

5. Machine learning approaches

6. Traditional machine learning methods

7. Unsupervised learning

8. k-means clustering

9. Dimensionality reduction techniques

10. Feature selection

11. Feature extraction/transformation

12. Clustering

13. Concept of clustering

14. Importance of clustering

15. Clustering algorithms

16. Hierarchical clustering

17. Partitioning-based clustering.

18. Density-based clustering

19. Model-based clustering

20. Fuzzy clustering

21. Applications of clustering in food science

21.1 Sensory analysis

21.2 Quality control

21.3 Ingredient profiling

22. Applications in food science of dimensionality reduction

22.1 Sensory analysis

22.2 Quality control

22.3 Ingredient profiling

22.4 Nutritional analysis

23. Challenges of clustering and dimensionality reduction in food science

24. Future trends in unsupervised learning in food science

25. Future directions of clustering

26. Future trends in dimensionality reduction

27. Conclusion

Further reading

6 - Deep learning approach for food science and bioprocess optimizations

2. Fundamentals of deep learning

2.1 Basic concepts and architectures (e.g., neural networks, CNNs, and RNNs)

3. Applications of deep learning in food science

3.1 Food safety and disease detection

3.2 Sensor

3.3 Food safety and quality assurance

4. Deep learning techniques in bioprocess optimization

5. Advantages

6. Challenges and limitations

6.1 Data availability and quality issues

6.2 Computational resource requirements

6.3 Model interpretability and transparency

6.4 Integration with existing system

7. Conclusion and future perspective

7 - Reinforcement learning in food industry applications: AI and ML applications in apple cold chain logistics

2. AI and ML applications in postharvest storage

2.1 Automated sorting and grading

2.2 Predictive quality monitoring

2.3 Dynamic environmental control

2.4 Predictive maintenance for storage equipment

3. Predictive quality monitoring for controlled atmosphere storage

4. Dynamic temperature regulation in controlled atmosphere storage.

5. Humidity optimization in controlled atmosphere storage

6. Gas composition management in controlled atmosphere storage

7. Energy-efficient technologies and practices in controlled atmosphere storage

7.1 AI-driven optimization in controlled atmosphere storage

8. Case studies: Adani Agri and CONCOR in controlled atmosphere storage

8.1 Adani Agri: Pioneering innovation in controlled atmosphere storage

8.2 CONCOR: Streamlining logistics in controlled atmosphere storage

9. Comparative analysis: AI utilization in cold chain logistics

9.1 Fresh Cold solutions: Precision in inventory management

9.2 FrostTech logistics: Autonomous cold storage management

9.3 CoolTrans express: AI-enhanced route optimization

10. Summary of comparative analysis

11. Case study survey at cold storages at a tier two city: Warangal, Telangana

11.1 Types of warehouses

11.2 Material handling systems

11.3 Inventory management systems

11.4 Stacking/racking systems

11.5 Storage bins

12. Materials and methods

13. Results and discussions

14. Conclusion

8 - Artificial intelligence techniques in digital interventions for promoting healthy eating: A systematic review

2. Related work

3. Methodology

3.1 Paper identification and search strategy

3.2 Selection and data extraction

3.3 Data analysis

4. Results

4.1 Trends in the application of AI interventions for healthy eating over the years (2013-2024)

4.1.1 Publication trend over the years (2013-2024): Year of publication, venue of publication, type of publication, and geographi ...

4.1.2 Key topics, theories, and models explored over the years (2013-2024)

4.1.3 Class of technology explored over the years (2013-2024)

4.1.4 Health conditions and intervention objectives explored over the years (2013-2024).

4.1.5 Types of study, study instruments, and user data employed over the years (2013-2024).

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

0-443-26469-4

0-443-26468-6

9780443264696

OCLC:

1545651356