Buch, Englisch, 448 Seiten
Buch, Englisch, 448 Seiten
ISBN: 978-1-394-36263-9
Verlag: Wiley John + Sons
Navigate digital food safety solutions and effective implementation strategies
Food Safety in the Digital Age explains how to modernize food safety practices in an era of globalized supply chains and rapid technological advancement. Written by a team of operations and microbiology experts, this resource connects classical food safety science with cutting-edge digital innovations designed for professionals, students, researchers, and policymakers worldwide.
The book thoroughly explores transformative technologies including blockchain traceability systems, IoT sensors, artificial intelligence, machine learning algorithms, and digital twins. Readers gain practical implementation insights while learning to meet the challenges posed by cybersecurity threats, data quality assurance, and equitable access across diverse contexts. Special emphasis on emerging markets, climate resilience strategies, sustainability integration, and human dimensions ensures technology enhances rather than replaces professional judgment.
The book includes: - Comprehensive coverage of blockchain, AI, IoT, digital twins, and other technologies revolutionizing food safety practice and regulatory compliance
- Real-world case studies demonstrating practical applications in restaurants, processing plants, supply chains, and emerging market contexts
- Critical analysis of cybersecurity frameworks and risk mitigation strategies tailored specifically to protect digital food systems
- Evidence-based guidance on integrating sustainability with food safety through precision agriculture, smart cold chains, and circular systems
- Expert insights on future trends including predictive surveillance, personalized safety tools, climate adaptation, and inclusive innovation approaches
Food Safety in the Digital Age equips food safety professionals, quality assurance managers, supply chain specialists, food science students, academic researchers, government regulators, and industry consultants with the scientific foundation and practical strategies necessary to build safer, more transparent, and resilient food systems.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Contents
List of Figures
List of Tables
List of Contributors
Preface
Section I Foundations of Digital Food Safety
1. Introduction to Food Safety in the Digital Age
Helen Onyeaka
1.1 Introduction
1.2 The Changing Face of Food Safety
1.3 Promise and Peril in the Digital Era
1.4 Why This Book, and Why Now?
1.5 Who Should Read This Book?
1.6 What This Book Covers
1.7 Conclusion
References
2. Digital Food Safety Ecosystem
Chijioke Nwoye Eze, Uju M. Nwauzoma, David Moses Omale, and Helen Onyeaka
2.1 Introduction
2.2 Components of Digital Food Safety Ecosystem
2.2.1 Data Analytics Platform
2.2.2 Internet of Things Sensors
2.2.3 Artificial Intelligence and Machine Learning
2.2.4 Blockchain and Distributed Ledger Technology in Digital Food Safety
2.2.5 Supply Chain Transparency and Traceability
2.2.6 Food Origin and Authenticity Verification
2.2.7 Secure Data Storage and Sharing
2.3 Applications of the Digital Food Ecosystem
2.3.1 Food Processing and Manufacturing
2.3.2 Real-time Monitoring and Control
2.3.3 Automated Quality Control and Inspection
2.3.4 Predictive Maintenance and Equipment Optimisation
2.3.5 Food Storage and Transportation
2.3.6 Food Retail and Service
2.3.7 Food Safety Monitoring and Inspection
2.3.8 Automated Inventory Management and Tracking
2.3.9 Personalised Nutrition and Allergen Alerts
2.3.10 Consumer Education and Empowerment
2.4 Benefits and Challenges
2.4.1 Benefits of a Digital Food Safety Ecosystem
2.4.2 Improved Food Safety and Reduced Risk
2.4.3 Increased Efficiency and Productivity
2.4.4 Enhanced Transparency and Accountability
2.4.5 Personalised Nutrition and Health Recommendations
2.4.6 Challenges of the Digital Food Safety Ecosystem
2.5 Conclusion
References
3. Regulatory Frameworks for Digital Food Safety
Malgorzata Szliter
3.1 Introduction
3.2 Evolution of Regulatory Requirements
3.2.1 Emerging Concerns Generated by Digital Food Safety Tools and Systems
3.2.1.1 Algorithmic Opacity
3.2.1.2 Standardisation and Interoperability Challenges
3.2.1.3 Data Security and Cybersecurity Issues
3.2.1.4 Equality Issues
3.2.2 New Regulatory Regimes and Priorities Towards Digital Food Safety Tools and Systems
3.3 Global Regulatory Landscape
3.3.1 International Regulatory Frameworks and Standards
3.3.1.1 Key Standards and Regulations Supporting the Digitalisation of Food Safety
3.3.1.2 Regulation of Digital Food Safety Systems and Tools
3.3.2 Regional and National Regulatory Frameworks and Standards
3.3.2.1 The European Union
3.3.2.2 The United States
3.3.2.3 The United Kingdom
3.3.2.4 China
3.3.2.5 Other Regions of the World
3.4 Challenges and Opportunities in Regulation
3.4.1 Challenges
3.4.2 Opportunities
3.5 Conclusion
References
4. Ethics and Governance in Digital Food Safety
Jacob Skelhorn-French
4.1 Introduction
4.2 Ethical Issues and Governance Requirements Related to Digital Food Safety
4.2.1 Equity and Access
4.2.2 Transparency and Trust
4.2.3 Responsibility and Accountability
4.2.4 Bias and Discrimination
4.2.5 Sustainability Concerns
4.3 Data Security and Privacy Issues in Food Computing
4.3.1 Data Security
4.3.2 Privacy Issues
4.4 Case Study
4.4.1 Case Study 1 - IBM Food Trust
4.4.2 Case Study 2 - Everledger
4.4.3 Lessons Learned from the Case Studies
4.4.3.1 Industrial-wide Collaboration Is Essential
4.4.3.2 Regulation Is Needed to Safeguard Ethical Behaviour
4.5 Conclusion and Future Outlook
References
Section II Core Technologies Driving Innovation
5. Blockchain Technology for Food Traceability and Transparency
Shruti Maheshwari, Swagata Ghosh, and Soumya Ghosh
5.1 Introduction
5.2 Understanding the Food Supply Chain and Its Challenges
5.3 Key Challenges in Today’s Food Supply Chain
5.4 How Blockchain Enables Food Traceability and Transparency
5.5 Key Features That Make Blockchain Ideal for Food Traceability
5.5.1 Immutability
5.5.2 Transparency and Role-based Accessibility
5.5.3 Decentralisation
5.5.4 Security and Cryptographic Assurance
5.5.5 Smart Contracts
5.6 Mapping the Journey: How Blockchain Tracks Food Products
5.7 Real-world Example: A Tomato’s Transparent Journey
5.8 Benefits of Implementing Blockchain in the Food Supply Chain
5.8.1 Enhanced Traceability and Real-time Visibility
5.8.2 Enhanced Food Safety and Efficient Outbreak Response
5.8.3 More Efficiency, Less Cost
5.8.4 Enhanced Consumer Trust Thanks to Transparency
5.8.5 Combatting Food Fraud and Counterfeiting
5.8.6 Better Supply Chain Coordination and Collaboration
5.9 Challenges and Barriers of Blockchain Adoption in the Food Industry
5.9.1 Scalability Issues in Data-heavy Contexts
5.9.2 Interoperability with Existing Systems
5.9.3 High Implementation and Maintenance Costs
5.9.4 Conflicts Over Data Privacy and Confidentiality
5.9.5 Regulatory Voids and Absence of Standards
5.9.6 Limited Technical Skills and Resistance to Change
5.9.7 Energy Consumption and Environmental Impact
5.10 Real-world Examples of Blockchain for Food Traceability
5.10.1 IBM Food Trust and Walmart: Changing Traceability in Seconds
5.10.2 VeChain: Assurance of Authenticity Around the World
5.10.3 Provenance: Tracing Ethical and Sustainable Products
5.10.4 Ambrosus: Integrating IoT for Quality Assurance
5.10.5 Carrefour: Empowering the Conscious Consumer
5.10.6 JD.com: Reinventing Trust in Online Food Sales
5.11 Future Trends and Opportunities
5.11.1 Integration with IoT and Sensors: Real-time, Granular Monitoring
5.11.2 Advancements in Blockchain Technology: Solving Scalability and Privacy
5.11.3 Increased Collaboration and Standardisation: Building a Unified Ecosystem
5.11.4 Focus on Sustainability and Ethical Sourcing: Empowering the Conscious Consumer
5.11.5 Blockchain-as-a-Service Platforms: Lowering the Barrier to Entry
5.12 Conclusion
References
6. Internet of Things for Food Safety Monitoring
Gu Pang
6.1 Introduction
6.2 IoT: Definitions, Components, and Key Characteristics
6.2.1 Definitions and Architecture of IoT
6.2.2 Key Characteristics and Benefits
6.3 IoT Applications in Food Processing Plants
6.3.1 Pain Points in Food Processing Plants
6.3.2 IoT’s Way to Address the Pain Points in Processing Plants
6.4 IoT Applications in Food Storage
6.4.1 Pain Points in Food Storage
6.4.2 IoT’s Way to Address the Challenges
6.5 IoT Applications in Food Transportation
6.5.1 Problems Associated with Food Transportation
6.5.2 IoT Applied for Transportation Optimisation
6.6 IoT for Quality Assurance and Legal Compliance
6.7 Case Study
6.7.1 Burnley Brewing Uses IoT to Monitor and Automate the Brewing Process
6.7.2 Fujitsu IoT for Dairy Farming and Clean Vegetables
6.7.3 DIG Restaurants
6.7.4 Lessons Learned from the Case Studies
6.8 Conclusion and Future Directions
References
7. Artificial Intelligence and Machine Learning for Predictive Food Safety
Gu Pang
7.1 Introduction
7.2 Fundamentals of Artificial Intelligence and Machine Learning
7.2.1 Definitions of AI
7.2.2 Categories of AI Systems
7.2.3 Key Concepts Under the Realm of AI
7.2.3.1 Machine Learning
7.2.3.2 Deep Learning
7.2.4 Role of Big Data in Training Predictive Models
7.3 AI/ML in Contamination Detection and Quality Control
7.3.1 Pathogen and Toxin Detection
7.3.2 Real-time Quality Evaluation in Supply Chains
7.3.3 Case Study
7.4 Predictive Risk Assessment and Modelling
7.4.1 AI for Early Warning Systems in Crops
7.4.2 Predictive Risk Modelling
7.4.3 Time-series Forecasting for Spoilage and Shelf-life Estimation
7.5 Enhancing Decision-making and Regulatory Compliance with AI
7.5.1 AI-driven Food Safety Compliance and Audits
7.5.2 Inventory and Recall Management
7.5.2.1 AI for Inventory Management
7.5.2.2 AI for Rapid Root-cause Analysis During Recalls
7.5.3 Case Study
7.6 Conclusions and Prospects
References
8. Big Data Analytics in Food Safety
Gu Pang
8.1 Introduction
8.2 Fundamentals of Big Data Analytics
8.2.1 Definitions of Big Data Analytics
8.2.2 Sources of Big Data in the Food Industry
8.2.2.1 Online Sources of Big Data
8.2.2.2 Offline Sources of Big Data
8.2.3 Characteristics of BDA
8.3 BDA in Food Spoilage and Pathogen Detection
8.4 BDA in Production Optimisation and Quality Control
8.4.1 BDA in Precision Agriculture
8.4.2 BDA in Food Processing
8.4.3 BDA in Food Quality and Authenticity
8.5 BDA in Market Analysis and Consumer Insights
8.5.1 Consumer Behaviour Analysis
8.5.2 Market Segmentation and Positioning
8.5.3 Market Trend Analysis
8.6 BDA in Compliance Auditing, Innovation, and Sustainability
8.6.1 Compliance Auditing
8.6.2 Innovation
8.6.3 Sustainability
8.7 Conclusion and Prospects
References
9. Augmented Reality and Virtual Reality in Food Safety
Mahsa Monirabbasi
9.1 Introduction
9.2 Fundamentals of AR and VR
9.2.1 Definitions
9.2.1.1 What Is Augmented Reality?
9.2.1.2 What Is Virtual Reality?
9.2.1.3 Comparison of AR, VR, MR, and XR Technologies
9.3 Augmented Reality in Food Safety
9.3.1 AR in Food-related Training and Education
9.3.2 AR for Better Dietary and Nutrition Assessment
9.3.3 AR in Food Production and Precision Farming
9.3.4 AR in Food Sensory Science
9.4 Virtual Reality in the Food Industry
9.4.1 VR in Food Education and Training
9.4.2 VR in Product Development and Prototyping
9.4.3 VR in Food Science
9.4.4 VR in Visualising the Food Supply Chain
9.5 Conclusion and Future Prospects
References
10. Nanotechnology in Food Safety Applications
Oladipo Iyabo Christianah, Lateef Agbaje, and Fashogbon Racheal Oluwayemisi
10.1 Introduction
10.1.1 Definition and Scope of Nanotechnology
10.1.1.1 Scope of Nanotechnology
10.1.2 Importance of Food Safety and Challenges in the Food Industry
10.1.3 How Nanotechnology Is Revolutionising Food Safety
10.2 Nanotechnology for Detecting Contaminants
10.2.1 Nanosensors in Food Safety
10.2.1.1 Roles of Nanosensors in Detecting Foodborne Pathogens
10.2.1.2 Detection of Chemical Contaminants (Pesticides, Heavy Metals, and Toxins)
10.2.2 Nanomaterials in Biosensors
10.2.2.1 Nanoparticle-based Nanosensors
10.2.2.2 Biosensors Based on Quantum Dots
10.2.2.3 Nanowire-based Biosensors
10.2.2.4 Nanorod-based Biosensors
10.2.2.5 Biosensors Based on Carbon Nanotubes
10.2.2.6 Biosensors Based on Dendrimers
10.2.2.7 Rapid Detection Mechanisms of Nanosensors
10.2.3 Smart Packaging Using Nanosensors
10.2.3.1 Detection of Gases from Spoiled Food (Ethylene, Ammonia Sensors)
10.3 Nanotechnology in Food Packaging for Safety Enhancement
10.3.1 Antimicrobial Nanomaterials
10.3.2 Barrier Enhancement for Food Packaging
10.3.3 Smart Labels That Inform Consumers About Food Safety
10.4 Potential Risks and Regulatory Considerations
10.4.1 Concerns About Nanoparticle Migration into Food
10.4.2 Possible Health Risks and Toxicity Studies
10.5 Future Perspectives and Innovations
10.5.1 AI and Nanotechnology Integration for Precision Food Safety Monitoring
10.5.2 Sustainable and Eco-friendly Nanotechnology in Food Packaging
10.6 Conclusion
References
11. Smart Sensors in Food Safety
Ornella Incerti
11.1 Introduction
11.2 Smart Sensing Systems
11.3 Type of Detection Technology
11.3.1 Electrochemical Detection Technology
11.3.2 Optical Detection Technology
11.3.2.1 Machine Vision
11.3.2.2 Near-infrared Spectroscopy
11.3.2.3 Hyperspectral Imaging
11.3.2.4 Multispectral Imaging
11.3.2.5 Raman Spectroscopy
11.3.2.6 Surface-enhanced Raman Spectroscopy
11.3.2.7 Terahertz Spectroscopy
11.3.2.8 Laser-induced Breakdown Spectroscopy
11.3.3 Machine Olfaction Technology
11.3.4 Machine Gustatory Technology
11.3.5 Intelligent Biosensors
11.3.5.1 Deep Learning-based Biosensors
11.3.5.2 IoT-based Intelligent Biosensors
11.4 Conclusions
References
Section III Digital Food Safety Applications and Challenges
12. Cybersecurity in Digital Food Systems
Gu Pang
12.1 Introduction
12.2 A Brief Recap of Digital Food Systems
12.2.1 What Are Digital Food Systems
12.2.2 Benefits of Digital Food Systems
12.2.3 Challenges Facing Digital Food Systems
12.3 Types of Cybersecurity Threats and Vulnerabilities in Digital Food Systems
12.3.1 Hacktivism and Ransomware Attacks
12.3.2 Malware
12.3.3 IoT Device Vulnerabilities
12.3.4 Social Engineering Attacks and Insider Threats
12.4 Root Causes of Cybersecurity Threats
12.4.1 Inadequate Cybersecurity Systems and Security Infrastructure
12.4.2 Skills Gap
12.4.3 Low Awareness on Cybersecurity
12.5 Solutions to Deal with Cyberattacks
12.5.1 Regulatory Frameworks and Industry Standards
12.5.2 Three Dimensions of Cybersecurity
12.5.3 Develop and Promote Best Practices
12.5.4 Improve Business Awareness on Cybersecurity
12.5.5 Foster Collaboration Networks
12.5.6 Future Trends and Emerging Solutions
12.6 Conclusion
References
13. Remote Audits and Inspections Using Digital Tools
Eduard Grau-Noguer, Janne Lundén, Arja Helena Kautto, Samuel Portaña, and Remo Suppi
13.1 Introduction
13.2 Remote Audits of Food Business Operators
13.3 Remote Meat Inspections
13.4 On-site Inspections Using Digital Tools
13.5 Conclusion
13.6 Acknowledgements
References
14. 3D Printing and Emerging Risks in Food Safety
Hui Sun
14.1 Introduction
14.2 Understanding 3D Food Printing
14.2.1 What Is 3D Food Printing
14.2.2 How Does 3D Printing Work
14.2.2.1 Basic Mechanism Underpins 3D Food Printing
14.2.2.2 Key Technologies
14.2.3 Current Research Landscape on 3D Food Printing
14.2.3.1 Keyword Analysis
14.2.3.2 Emerging Trends
14.2.4 Impacts of 3D-printed Foods on the Food Industry
14.2.4.1 Mass Customisation and Customised Food Supply Chain
14.2.4.2 Professional Culinary and Personalised Nutrition in Daily Life
14.2.4.3 Support of Sustainability
14.2.4.4 Additional Concerns
14.3 Risks Associated with 3D Food Printing
14.3.1 Food Safety Risks
14.3.1.1 Microbial Contamination During Printing
14.3.1.2 Harmful Substances from Printer Components
14.3.1.3 Potential Risks from Gels and Other Food Additives
14.3.2 Nutrition Quality Risks
14.3.2.1 Nutrition Loss Caused by Ultra-processing
14.3.2.2 Loss of Food Matrix
14.3.3 Risks from Printing Devices
14.3.4 Privacy and Intellectual Property Risks
14.4 Regulatory Challenges
14.5 Conclusion and Future Outlook
References
15. Digital Food Safety in Emerging Markets
Shruti Maheshwari, Swagata Ghosh, and Soumya Ghosh
15.1 Introduction
15.2 The Unique Landscape of Food Safety in Emerging Markets
15.2.1 The Role of Smallholder Farmers
15.2.2 Informal Food Vendors and Traditional Markets
15.2.3 Fragmented and Non-digital Supply Chains
15.2.4 Infrastructure Deficits
15.2.5 Regulatory Gaps and Weak Enforcement
15.2.6 Layered Complexity from Macro-trends
15.3 Digital Innovations Transforming Food Safety
15.3.1 Internet of Things
15.3.2 Blockchain
15.3.3 Mobile Applications
15.3.4 Artificial Intelligence and Predictive Analytics
15.3.5 Remote Sensing and Geographic Information Systems
15.3.6 Digital Marketplaces and Platforms
15.4 Challenges to Digital Adoption in Developing Countries
15.4.1 Infrastructure Deficits
15.4.2 Cost and Financial Barriers
15.4.3 Limited Digital Literacy and Technical Skills
15.4.4 Institutional and Governance Challenges
15.4.5 Policy and Regulatory Barriers
15.4.6 Cultural and Behavioural Resistance
15.4.7 Sustainability and Scalability of Pilot Project
15.5 Opportunities and Innovations in Digital Food Safety
15.5.1 Mobile-based Solutions
15.5.2 Localised, Low-cost Innovations
15.5.3 Public – Private Innovation Hubs
15.5.4 Consumer Engagement and Transparency
15.5.5 Data and Risk Mapping
15.6 Case Studies: Real-world Applications of Digital Food Safety in Emerging Markets
15.6.1 India – FSSAI’s Food Safety Connect Mobile App
15.6.2 Kenya – Twiga Foods Digital Aggregation Platform
15.6.3 Indonesia – Vendor Hygiene Rating via Apps
15.7 Policy and Strategic Recommendations
15.7.1 Strengthen Digital Infrastructure
15.7.2 Build Institutional Capacity
15.7.3 Encourage Innovation and Public–Private Cooperation
15.7.4 Encourage Stakeholders to be Digitally Literate
15.7.5 Make Regulatory Sandboxes Active
15.8 Conclusion
References
16. Cross-sector Collaboration for Digital Food Safety
Ahmad Bhatti, and Angela Marqui
16.1 Introduction
16.2 Importance of Cross-sector Collaboration for Digital Food Safety
16.2.1 Key Stakeholders and Their Interests Related to Food Safety
16.2.1.1 Governments and Regulatory Bodies
16.2.1.2 Food Producers
16.2.1.3 Academic and Research Institutions
16.2.1.4 Civil Society Organisations and Non-governmental Organisations
16.2.1.5 Consumers
16.2.2 The Imperative for Cooperation
16.2.3 Challenges in Cross-sector Collaboration
16.3 Case Studies
16.3.1 The Global Food Safety Initiative
16.3.2 The EU Digital Strategy
16.3.2.1 Partnership Between EFSA and Member States
16.3.2.2 Partnership Between EFSA and Technology Companies
16.3.3 China National Centre for Food Safety Risk Assessment
16.3.4 Private Sector Collaboration: IBM and Walmart
16.3.4.1 Business-to-business Partnership
16.3.4.2 Business-academic Collaboration to Serve Local Needs
16.4 Lessons Learned from the Case Studies
16.5 Conclusion
References
17. Food Fraud Detection Through Digital Solutions
Helen Onyeaka, Rachel Fran Mansa, and Martin Maduka Ejeagwu
17.1 Introduction
17.2 An Overview of Food Fraud in the Digital Era
17.2.1 Definition
17.2.2 Forms and Consequences of Food Fraud
17.2.3 Difficulties in Identifying and Preventing Food Fraud
17.3 Digital Technologies for Food Fraud Detection
17.3.1 Blockchain and Distributed Ledger Technology
17.3.1.1 A Brief Recap of Blockchain
17.3.1.2 Application of Blockchain for Food Safety
17.3.1.3 Case Studies
17.3.2 Artificial Intelligence and Machine Learning
17.3.2.1 A Brief Recap of AI and ML
17.3.2.2 Application of AI and ML to Detect Food Fraud
17.3.2.3 Case Study
17.3.3 Internet of Things and Smart Sensors
17.3.3.1 A Brief Recap of IoT
17.3.3.2 Application of IoT to Detect Food Fraud
17.3.3.3 Case Study
17.4 Challenges Associated with Digital Solutions
17.5 Conclusion and Future Prospects
References
18. Digital Solutions for Allergen Management
Helen Onyeaka
18.1 Introduction
18.2 The Complexity of Allergen Risks in the Food Supply Chain
18.2.1 Food Allergy
18.2.2 Adverse Reactions of Food Allergy
18.2.3 Sources of Allergen Contamination
18.2.4 Regulatory Landscape and Limitations
18.3 Digital Traceability and Allergen Monitoring
18.3.1 Blockchain for Real-time Traceability in Ingredient Sourcing
18.3.2 Smart Labelling Systems and Digital Twins in Allergen Monitoring
18.3.2.1 Smart Labelling
18.3.2.2 Digital Twin
18.4 Advanced Allergen Detection Technologies
18.4.1 Nanomaterial-enhanced Biosensors
18.4.2 Portable On-site Allergen Detectors and Consumer-facing Innovations
18.4.2.1 Portable Allergen Detectors
18.4.2.2 Consumer-facing Innovations
18.5 AI and ML in Allergen Risk Prediction
18.5.1 AI for Faster Detection
18.5.2 AI and ML for Allergic Prediction
18.5.3 AI and ML for Personalised Allergen Management
18.5.4 AI for Allergen Management in Food Manufacturing
18.6 Conclusion
References
19. Personalised Food Safety, Leveraging Consumer Data
Hui Sun
19.1 Introduction
19.2 Customisation Through Innovation, the Rise of Personalised Food Service
19.2.1 Importance of Personalisation for the Food Industry
19.2.2 Types of Personalised Services in the Food Industry
19.2.2.1 Personalised Nutrition
19.2.2.2 Customisable Food Products
19.2.2.3 Personalised Grocery Shopping
19.2.2.4 Personalised Support for the Selection of Functional Foods
19.3 The Role of Consumer Data in Personalised Food Service
19.3.1 Types of Consumer Data
19.3.2 Sources of Consumer Data
19.4 AI in Customised Food Safety
19.4.1 Explainable AI, Personalised Dietary Recommendations with Justifications
19.4.2 AI-supported Predictive Marketing to Improve Food Safety
19.4.3 AI-driven System to Support Healthier Food Choices
19.4.4 NLP-enabled Personalised Nutritional Recommendations
19.5 Internet of Things and Smart Devices
19.6 Data Security and Privacy Considerations
19.6.1 Challenges
19.6.2 Guiding Principles
19.6.2.1 Guarantee That the Control of Individual Data Is Maintained by the Individual
19.6.2.2 Building Collaborative Frameworks for Data Security and Privacy
19.6.2.3 Implement Robust and Transparent Data Protection Measures by Design
19.7 Conclusions
References
Section IV The Future of Digital Food Safety
20. Digital Tools for Foodborne Disease Surveillance
Ejeagba O. Imo
20.1 Introduction
20.2 Foodborne Diseases
20.2.1 Methods of Surveillance
20.3 Digital Surveillance
20.3.1 Digital Tools
20.4 Challenges Facing the Implementation of Digital Tools in Foodborne Disease Surveillance
20.4.1 Underreporting
20.4.2 Overreporting
20.4.3 Data Accuracy and Reliability
20.4.4 Data Integration and Standardisation
20.4.5 Privacy and Security Concerns
20.4.6 Resource Limitations in Low- and Middle-income Countries
20.5 Recommendations for Successful Implementation and Future Directions
20.5.1 Enhance Data Accuracy Through Standardisation
20.5.2 Promote Public Awareness and Engagement
20.5.3 Focus on Data Privacy and Security
20.5.4 Enhance Training and Capacity Building
20.5.5 Implement Real-time Reporting and Feedback Mechanisms
20.5.6 Incorporation of Artificial Intelligence and Machine Learning
20.5.7 Use of Blockchain for Enhanced Traceability
20.6 Conclusion
References
21. Integrating Sustainability into Digital Food Safety
Helen Onyeaka and Gu Pang
21.1 Introduction
21.2 The Intersection of Food Safety and Sustainability
21.2.1 Food Safety: Scopes, Goals, and Evolving Challenges
21.2.2 Sustainability Challenges Facing the Food Sector
21.2.3 Aligning Digital Food Safety with Global Sustainability Frameworks
21.3 Digital Technologies Enabling Sustainable Food Safety
21.3.1 Artificial Intelligence and Machine Learning
21.3.1.1 A Brief Recap of AI and ML
21.3.1.2 Data-driven Predictions Aligned with Environmental Targets
21.3.1.3 Advanced Contamination Detection and Rapid Response
21.3.1.4 AI for Precision Agriculture
21.3.2 Blockchain for Transparency and Traceability
21.3.2.1 A Brief Recap of Blockchain
21.3.2.2 Supply Chain Transparency
21.3.2.3 Ethical Sourcing and Compliance Verification to Reduce Fraud
21.3.3 Smart Sensors and IoT for Real-time Monitoring
21.3.3.1 A Brief Recap of IoT
21.3.3.2 IoT-enabled Cold Chain Monitoring to Prevent Food Waste and Loss
21.3.3.3 Efficient Resource Utilisation in Farming and Food Production
21.4 Circular Food Economy: A Holistic Approach
21.5 Challenges and Trade-offs
21.5.1 Technological Challenges
21.5.2 Economic Barriers
21.5.3 Social and Ethical Trade-offs
21.6 Conclusions
References
22. Digital Transformation for Climate-resilient Food Safety
Joseph Sanderson
22.1 Introduction
22.2 Climate Change and Its Impacts on Food Safety
22.2.1 Climate Change: What Is It?
22.2.2 Impacts of Climate Change on Food Safety
22.2.2.1 Plant and Animal Health
22.2.2.2 Contamination by Biological Hazards
22.2.2.3 Chemical Hazards
22.2.2.4 Loss of Food Sources
22.3 Digital Transformation for Climate-resilient Food Safety
22.3.1 Enhanced Monitoring, Predictive Risk Analytics, and Early Warning
22.3.2 Climate-resilient Agricultural Practices Enabled by Digital Technologies
22.3.3 End-to-end Traceability for Faster Contamination Detection and Authentication
22.3.4 Supply Chain Resilience and Real-time Adjustments
22.4 Case Studies
22.4.1 Merlin AI: AI-powered Sorting to Detect Climate-Induced Contaminants
22.4.2 AgriDigital: Blockchain and Smart Contracts for Drought-affected Regions
22.4.3 Cold Chain Monitoring by Cainiao Network
22.4.4 Lessons Learned from the Case Studies
22.5 Conclusions
References
23. Applications, Advancement, and Future Directions of the Use of Artificial Intelligence in Microbiology
Fera R. Dewi, Edward Terhemen Akange, and Olumide A. Odeyemi
23.1 Introduction
23.1.1 Operational Pathway in Artifical Intelligence
23.1.2 AI Techniques
23.1.3 Classification of AI
23.1.3.1 Supervised Learning and Unsupervised Learning
23.1.3.2 Unsupervised Learning
23.1.3.3 Reinforcement Learning in Microbiology
23.1.4 Smart Operation of AI in Microbiology
23.2 Application of AI
23.2.1 Microbial Diagnostic
23.2.1.1 Automated Detection of Microbial Infection
23.2.1.2 AI in Disease Prediction and Prevention
23.2.1.3 Antimicrobial Resistance Prediction
23.2.2 Predictive Food Microbiology
23.2.2.1 Predicting Food Spoilage and Contamination
23.2.2.2 Ensuring Food Safety Through Microbial Monitoring
23.2.3 Environmental Microbiology
23.2.3.1 Monitoring and Predicting Microbial Activity in the Ecosystem
23.2.3.2 AI in Wastewater Treatment and Pollution Control
23.2.4 Microbial Genomics and Metagenomics
23.2.4.1 Advances in Genome Sequencing and Annotation Using AI
23.2.4.2 AI’s Role in Microbiome Research
23.3 Conclusion
References
24. Emerging Trends and Future Directions in Digital Food Safety
Gu Pang
24.1 Introduction
24.2 Frontier Technologies Redefining Food Safety
24.2.1 Cloud–Edge Hybrid Computing: Bridging Real-time Processing with Scalable Cloud Power
24.2.2 Integration of Multi-omics and AI for Hazard Detection
24.3 Challenges
24.3.1 Emerging Contaminants and New Risks
24.3.2 Regulatory Gaps and Lags
24.3.3 Supply Chain Disruptions Caused by War, Trade Disputes, and Regional Unrest
24.4 Future Opportunities and Policy Directions
24.4.1 Global Collaboration Frameworks
24.4.2 Sustainable and Inclusive Innovations
24.4.2.1 Design of Low-cost AI and Digital Tools for Smallholder Farmers
24.4.2.2 Equitable Deployment Frameworks
24.4.2.3 Inclusive Governance and Capacity Building
24.5 Conclusion
References
Index
