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Table of Contents
Intro
Preface
Contents
1 Machine Learning Application in Food Safety, Production, and Quality
1.1 Introduction
1.2 An Introduction to Food Supply Chain
1.2.1 Food Safety
1.2.1.1 Foodborne Illness
1.2.1.2 Foodborne Disease Outbreaks
1.2.2 Food Spoilage and Quality
1.2.2.1 Food Authenticity
1.2.2.2 Food Post-harvesting
1.2.3 Food Production Process
1.2.3.1 Food Harvesting
1.2.3.2 Food Packaging
1.2.3.3 Food Traceability
1.2.3.4 Food Distribution
1.2.3.5 Food Storage
1.3 An Introduction to Machine Learning
1.3.1 Machine Learning Applications in Food Safety
1.3.2 Machine Learning Applications in Food Quality
1.3.3 Machine Learning Applications in Food Production
1.4 Conclusion
References
2 Foodborne Bacterial PathogenBig Data
Genomic Analysis
2.1 Introduction
2.2 Whole Genome Sequencing
2.2.1 WGS in Source Attribution
2.2.2 WGS in Disease Surveillance
2.2.3 Antimicrobial Resistance, Virulence Potential, and Risk Analysis
2.2.4 WGS Technologies
2.2.4.1 First-Generation Sequencing: Sanger Shotgun Approach
2.2.4.2 Second-Generation Sequencing: The Massively Parallel Approach
2.2.4.3 Third-Generation Sequencing: The Long-Read Approach
2.3 Bioinformatics: Algorithms and Databases
2.4 Future Opportunities and Challenges for WGS
2.4.1 Predicting Emerging Treats
2.4.2 Low- and Middle-Income Countries
2.4.3 Culture-Independent Diagnostic Tests
2.4.3.1 Metagenomic Sequencing
References
3 Foodborne Viral Pathogen Big Data: Genomic Analysis
3.1 Introduction
3.1.1 Norovirus
3.1.2 HAV
3.1.3 HEV
3.1.4 SARS-CoV-2
3.1.5 WGS
3.2 Applications
3.2.1 Surveillance and Source Attribution
3.2.2 Analysis of Variants and Viral Evolution
3.2.3 Predictive Analytics
3.3 Conclusion and Future Perspectives
References
4 The Use of Big Data in the Field of Food Mycology and Mycotoxins
4.1 Introduction
4.2 Food Mycology: Past and Present
4.3 Evolution of Food Mycological Methods
4.3.1 Methods for Quantifying Fungal Growth
4.3.2 Cultural Methods
4.3.3 The Impact of Polyphasic Approaches on Mycological Studies
4.3.4 Molecular Techniques on Fungal Taxonomy
4.4 The Omic Tools (Genomics, Transcriptomics, Metagenomics, Proteomics, and Metabolomics) in Food Mycology for the Generation of Big Data
4.4.1 Genomics
4.4.2 Transcriptomics
4.4.3 Metagenomics
4.4.4 Metabolomics
4.4.5 Proteomics
4.5 The Usefulness of Big Data Storage
4.6 How to Use Big Data to Find Strategies to Prevent and Control Fungi and Mycotoxins
References
5 Big Data and its Role in Mitigating Food Spoilage and Quality Deterioration along the Supply Chain
5.1 Introduction
5.2 Food Spoilage and Shelf Life
5.2.1 Shelf Life Open Dates as Measures of Food Spoilage.
Preface
Contents
1 Machine Learning Application in Food Safety, Production, and Quality
1.1 Introduction
1.2 An Introduction to Food Supply Chain
1.2.1 Food Safety
1.2.1.1 Foodborne Illness
1.2.1.2 Foodborne Disease Outbreaks
1.2.2 Food Spoilage and Quality
1.2.2.1 Food Authenticity
1.2.2.2 Food Post-harvesting
1.2.3 Food Production Process
1.2.3.1 Food Harvesting
1.2.3.2 Food Packaging
1.2.3.3 Food Traceability
1.2.3.4 Food Distribution
1.2.3.5 Food Storage
1.3 An Introduction to Machine Learning
1.3.1 Machine Learning Applications in Food Safety
1.3.2 Machine Learning Applications in Food Quality
1.3.3 Machine Learning Applications in Food Production
1.4 Conclusion
References
2 Foodborne Bacterial PathogenBig Data
Genomic Analysis
2.1 Introduction
2.2 Whole Genome Sequencing
2.2.1 WGS in Source Attribution
2.2.2 WGS in Disease Surveillance
2.2.3 Antimicrobial Resistance, Virulence Potential, and Risk Analysis
2.2.4 WGS Technologies
2.2.4.1 First-Generation Sequencing: Sanger Shotgun Approach
2.2.4.2 Second-Generation Sequencing: The Massively Parallel Approach
2.2.4.3 Third-Generation Sequencing: The Long-Read Approach
2.3 Bioinformatics: Algorithms and Databases
2.4 Future Opportunities and Challenges for WGS
2.4.1 Predicting Emerging Treats
2.4.2 Low- and Middle-Income Countries
2.4.3 Culture-Independent Diagnostic Tests
2.4.3.1 Metagenomic Sequencing
References
3 Foodborne Viral Pathogen Big Data: Genomic Analysis
3.1 Introduction
3.1.1 Norovirus
3.1.2 HAV
3.1.3 HEV
3.1.4 SARS-CoV-2
3.1.5 WGS
3.2 Applications
3.2.1 Surveillance and Source Attribution
3.2.2 Analysis of Variants and Viral Evolution
3.2.3 Predictive Analytics
3.3 Conclusion and Future Perspectives
References
4 The Use of Big Data in the Field of Food Mycology and Mycotoxins
4.1 Introduction
4.2 Food Mycology: Past and Present
4.3 Evolution of Food Mycological Methods
4.3.1 Methods for Quantifying Fungal Growth
4.3.2 Cultural Methods
4.3.3 The Impact of Polyphasic Approaches on Mycological Studies
4.3.4 Molecular Techniques on Fungal Taxonomy
4.4 The Omic Tools (Genomics, Transcriptomics, Metagenomics, Proteomics, and Metabolomics) in Food Mycology for the Generation of Big Data
4.4.1 Genomics
4.4.2 Transcriptomics
4.4.3 Metagenomics
4.4.4 Metabolomics
4.4.5 Proteomics
4.5 The Usefulness of Big Data Storage
4.6 How to Use Big Data to Find Strategies to Prevent and Control Fungi and Mycotoxins
References
5 Big Data and its Role in Mitigating Food Spoilage and Quality Deterioration along the Supply Chain
5.1 Introduction
5.2 Food Spoilage and Shelf Life
5.2.1 Shelf Life Open Dates as Measures of Food Spoilage.