Cover Image

Smart food safety /

Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Lu, Xiaonan (Editor)
Format: eBook
Language:English
Published: San Diego, CA : Academic Press, 2024.
Series:Advances in food and nutrition research ; v.111.
Subjects:
Food > Safety measures.
Food > Safety measures > Technological innovations.
Food Safety
Food Safety > methods
Aliments > Sécurité > Mesures.
Aliments > Sécurité > Mesures > Innovations.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Series Page
  • Advances in Food and Nutrition Research
  • Copyright
  • Contents
  • Contributors
  • Preface
  • Reference
  • Chapter One: Smart food packaging: Recent advancement and trends
  • 1 Introduction
  • 2 Active packaging
  • 2.1 Oxygen scavengers
  • 2.2 CO2 emitters
  • 2.3 Ethylene scavengers
  • 2.4 Antimicrobial and antifungal materials
  • 2.4.1 Essential oils
  • 2.4.2 Metallic nanomaterials
  • 2.5 Smart active packaging
  • 3 Intelligent packaging
  • 3.1 Indicators
  • 3.1.1 Integrity indicators
  • 3.1.2 Time temperature indicator (TTI)
  • 3.1.3 Freshness indicators
  • 3.2 Sensors
  • 3.2.1 Gas sensors
  • 3.2.2 Biosensors
  • 3.3 Data carrier
  • 3.3.1 Barcodes
  • 3.3.2 Radio frequency identification (RFID)
  • 3.4 Smart intelligent packaging
  • 4 Dual-function smart packaging
  • 5 Challenges and opportunities
  • References
  • Chapter Two: Frontiers of machine learning in smart food safety
  • 1 Introduction
  • 1.1 Definition of smart food safety
  • 1.2 The role of ML in advancing food safety
  • 1.2.1 Pre-harvest stage
  • 1.2.2 Post-harvest stage
  • 1.3 Current state of smart food safety
  • 2 Application of ML techniques in food safety
  • 2.1 Food quality inspection and detection
  • 2.2 Recognition of food fraud and adulteration
  • 2.3 Advanced food processing and packaging monitoring
  • 2.4 Raw material traceability and supply chain verification
  • 2.5 Early warning system of foodborne illness outbreaks
  • 3 Case studies of cutting-edge ML applications
  • 3.1 Predicting and improving complex beer flavor through machine learning
  • 3.2 Exploring deep learning's role in ensuring food safety: an exploration of natural language processing and time-series forecasting in food safety
  • 3.3 Utilizing crowdsourcing and ML to identify potential foodborne outbreaks through social media data analysis
  • 4 Challenges and potential solutions in implementing ML for smart food safety
  • 4.1 Challenges in implementing smart food safety systems
  • 4.2 Insights into potential solutions
  • 5 Conclusion and future perspectives
  • 5.1 Emerging trends in ML technologies
  • 5.2 Key influential social and industrial factors for smart food safety
  • 5.3 Regulatory and policy shifts in the era of AI-driven food safety
  • Declaration of AI and AI-assisted technologies in the writing process