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Area of Science:

  • Food Science
  • Computer Science
  • Data Science

Background:

  • Food safety is crucial for public health, preventing consumer exposure to hazardous products.
  • Artificial intelligence (AI) offers advanced capabilities for analyzing large datasets in food safety.
  • Existing research highlights AI applications, but a synthesized overview of systematic reviews is lacking.

Purpose of the Study:

  • To provide a comprehensive tertiary analysis of AI applications in food safety.
  • To identify trends in AI-driven food safety research, including data sources, hazards, and algorithms.
  • To highlight challenges and future directions for AI in food safety.

Main Methods:

  • Systematic tertiary analysis of secondary studies.
  • Identification and synthesis of trends from existing systematic reviews on AI in food safety.
  • Categorization of AI applications by food type, data source, hazard, and algorithm.

Main Results:

  • Dairy products are the most studied food category, with sensing data as the primary source.
  • Neural networks are the most common AI algorithm used.
  • Applications predominantly focus on chemical hazard detection, with limited use of unstructured data.

Conclusions:

  • AI, particularly neural networks, shows significant promise in detecting chemical food safety hazards.
  • There is a need to explore AI applications utilizing unstructured data for comprehensive food safety monitoring.
  • Large language models (LLMs) present a future avenue for advancing food safety and regulatory compliance.