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Related Experiment Videos

A Machine Learning Framework for Meat Safety Risk Assessment with Cross-Domain Data Fusion.

Liying Wu1, Yanna Ke1, Yiming Huang2

  • 1Shanghai Institute of Quality Inspection and Technical Research Co., Ltd., Shanghai 201114, China.

Journal of Food Protection
|May 7, 2026
PubMed
Summary
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A new framework assesses meat supply chain risks using advanced data analysis. It identifies pork and fresh meat as high-risk, with a growing threat from biological hazards like pathogens.

Area of Science:

  • Food safety
  • Risk assessment
  • Supply chain management

Background:

  • Global meat supply chains face increasing complexity and consumption, elevating public health risks.
  • High-volume consumption regions are particularly vulnerable to these escalating risks.

Purpose of the Study:

  • To develop a novel risk assessment framework for global meat supply chains.
  • To integrate multi-source heterogeneous data for comprehensive risk evaluation.

Main Methods:

  • Implementation of Large Language Model (LLM)-enhanced data governance.
  • Utilizing a Delphi-Analytic Hierarchy Process (AHP) dual-dimensional indicator system.
  • Employing an optimized Random Forest model for predictive analysis.

Main Results:

Keywords:
Delphi-AHPLLMMicrobiological surveillancePathogen persistenceRandom forestRisk predictive analytics

Related Experiment Videos

  • Achieved predictive accuracy ranging from 88.25% to 99.24%.
  • Identified pork as the highest-risk meat category and fresh meat as the riskiest processing type.
  • Observed a shift towards biological hazards (pathogens) dominating over chemical contaminants since 2022.

Conclusions:

  • The data-driven framework enables proactive and targeted risk management in meat safety.
  • The tool supports regulatory decision-making for enhanced public health protection.
  • Highlights the evolving nature of meat safety risks, emphasizing biological over chemical threats.