Application of Machine Learning in Food Safety Risk Assessment

Area of Science:

• Food Science
• Computer Science
• Data Science

Background:

• Globalization of supply chains increases food safety complexity.
• Advanced risk assessment approaches are necessary.
• Machine learning (ML) and deep learning (DL) offer potential solutions.

Purpose of the Study:

• To review the role of ML and DL in intelligent food safety management.
• To summarize recent advances in ML and DL applications for food safety.
• To identify future research directions.

Main Methods:

• Systematic review of ML and DL applications in food safety.
• Focus on biotoxin detection, heavy metal contamination, pesticide/veterinary drug residues, and microbial risk prediction.
• Analysis of traditional algorithms (SVM, Random Forests) and unsupervised methods (K-means, Hierarchical Clustering).
• Evaluation of deep learning architectures (CNNs, RNNs, Transformers).

Main Results:

• Traditional ML algorithms show strong performance in classification and risk evaluation.
• Unsupervised methods aid pattern recognition in unlabeled data.
• Deep learning architectures improve detection accuracy and efficiency through automated feature extraction and multimodal data integration.

Conclusions:

• ML and DL are transformative for intelligent food safety management.
• Future work should prioritize model interpretability and multi-modal data fusion.
• Integration into Hazard Analysis and Critical Control Points (HACCP) systems is recommended for real-time management.