Advancing food security: The role of machine learning in pathogen detection

Abstract

Machine Learning (ML) has emerged as an important advancement in pathogen detection, particularly in the field of food safety. This paper reviews current advances and the application of machine learning in real-time foodborne pathogen detection and risk assessment. ML accelerates pathogen identification processes by leveraging AI-biosensing and deep learning models, significantly reducing detection times and potentially increasing accuracy rates, as indicated in several studies. The study investigates a variety of real-world applications and case studies, including the detection of Escherichia coli, Pseudomonas aeruginosa, Magnaporthe oryzae, demonstrating ML's efficiency in quick pathogen detection, disease prediction, and contamination source identification. These applications show significant benefits in terms of epidemic prevention, customer safety, and operational efficiency. However, challenges persist, particularly with data quality, model interpretability, and regulatory compliance. The review emphasizes the importance of transparent ML models and rigorous validation in meeting regulatory standards. Future possibilities include combining ML with new technologies like the Internet of Things (IoT) and blockchain to provide comprehensive, real-time food safety management. This integration promises to improve real-time monitoring, traceability, and transparency throughout the food supply chain.