Machine Learning-Driven Strategies for Pathogen Detection and Food Safety Management

Dear Colleagues,

Every year, according to estimates from the WHO, over 600 million people suffer from foodborne illnesses, resulting in approximately 420,000 deaths worldwide. While many of these diseases are preventable, traditional pathogen detection methods—such as culture-based techniques, immunoassays, and PCR—have notable limitations in terms of time, complexity, and accuracy.

Machine learning (ML), a branch of artificial intelligence (AI), offers a promising solution for improving foodborne pathogen detection. Unlike conventional methods, ML allows for the rapid analysis of large datasets from diverse sources, such as genetic sequencing, environmental monitoring, and microbiological testing. By utilizing advanced techniques like neural networks, ML can quickly identify pathogens, predict risks, and trace the source of infection outbreaks. This automated process speeds up detection, providing real-time monitoring and significantly reducing the risks associated with foodborne diseases.

In addition to pathogen detection, ML enhances food safety systems by minimizing false positives and negatives, optimizing production processes, and continuously monitoring data through technologies like the Internet of Things (IoT) and edge computing. This proactive approach improves safety throughout the food supply chain, reducing the costs related to waste, product recalls, and unnecessary testing.

However, challenges remain, including issues related to data quality, model transparency, and regulatory compliance. To fully unlock the potential of ML in food safety, collaboration among industry, regulatory bodies, and researchers is crucial. Investing in explainable AI techniques (e.g., LIME—Local Interpretable Model-Agnostic Explanations—and SHAP—Shapley Additive Explanations) can improve model interpretability, fostering greater trust and ensuring adherence to regulatory standards.

Looking ahead, integrating ML with emerging technologies like blockchain can further enhance real-time traceability and management of food safety, improving the prevention and control of foodborne illness outbreaks. As ML evolves, it is poised to play a pivotal role in strengthening food safety systems and protecting public health.

This Special Issue aims to explore the latest advancements in ML applications for pathogen detection in food safety. Contributions will focus on various ML techniques, real-time monitoring, and risk prediction. This Special Issue will also address challenges related to data quality, transparency, and regulatory compliance, with an emphasis on future research and development to harness the full potential of ML in pathogen identification and food safety management.

Dr. Maria Schirone
Prof. Antonello Paparella
Guest Editors

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• machine learning (ML) pathogen detection
• food safety
• pathogen monitoring
• disease severity
• artificial intelligence health solutions
• food safety risk assessment