AI-Driven Predictive Microbiology with Real-Time Sensors for Next-Generation Food Safety

Abstract

Food safety in modern processing environments requires monitoring strategies that are faster, more adaptive, and more predictive than traditional microbiological approaches. This systematic narrative review examines recent advances in AI-enhanced predictive microbiology and real-time sensor technologies, focusing on their potential to transform contamination detection and microbial risk assessment. A structured search was conducted across Scopus, Web of Science, PubMed, IEEE Xplore, ScienceDirect, and supplementary sources such as Google Scholar to identify studies published between 2000 and 2024 that addressed computational modeling, sensor technologies, or integrated food safety systems. Findings show that machine learning and deep learning models provide superior capability for modeling nonlinear microbial responses across diverse food matrices, while modern optical, biosensing, electrochemical, and spectroscopic sensors generate continuous high-resolution data streams to enhance situational awareness during processing. When combined through cloud or edge computing infrastructures, these tools enable dynamic prediction, rapid anomaly detection, and automated decision support. Despite these advances, challenges remain, including data harmonization, model interpretability, sensor reliability, and the lack of standardized validation frameworks for industrial implementation. Overall, the convergence of AI analytics and real-time sensing technologies represents a promising pathway toward next-generation food safety systems that are predictive, responsive, and capable of autonomous decision-making across increasingly complex global supply chains.