Integrating microbiome source tracking, machine learning, and precision intervention for data-driven control of microbial contamination in food processing

The microbial contamination in food processing environments is jointly influenced by complex interactions among raw materials, equipment, water, air, personnel, microbial communities, and processing conditions. Consequently, routine monitoring and fixed-frequency sanitation programs may be insufficient for managing persistent or intermittent contamination. This review synthesizes recent evidence on microbiome-based source tracking, predictive microbiology and machine learning, and targeted decontamination technologies, with a focus on potential pathways for their integration into data-driven food safety management. A literature search was conducted in Scopus, Web of Science, and PubMed for peer-reviewed studies published between 2020 and 2026, and a narrative synthesis approach was adopted to integrate the findings due to considerable heterogeneity in study designs, data types, and outcome measures. The reviewed evidence indicates that microbiome analysis and multi-omics approaches can improve the characterization of contamination reservoirs, while predictive models can integrate environmental, process, and microbiological data to support risk prioritization. However, the reliability of source attribution remains constrained by sequencing biases, incomplete reference databases, contamination interference, and limited taxonomic resolution. Machine learning applications are further limited by small and highly heterogeneous dataset sizes, information leakage, overfitting, distribution shifts, and insufficient external validation. Precision decontamination technologies show promise but face challenges in cost, scale-up, process integration, and regulatory validation. Based on these findings, this review proposes a conceptual "Sense-Trace-Predict-Act-Learn" framework for linking environmental monitoring, source investigation, risk prediction, targeted intervention, and post-intervention evaluation. This framework is intended as a decision-support roadmap rather than a validated autonomous industrial system. Future advances will depend on standardized data infrastructure, prospective industrial validation, uncertainty-aware modeling approaches, and integration with existing food safety management systems.