Beyond predictive accuracy: A case for mechanism-informed, uncertainty-aware machine learning in food microbiology

Foodborne illness and avoidable food waste both depend on decisions about how microorganisms behave in a food. Machine-learning (ML) models can achieve strong predictive accuracy within a defined data domain, but a held-out test error alone does not establish reliability for food-safety decisions. Conventional predictive-microbiology models are usually empirical rather than fully mechanistic, although their parameters have biological interpretation; ML offers flexible learning of complex, high-dimensional relationships. Mechanism-informed hybrids can incorporate kinetic or physiological constraints within a learning workflow. Such constraints may improve plausibility, data efficiency, and interpretability within a declared applicability domain, but they do not confer universal or unrestricted extrapolation. We outline four hybrid design patterns and propose a food-specific validation framework that includes context-relevant external validation, calibrated predictive uncertainty, and decision-focused evaluation.