From detection to prediction: AI-integrated molecular and biosensing approaches for psychrotrophic and thermophilic spoilage bacteria in dairy systems.

Psychrotrophic Gram-negative bacteria and thermophilic or thermoduric spore-forming bacteria are major causes of quality deterioration and shelf-life loss in milk and dairy products. Their impact is mediated primarily by heat-stable peptidases and lipases, heat-resistant spores and biofilms that persist in processing environments; with the notable exception of toxigenic members of the Bacillus cereus group, most of these organisms are spoilage rather than safety hazards. Culture-based methods remain the reference standard but are slow and provide single time-point information. This review critically evaluates molecular and sensing methods, including quantitative and digital PCR, isothermal amplification, CRISPR-Cas assays, biosensors, viability assays, spectroscopy and (meta)omics, with explicit attention to whether performance has been demonstrated in naturally contaminated dairy matrices rather than in buffer or spiked samples, and examines how their outputs could inform predictive microbiology and machine learning. A classification framework based on growth-temperature range, heat resistance, sporulation and dairy relevance is provided, and each detection-to-prediction pathway is graded as demonstrated in dairy, partly demonstrated or proposed. Validated dairy applications currently exist mainly for individual detection assays and for mechanistic or stochastic shelf-life models driven by culture-derived data; prospective, validated workflows in which molecular or biosensor outputs are used as model inputs to forecast spoilage have not yet been reported. Because heat-stable enzymes can spoil ultra-high-temperature products after the producing cells have been inactivated, viable counts alone are insufficient predictors of such defects. Key barriers include matrix effects, small and imbalanced datasets, domain shift, limited external validation and poorly defined intended use. Detection-to-prediction integration should therefore be regarded as an emerging research direction rather than an established industrial capability.