Fresh Dairy Quality in the Era of Smart Food Systems: Deterioration Pathways, Analytical Technologies, and Predictive Modeling

This review critically examines product-specific deterioration pathways in fresh dairy products and evaluates how analytical technologies, kinetic models, chemometric tools, and machine learning approaches support quality assessment and shelf-life prediction. Current evidence shows that many studies identify storage-related changes; however, few can predict the point at which a product reaches its acceptability limit and end of shelf-life. Reliable shelf-life prediction therefore requires a clear definition of the dominant deterioration pathway, a representative quality indicator, and a well-defined microbiological, physicochemical, structural, or sensory acceptability threshold. The analytical signal must demonstrate a validated mechanistic or predictive relationship with the selected quality threshold, rather than merely correlate with storage duration. Adopting an endpoint-centered framework would enable a more efficient integration of deterioration mechanisms, analytical measurements, and predictive modeling approaches, thereby providing a stronger scientific basis for shelf-life determination and quality management in fresh dairy products. Future progress will depend on the generation of representative multi-batch datasets, independent validation, realistic time–temperature histories, and model evaluation across diverse storage and distribution scenarios.