Optimizing Pasteurized Fluid Milk Shelf-Life Through Microbial Spoilage Reduction

Abstract

Psychrotolerant spore-forming bacteria, entering raw milk primarily on-farm, represent a major challenge for fluid milk processors due to the ability of these bacteria to survive heat treatments used for milk processing (e.g., pasteurization) and to cause premature spoilage. Importantly, fluid milk processors require tools to identify optimal strategies for reducing spore-forming bacteria, thereby extending product shelf-life by delaying spoilage. Potential strategies include (i) introducing farm-level premium payments (i.e., bonuses) based on spore-forming bacteria counts in raw milk and (ii) investing in spore reduction technologies at the processing level of the fluid milk supply chain. In this study, we apply an optimization methodology to the problem of milk spoilage due to psychrotolerant spore-forming bacteria and propose two novel mixed-integer linear programming models that assess improving milk shelf-life from the dairy processors' perspective. Our first model, imposed to a budgetary constraint, maximizes milk's shelf-life to cater to consumers who prefer milk with a long shelf-life. The second model minimizes the budget required to perform operations to produce milk with a shelf-life of a certain length geared to certain customers. We generate case studies based on real-world data from multiple sources and perform a comprehensive computational study to obtain optimal solutions for different processor sizes. Results demonstrate that optimal combinations of interventions are dependent on dairy processors' production volume and quality of raw milk from different producers. Thus, the developed models provide novel decision support tools that will aid individual processors in identifying the optimal approach to achieving a desired milk shelf-life given their specific production conditions and motivations for shelf-life extension.