Development and deployment of a supply-chain digital tool to predict fluid milk spoilage due to psychrotolerant sporeformers

Psychrotolerant sporeformers pose a challenge to maintaining fluid milk quality. Dynamic temperature changes along the supply chain can favor germination and growth of these bacteria lead spoilage. In this study, we aim expand on our previous work predicting spoilage due psychrotolerant sporeformers. The key model innovations include (i) ability account for changing temperatures chain, (ii) deployed user-friendly interface allow easy access model. Using frequencies concentrations 8 Bacillales subtypes specific collected in New York, simulated sporeformer half-gallons high-temperature short-time (HTST) pasteurized transported from processing facility retail store then consumer. Monte Carlo simulations predicted that 44.3% were spoiled (defined as having bacterial concentration > 20,000 cfu/mL, conservative estimate represents Pasteurized Milk Ordinance [PMO] regulatory limit) after 21 d refrigerated storage at consumer's home. Model validations showed was most accurate mean low (i.e., 3 4°C; compared with higher 6 10°C) within first consumer storage, root square error 0.29 0.34 log10cfu/mL, respectively. Global sensitivity analyses indicated home temperature, facility-to-retail transportation initial spore influential factors shelf-life. What-if scenarios microfiltration be effective strategy reduce implementation (assumed by 2.2 log10cfu/mL) percentage 17.0 points could delay date which 50% 25 35. Overall, is readily digital tool assessing evaluating effectiveness intervention strategies, including those target different stages.