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

Abstract

ABSTRACT: Psychrotolerant sporeformers pose a challenge to maintaining fluid milk quality. Dynamic temperature changes along the supply chain can favor the germination and growth of these bacteria and lead to fluid milk spoilage. In this study, we aim to expand on our previous work on predicting milk spoilage due to psychrotolerant sporeformers. The key model innovations include (1) the ability to account for changing temperatures along the supply chain, and (2) a deployed user-friendly interface to allow easy access to the model. Using the frequencies and concentrations of 8 Bacillales subtypes specific to fluid milk collected in New York, the model simulated sporeformer growth in half-gallons of high-temperature, short-time (HTST) pasteurized fluid milk transported from processing facility to retail store and then to consumer. The Monte Carlo simulations predicted that 44.3% of half-gallons of milk were spoiled (defined as having a bacterial concentration >20,000 cfu/mL, a conservative estimate that represents the Pasteurized Milk Ordinance regulatory limit) after 21 d of refrigerated storage at consumer's home. Model validations showed that the model was the most accurate in predicting the mean sporeformer concentration at low temperatures (i.e., at 3°C and 4°C; compared with higher temperatures at 6°C and 10°C) within the first 21 d of consumer storage, with a root mean square error of 0.29 and 0.34 log10 cfu/mL, respectively. Global sensitivity analyses indicated that home storage temperature, facility-to-retail transportation temperature, and initial spore concentration were the 3 most influential factors for predicting milk spoilage on d 21 of shelf life. What-if scenarios indicated that microfiltration was predicted to be the most effective strategy to reduce spoilage. The implementation of this strategy (assumed to reduce initial spore concentration by 2.2 log10 cfu/mL) was predicted to reduce the percentage of spoiled milk by 17.0 percentage points on d 21 of storage and could delay the date by which 50% of half-gallons of milk were spoiled, from d 25 to 35. Overall, the model is readily deployed as a digital tool for assessing fluid milk spoilage along the supply chain and evaluating the effectiveness of intervention strategies, including those that target storage temperatures at different supply chain stages.

Keywords

• fluid milk
• spoilage
• Monte Carlo simulation
• predictive model