A predictive growth model of Staphylococcus aureus during temperature abuse conditions

Authors: Juneja, Vijay; Osoria, Marangeli; KAPOOR, HARSIMRAN - University Of Georgia; GUPTA, PRIYANKA - University Of Central Oklahoma; SALAZAR, JOELLE - Us Food & Drug Administration (FDA); SHRESTHA, SUBASH - Cargill, Incorporated; BAG, SUBRATA - West Bengal University Of Animal & Fishery Sciences; MISHRA, ABHINAV - University Of Georgia

Submitted to: Food Research International
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/21/2025
Publication Date: 2/23/2025
Citation: Juneja, V.K., Osoria, M., Kapoor, H.K., Gupta, P., Salazar, J.K., Shrestha, S., Bag, S.K., Mishra, A. 2025. A predictive growth model of Staphylococcus aureus during temperature abuse conditions. Food Research International. https://doi.org/10.1016/j.foodres.2025.116032.
DOI: https://doi.org/10.1016/j.foodres.2025.116032

Interpretive Summary: Staphylococcus aureus continues to be a pathogen of concern to the food industry. It is an intoxication resulting from ingesting heat-resistant, pre-formed enterotoxin elaborated during massive growth of S. aureus in temperature abused foods. We collected growth data and developed a dynamic model for the pathogen to estimate its growth at temperatures encountered during emerging food processing operations, such as during low-temperature long-time cooking. The predictive model will assist regulatory agencies and food industries in predicting potential S. aureus behavior in food products stored at non-isothermal temperatures and to determine compliance of food processing operations with regulatory performance standards.

Technical Abstract: Staphylococcus aureus is frequently linked to foodborne outbreaks due to improper handling and processing of food through the supply chain. The primary contributing factor leading to Staphylococcus food poisoning is that some foods are not cooked after handling or are not appropriately refrigerated during storage. A predictive model for S. aureus was developed and validated using growth kinetic data. The growth data was collected in the Tryptic Soy Broth at isothermal temperatures from 7 to 48.9 ' (7, 10, 15, 20, 25, 30, 35, 40, 45, and 48.9 '); however, no growth occurred at 7 and 48.9 °C. Baranyi model was developed to fit the growth data, and Ratkowsky's secondary model was fitted to the growth rates with respect to temperature. Both primary and secondary models fitted the growth data well, as depicted by the goodness of fit measures (high R2, low RMSE/SSE). The average h0 value was 5.06 across all growth temperatures (10 to 45 '). The maximum growth temperature was 47.3', while the minimum was 5.7 '. Bacteria growth was estimated under dynamic temperature profiles by solving the differential form of the Baranyi model in combination with the Ratkowsky model equation for rate constants using the fourth-order Runge-Kutta method. The dynamic model was developed and validated using growth data obtained with two sinusoidal temperature profiles, 10-30 ' and 25-45 ' for 30 h and 24 h. Data for these two profiles were assessed using acceptable prediction zone analysis; 78.9% of the observed growth observations were within the acceptable prediction zone (-1.0 to 0.5 log10 CFU/mL), although the model may overestimate or underestimate at some points, generally <1 log. The model will assist in estimating the growth of S. aureus in temperature abuse conditions.