Rutgers Professor Don Schaffner and Stockton University data scientist Clifton Baldwin continue their productive collaboration with models for predicting the time to 3-log, 4-log, and 5-log reduction of Salmonella, E. coli O157:H7, and Listeria monocytogenes in commercially prepared salad dressings, and includes a component for estimating pathogen concentrations in products based on historical ingredient testing data.
- They developed models to predict pathogen inactivation in commercial salad dressings, with key factors being temperature, pH, spice content, water content, fruit content, sugar content, and vegetable content.
- They found that incubation temperature and formulation pH were highly significant in predicting E. coli O157:H7 inactivation, while water content was the most critical factor for Salmonella inactivation.
- They concluded that their analysis would help develop risk-based approaches to ensure the safety of commercially prepared salad dressings.
They conclude that the models developed in this paper can only explain 42-47% of the variability in the data, and additional research is needed to uncover more sources of variability and develop more robust models.
Schaffner DW, Baldwin WC. Models for the Inactivation of Foodborne Pathogens in Salad Dressing from Challenge Studies. J Food Prot. 2024 Dec;87(12):100384. doi: 10.1016/j.jfp.2024.100384. Epub 2024 Oct 16. PMID: 39419397. https://www.sciencedirect.com/science/article/pii/S0362028X24001686?via%3Dihub
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