A Dynamic Secondary Model to Describe Survival of Salmonella in Low-water Activity (aw) Foods

Introduction: Survival of Salmonella in low water activity (a_w) foods is, often, characterized by a relatively fast initial decline, followed by long term persistence with slow or no decline over time, which is best described by the Weibull model. Temperature and a_w are the main influencing factors. Storage conditions are rarely constant and there is no available model that can accommodate this.

 Purpose: The objective of this study was to develop a general predictive model able to include dynamic conditions for Salmonella survival at temperatures below 40°C and a_w levels below 0.7.

Methods: Salmonella survival data (86 curves) on tree nuts and spices, temperatures between 4 and 37°C and a_w between 0.11 and 0.68, with measurements of temperature and a_w at every point in the curve, were collected from the literature. A set of differential equations based on the log-linear survival model, including a parameter representing the initial adaptation phase of the cell to the environmental conditions, was fit to the data. A secondary model based on the gamma concept was used to account for the impact of temperature and a_w. Model performance was compared to independent Weibull models using the adjusted R²_adj and the Bayesian Information Criteria (BIC), the latter penalizing a model for its number of parameters.

Results: Statistical analyses show superior performance of the developed model (BIC=1405) as compared to the Weibull model (BIC=1580). The model is more efficient than independent Weibull models, with only a slight decrease in the R²_adj values (0.90 compared to 0.94 for the Weibull models).

Significance: The model simulates survival of Salmonella over time, under varying storage conditions. It will increase precision in survival estimates for risk assessments of Salmonella in low a_w foods.