MICROBIAL MODELING AND BIOINFORMATICS FOR FOOD SAFETY AND SECURITY
Location: Residue Chemistry and Predictive Microbiology
Title: Dynamic model for predicting growth of salmonella spp. in ground sterile pork
Submitted to: Food Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 7, 2010
Publication Date: June 10, 2010
Citation: Velugoti, P.R., Bohra, L.K., Juneja, V.K., Huang, L., Subbiah, J., Wessling, A.L., Thippareddi, H. 2010. Dynamic model for predicting growth of salmonella spp. in ground sterile pork. Food Microbiology. 28:796-803.
Interpretive Summary: Salmonella is a foodborne pathogen very frequently associated with raw meats. Different growth kinetics models are used to characterize the behavior of the pathogen in meat. These models are either experimentally derived or are based on fundamental growth theories. This study investigated the growth kinetics of this microorganism in raw pork and developed a dynamic model that can be used to estimate the growth in raw meat under different temperature conditions. The findings will be used by food processors and regulatory agencies to assess the hazards in contaminated pork during storage and distribution.
Predictive model for Salmonella spp. growth in ground pork was developed and validated using kinetic growth data. Salmonella spp. kinetic growth data in ground pork was collected at several isothermal conditions (between 10 and 45C) and Baranyi model was fitted to describe the growth at each temperature, separately. The maximum growth rates estimated from the Baranyi model were modeled as a function of temperature using a modified Ratkowsky equation. To estimate bacterial growth under dynamic temperature conditions, the differential form of the Baranyi model, in combination with the modified Ratkowsky equation for rate constants, was solved numerically using fourth order Runge-Kutta method. The dynamic model was validated using five different dynamic temperature profiles (linear cooling, exponential cooling, linear heating, exponential heating, and sinusoidal). Performance measures, root mean squared error, accuracy factor, and bias factor were used to evaluate the model performance, and were observed to be satisfactory. The dynamic model can estimate the growth of Salmonella spp. in pork within a 0.5 log accuracy under both linear and exponential cooling profiles, although the model may overestimate or underestimate at some data points, which were generally less than1 log. Under sinusoidal temperature profiles, the estimates from the dynamic model were also within 0.5 log of the observed values. However, underestimation could occur if the bacteria were exposed to temperatures below the minimum growth temperature of Salmonella spp., since low temperature conditions could alter the cell physiology. To obtain an accurate estimate of Salmonella spp. growth using the models reported in this work, it is suggested that the models be used at temperatures above 7C, the minimum growth temperature for Salmonella spp. in pork.