|Melendres, Martin - CIAD, MEXICO|
|Gumudavelli, Vinod - UNIV. OF NEBRASKA|
|Subbia, Jeyamkondan - UNIV. OF NEBRASKA|
|Thippareddi, Harshavardhan - UNIV. OF NEBRASKA|
Submitted to: Food Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: August 7, 2006
Publication Date: March 1, 2007
Citation: Juneja, V.K., Melendres, M.V., Huang, L., Gumudavelli, V., Subbia, J., Thippareddi, H. 2007. Modeling the effect of temperature on growth of salmonella in chicken. Food Microbiology. 24:328-335. Interpretive Summary: Salmonella is a pathogen of major concern for the food industry due to its association with several outbreaks of foodborne illness. Chicken products are commonly implicated as transmission vehicles in these outbreaks. This emphasizes the need to characterize the behavior of the pathogen at different temperatures to provide an adequate degree of protection against survival of Salmonella spp. We developed a dynamic mathematical model for predicting the fate of this pathogen in chicken. The model can be used to predict Salmonella growth and survival at changing temperatures encountered during cooking. This information will be of immediate use to consumers and to the food industry and regulatory agencies to aid in the development of guidelines to ensure the safety of the food supply.
Technical Abstract: Growth data of Salmonella in chicken were collected at several isothermal conditions (10, 15, 20, 25, 28, 32, 35, 37, 42, and 45 deg. C) and were then fitted into primary models, namely the logistic model, modified Gompertz model and Baranyi model. Measures of goodness-of-fit such as mean square error, pseudo-R2, -2 log likelihood, Akaike’s information and Sawa’s Bayesian information criteria were used for comparison for these primary models. Based on these criteria, modified Gompertz model described growth data the best, followed by the Baranyi model, and then the logistic model. The maximum growth rates obtained from each primary model were then modeled as a function of temperature using the modified Ratkowsky model. Pseudo-R2 values for this secondary model describing growth rate obtained from Baranyi, modified Gompertz, and logistic models were 0.999, 0.980, and 0.990, respectively. Mean square error values for corresponding models were 0.0002, 0.0008, and 0.0009, respectively. Both measures clearly show that the Baranyi model performed better than the modified Gompertz model or the logistic model.