Submitted to: International Journal of Food Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/24/2019
Publication Date: 8/27/2019
Citation: Huang, L. 2019. Dynamic analysis of growth of Salmonella spp. in raw ground beef – estimation of kinetic parameters, sensitivity analysis, and markov chain Monte Carlo simulation. International Journal of Food Microbiology. 108:106878. https://doi.org/10.1016/j.foodcont.2019.106878.
Interpretive Summary: Nontyphoidal Salmonella (NS) are major foodborne pathogens often associated with raw and undercooked meats. The study was conducted to investigate the kinetics of growth and survival of NS in raw ground beef and to develop mathematical models to accurately predict the microbial growth and survival under dynamically changing temperature conditions. The models developed in this study can be used to predict the growth and survival and conduct risk assessment of NB in ground beef.
Technical Abstract: Nontyphoidal Salmonella are major foodborne pathogens often associated with raw and undercooked meats. The study was conducted to investigate the kinetics of growth and survival of a 6-strain cocktail, representing 5 serovars of Salmonella enterica, in raw and irradiated ground beef under 6 dynamic storage temperatures between 1 to 45 deg C. One-step dynamic analysis was used to directly construct predictive models for both background microbiota and Salmonella from dynamic temperature profiles. The analytical results showed that the minimum growth temperature (Tmin) for Salmonella spp. in raw ground beef was 9.9 deg C, and its population would gradually decrease at the rate of 0.14 log CFU/g per week per deg C away from this temperature. Above 17.3 deg C, Salmonella would grow faster than the background microbiota. Scaled sensitivity coefficients were calculated to determine the identifiability of the kinetic parameters. The models were validated using the estimated kinetic parameters to predict the dynamic growth of Salmonella. The results showed that the root-mean-square-errors (RMSE) of prediction were identical to those of model development (0.6 log CFU/g). Using the estimated kinetic parameters as the prior information, Markov Chain Monte Carlo (MCMC) was used for posterior analysis to simulate the growth of Salmonella in raw ground beef. The results showed that the experimental observed growth data were mostly within the range of mean ± standard deviation of the simulation. The RMSE of prediction by MCMC was only 0.2 log CFU/g, showing that MCMC can be used to accurately predict the growth of Salmonella in raw ground beef.