Abstract: The purpose of this study was to develop a dynamic prediction model of Listeria monocytogenes growth in fresh pork. Pork samples inoculated with a three-strain cocktail of L. monocytogenes were incubated under three groups of dynamic temperature conditions (1–45 ℃) to observe the growth of L. monocytogenes. The growth data of L. monocytogenes were analyzed by the one-step method and fitted to two different primary models, Baranyi and two-compartment models and then to the Cardinal secondary model. The results showed that both Baranyi-Cardinal model and Two-compartment-Cardinal model were equally suitable to describe the growth of L. monocytogenes in pork. The minimum, optimum and maximum growth temperatures of L. monocytogenes estimated by the Baranyi-Cardinal model were 0.94, 38.37 and 45.36 ℃, and those estimated by the Two-compartment-Cardinal model were 1.03, 37.96, 45.58 ℃, respectively. The optimum growth rate and the maximum growth density estimated by the Baranyi-Cardinal model were 0.891 and 0.858 h-1, and those estimated by the Two-compartment-Cardinal model were 9.07 and 9.09 (lg(CFU/g)), respectively. Model validation was conducted by using another four groups of dynamic growth experiments and three groups of isothermal growth experiments (4, 20 or 37 ℃). The obtained results showed the models could accurately predict the growth behavior of L. monocytogenes under both dynamic temperature and isothermal conditions. The root mean square error (RMSE) was between 0.13 and 0.48 (lg(CFU/g)), and the residual error obeyed the normal distribution with a mean value of ?0.02 (lg(CFU/g)) and a standard deviation of 0.29 (lg(CFU/g)). At last, numerical simulation of the growth of L. monocytogenes in fresh pork under cold storage in a household refrigerator was conducted, showing the application potential of the models. The results of this study are useful for growth prediction and risk assessment of L. monocytogenes in fresh pork.

Key words: fresh pork; Listeria monocytogenes; growth prediction; one-step method; numerical simulation