Development and validation of a molecular predictive model to describe the growth of Listeria monocytogenes in vacuum-packaged chilled pork

The aim of this study was to develop a molecular predictive model from appropriate real-time PCR methods, so as to describe the growth of a cocktail of Listeria monocytogenes strains in vacuum-packaged chilled pork during storage at selected temperature conditions (4, 10, 15, 20 and 25 °C). We compared this model with a traditional predictive model which used original data obtained by conventional microbiological methods. Real-time PCR was successfully used in the construction of a predictive model. A sigmoidal trend was observed for all growth curves, and four primary growth models (modified Gompertz, Baranyi, Logistic and Huang) could be used to fit the growth curves. The R2 values were >0.97 and MSE values were 0.2198 log cfu/mL in all models used. Most of the Bf and Af values were within the limit of 1.0 ≤ Bf ≤ Af ≤ 1.1, except for one obtained by real-time PCR at 25 °C. The F test showed that the modified Gompertz, Logistic and Baranyi models were sufficient to describe growth curves, but the Huang model was rejected twice in ten cases. No difference was observed in accuracy between the molecular and traditional predictive models for most of growth curves when assessed by F test. Further, no differences in both growth rate and lag phase were observed between real-time PCR and conventional microbiological methods. The application of molecular predictive model not only can aid to establish models of certain pathogens more accurately in the presence of other bacteria, but also save time and labor. Thereby, it will reduce the risk of pathogens and enhance the safety of meat and meat products. ► We develop molecular predictive model in which all the original data were derived from appropriate real-time PCR method, to describe the growth of L. monocytogenes in vacuum-packaged chilled pork. ►There was no difference between molecular and traditional predictive model. ►Molecular predictive model can aid to establish models of certain pathogen more accurately in the presence of other bacteria. ►This model can reduce considerable time and input of labor save time and labor.