A predictive model that evaluates the effect of growth conditions on the thermal resistance of Listeria monocytogenes

Abstract

A predictive model for Listeria monocytogenes was developed using cells grown in different pH and milkfat levels before subsequent thermal inactivation in identical pH and milkfat conditions. Inactivation of the cells used combinations of temperature (55, 60, 65 °C), pH (5.0, 6.0, 7.0), and milkfat (0%, 2.5%, 5.0%) in a complete 3×3×3 factorial design with each test done in triplicate. A modified Gompertz equation was used to model nonlinear survival curves with the following three parameter estimates: A for the shouldering region, B for the maximum death rate, and C for the tailing region. All treatment sets were analyzed together in a regression model using the modified Gompertz equation. There was good confidence in the overall model when it was used to predict values for the entire data set. The correlation of determination, R 2, between the observed log surviving fraction (LSF) of cells from each of the conditions studied in the experiment, for the overall model was 0.811. For the A and B parameter estimates, temperature or milkfat alone, and the interaction of temperature and milkfat significantly ( p<0.05) affected the shouldering region and maximum death rate of a survival curve, respectively. These results were compared to a previously published predictive model, generated for cells grown under optimum conditions (pH 7.0, 0% milkfat), where pH was the only significant ( p<0.05) factor affecting the shoulder region. These results suggested that the conditions of the growth environment had an important impact on survival curve shape and the estimates of the predictive model. Specifically, there were more factor interactions involving temperature and milkfat level. These growth factors affected the shoulder region and maximum rate of death of the survival curve when cells were grown in identical medium conditions to which they were heated. Differences related to shouldering and inactivation rates for cells grown in different conditions may have important and practical importance for estimating inactivation of L. monocytogenes. This study provides some evidence on the importance of growing conditions when evaluating microbial heat resistance.