A probabilistic model for predicting Enterobacter sakazakii inactivation in trypticase soy broth (TSB) and infant formula (IF) by high-pressure processing was developed. The modeling procedure is based on a previous model (S. Koseki and K. Yamamoto, Int. J. Food Microbiol. 116:136-143, 2007) that describes the probability of death of bacteria. The model developed in this study consists of a total of 300 combinations of pressure (400, 450, 500, 550, or 600 MPa), pressure-holding time (1, 3, 5, 10, or 20 min), temperature (25 or 40 degrees C), inoculum level (3, 5, or 7 log(10) CFU/ml), and medium (TSB or IF), with each combination tested in triplicate. For each replicate response of E. sakazakii, survival and death were scored with values of 0 and 1, respectively. Data were fitted to a logistic regression model in which the medium was treated as a dummy variable. The model predicted that the required pressure-holding times at 500 MPa for a 5-log reduction in IF with 90% achievement probability were 26.3 and 7.9 min at 25 and 40 degrees C, respectively. The probabilities of achieving 5-log reductions in TSB and IF by treatment with 400 MPa at 25 degrees C for 10 min were 92 and 3%, respectively. The model enabled the identification of a minimum processing condition for a required log reduction, regardless of the underlying inactivation kinetics pattern. Simultaneously, the probability of an inactivation effect under the predicted processing condition was also provided by taking into account the environmental factors mentioned above.

Escherichia coli, as a model microorganism, was treated in phosphate-buffered saline under high hydrostatic pressure between 100 and 300 MPa, and the inactivation dynamics was investigated from the viewpoint of predictive microbiology. Inactivation data were curve fitted by typical predictive models: logistic, Gompertz and Weibull functions. Weibull function described the inactivation curve the best. Two parameters of Weibull function were calculated for each holding pressure and their dependence on holding pressure was obtained by interpolation. With the interpolated parameters, inactivation curves were simulated and compared with the experimental data sets.