Multiple regression model for thermal inactivation of Listeria monocytogenes in liquid food products

A multiple regression model was constructed for thermal inactivation of Listeria monocytogenes in liquid food products, based on 802 sets of data with 51 different strains and 6 cocktails of strains published from 1984 to 2010. Significant variables, other than inactivation temperature, were pH, sodium chloride content, sugar content, the temperature of growth or storage before inactivation, in addition to a heat shock before inactivation. The constructed model for thermal inactivation of L monocytogenes has a reduced variability as these variables are known to influence the thermal resistance (and these are known or controllable in practice). Mean simulation results of inactivation of L monocytogenes during pasteurisation (20 s, 76 degrees C) of raw milk (calculated mean level after growth 14 cfu/l) were comparable with results of a single regression model constructed from inactivation data found in experiments in milk only (175 data sets, 18 strains/cocktails). Both models predicted a probability of survival of less than 1 in a billion litres. The study shows that multiple regression modelling can be used to obtain a model from all data available, with a limited and realistic uncertainty level, while retaining the variability of heat resistance due to the 51 strains and 6 cocktails of strains (unknown and not controllable in practice). (C) 2012 Elsevier Ltd. All rights reserved.