Development and validation of a regression model for Listeria monocytogenes growth in roast beefs

Food business operators are responsible for food safety and assessment of shelf lives for their ready-to-eat products. For assisting them, a customized software based on predictive models, ListWare, is being developed. The aim of this study was to develop and validate a predictive model for the growth of Listeria monocytogenes in sliced roast beef. A challenge study was performed comprising 51 different combinations of variables. The growth curves followed the Baranyi and Roberts model with no clear lag phase and specific growth rates in the range <0.005–0.110 hr−1. A linear regression model was developed based on 528 observations and had an adjusted R-square of 0.80. The significant predictors were storage temperature, sodium lactate, interactions between sodium acetate and temperature, and MAP packaging and temperature. The model was validated in four laboratories in three countries. For conditions where the model predicted up to + log 2 cfu/g Listeria concentration, the observed concentrations were true or below the predicted concentration in 90% of the cases. For the remaining 10%, the roast beef was coated with spices and therefore different from the others. The model will be implemented in ListWare web-application for calculation of “Listeria shelf life”.