Author(s)

V. Popov, R. Gospavic, J. Kreyenschmidt & S. Bruckner

Abstract

The application of a Quantitative Microbial Risk Assessment (QMRA) in food supply chains requires accurate prediction of microbial growth under variable temperature conditions. A dynamical growth model under variable temperature conditions was developed using raw data for microbial growth of Pseudomonas spp. in poultry under aerobic conditions from a food supply chain in Germany. The primary model was developed using measurement data under a set of fixed temperatures and constant environmental conditions. The two primary models used for modelling the growth under constant temperature conditions were: Baranyi and modified Gompertz. As a secondary model the square root model was used for maximal growth rate in the Baranyi model and polynomial interpolation for the modified Gompertz model. In the case of the Baranyi model the main assumption, that the initial physiological state of the inoculum is constant and independent of the environmental parameters, is used. The generated dynamical models are validated by comparing the predictions obtained by both models with data measured under dynamical temperature conditions. Two temperature profiles were used for comparison and good agreement has been found between experimental data and model predictions. Keywords: microbial growth, predictive microbiology, pseudomonas spp., poultry.

Keywords

microbial growth, predictive microbiology, pseudomonas spp., poultry