Bayesian global regression model relating product characteristics of intermediate moisture food products to heat inactivation parameters for Salmonella Napoli and Eurotium herbariorum mould spores

Thermal inactivation of pathogenic and spoilage organisms in low and intermediate moisture foods is of critical importance for guaranteeing microbiological safety and stability of these products. Producers tendentially reduce salt in low and intermediate moisture foods because of nutritional health considerations, but it is unclear how this affects microbial inactivation rates during pasteurization. In this study we predict the time to achieve a pre-defined 6-log reduction for Salmonella enterica subsp. enterica serovar Napoli (hereafter: S. Napoli) and Eurotium herbariorum mould spores (hereafter: E. herbariorum spores) and the relationship with product characteristics. We tested 31 design products for heat inactivation of S. Napoli and 29 design products for heat inactivation of E. herbariorum spores. We used a global Bayesian regression combining primary Weibull models with a secondary regression model to relate pasteurization temperature and product characteristics (water activity (aw), pH, and fractions of sodium chloride, sucrose and oil on product) to microbial counts. With this model, we predict the time to 6-log reduction. Thermal inactivation rates were much higher for vegetative S. Napoli than for E. herbariorum spores. Also, inactivation curves were non-linear for many experiments. There were significant associations between the Weibull model parameters and temperature, and pH and aw for S. Napoli and E. herbariorum spores, respectively. We parameterized an inactivation model for S. Napoli and E. herbariorum spores using design products with a broad range of characteristics and showed how the simplified approach of using D-values does not accurately describe the non-linearity of thermal inactivation for both types of organism. Results of our model can be used to produce accurate heat inactivation predictions as input for the pasteurization process in factories where intermediate moisture foods are manufactured. •Global Bayesian regression model combining primary and secondary inactivation models.•Pasteurization model for safety and spoilage: vegetative Salmonella Napoli and Eurotium herbariorum spores•Proposes minimally required pasteurization times for Intermediate Moisture Foods products as function of product properties and pasteurization temperature.