Predicting growth rates and growth boundary of Listeria monocytogenes — An international validation study with focus on processed and ready-to-eat meat and seafood

Abstract

The performance of six predictive models for Listeria monocytogenes was evaluated using 1014 growth responses of the pathogen in meat, seafood, poultry and dairy products. The performance of the growth models was closely related to their complexity i.e. the number of environmental parameters they take into account. The most complex model included the effect of nine environmental parameters and it performed better than the other less complex models both for prediction of maximum specific growth rates (micro(max) values) and for the growth boundary of L. monocytogenes. For this model bias and accuracy factors for growth rate predictions were 1.0 and 1.5, respectively, and 89% of the growth/no-growth responses were correctly predicted. The performance of three other models, including the effect of five to seven environmental parameters, was considered acceptable with bias factors of 1.2 to 1.3. These models all included the effect of acetic acid/diacetate and lactic acid, one of the models also included the effect of CO(2) and nitrite but none of these models included the effect of smoke components. Less complex models that did not include the effect of acetic acid/diacetate and lactic acid were unable to accurately predict growth responses of L. monocytogenes in the wide range of food evaluated in the present study. When complexity of L. monocytogenes growth models matches the complexity of foods of interest, i.e. the number of hurdles to microbial growth, then predicted growth responses of the pathogen can be accurate. The successfully validated models are useful for assessment and management of L. monocytogenes in processed and ready-to-eat (RTE) foods.

Establishing Equivalence for Microbial-Growth-Inhibitory Effects (“Iso-Hurdle Rules”) by Analyzing Disparate Listeria monocytogenes Data with a Gamma-Type Predictive Model

“…This validation procedure is highly recommended in predictive microbiology. It is performed by comparing predicted and observed growth rates visually and through indices of performance (3,24,29). In the present study, the new data generation was limited; only 20 experimental conditions were tested to assess the inhibitory effect of five factors.…”

“…This applies particularly to readyto-eat foods that are able to support the growth of L. monocytogenes and that may pose a risk for public health. Several predictive growth models have been already developed (1,24,35). Among them, modular models such as the gamma-type models developed by Zwietering et al (45) allow the quantification of individual and combined preservative factors or hurdle effects on the bacterial growth rate.…”

“…The secondary model is based on the gamma concept (45), which consists of independently quantifying the individual effect of each factor involved in the observed bacterial behavior. The gamma concept has been successfully applied on L. monocytogenes in the last 20 years (2,10,22,24).…”

Towards transparent and consistent exchange of knowledge for improved microbiological food safety

“…However, dealing with modelling practices associated with these data, the parameters obtained after fitting models to dataset are now generally included in scientific publications. To apply and reuse these data, the related environmental conditions are needed and this is an area where more detailed reporting in scientific publications would be beneficial, as pointed out by predictive microbiology validation studies [7]. To improve transparency some predictive microbiology application software explicitly share the equations and their parameter values and others include references to the scientific studies where models were developed (M Filter et al, 8th International Conference on Predictive Modelling in Food, Paris, France, September 2013).…”

“…In addition, results from product validation studies have been used to improve the reliable application of models for assessment and management of microbiological food safety. These product validation results included values for indices for model performance and the range of applicability for specific models with respect to the food products and environmental conditions for which the models were successfully validated [7]. To ensure the complete exploitation of knowledge it would be essential to share, for example, firstly, raw data collection, secondly, criteria of inclusion in the analysis, thirdly, script/tool/algorithms used in the modelling generation process, and finally, complete description of food product or laboratory media characteristics.…”