Cardinal parameter meta-regression models describing Listeria monocytogenes growth in broth

Since Listeria monocytogenes has a high case-fatality rate, substantial research has been devoted to estimate its growth rate under different conditions of temperature, pH and water activity (aw). In this study, published findings on L. monocytogenes growth in broth were extracted and unified by constructing meta-regression models based on cardinal models for (i) temperature (CM[T]), (ii) temperature and pH (CM[T][pH]), and (iii) temperature, pH and aw (CM[T][pH][aw]). After assessing all the sources retrieved between 1988 until 2017, forty-nine primary studies were considered appropriate for inclusion. Apart from the modelling variables, study characteristics such as: type of broth (BHI, TSB, TPB), reading method (colony-forming-units, CFU; or binary-dilution optical density methods, OD), inoculum concentration and strain serotype, were also extracted. Meta-regressions based on CM[T] and CM[T][pH] were fitted on subsets of the 2009 growth rate measures and revealed that type of broth and reading method significantly modulated the cardinal parameter estimates. In the most parsimonious CM[T][pH][aw] meta-regression model, whereby the variability due to type of broth was extracted in a nested random-effects structure, the optimum growth rate ?opt of L. monocytogenes was found to be lower when measured as CFU (0.947 h-1; SE=0.094 h-1) than when measured as OD (1.289 h-1; SE=0.092 h-1). Such a model produced the following cardinal estimates: Tmin=-1.273°C (SE=0.179 °C), Topt=37.26°C (SE=0.688 °C), Tmax=45.12°C (SE=0.013 °C), pHmin=4.303 (SE=0.014), pHopt=7.085 (SE=0.080), pHmax=9.483 (SE=0.080), aw min=0.894 (SE=0.002) and aw opt=0.995 (SE=0.001). Integrating the outcomes from numerous L. monocytogenes growth experiments, this meta-analysis has estimated pooled cardinal parameters that can be used as reference values in quantitative risk assessment studies.Foundation for Food Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES to CIMO (UIDB/00690/2020). U. Gonzales-Barron acknowledges the national funding by FCT, P.I., through the Institutional Scientific Employment Program contractinfo:eu-repo/semantics/publishedVersio

Combining fuzzy querying of imprecise data and predictive microbiology using category-based reasoning for prediction of the possible microbial spoilage in foods: application to [i]Listeria monocytogen[/i]es

Communication présentée au "3rd International Conference on Predictive Modelling in Foods, Leuven, Belgium12–15 September 2000"Various predictive models of microbial behavior have been created and extensive data collection has been done by numerous private or public laboratories. However, significant differences between predicted and observed values in foods have been observed and need to be stressed, understood and explained as much as possible. In this paper, we present a software tool (currently at the level of a prototype) able: (i) to store in a database all relevant information expressed on one hand as qualitative or quantitative data and on the other hand as precise or imprecise data; (ii) to retrieve the more relevant information from the database using queries where criteria may be expressed as fuzzy values in order to enhance the flexibility of the search; (iii) to compute, in addition to the nearest data, an estimation of searched values using statistical models. The architecture of this software tool is structured as a category-based reasoning system. Example queries about Listeria monocytogenes (L. monocytogenes) illustrate the functionalities of this tool

Validation of a stochastic modelling approach for Listeria monocytogenes growth in refrigerated foods

International audienceA stochastic modelling approach was developed to describe the distribution of Listeria monocytogenes contamination in foods throughout their shelf life. This model was designed to include the main sources of variability leading to a scattering of natural contaminations observed in food portions: the variability of the initial contamination, the variability of the biological parameters such as cardinal values and growth parameters, the variability of individual cell behaviours, the variability of pH and water activity of food as well as portion size, and the variability of storage temperatures. Simulated distributions of contamination were compared to observed distributions obtained on 5 day-old and 11 day-old cheese curd surfaces artificially contaminated with between 10 and 80 stressed cells and stored at 14°C, to a distribution observed in cold smoked salmon artificially contaminated with approximately 13 stressed cells and stored at 8°C, and to contaminations observed in naturally contaminated batches of smoked salmon processed by 10 manufacturers and stored for 10 days a 4°C and then for 20 days at 8°C. The variability of simulated contaminations was close to that observed for artificially and naturally contaminated foods leading to simulated statistical distributions properly describing the observed distributions. This model seems relevant to take into consideration the natural variability of processes governing the microbial behaviour in foods and is an effective approach to assess, for instance, the probability to exceed a critical threshold during the storage of foods like the limit of 100 CFU/g in the case of L. monocytogenes