Combining fuzzy querying of imprecise data and predictive microbiology using category-based reasoning for prediction of the possible microbial spoilage in foods: application to Listeria monocytogenes.

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.

Significance of inoculum size in the lag time of Listeria monocytogenes.

The lag time of Listeria monocytogenes growing under suboptimal conditions (low nutrient concentrations, pH 6, and 6.5 degrees C) was extended when the inoculum was severely stressed by starvation and the inoculum size was very small. Predictive microbiology should deal with bacterial stress and stochastic approaches to improve its value for the agro-food industry.

Predictive microbiology of dairy products: influence of biological factors affecting growth of Listeria monocytogenes.

The growth potential of Listeria monocytogenes was evaluated at low temperature in sterilized milk and raw dairy products. Sterilized and raw milk were inoculated with different strains of L. monocytogenes in 2 physiological states and at various contamination levels.