A normal distribution and a mixture model of two normal distributions in a Bayesian approach using prevalence and concentration data were used to establish the distribution of contamination of the food-borne pathogenic bacteria Listeria monocytogenes in unprocessed and minimally processed fresh vegetables. A total of 165 prevalence studies, including 15 studies with concentration data, were taken from the scientific literature and from technical reports and used for statistical analysis. The predicted mean of the normal distribution of the logarithms of viable L. monocytogenes per gram of fresh vegetables was ؊2.63 log viable L. monocytogenes organisms/g, and its standard deviation was 1.48 log viable L. monocytogenes organisms/g. These values were determined by considering one contaminated sample in prevalence studies in which samples are in fact negative. This deliberate overestimation is necessary to complete calculations. With the mixture model, the predicted mean of the distribution of the logarithm of viable L. monocytogenes per gram of fresh vegetables was ؊3.38 log viable L. monocytogenes organisms/g and its standard deviation was 1.46 log viable L. monocytogenes organisms/g. The probabilities of fresh unprocessed and minimally processed vegetables being contaminated with concentrations higher than 1, 2, and 3 log viable L. monocytogenes organisms/g were 1.44, 0.63, and 0.17%, respectively. Introducing a sensitivity rate of 80 or 95% in the mixture model had a small effect on the estimation of the contamination. In contrast, introducing a low sensitivity rate (40%) resulted in marked differences, especially for high percentiles. There was a significantly lower estimation of contamination in the papers and reports of 2000 to 2005 than in those of 1988 to 1999 and a lower estimation of contamination of leafy salads than that of sprouts and other vegetables. The interest of the mixture model for the estimation of microbial contamination is discussed.Quantitative microbial risk assessment (QMRA) is in rapid development in the area of food safety. To obtain a quantitative exposure assessment or a quantitative risk characterization for a given food and a given food-borne pathogen, statistical distributions of microbial concentrations are used as input values (such as bacterial concentrations in raw food materials subjected to further process) and/or as output values (predicted contamination in foods after processing and/or storage) (35,75). Microbial contaminations in foods are expressed in two forms: (i) prevalence data (percentage of positive samples in a given study, i.e., growth/no growth of a target pathogen after enrichment in an appropriate broth of an aliquot of a food sample) and (ii) concentration data expressed as CFU per gram or CFU per milliliter. Data on the concentration of pathogenic bacteria in foods, while scarce, are essential to QMRA.Previous works on QMRA did not consider low levels of concentrations, i.e., concentrations below the threshold of detection of microbiological methods (6,15,44...
Spoilage can be evaluated by separating and determining biogenic amines by various techniques, notably high-performance liquid chromatography. Previous studies have not taken into account how the muscle tissue matrix affects the assay. We demonstrate a matrix effect in plaice and whiting and show that it changes during spoilage. This effect should be taken into account when plotting regression lines relating the quantity of amine to the biogenic amine/internal standard ratio.
Comment citer ce document : Buche, P., Dervin, C., Haemmerlé, O., . Fuzzy querying of incomplete, imprecise, and heterogeneously structured data in the relational model using ontologies and rules.IEEE
AbstractIn this paper, we present a new method, called multi-view fuzzy querying, which permits to query incomplete, imprecise and heterogeneously structured data stored in a relational database. This method has been implemented in the MIEL software.MIEL is used to query the Sym'Previus database which gathers information about the
Describing the Sym'Previus project, the software and its deliverable facilities is the aim of this present paper. This software concerns all the partners of the food industry who are involved in the management of food safety and allows food-borne pathogen behaviour in food to be predicted, as function of the environment (nature of the food, manufacturing process, conditions of conservation). This analysis of microbial behaviour has been possible thanks to the progress made in predictive microbiology since the 1980s. Sym'Previus offers to food industry professionals and their partners the possibility of applying this progress, by giving access to a database, to simulation systems and expertise.
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