Microbiologie quantitative et estimation des risques MQER

Maisons-Alfort, France

Microbiologie quantitative et estimation des risques MQER

Maisons-Alfort, France

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Cornu M.,Microbiologie quantitative et estimation des risques MQER | Cornu M.,Institute for Radiological Protection and Nuclear Safety | Billoir E.,French National Institute for Agricultural Research | Billoir E.,University Claude Bernard Lyon 1 | And 3 more authors.
Food Microbiology | Year: 2011

Competition between background microflora and microbial pathogens raises questions about the application of predictive microbiology in situ, i.e., in non-sterile naturally contaminated foods. In this article, we present a review of the models developed in predictive microbiology to describe interactions between microflora in foods, with a special focus on two approaches: one based on the Jameson effect (simultaneous deceleration of all microbial populations) and one based on the Lotka-Volterra competition model. As an illustration of the potential of these models, we propose various modeling examples in estimation and in prediction of microbial growth curves, all related to the behavior of Listeria monocytogenes with lactic acid bacteria in three pork meat products (fresh pork meat and two types of diced bacon). © 2010 Elsevier Ltd.


Billoir E.,French National Institute for Agricultural Research | Billoir E.,University Claude Bernard Lyon 1 | Denis J.-B.,French National Institute for Agricultural Research | Cammeau N.,Microbiologie quantitative et estimation des risques MQER | And 5 more authors.
Risk Analysis | Year: 2011

To assess the impact of the manufacturing process on the fate of Listeria monocytogenes, we built a generic probabilistic model intended to simulate the successive steps in the process. Contamination evolution was modeled in the appropriate units (breasts, dice, and then packaging units through the successive steps in the process). To calibrate the model, parameter values were estimated from industrial data, from the literature, and based on expert opinion. By means of simulations, the model was explored using a baseline calibration and alternative scenarios, in order to assess the impact of changes in the process and of accidental events. The results are reported as contamination distributions and as the probability that the product will be acceptable with regards to the European regulatory safety criterion. Our results are consistent with data provided by industrial partners and highlight that tumbling is a key step for the distribution of the contamination at the end of the process. Process chain models could provide an important added value for risk assessment models that basically consider only the outputs of the process in their risk mitigation strategies. Moreover, a model calibrated to correspond to a specific plant could be used to optimize surveillance. © 2010 Society for Risk Analysis.

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