Inferring an Augmented Bayesian Network to Confront a Complex Quantitative Microbial Risk Assessment Model with Durability Studies: Application to Bacillus Cereus on a Courgette Purée Production Chain

Authors

  • Clémence Sophie Rigaux Ancelet,

    Corresponding author
    • INRA, UR 1204, Met@risk, Food Risk Analysis Methodologies, Paris, France
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  • Frédéric Carlin,

    1. INRA, UMR 408, Sécurité et Qualité des Produits d'Origine Végétale, Avignon, France
    2. Université d'Avignon et des Pays de Vaucluse, UMR 408, Sécurité et Qualité des Produits d'Origine Végétale, Avignon, France
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  • Christophe Nguyen-thé,

    1. INRA, UMR 408, Sécurité et Qualité des Produits d'Origine Végétale, Avignon, France
    2. Université d'Avignon et des Pays de Vaucluse, UMR 408, Sécurité et Qualité des Produits d'Origine Végétale, Avignon, France
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  • Isabelle Albert

    1. INRA, UR 1204, Met@risk, Food Risk Analysis Methodologies, Paris, France
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Clémence Rigaux, INRA, UR 1204, Met@risk, Food Risk Analysis Methodologies, F-75005 Paris, France; clemence.rigaux@paris.inra.fr.

Abstract

The Monte Carlo (MC) simulation approach is traditionally used in food safety risk assessment to study quantitative microbial risk assessment (QMRA) models. When experimental data are available, performing Bayesian inference is a good alternative approach that allows backward calculation in a stochastic QMRA model to update the experts’ knowledge about the microbial dynamics of a given food-borne pathogen. In this article, we propose a complex example where Bayesian inference is applied to a high-dimensional second-order QMRA model. The case study is a farm-to-fork QMRA model considering genetic diversity of Bacillus cereus in a cooked, pasteurized, and chilled courgette purée. Experimental data are Bacillus cereus concentrations measured in packages of courgette purées stored at different time-temperature profiles after pasteurization. To perform a Bayesian inference, we first built an augmented Bayesian network by linking a second-order QMRA model to the available contamination data. We then ran a Markov chain Monte Carlo (MCMC) algorithm to update all the unknown concentrations and unknown quantities of the augmented model. About 25% of the prior beliefs are strongly updated, leading to a reduction in uncertainty. Some updates interestingly question the QMRA model.

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