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Sensitivity Analysis for Critical Control Points Determination and Uncertainty Analysis to Link FSO and Process Criteria: Application to Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk

Authors

  • Matieyendou Lamboni,

    Corresponding author
    1. EC-Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy
    2. French Agency for Food, Environmental and Occupational Health and Safety (ANSES), Maisons-Alfort, France
    3. Centre National Interprofessionnel de l'Economie Laitière (CNIEL), Paris, France
    • Address corresponding to Matieyendou Lamboni, EC-Joint Research Centre, Institute for Environment and Sustainability, Via Fermi 2749, I-21027, Ispra, Italy; matieyendou.lamboni@gmail.com.

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  • Moez Sanaa,

    1. French Agency for Food, Environmental and Occupational Health and Safety (ANSES), Maisons-Alfort, France
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  • Fanny Tenenhaus-Aziza

    1. Centre National Interprofessionnel de l'Economie Laitière (CNIEL), Paris, France
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Abstract

Microbiological food safety is an important economic and health issue in the context of globalization and presents food business operators with new challenges in providing safe foods. The hazard analysis and critical control point approach involve identifying the main steps in food processing and the physical and chemical parameters that have an impact on the safety of foods. In the risk-based approach, as defined in the Codex Alimentarius, controlling these parameters in such a way that the final products meet a food safety objective (FSO), fixed by the competent authorities, is a big challenge and of great interest to the food business operators. Process risk models, issued from the quantitative microbiological risk assessment framework, provide useful tools in this respect. We propose a methodology, called multivariate factor mapping (MFM), for establishing a link between process parameters and compliance with a FSO. For a stochastic and dynamic process risk model of math formula in soft cheese made from pasteurized milk with many uncertain inputs, multivariate sensitivity analysis and MFM are combined to (i) identify the critical control points (CCPs) for math formula throughout the food chain and (ii) compute the critical limits of the most influential process parameters, located at the CCPs, with regard to the specific process implemented in the model. Due to certain forms of interaction among parameters, the results show some new possibilities for the management of microbiological hazards when a FSO is specified.

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