Training in tools to develop quantitative microbial risk assessment along the food chain of Spanish products

Abstract Food safety is a widespread challenge. Every year it is estimated that almost 1 in 10 people in the world fall ill after eating contaminated food resulting in over 400,000 deaths. The risk of outbreaks is higher when consuming ready‐to‐eat (RTE) products because they are eaten without a fur...

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Bibliographic Details
Main Authors: Alessandro Zambon, Alberto Garre Perez, Sara Spilimbergo, Pablo S Fernández Escámez
Format: Article
Language:English
Published: Wiley 2022-12-01
Series:EFSA Journal
Subjects:
Online Access:https://doi.org/10.2903/j.efsa.2022.e200903
Description
Summary:Abstract Food safety is a widespread challenge. Every year it is estimated that almost 1 in 10 people in the world fall ill after eating contaminated food resulting in over 400,000 deaths. The risk of outbreaks is higher when consuming ready‐to‐eat (RTE) products because they are eaten without a further cooking process that could inactivate pathogenic microorganisms. Hence, food processing is essential to increase the safety of RTE products. Microbiological risk assessment (MRA) integrates food science, microbiology and data science to provide a comprehensive understanding of the safety of the food system. MRA provides qualitative and/or quantitative information to decision makers, which might promote the adoption of better food practices. In this contest, this project aims to study and implement tools for quantitative microbial risk assessment (QMRA) of food products along the food chain. A common RTE product (cured ham) from Spain was used as a case study. Following, the exposure assessment model was implemented using mathematical models and statistical software to describe the microbial behaviour along the food chain. The study presents the possibility to identify the risk exposure in different scenarios (e.g. growth during different storage conditions, inactivation induced by traditional or innovative decontamination techniques), showing the flexibility of the predictive tools developed.
ISSN:1831-4732