Literature DB >> 29579908

Quantitative microbiological risk assessment in food industry: Theory and practical application.

Jeanne-Marie Membré1, Géraldine Boué2.   

Abstract

The objective of this article is to bring scientific background as well as practical hints and tips to guide risk assessors and modelers who want to develop a quantitative Microbiological Risk Assessment (MRA) in an industrial context. MRA aims at determining the public health risk associated with biological hazards in a food. Its implementation in industry enables to compare the efficiency of different risk reduction measures, and more precisely different operational settings, by predicting their effect on the final model output. The first stage in MRA is to clearly define the purpose and scope with stakeholders, risk assessors and modelers. Then, a probabilistic model is developed; this includes schematically three important phases. Firstly, the model structure has to be defined, i.e. the connections between different operational processing steps. An important step in food industry is the thermal processing leading to microbial inactivation. Growth of heat-treated surviving microorganisms and/or post-process contamination during storage phase is also important to take into account. Secondly, mathematical equations are determined to estimate the change of microbial load after each processing step. This phase includes the construction of model inputs by collecting data or eliciting experts. Finally, the model outputs are obtained by simulation procedures, they have to be interpreted and communicated to targeted stakeholders. In this latter phase, tools such as what-if scenarios provide an essential added value. These different MRA phases are illustrated through two examples covering important issues in industry. The first one covers process optimization in a food safety context, the second one covers shelf-life determination in a food quality context. Although both contexts required the same methodology, they do not have the same endpoint: up to the human health in the foie gras case-study illustrating here a safety application, up to the food portion in the brioche case-study illustrating here a quality application.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Microbial growth; Microbial inactivation; Predictive microbiology; Probabilistic techniques; QMRA

Mesh:

Year:  2017        PMID: 29579908     DOI: 10.1016/j.foodres.2017.11.025

Source DB:  PubMed          Journal:  Food Res Int        ISSN: 0963-9969            Impact factor:   6.475


  4 in total

1.  Concentration of soil-transmitted helminth eggs in sludge from South Africa and Senegal: A probabilistic estimation of infection risks associated with agricultural application.

Authors:  Isaac Dennis Amoah; Poovendhree Reddy; Razak Seidu; Thor Axel Stenström
Journal:  J Environ Manage       Date:  2017-12-09       Impact factor: 6.789

2.  Modeling the Reduction of Salmonella spp. on Chicken Breasts and Wingettes during Scalding for QMRA of the Poultry Supply Chain in China.

Authors:  Xingning Xiao; Wen Wang; Xibin Zhang; Jianmin Zhang; Ming Liao; Hua Yang; Qiaoyan Zhang; Chase Rainwater; Yanbin Li
Journal:  Microorganisms       Date:  2019-06-06

3.  Modeling the Growth and Interaction Between Brochothrix thermosphacta, Pseudomonas spp., and Leuconostoc gelidum in Minced Pork Samples.

Authors:  Emilie Cauchie; Laurent Delhalle; Ghislain Baré; Assia Tahiri; Bernard Taminiau; Nicolas Korsak; Sophie Burteau; Papa Abdoulaye Fall; Frédéric Farnir; Georges Daube
Journal:  Front Microbiol       Date:  2020-04-09       Impact factor: 5.640

4.  Evaluation of the Stress Tolerance of Salmonella with Different Antibiotic Resistance Profiles.

Authors:  Xingning Xiao; Biao Tang; Siyi Liu; Yujuan Suo; Hua Yang; Wen Wang
Journal:  Biomed Res Int       Date:  2021-09-14       Impact factor: 3.411

  4 in total

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