Abstract

Higher education students usually ask for more training based on case studies. This was addressed by designing a specific food safety module (24 hours) in which students were shown how to predict microbiological risks in food products i.e. they were asked to determine product shelf-life according to product formulation, preservation methods and consumption habits using predictive microbiology tools. Working groups of four students first identified the main microbiological hazards associated with a specific product. To perform this task, they were given several documents including guides for good hygiene practices, reviews on microbiological hazards in the food sector, flow sheets, etc… After three-hours of work, the working groups prepared and gave an oral presentation in front of their classmates and professors. This raised comments and discussion that allowed students to adjust their conclusions before beginning the next step of their work. This second step consisted in the evaluation of the safety risk associated with the two major microbiological hazards of the product studied, using predictive microbiology. Students then attended a general lecture on the different tools of predictive microbiology and tutorials (6 hours) that made them familiar with the modelling of bacterial growth or inactivation. They applied these tools (9 hours) to predict the shelf-life of the studied product according to various scenarios of preservation (refrigeration, water activity, concentration of salt or acid, modified atmosphere, etc…) and/or consumption procedures (cooking). The module was concluded by oral presentations of each working group and included student evaluation (3 hours).

Highlights

  • 1 Introduction: Objectives of the training. These days, students prefer having less theoretical lectures and more case studies that are relevant to their future professional issues

  • Students were given one food product and were asked to determine which mode of preservation would be best and how long the associated shelflife of this product should be. These questions were professionally relevant as the determination of shelf-life of food products is the responsibility of manufacturers who increasingly employ predictive microbiology tools

  • This food safety module is an optional module intended for engineering students (Year 4, second year in AgroParisTech) or master students interested in microbiological food safety

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Summary

Organization of the training

This food safety module is an optional module intended for engineering students (Year 4, second year in AgroParisTech) or master students (first year) interested in microbiological food safety. A first assessment of the group was done from this oral presentation This part of the project intended to develop students’ ability to determine a product shelf-life depending on various scenarios of preservation. The shelf-life of a product can be selected by different means that are listed in the annexe II of the European regulation (EC) n°2073/2005 (ItieHafez & Danan, 2014) In this training, the students employed predictive microbiology to predict the growth or the inactivation of microorganisms in relation to their environment using different mathematical models, databases and software (Delhalle, Daube, Adolphe, Crevecoeur, & Clinquart, 2012; Tenenhaus-Aziza & Ellouze, 2015). Students first attended a general lecture on predictive microbiology together with additional tutorials that helped students to better understand the modelling and its limits and to manipulate several tools of predictive microbiology (software, web-based resources) Afterwards, students applied these recently acquired skills to determine shelf-life of their own products according to various scenarios they imagined. The repetition of similar exercises, first in tutorials and with more freedom in the framework of a project, allowed students to reformulate the questions, to appropriate and integrate the approach and its implementation

Tutorials to learn tools in predictive microbiology
Team work project: impact of environmental parameters on growth rate
Conclusions
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