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).
References
1.
Baranyi J, Roberts TA. A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology. 1994;23(3–4):277–94.
2.
Baranyi J, Tamplin M. Combase: a common database on microbial responses to food environments. Journal of Food Protection. 2004;(9).
3.
Carlin F, Albagnac C, Rida A, Guinebretière MH, Couvert O, Nguyen-the C. Variation of cardinal growth parameters and growth limits according to phylogenetic affiliation in the Bacillus cereus Group. Consequences for risk assessment. Food Microbiology. 2013;33(1):69–76.
4.
Couvert O, Gaillard S, Savy N, Mafart P, Leguérinel I. Survival curves of heated bacterial spores: effect of environmental factors on Weibull parameters. International Journal of Food Microbiology. 2005;101(1):73–81.
5.
Delhalle L, Daube G, Adolphe Y, Crevecoeur S, Clinquart A. A review of growth models in predictive microbiology to ensure food safety. Biotechnologie Agronomie Societe et Environnement. 2012;(3):369–81.
6.
Geeraerd AH, Valdramidis VP, Van Impe JF. GInaFiT, a freeware tool to assess non-log-linear microbial survivor curves. International Journal of Food Microbiology. 2005;102(1):95–105.
7.
Itie-Hafez S, Danan C. 2014;35–7.
8.
James SJ, Evans J, James C. A review of the performance of domestic refrigerators. Journal of Food Engineering. 2008;87(1):2–10.
9.
Lagendijk E, Assere A, Derens E, Carpentier B. Domestic refrigeration practices with emphasis on hygiene: analysis of a survey and consumer recommendations. Journal of Food Protection. 2008;(9):1898–904.
10.
McClure PJ, de W. Blackburn C, Cole MB, Curtis PS, Jones JE, Legan JD, et al. Modelling the growth, survival and death of microorganisms in foods: the UK Food Micromodel approach. International Journal of Food Microbiology. 1994;23(3–4):265–75.
11.
MEMBRE J, LEPORQ B, VIALETTE M, METTLER E, PERRIER L, THUAULT D, et al. Temperature effect on bacterial growth rate: quantitative microbiology approach including cardinal values and variability estimates to perform growth simulations on/in food. International Journal of Food Microbiology. 2005;100(1–3):179–86.
12.
Ratkowsky D, Olley J, Mcmeekin T, Ball A. Relationship between temperature and growth-rate of bacterial cultures. Journal of Bacteriology. 1982;(1):1–5.
13.
Ross T, Dalgaard P. Modeling microbial responses in food. 2004;63–150.
14.
Rosso L, Bajard S, Flandrois J, Lahellec C, Fournaud J, Veit P. Differential growth of listeria monocytogenes at 4 and 8 degrees c: consequences for the shelf life of chilled products. Journal of Food Protection. 1996;(9):944–9.
15.
Rosso L, Lobry JR, Flandrois JP. An Unexpected Correlation between Cardinal Temperatures of Microbial Growth Highlighted by a New Model. Journal of Theoretical Biology. 1993;162(4):447–63.
16.
Tenenhaus-Aziza F, Ellouze M. Software for predictive microbiology and risk assessment: A description and comparison of tools presented at the ICPMF8 Software Fair. Food Microbiology. 2015;45:290–9.
17.
Zwietering M, Jongenburger I, Rombouts F, Van’t Riet K. Modeling of the bacterial growth curve. Applied and environmental microbiology. 1990;(6):1875–81.
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.