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Modelling the fate of Listeria monocytogenes during manufacture and ripening of smeared cheese made with pasteurised or raw milk
- Schvartzman, M.S., Maffre, A., Tenenhaus-Aziza, F., Sanaa, M., Butler, F., Jordan, K.
- International journal of food microbiology 2011 v.145S1 pp. S31
- Listeria monocytogenes, cheese milk, cheese ripening, cheeses, growth models, lactates, logit analysis, manufacturing, microbial growth, pasteurization, pasteurized milk, pathogens, prediction, raw milk, risk, temperature, water activity
- The dynamics of the physicochemical characteristics of foods help to determine the fate of pathogens throughout processing. The aim of this study was to assess the behaviour of Listeria monocytogenes during cheesesmaking and ripening and to model the growth observed under the dynamic conditions of the cheese. A laboratory scale cheese was made in 4 independent replicates from pasteurised or raw cow's milk, artificially contaminated with L. monocytogenes. No growth of L. monocytogenes occurred during raw milk cheese-making, whereas growth did occur in pasteurised milk. During ripening, growth occurred in raw milk cheese, but inactivation occurred in pasteurised milk cheese. The behaviour observed for L. monocytogenes was modelled using a logistic primary model coupled with a secondary cardinal model, taking into account the effect of physicochemical conditions (temperature, pH, water activity and lactate). A novel statistical approach was proposed to assess the optimal growth rate of a microorganism from experiments performed in dynamic conditions. This complex model had an acceptable quality of fit on the experimental data. The estimated optimum growth rates can be used to predict the fate of L. monocytogenes during cheese manufacture in raw or pasteurized milk in different physicochemical conditions. The data obtained contributes to a better understanding of the potential risk that L. monocytogenes presents to cheese producers (growth on the product, if it is contaminated) and consumers (the presence of high numbers) and constitutes a very useful set of data for the completion of chain-based modelling studies.