MODELAGEM MATEMÁTICA DO CRESCIMENTO DE LISTERIA MONOCYTOGENES EM QUEIJO BRANCO FATIADO: UMA ABORDAGEM UTILIZANDO O SOFTWARE MICRORISK LAB

Abstract

Cheese is a ready-to-eat dairy product susceptible to the growth of various deteriorating and pathogenic microorganisms, among which Listeria monocytogenes stand out. This bacterium can enter cheese processing environments through raw milk and adhere to food contact surfaces and form biofilms, which are difficult to eliminate and may result in cheese cross-contamination. In this context, the objective of this work was to model the growth of L. monocytogenes in slices of white cheese under different isothermal storage conditions using the MicroRisk Lab software. Data on the L. monocytogenes growth at temperatures between 5 °C and 25 °C of storage were acquired from research by Uhlich et al. (2006), published in the Journal of Food Safety, available on the ComBase database. The Baranyi and Roberts model was fitted to growth curves using the MicroRisk Lab, available online. According to the results, it can be stated that the model performed well to describe the growth of L. monocytogenes in white cheese slices at the five evaluated temperatures, with R2 values ≥ 0.978. The secondary models well represented the influence of temperature on the maximum specific growth velocity, with a better coefficient of determination value for the Arrhenius-type model (R2 = 0.994). Furthermore, it was possible to carry out two simulations of the growth of L. monocytogenes, using ComBase, in two different conditions of temperature oscillation (5-10-15-20 °C and 25-20-15-10 °C), applying the differential equation of the Baranyi and Roberts model. It can be verified that the model was able to describe the microbial behavior, using the secondary model of the Arrhenius type. While the predictions using ComBase presented a behavior closer to the one simulated in this work for the oscillation condition starting from high temperatures. However, experimental data are needed to validate the predictive model under non-isothermal storage conditions. Thus, it can be concluded that the application of predictive models and user-friendly software can be used to guide owners of restaurants, establishments, and dairy industries, to properly prepare and store sliced fresh cheese and other related products to prevent growth from L. monocytogenes.