Validation via challenge test of a dynamic growth-death model for the prediction of Listeria monocytogenes kinetics in Pecorino di Farindola cheese.

Pecorino di Farindola is a typical cheese produced in the area surrounding the village of Farindola, located in the Abruzzo Region (central Italy), unique among Italian cheese because only raw ewe milk and pig rennet are used for its production. In the literature it is well documented that raw milk is able to support the growth of pathogenic microorganisms such as Listeria monocytogenes. Predictive microbiology can be useful in order to predict growth-death kinetics of pathogenic bacteria, on the basis of known environmental conditions. Aim of this study was to compare predictions obtained from a model, originally designed to predict the kinetics of L. monocytogenes in the dynamic growth-death environment of drying fresh sausage, with the results of challenge tests performed during the ripening of Pecorino di Farindola produced from artificially contaminated raw ewe milk. A challenge test was carried out using ewe raw milk inoculated with L. monocytogenes, in order to produce Pecorino di Farindola cheese stored at 18 °C for 149 days of ripening. During the ripening period, pH and a values decreased in all samples analysed; lactic acid bacteria become the prevailing microbial population, while for L. monocytogenes a period of stability (neither growth nor death) followed the initial situation. The growth inhibition and the following inactivation may mostly be due to competition with the autochthonous microbiota and to the reduction of water activity. Mathematical modelling was used in order to predict microbial kinetics in the dynamic ripening environment, joining growth and death patterns in a continuous way, and including the highly uncertain growth/no growth range separating the two regions. The effect of lactic acid bacteria on the growth of pathogens was also included. Predicted microbial kinetics were satisfactory, as confirmed by the absence of statistically significant difference between observed and predicted values (p > 0.05). The present study proved, via challenge tests, that a dynamic growth/death model, previously used for a meat product, can be fruitfully used in cheese characterized by active competitive microbiota and progressive drying during ripening.