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

Challenge Test as Special Tool to Estimate the Dynamic of Listeria monocytogenes and Other Foodborne Pathogens

Over 23 million cases of foodborne disease (FBD) occur in Europe each year, with over 4700 deaths. Outbreaks of FBD have a significant impact on our society due to the high economic losses they cause (hospital treatment of affected patients and destruction of contaminated food). Among its health objectives, the European Union has set itself the goal of reducing the incidence of the main FBDs, approving various regulations that codify requirements in order to produce food that is “safe” for human consumption. Among these rules, Regulation 2005/2073 establishes precise food safety criteria for foods that are judged to be most at risk of causing episodes of FBD. The food business operator (FBO) must know their food better and know how to estimate whether a food can support the growth of food pathogens or if they are able to hinder it during the food’s shelf life. It is becoming crucial for each FBO to schedule specific laboratory tests (challenge tests) to establish the growth potential of individual pathogens and their maximum growth rate. In 2008 the European Union published the guidelines for programming the challenge tests for Listeria monocytogenes in RTE foods. These guidelines were further implemented in 2014 and again in 2019. In June 2019 the UNI EN ISO 20976-1 was published, which contains indications for setting up and carrying out challenge tests for all foodborne pathogens in all foods. In this article, we compare the three official documents to highlight their common aspects and differences, highlighting the advantages and disadvantages that each of them offers for those who have to set up a challenge test for the various foodborne pathogens. Our conclusion is that the challenge test is today the most effective tool to estimate the dynamics and growth potential of pathogenic microorganisms in food, if it is designed and implemented in a scrupulous way. It is important to develop a rational experimental design for each challenge test, and for each food, and this requires professionals who are experts in this specific field of study and who must be properly trained.

From Cheese-Making to Consumption: Exploring the Microbial Safety of Cheeses through Predictive Microbiology Models

Cheeses are traditional products widely consumed throughout the world that have been frequently implicated in foodborne outbreaks. Predictive microbiology models are relevant tools to estimate microbial behavior in these products. The objective of this study was to conduct a review on the available modeling approaches developed in cheeses, and to identify the main microbial targets of concern and the factors affecting microbial behavior in these products. Listeria monocytogenes has been identified as the main hazard evaluated in modelling studies. The pH, a_w, lactic acid concentration and temperature have been the main factors contemplated as independent variables in models. Other aspects such as the use of raw or pasteurized milk, starter cultures, and factors inherent to the contaminating pathogen have also been evaluated. In general, depending on the production process, storage conditions, and physicochemical characteristics, microorganisms can grow or die-off in cheeses. The classical two-step modeling has been the most common approach performed to develop predictive models. Other modeling approaches, including microbial interaction, growth boundary, response surface methodology, and neural networks, have also been performed. Validated models have been integrated into user-friendly software tools to be used to obtain estimates of microbial behavior in a quick and easy manner. Future studies should investigate the fate of other target bacterial pathogens, such as spore-forming bacteria, and the dynamic character of the production process of cheeses, among other aspects. The information compiled in this study helps to deepen the knowledge on the predictive microbiology field in the context of cheese production and storage.