Probabilistic modeling approach for evaluating the compliance of ready-to-eat foods with new European Union safety criteria for Listeria monocytogenes.

Among the new microbiological criteria that have been incorporated in EU Regulation 2073/2005, of particular interest are those concerning Listeria monocytogenes in ready-to eat (RTE) foods, because for certain food categories, they no longer require zero tolerance but rather specify a maximum allowable concentration of 100 CFU/g or ml. This study presents a probabilistic modeling approach for evaluating the compliance of RTE sliced meat products with the new safety criteria for L. monocytogenes. The approach was based on the combined use of (i) growth/no growth boundary models, (ii) kinetic growth models, (iii) product characteristics data (pH, a(w), shelf life) collected from 160 meat products from the Hellenic retail market, and (iv) storage temperature data recorded from 50 retail stores in Greece. This study shows that probabilistic analysis of the above components using Monte Carlo simulation, which takes into account the variability of factors affecting microbial growth, can lead to a realistic estimation of the behavior of L. monocytogenes throughout the food supply chain, and the quantitative output generated can be further used by food managers as a decision-making tool regarding the design or modification of a product's formulation or its "use-by" date in order to ensure its compliance with the new safety criteria. The study also argues that compliance of RTE foods with the new safety criteria should not be considered a parameter with a discrete and binary outcome because it depends on factors such as product characteristics, storage temperature, and initial contamination level, which display considerable variability even among different packages of the same RTE product. Rather, compliance should be expressed and therefore regulated in a more probabilistic fashion.