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

Abstract

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.

FIG. 1.

pH and a_w values of sliced RTE meat products and growth/no growth boundaries (50% probability level) of L. monocytogenes at 4, 10, and 15°C predicted by the model of Koutsoumanis and Sofos (10). Products to the right of a growth boundary do not support growth of L. monocytogenes at the specified storage temperature. The shaded area indicates products that are automatically considered unable to support growth of L. monocytogenes according to EC Regulation 2073/2005.

FIG. 2.

Mean temperatures in display cabinet refrigerators in the Greek retail market.

FIG. 3.

Cumulative distribution of the probability of growth of L. monocytogenes in bresaola (product 2 in Table 1) (a) and pork shoulder (product 87 in Table 1) (b) and percent of packages that are able or unable to support growth of the pathogen during storage in retail settings.

FIG. 4.

Distribution of predicted L. monocytogenes concentration in contaminated bresaola (product 2 in Table 1) (a) and pork shoulder (product 87 in Table 1) (b) packages at the end of the shelf life in the retail setting.

FIG. 5.

Effect of shelf life modifications on the cumulative probability distribution of the L. monocytogenes concentration in contaminated pork shoulder packages (product 87 in Table 1) at the end of shelf life. □, current shelf life of 113 days; ○, shelf life of 50 days; ▵, shelf life of 36 days. Dotted lines indicate the level corresponding to compliance with the 100-CFU/g safety criterion.

FIG. 6.

Effect of modifications of product formulation on the cumulative probability distribution of L. monocytogenes concentration in contaminated pork shoulder packages (product 87 in Table 1) at the end of the shelf life. □, current formulation (pH = 5.49, a_w = 0.943, NaNO₂ = 50 ppm); ○, modified formulation (pH = 5.49, a_w = 0.930, NaNO₂ = 100 ppm). Dotted lines indicate the level corresponding to compliance with the new safety criteria.