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Foodborne diseases constitute a major concern in societies, and their causes are aimed to be identified and minimized. Only in the last few years, this is encouraged by the application of risk assessment, management, and communication. This work presents a probabilistic quantitative microbiological risk assessment and management of Listeria monocytogenes in ready-to-eat lettuce salads in Spain. For risk assessment, a guideline provided by Codex Alimentarius was followed. Food chain was modeled from processing of raw material at the factory up to consumption. Different assumptions were made to describe the variables of the model by probability distributions or mathematical models. Monte Carlo simulations of the model were run to estimate the number of cases in low-risk and high-risk populations. Although results deviated from the number of cases observed in Spain, given an ideal situation of 100% compliance of the microbiological criterion ≤100 cfu/g throughout the shelf-life of the product, the resulting number of cases was near the real situation. From the 4 risk management measures simulated, the injection of a mixture of gases into packages at manufacture (CO 2 about 5.5%, O 2 about 3%, and N 2 for the balance) was the most effective in reducing the number of cases, followed by 4 d of storage at home and prevention of high-risk consumers from consumption of ready-to-eat lettuce salads. More research and cooperation between different stakeholder organizations are needed in order to progressively improve the model. With this work, a breakthrough has been made with regards to risk assessment and management procedures and implementation.

A response surface model of Listeria monocytogenes' growth rate was built in this study under different temperatures (10°C, 15°C, 20°C, 25°C and 30°C) and acid concentrations: citric acid (0-0.4%) and ascorbic acid (0-0.4%); two ingredients which are often used in the food industry as preservatives. Mathematical validation was performed with additional samples at different conditions within the range of the model, obtaining acceptable values of root mean square error (0.0466), standard error of prediction (18.84%), bias factor (1.05) and accuracy factor (1.16). The inhibitory effect on growth was more effective with citric acid than ascorbic acid, possibly due to the major dissociation of citric acid occurring inside microbial cells. The different conditions considered in the model will potentially allow L. monocytogenes' response to be predicted in foods having a similar composition to the chemical and physical factors set out in this paper.

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