Quantitative risk assessment of haemolytic and uremic syndrome linked to O157:H7 and non-O157:H7 Shiga-toxin producing Escherichia coli strains in raw milk soft cheeses.

Shiga-toxin producing Escherichia coli (STEC) strains may cause human infections ranging from simple diarrhea to Haemolytic Uremic Syndrome (HUS). The five main pathogenic serotypes of STEC (MPS-STEC) identified thus far in Europe are O157:H7, O26:H11, O103:H2, O111:H8, and O145:H28. Because STEC strains can survive or grow during cheese making, particularly in soft cheeses, a stochastic quantitative microbial risk assessment model was developed to assess the risk of HUS associated with the five MPS-STEC in raw milk soft cheeses. A baseline scenario represents a theoretical worst-case scenario where no intervention was considered throughout the farm-to-fork continuum. The risk level assessed with this baseline scenario is the risk-based level. The impact of seven preharvest scenarios (vaccines, probiotic, milk farm sorting) on the risk-based level was expressed in terms of risk reduction. Impact of the preharvest intervention ranges from 76% to 98% of risk reduction with highest values predicted with scenarios combining a decrease of the number of cow shedding STEC and of the STEC concentration in feces. The impact of postharvest interventions on the risk-based level was also tested by applying five microbiological criteria (MC) at the end of ripening. The five MCs differ in terms of sample size, the number of samples that may yield a value larger than the microbiological limit, and the analysis methods. The risk reduction predicted varies from 25% to 96% by applying MCs without preharvest interventions and from 1% to 96% with combination of pre- and postharvest interventions.

Software for predictive microbiology and risk assessment: a description and comparison of tools presented at the ICPMF8 Software Fair.

The 8th International Conference on Predictive Modelling in Food was held in Paris, France in September 2013. One of the major topics of this conference was the transfer of knowledge and tools between academics and stakeholders of the food sector. During the conference, a "Software Fair" was held to provide information and demonstrations of predictive microbiology and risk assessment software. This article presents an overall description of the 16 software tools demonstrated at the session and provides a comparison based on several criteria such as the modeling approach, the different modules available (e.g. databases, predictors, fitting tools, risk assessment tools), the studied environmental factors (temperature, pH, aw, etc.), the type of media (broth or food) and the number and type of the provided micro-organisms (pathogens and spoilers). The present study is a guide to help users select the software tools which are most suitable to their specific needs, before they test and explore the tool(s) in more depth.

Is there a linear relationship between the dose of ruminant trans-fatty acids and cardiovascular risk markers in healthy subjects: results from a systematic review and meta-regression of randomised clinical trials.

The effects of ruminant (R) trans-fatty acids (TFA) on the risk of CVD are still under debate. It could be argued that the lack of the effect of R-TFA may be the result of the small amount of their intake. Taking into consideration the growing available data from intervention studies, we carried out a systematic review and meta-regression to assess the impact of R-TFA intake levels on changes in the total cholesterol: HDL-cholesterol (TC:HDL-C) ratio. A systematic review of the literature was conducted and thirteen randomised clinical trials were included, yielding a total of twenty-three independent experimental groups of subjects. A univariate random-effects meta-regression approach was used to quantify the relationship between the dose of R-TFA and changes in the TC:HDL-C ratio. To consider several potential modifiers such as subject and dietary characteristics, a multivariate regression analysis was performed. We found no relationship between R-TFA intake levels of up to 4.19% of daily energy intake (EI) and changes in cardiovascular risk factors such as TC:HDL-C and LDL-cholesterol (LDL-C):HDL-C ratios. In addition, a multivariate regression analysis that included other dietary variables, as well as subject baseline characteristics, confirmed that doses of R-TFA did not significantly influence the changes in the lipid ratio. Our findings showed that doses of R-TFA did not influence the changes in the ratios of plasma TC:HDL-C and LDL-C:HDL-C. These data suggest that TFA from natural sources, at least at the current levels of intake and up to 4.19% EI, have no adverse effects on these key CVD risk markers in healthy people.

Sensitivity analysis for critical control points determination and uncertainty analysis to link FSO and process criteria: application to Listeria monocytogenes in soft cheese made from pasteurized milk.

Microbiological food safety is an important economic and health issue in the context of globalization and presents food business operators with new challenges in providing safe foods. The hazard analysis and critical control point approach involve identifying the main steps in food processing and the physical and chemical parameters that have an impact on the safety of foods. In the risk-based approach, as defined in the Codex Alimentarius, controlling these parameters in such a way that the final products meet a food safety objective (FSO), fixed by the competent authorities, is a big challenge and of great interest to the food business operators. Process risk models, issued from the quantitative microbiological risk assessment framework, provide useful tools in this respect. We propose a methodology, called multivariate factor mapping (MFM), for establishing a link between process parameters and compliance with a FSO. For a stochastic and dynamic process risk model of Listeriamonocytogenes in soft cheese made from pasteurized milk with many uncertain inputs, multivariate sensitivity analysis and MFM are combined to (i) identify the critical control points (CCPs) for L.monocytogenes throughout the food chain and (ii) compute the critical limits of the most influential process parameters, located at the CCPs, with regard to the specific process implemented in the model. Due to certain forms of interaction among parameters, the results show some new possibilities for the management of microbiological hazards when a FSO is specified.

Risk-Based Approach for Microbiological Food Safety Management in the Dairy Industry: The Case of Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk.

According to Codex Alimentarius Commission recommendations, management options applied at the process production level should be based on good hygiene practices, HACCP system, and new risk management metrics such as the food safety objective. To follow this last recommendation, the use of quantitative microbiological risk assessment is an appealing approach to link new risk-based metrics to management options that may be applied by food operators. Through a specific case study, Listeria monocytogenes in soft cheese made from pasteurized milk, the objective of the present article is to practically show how quantitative risk assessment could be used to direct potential intervention strategies at different food processing steps. Based on many assumptions, the model developed estimates the risk of listeriosis at the moment of consumption taking into account the entire manufacturing process and potential sources of contamination. From pasteurization to consumption, the amplification of a primo-contamination event of the milk, the fresh cheese or the process environment is simulated, over time, space, and between products, accounting for the impact of management options, such as hygienic operations and sampling plans. A sensitivity analysis of the model will help orientating data to be collected prioritarily for the improvement and the validation of the model. What-if scenarios were simulated and allowed for the identification of major parameters contributing to the risk of listeriosis and the optimization of preventive and corrective measures.

Behavior of different Shiga toxin-producing Escherichia coli serotypes in various experimentally contaminated raw-milk cheeses.

Shiga toxin-producing Escherichia coli (STEC) is an important cause of food-borne illness. The public health implication of the presence of STEC in dairy products remains unclear. Knowledge of STEC behavior in cheeses would help to evaluate the human health risk. The aim of our study was to observe the growth and survival of experimentally inoculated STEC strains in raw-milk cheeses manufactured and ripened according to five technological schemes: blue-type cheese, uncooked pressed cheese with long ripening and with short ripening steps, cooked cheese, and lactic cheese. Cheeses were contaminated with different STEC serotypes (O157:H7, O26:H11, O103:H2, and O145:H28) at the milk preparation stage. STEC growth and survival were monitored on selective media during the entire manufacturing process. STEC grew (2 to 3 log(10) CFU · g(-1)) in blue-type cheese and the two uncooked pressed cheeses during the first 24 h of cheese making. Then, STEC levels progressively decreased in cheeses that were ripened for more than 6 months. In cooked cheese and in lactic cheese with a long acidic coagulation step (pH < 4.5), STEC did not grow. Their levels decreased after the cooking step in the cooked cheese and after the coagulation step in the lactic cheese, but STEC was still detectable at the end of ripening and storage. A serotype effect was found: in all cheeses studied, serotype O157:H7 grew less strongly and was less persistent than the others serotypes. This study improves knowledge of the behavior of different STEC serotypes in various raw-milk cheeses.