Bayesian global regression model relating product characteristics of intermediate moisture food products to heat inactivation parameters for Salmonella Napoli and Eurotium herbariorum mould spores.

Thermal inactivation of pathogenic and spoilage organisms in low and intermediate moisture foods is of critical importance for guaranteeing microbiological safety and stability of these products. Producers tendentially reduce salt in low and intermediate moisture foods because of nutritional health considerations, but it is unclear how this affects microbial inactivation rates during pasteurization. In this study we predict the time to achieve a pre-defined 6-log reduction for Salmonella enterica subsp. enterica serovar Napoli (hereafter: S. Napoli) and Eurotium herbariorum mould spores (hereafter: E. herbariorum spores) and the relationship with product characteristics. We tested 31 design products for heat inactivation of S. Napoli and 29 design products for heat inactivation of E. herbariorum spores. We used a global Bayesian regression combining primary Weibull models with a secondary regression model to relate pasteurization temperature and product characteristics (water activity (aw), pH, and fractions of sodium chloride, sucrose and oil on product) to microbial counts. With this model, we predict the time to 6-log reduction. Thermal inactivation rates were much higher for vegetative S. Napoli than for E. herbariorum spores. Also, inactivation curves were non-linear for many experiments. There were significant associations between the Weibull model parameters and temperature, and pH and aw for S. Napoli and E. herbariorum spores, respectively. We parameterized an inactivation model for S. Napoli and E. herbariorum spores using design products with a broad range of characteristics and showed how the simplified approach of using D-values does not accurately describe the non-linearity of thermal inactivation for both types of organism. Results of our model can be used to produce accurate heat inactivation predictions as input for the pasteurization process in factories where intermediate moisture foods are manufactured.

Quantifying the sources of Salmonella on dressed carcasses of pigs based on serovar distribution.

Salmonella serotyping data, qualitatively described by van Hoek et al. (2012), were used to quantify potential sources of Salmonella in a Dutch pig slaughterhouse. Statistical tests to compare per-day Salmonella prevalence and serotyping data from multiple points in the chain were used to find transmission pathways. A statistical model based on serotyping data was developed to attribute Salmonella on dressed carcasses to the most likely source. Approximately two-third of dressed carcasses carrying Salmonella on the medial surface had been contaminated by house flora. For carcasses carrying Salmonella on the distal surface, transient Salmonella from incoming pigs was a more important source. The relevance of the different sources of Salmonella varied within and between sampling days. Results were compared to those of another modeling approach, in which Salmonella concentration data from the same samples were used (Smid et al., 2012). They mostly agreed. The approach chosen by an individual slaughterhouse depends on the data that are collected.

A biotracing model of Salmonella in the pork production chain.

In biotracing systems, downstream chain information and model-based approaches are used to trace the sources of microbial contamination in a food chain. This article includes the results of a biotracing model for Salmonella in the pork slaughter process chain. A Bayesian belief network model was used in which information on the Salmonella level at different locations in the slaughterhouse were used in combination with prior knowledge about the dynamics of Salmonella throughout the slaughter line. Data collected in a Dutch slaughterhouse were used to specify prior beliefs about the model inputs and to iteratively refine the distributions of the parameters in the model to obtain an optimal description of that specific slaughterhouse. The primary purpose of the model is to trace the sources of contamination for individual Salmonella-positive carcasses at the end of the slaughter line. The model results indicated that house flora on or in the carcass splitter was the source of contamination for many carcasses, especially for those that carried contamination on the cutting side. The results also indicated that the parameter values of the model may be subject to temporal variation and can be used as a tool to provide estimates of such trends. This model illustrates the concept of biotracing, gives insight into the dynamics of Salmonella in the slaughter line, and indicates the sites in the line where data collection is most effective for biotracing. This biotracing model was implemented as an interactive computer application, which is a step in the process toward an operational biotracing system by which a stakeholder can initiate immediate responses to Salmonella contamination and other hazards in the pork slaughterhouse.

A practical framework for the construction of a biotracing model: application to Salmonella in the pork slaughter chain.

A novel purpose of the use of mathematical models in quantitative microbial risk assessment (QMRA) is to identify the sources of microbial contamination in a food chain (i.e., biotracing). In this article we propose a framework for the construction of a biotracing model, eventually to be used in industrial food production chains where discrete numbers of products are processed that may be contaminated by a multitude of sources. The framework consists of steps in which a Monte Carlo model, simulating sequential events in the chain following a modular process risk modeling (MPRM) approach, is converted to a Bayesian belief network (BBN). The resulting model provides a probabilistic quantification of concentrations of a pathogen throughout a production chain. A BBN allows for updating the parameters of the model based on observational data, and global parameter sensitivity analysis is readily performed in a BBN. Moreover, a BBN enables "backward reasoning" when downstream data are available and is therefore a natural framework for answering biotracing questions. The proposed framework is illustrated with a biotracing model of Salmonella in the pork slaughter chain, based on a recently published Monte Carlo simulation model. This model, implemented as a BBN, describes the dynamics of Salmonella in a Dutch slaughterhouse and enables finding the source of contamination of specific carcasses at the end of the chain.

Modelling the number of viable vegetative cells of Bacillus cereus passing through the stomach.

AIMS: Model the number of viable vegetative cells of B. cereus surviving the gastric passage after experiments in simulated gastric conditions. MATERIALS AND METHODS: The inactivation of stationary and exponential phase vegetative cells of twelve different strains of Bacillus cereus, both mesophilic and psychrotrophic strains isolated from food and faeces from healthy and ill individuals, in simulated gastric conditions was determined using decimal reduction times at low pH (D(pH)). Subsequently inactivation rates were calculated. Inclusion of the inactivation rates into models describing the course of the gastric pH after the consumption of meal of solid food and the transfer of food from the stomach to the small intestine resulted in numbers of viable Bacillus cereus vegetative cells able to pass the stomach. CONCLUSIONS: According to the model, 3-26% of the ingested vegetative cells from Bacillus cereus may survive the gastric passage, dependent on the growth phase of the vegetative cells, the type of strains, and the age of the consumer. SIGNIFICANCE AND IMPACT OF THE STUDY: Vegetative cells of Bacillus cereus may be involved in the onset of diarrhoeal disease to a greater extent than expected since up to 26% of the ingested cells survive simulated gastric conditions.

Towards an integrated approach in supporting microbiological food safety decisions.

Decisions on food safety involve consideration of a wide range of concerns including the public health impact of foodborne illness, the economic importance of the agricultural sector and the food industry, and the effectiveness and efficiency of interventions. To support such decisions, we propose an integrated scientific approach combining veterinary and medical epidemiology, risk assessment for the farm-to-fork food chain as well as agricultural and health economy. Scientific advice is relevant in all stages of the policy cycle: to assess the magnitude of the food safety problem, to define the priorities for action, to establish the causes for the problem, to choose between different control options, to define targets along the food chain and to measure success.

The acute toxicity of selected alkylphenols to young and adult Daphnia magna.

Differences in sensitivity toward toxicants between young and adult individuals in a population are assumed to be primarily associated with their difference in body size. This assumption plays a key role in the modeling of effects of variable concentrations of toxicants on nonhomogeneous populations. The hazard-based no-effect-concentrations (NECs), killing rates, and elimination rates, estimated from the survival data of a series of acute toxicity tests with young and adults of Daphnia magna and six alkylphenols, were used to evaluated this assumption. The results lead to the conclusion that young and adult D. magna were equally sensitive in terms of NEC and killing rate and that the observed differences in elimination rates could be explained on the basis of a difference in body size. Furthermore, it was found that elimination rates estimated on the basis of the survival data were consistently smaller than those expected on the basis of a QSAR for Daphnia pulex, a comparable species. This discrepancy was likely due to a decreased uptake and elimination during a period of immobilization prior to death. Since it is unknown to what extent immobilized individuals are able to recover from short-term exposures, the observed deviation clearly identifies a complicating factor in the modeling of effects of variable concentrations of toxicants.