Variability and uncertainty analysis of the cross-contamination ratios of salmonella during pork cutting

A Stochastic Model to Assess the Effect of Meat Inspection Practices on the Contamination of the Pig Carcasses

The objective of meat inspection is to promote animal and public health by preventing, detecting, and controlling hazards originating from animals. With the improvements of sanitary level in pig herds, the hazards profile has shifted and the inspection procedures no longer target major foodborne pathogens (i.e., not risk based). Additionally, carcass manipulations performed when searching for macroscopic lesions can lead to cross-contamination. We therefore developed a stochastic model to quantitatively describe cross-contamination when consecutive carcasses are submitted to classic inspection procedures. The microbial hazard used to illustrate the model was Salmonella, the data set was obtained from Brazilian slaughterhouses, and some simplifying assumptions were made. The model predicted that due to cross-contamination during inspection, the prevalence of contaminated carcass surfaces increased from 1.2% to 95.7%, whereas the mean contamination on contaminated surfaces decreased from 1 logCFU/cm² to -0.87 logCFU/cm², and the standard deviations decreased from 0.65 to 0.19. These results are explained by the fact that, due to carcass manipulations with hands, knives, and hooks, including the cutting of contaminated lymph nodes, Salmonella is transferred to previously uncontaminated carcasses, but in small quantities. These small quantities can easily go undetected during sampling. Sensitivity analyses gave insight into the model performance and showed that the touching and cutting of lymph nodes during inspection can be an important source of carcass contamination. The model can serve as a tool to support discussions on the modernization of pig carcass inspection.

Investigations into Salmonella contamination in feed production chain in Karst rural areas of China

In order to understand the status of Salmonella contamination of feed production chain in Karst rural areas, southwest of China, a total of 1077 feed samples including animal feed materials and feed products were randomly collected from different sectors of feed chain covering feed mills, farms, and feed sales in nine regions of Karst rural areas between 2009 and 2012, to conduct Salmonella test. The different positive rates with Salmonella contamination were detected, the highest was 4.7 % in 2009, the lowest was 0.66 % in 2011, while 4.3 % in 2010, 2.8 % in 2012, respectively. Twelve types of feed including concentrate, complete, self-made, and feed ingredients were inspected. Salmonella contamination mainly concentrated on animal protein material such as meat meal, meat and bone meal, feather meal, blood meal, and fish meal. No Salmonella contamination was detected in feed yeast, microbial protein, rapeseed, and soybean meal. Salmonella contamination existed in each sector of feed production chain. This investigation provided a basic reference for feed production management and quality control in feed production chain in Karst rural areas of China.

Beyond QMRA: Modelling microbial health risk as a complex system using Bayesian networks

BACKGROUND

Quantitative microbial risk assessment (QMRA) is the current method of choice for determining the risk to human health from exposure to microorganisms of concern. However, current approaches are often constrained by the availability of required data, and may not be able to incorporate the many varied factors that influence this risk. Systems models, based on Bayesian networks (BNs), are emerging as an effective complementary approach that overcomes these limitations.

OBJECTIVES

This article aims to provide a comparative evaluation of the capabilities and challenges of current QMRA methods and BN models, and a scoping review of recent published articles that adopt the latter for microbial risk assessment. Pros and cons of systems approaches in this context are distilled and discussed.

METHODS

A search of the peer-reviewed literature revealed 15 articles describing BNs used in the context of QMRAs for foodborne and waterborne pathogens. These studies were analysed in terms of their application, uses and benefits in QMRA.

DISCUSSION

The applications were notable in their diversity. BNs were used to make predictions, for scenario assessment, risk minimisation, to reduce uncertainty and to separate uncertainty and variability. Most studies focused on a segment of the exposure pathway, indicating the broad potential for the method in other QMRA steps. BNs offer a number of useful features to enhance QMRA, including transparency, and the ability to deal with poor quality data and support causal reasoning.

CONCLUSION

The method has significant untapped potential to describe the complex relationships between microbial environmental exposures and health.