Characterization of the transfer probability of Salmonella ser. Typhimurium between pork and a cutting knife in an experimental model
•Experiments on Salmonella transfer between a knife and pork were performed.•Bayesian inference was used to estimate uncertainty of the transfer probability.•Transfer probability was 0.03 from knife to pork.•Transfer probability was 0.0042 from pork to knife.•The results are useful for the consumer...
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Veröffentlicht in: | Microbial risk analysis 2022-08, Vol.21, p.100203, Article 100203 |
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Sprache: | eng |
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Zusammenfassung: | •Experiments on Salmonella transfer between a knife and pork were performed.•Bayesian inference was used to estimate uncertainty of the transfer probability.•Transfer probability was 0.03 from knife to pork.•Transfer probability was 0.0042 from pork to knife.•The results are useful for the consumer phase in risk assessment models.
Cross-contamination is an important event for bacterial transfer throughout the pork production chain. In Brazil, Salmonella sp. is the most relevant hazard in the pork industry, and further knowledge concerning its contamination is essential for in-depth risk assessments. Thus, we aimed to assess the transfer probability of Salmonella sp. between a knife and pork in a domestic kitchen scenario to provide parametrization for incorporating transfer of Salmonella sp. in risk assessment models. To estimate Salmonella Typhimuium transfer rates between contaminated pork and a knife blade during cutting, 23 independent experiments were performed. A Bayesian inference was utilized to determine the transfer probability, capturing the uncertainty generated in the transfer probability experiments. The mean transfer probability was 0.03 for knife to pork [0.029; 0.032] 95% credible interval (CrI) and 0.0042 for pork to knife [0.0041; 0.0043] 95% CrI. The probabilistic estimate of the transfer probability of Salmonella sp. during pork cutting gives insights on a relevant parameter for the consumer phase of the pork production industry in Brazil, allowing for enhanced risk assessment models. |
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ISSN: | 2352-3522 2352-3530 |
DOI: | 10.1016/j.mran.2022.100203 |