Cross-contamination in the kitchen: A model for quantitative microbiological risk assessment.

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ID: 277405
2023
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Abstract
A quantitative microbiological risk assessment model for the cross-contamination transmission route in the kitchen (KCC) is presented. Bacteria are transmitted from contaminated (chicken) meat to hands, kitchen utensils, and other surfaces, subsequently contaminating a salad. The model aims to estimate the fraction of bacteria on the meat that is ingested due to cross-contamination, determine the importance of the different transmission routes, and assess the effect of scenarios (interventions) on the fraction ingested. The cross-contamination routes defined, bacterial source-to-recipient transfer fractions as available and derived from literature, and important characteristics (e.g., washing in cold water vs. hot water with soap) shaped the KCC model. With this model, 32 scenarios of an eight-step preparation of a "meat and salad" meal in a domestic kitchen were stochastically simulated. The "cutting board-salad" route proved dominant and the salad plays a major role in the final exposure. A realistic scenario (washing hands, cutting board, and knife with cold water after cutting the meat) estimates that a mean fraction of 3.2E - 3 of the bacteria on the meat is ingested. In the case of "hand washing with hot water and soap" and "cutting board and knife replacement," the mean fraction ingested is 3.6E - 6. For a subsequent meal, where the contaminated sources were kitchen fomites, the estimated mean fraction is 4.3E - 4. In case of hamburger, part of the bacteria is unavailable for cross-contamination, resulting in a mean fraction ingested of about 5.4E - 5. The role of the dishcloth in cross-contamination transmission proved to be minor.
Reference Key
iulietto2023crosscontaminationrisk Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Iulietto, Maria Francesca;Evers, Eric G;
Journal Risk analysis : an official publication of the Society for Risk Analysis
Year 2023
DOI
10.1111/risa.14232
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