ARS | Publication request: Mathematical modeling the cross-contamination of Escherichia coli O157:H7 on the surface of ready-to-eat meat product while slicing

Submitted to: Food Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 20, 2009
Publication Date: February 1, 2010
Citation: Sheen, S., Hwang, C. 2010. Mathematical modeling the cross-contamination of Escherichia coli O157:H7 on the surface of ready-to-eat meat product while slicing. Food Microbiology. 27:37-43.

Interpretive Summary: Microbial cross-contamination either at home or the production site is one of the major food contamination sources and may lead to the foodborne illness. The knowledge regarding Escherichia coli O157:H7 surface transfer on ready-to-eat (RTE) deli meat and the slicer used for slicing different RTE products are needed to ensure RTE food safety. The objectives of this study were to investigate and to model the surface cross-contamination of Escherichia coli O157:H7 during slicing operation. A five-strain cocktail of E. coli O157:H7 was directly inoculated onto a slicer blade (carbon steel) to an initial level of about ten thousand to one hundred million microbial counts per blade, and then the deli meat (ham) was sliced to a thickness of 1-2 mm (direct blade inoculation to ham). In another cross-contamination scenario, a clean blade was used to slice ham that was previously inoculated with E. coli O157:H7 followed by slicing of ham that was not inoculated (contaminated-ham to blade to ham). The experimental results (positive E. coli O157:H7) from low microbial counts per test (ten thousand to one hundred thousand counts) were random on first 100 sliced ham. Generally speaking, the cross-contamination may last longer in the “contaminated-ham to blade to ham” than in the “direct blade inoculation to ham” case. The models, therefore, developed (using 100000 counts and higher) are microbial load, slicing ham number and contamination route dependent. The surface cross-contamination trend indicated either a power or an exponential decaying mode during slicing process. The models may be applied to predict the low level E. coli O157:H7 cross-contamination (100000 counts and lower) during ham slicing process and serve as a building block in microbial risk assessment.

Technical Abstract: Microbial cross-contamination either at home or production site is one of the major factors of causing contamination of foods and leading to the foodborne illness. The knowledge regarding Escherichia coli O157:H7 surface transfer on ready-to-eat (RTE) deli meat and the slicer used for slicing different RTE products are needed to ensure RTE food safety. The objectives of this study were to investigate and to model the surface cross-contamination of Escherichia coli O157:H7 during slicing operation. A five-strain cocktail of E. coli O157:H7 was inoculated directly onto a slicer’s round blade rim area at an initial level of ca. 4, 5, 6, 7 or 8 log CFU/blade (ca. 3, 4, 5, 6 or 7 log CFU/cm2 of the blade edge area), and then the RTE deli meat (ham) was sliced to a thickness of 1-2 mm. For another cross-contamination scenario, a clean blade was initially used to slice ham which was pre-surface-inoculated with E. coli O157:H7 (ca. 4, 5, 6, 7 or 8 log CFU/100 cm2 area), then, followed by slicing un-inoculated ham. Results showed that the developed empirical models were reasonably accurate in describing the transfer trend/pattern of E. coli O157:H7 between the blade and ham slices when the total inoculum level was greater than or equal to a 5 log CFU on the ham or blade. With an initial inoculum level less than or equal to a 4 log CFU, the experimental data showed a rather random microbial surface transfer pattern. The models, i.e. a power equation for direct blade-surface-inoculation, and an exponential equation for ham-surface-inoculation are microbial load and sequential slice index dependent. The surface cross-contamination prediction of E. coli O157:H7 for sliced deli meat (ham) using the developed models were demonstrated. The empirical models may provide a useful tool in developing the RTE meat risk assessment.