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" The Effect of Consumer Behaviors on Cross-Contamination While Preparing Meals in a Consumer Kitchen "
Kirchner, Margaret Katherine
Stevenson, Clinton
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Latin Dissertation
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English
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| Record Number
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1058124
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| Doc. No
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TL57241
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| Main Entry
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Kirchner, Margaret Katherine
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| Title & Author
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The Effect of Consumer Behaviors on Cross-Contamination While Preparing Meals in a Consumer Kitchen\ Kirchner, Margaret KatherineStevenson, Clinton
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| College
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North Carolina State University
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| Date
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2020
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| Degree
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Ph.D.
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2020
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| Note
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211 p.
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| Abstract
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Cross-contamination of raw food to other surfaces, hands, and foods is a serious issue at the home and in foodservice. It can result in the transfer of potentially harmful pathogens to hands, kitchen surfaces, or Ready-to-Eat (RTE) foods. Improper food handling behaviors, like failure to wash hands, improper or incomplete handwashing, chopping raw and RTE foods on the same surfaces, and more. It is estimated that one in five cases of foodborne illnesses is acquired in the home; a venue where cross-contamination can occur. However, how bacteria move around a kitchen environment when consumers are preparing food is not well characterized. In this dissertation, cross-contamination that occurs in the home while preparing both raw turkey patties and skin-in-bone-on chicken thighs was quantitative analyzed, by both frequency and transfer efficiency, in conjunction with the food safety behaviors displayed by participants during the meal preparation. In year one, participants (n=371) prepared a meal of turkey patties, containing the bacteriophage MS2 as a surrogate, and a RTE lettuce salad while being recorded using cameras. Environmental sampling was then performed on various kitchen surfaces and a lettuce sample to assess the frequency and degree of cross-contamination that occurred across the kitchen through RT-qPCR. About half of the participants were shown a video on proper thermometer use as a food safety intervention; the other half served as a control. Most surfaces were only cross-contaminated £ 20% of the time, with the exception of spice containers, positive 48% of the time. The highest MS2 concentrations, exceeding 8 log10 viral genome equivalent copies (GEC) per surface on average, were also attributed to the spice containers. These results together indicate that spice containers could serve as a vehicle of cross-contamination. Transfer efficiency, expressed as a percentage, was relatively low, ranging from an average of 0.002 - 0.07%, but more work needs to be done with risk modeling to determine how this fits into the development of foodborne illness. A total of 367 lettuce samples were taken (222 salad lettuce and 145 garnish lettuce) and 9.9% of the salad lettuce samples were positive for MS2 and 22.1% of the garnish lettuce samples were positive. The garnish had a higher average concentration of the surrogate compared to the salad lettuce (5.9+0.78 vs. 5.5+ 0.45 log10 GEC per sample for garnish vs. salad, respectively). The transfer efficiency from the raw ground turkey to the garnish and salad lettuce were 1.9 x 10-3 and 1.6 x 10-4 per sample, respectively. The intervention did not have any effect on the cross-contamination of the kitchen surfaces or the lettuce. For year two, participants (n=281) were instructed to prepare a meal of bone-in skin on chicken thighs and a RTE vegetable salad while being recorded. Similarly to year one, half of the participants were given an intervention, an email about not washing chicken, and the other half served as a control group. Unlike year one, the surrogate used to inoculate the chicken thighs was E. coli DH5-a, a non-pathogenic strain that behaves similarly to pathogenic E. coli, and samples were enumerated through plating on selective media. The sink before the participant cleaned it had the highest frequency of contamination, regardless of if the chicken was washed or not, though they were contaminated more frequently when the chicken was washed (60.3% for washers and 35.6% for non-washers, p=0.0011). Cleaning of the inner sink and countertop resulted in a significant decrease in the frequency of contamination for both washers and non-washers (p<0.050). Sinks before cleaning and environmental swabs taken from the countertop at a distance of 0-6” from the sink had the highest concentration of E. coli, but all surfaces had a relatively low frequency of contamination. The intervention did not affect the frequency or degree of salad contamination. In order to determine how consumer behaviors, notably handwashing and touch-based behaviors, effect cross-contamination, impact the of risk cross-contamination of kitchen surfaces when preparing a meal, data from year one was analyzed. Behavioral coding was performed for handwashing and touch-based behaviors using the available videos (n=278) and the microbiological data was used to represent cross-contamination. Cross-contamination risk was defined as the likelihood and degree (i.e. contaminant concentration) of MS2 transferred to surfaces. The three most significant predictors out of the handwashing variables were (1) the percent of the time handwashing was attempted, (2) the average handwashing efficacy score, and (3) the average scrub time; with the risk of cross-contamination decreasing as those three variables increased. The best touch-based predictors varied by surface. After assessing handwashing and touch variables together using multiple backwards stepwise and logistic regressions, both handwashing and touch-based variables were found to be good predictors of cross-contamination; with the percentage of raw product touches and the number of times a surface was touched as the most commonly significant predictors. However, those predictors with a protective effect on cross-contamination (OR< 1; with tight confidence intervals) were handwashing predictors. Overall, these results provide a better understanding of how cross-contamination occurs in a home kitchen during meal preparation, characterizes how microbes move around a home kitchen, and what food safety behaviors significantly impact cross-contamination. These data can be used to inform food safety regulation and education efforts as well as provide ample information to be used in predictive and risk modeling.
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| Descriptor
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Food science
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Organizational behavior
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Pathology
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Stevenson, Clinton
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North Carolina State University
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