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KMID : 0881720200350010051
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Journal of Food Hygiene and Safety
2020 Volume.35 No. 1 p.51 ~ p.59
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Quantitative Microbial Risk Assessment of Pathogenic Vibrio through Sea Squirt Consumption in Korea
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Ha Ji-Myeong
Lee Jee-Yeon
Oh Hye-min
Shin Il-Shik
Kim Young-Mog
Park Kwon-Sam
Yoon Yo-Han
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Abstract
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This study evalutated the risk of foodborne illness from Vibrio spp. (Vibrio vulnificus and Vibrio cholerae) through sea squirt consumption. The prevalence of V. vulnificus and V. cholerae in sea squirt was evaluated, and the predictive models to describe the kinetic behavior of the Vibrio in sea squirt were developed. Distribution temperatures and times were collected, and they were fitted to probabilistic distributions to determine the appropriate distributions. The raw data from the Korea National Health and Nutrition Examination Survey 2016 were used to estimate the consumption rates and amount of sea squirt. In the hazard characterization, the Beta-Poisson model for V. vulnificus and V. cholerae infection was used. With the collected data, a simulation model was prepared and it was run with @RISK to estimate probabilities of foodborne illness by pathogenic Vibrio spp. through sea squirt consumption. Among 101 sea squirt samples, there were no V. vulnificus positive samples, but V. cholerae was detected in one sample. The developed predictive models described the fates of Vibrio spp. in sea squirt during distribution and storage, appropriately shown as 0.815-0.907 of R 2 and 0.28 of RMSE. The consumption rate of sea squirt was 0.26%, and the daily consumption amount was 68.84 g per person. The Beta-Poisson model [P=1-(1+Dose/β) -α ] was selected as a dose-response model. With these data, a simulation model was developed, and the risks of V. vulnificus and V. cholerae foodborne illness from sea squirt consumption were 2.66×10 -15 , and 1.02×10 -12 , respectively. These results suggest that the risk of pathogenic Vibrio spp. in sea squirt could be considered low in Korea.
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KEYWORD
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Vibrio vulnificus, Vibrio cholerae, Sea squirt, Risk assessment, Predictive model
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