Quantitative Microbial Risk Assessment Model for Staphylococcus aureus in Kimbab

김밥에서의 Staphylococcus aureus에 대한 정량적 미생물위해평가 모델 개발

  • Bahk, Gyung-Jin (National Food Safety & Toxicology Center, Michigan State University) ;
  • Oh, Deog-Hwan (School of Biotechnology and Bioengineering, Kangwon National University) ;
  • Ha, Sang-Do (Department of Food Science and Technology, Chung-Ang University) ;
  • Park, Ki-Hwan (Department of Food Science and Technology, Chung-Ang University) ;
  • Joung, Myung-Sub (Korea Health Industry Development Institute) ;
  • Chun, Suk-Jo (Korea Health Industry Development Institute) ;
  • Park, Jong-Seok (Korea Food and Drug Administration) ;
  • Woo, Gun-Jo (Korea Food and Drug Administration) ;
  • Hong, Chong-Hae (Department of Veterinary Medicine, Kangwon National University)
  • Published : 2005.06.30


Quantitative microbial risk assessment (QMRA) analyzes potential hazard of microorganisms on public health and offers structured approach to assess risks associated with microorganisms in foods. This paper addresses specific risk management questions associated with Staphylococcus aureus in kimbab and improvement and dissemination of QMRA methodology, QMRA model was developed by constructing four nodes from retail to table pathway. Predictive microbial growth model and survey data were combined with probabilistic modeling to simulate levels of S. aureus in kimbab at time of consumption, Due to lack of dose-response models, final level of S. aureus in kimbeb was used as proxy for potential hazard level, based on which possibility of contamination over this level and consumption level of S. aureus through kimbab were estimated as 30.7% and 3.67 log cfu/g, respectively. Regression sensitivity results showed time-temperature during storage at selling was the most significant factor. These results suggested temperature control under $10^{\circ}C$ was critical control point for kimbab production to prevent growth of S. aureus and showed QMRA was useful for evaluation of factors influencing potential risk and could be applied directly to risk management.


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