Kim Kyungmi, Lee Heeyoung, Lee Soomin, Kim Sejeong, Lee Jeeyeon, Ha Jimyeong, Yoon Yohan
Department of Food and Nutrition, Sookmyung Women's University, Seoul 04310, Korea.
Risk Analysis Research Center, Sookmyung Women's University, Seoul 04310.
Korean J Food Sci Anim Resour. 2017;37(4):579-592. doi: 10.5851/kosfa.2017.37.4.579. Epub 2017 Aug 31.
This study assessed the quantitative microbial risk of non-enterohemorrhagic (EHEC). For hazard identification, hazards of non-EHEC in natural and processed cheeses were identified by research papers. Regarding exposure assessment, non-EHEC cell counts in cheese were enumerated, and the developed predictive models were used to describe the fates of non-EHEC strains in cheese during distribution and storage. In addition, data on the amounts and frequency of cheese consumption were collected from the research report of the Ministry of Food and Drug Safety. For hazard characterization, a dose-response model for non-EHEC was used. Using the collected data, simulation models were constructed, using software @RISK to calculate the risk of illness per person per day. Non-EHEC cells in natural- (n=90) and processed-cheese samples (n=308) from factories and markets were not detected. Thus, we estimated the initial levels of contamination by Uniform distribution × Beta distribution, and the levels were -2.35 and -2.73 Log CFU/g for natural and processed cheese, respectively. The proposed predictive models described properly the fates of non-EHEC during distribution and storage of cheese. For hazard characterization, we used the Beta-Poisson model (α=2.21×10, N=6.85×10). The results of risk characterization for non-EHEC in natural and processed cheese were 1.36×10 and 2.12×10 (the mean probability of illness per person per day), respectively. These results indicate that the risk of non-EHEC foodborne illness can be considered low in present conditions.
本研究评估了非肠出血性大肠杆菌（EHEC）的定量微生物风险。在危害识别方面，通过研究论文确定了天然奶酪和加工奶酪中非EHEC的危害。在暴露评估方面，对奶酪中的非EHEC细胞计数进行了枚举，并使用所建立的预测模型来描述非EHEC菌株在奶酪分销和储存过程中的命运。此外，从食品药品安全部的研究报告中收集了奶酪消费数量和频率的数据。在危害特征描述方面，使用了非EHEC的剂量反应模型。利用收集到的数据，构建了模拟模型，使用@RISK软件计算每人每天的患病风险。未检测到来自工厂和市场的天然奶酪（n = 90）和加工奶酪样品（n = 308）中的非EHEC细胞。因此，我们通过均匀分布×贝塔分布估计了初始污染水平，天然奶酪和加工奶酪的污染水平分别为-2.35和-2.73 Log CFU/g。所提出的预测模型恰当地描述了非EHEC在奶酪分销和储存过程中的命运。在危害特征描述方面，我们使用了贝塔-泊松模型（α = 2.21×10，N = 6.85×10）。天然奶酪和加工奶酪中非EHEC的风险特征描述结果分别为1.36×10和2.12×10（每人每天患病的平均概率）。这些结果表明，在当前条件下，非EHEC食源性疾病的风险可被认为较低。
