식품산업체에서 검사한 자료의 일반세균수는 대부분 불규칙 분포를 나타낸다. 최근 이러한 불규칙적인 경향에 대하여 미생 물수의 lognormal 분포 특성을 이용하는 확률분포 모델을 적용함으로써 좀더 정확한 변화의 특성을 밝히고 있다. 확률분포 모델을 이용하면 일반세균수와 대장균군 등의 분석결과에 대해 과거의 단순한 경향분석을 벗어나, 유용한 분석이 가능한 것으로 나타나고 있 다. 본 연구는 냉동만두류를 생성하는 2개 업체의 각각 1년 동안 실험된 일반세균 자료를 갖고, 확률분포 모델 활용의 타당성과 업체간의 위생수준 상호비교 그리고 예상되는 위해 발생 예측에 적용 검토하였다. 그 결과 정량적인 미생물 자료에 대한 확률분포 모델의 적용은 식품산업체에서 일반세균수에 의한 위해발생 가능성을 예측하고 안전관리 수준을 결정하는데 유용한 것으로 판단되었다.

미생물학적 위해성 평가(Micribial risk assessment: MRA)에서 노출평가시 확률적 접근방법은 식중독 발생에 관련된 많은 위해 미생물의 다양성과 변이성 그리고 불확실성에 대한 분석이 가능하고 과적의 접근방법인 point estiamte 보다 훨씬 더 현실성이 반영된 결과를 제공할 수 있어 현재 MRA의 노출평가에서 가장 효과적인 방법으로 인정되고 있다. 본 연구는 MRA 방법론 중 노출평가에서 이용될 수 있는 수학적 확률분포 모델을 이용하여 위해 미생물의 발생수준을 추정하는 방법론을 제시하고, 이를 바탕으로 국내 식육에서의 Listeria monocytogenes에 대한 오염수준을 추정하였으며, 그 결과 국내 식육에서의 L. monocytogenes의 오염수준은 평균 (50_(th) percentile) -4.08 Log CFU/g이며, 최소 (5_(th) percentile) -4.88 Log CFU/g, 최대 (95_(th) percentile) -3.56 Log CFU/g의 범위를 갖는 것으로 추정되었다.

In this study, occurrence of aflatoxin M₁ (AFM₁) in domestic milk and milk products was determined. The level of AFM₁ in market milk (0.047 ppb) was lower than that in raw milk (0.083 ppb) but this looks like that is due to dilution in collecting process rather than the effect of sterilization. In the case of nonfat dry milk, level of AFM₁ appeared high by 0.24 ppb but it is thought to be not different from market milk actually because nonfat dry milk is diluted at intake. In the case of ice cream, finished products were contaminated with AFM₁ of 0.020 ppb and also have the possibility of the contamination of AFB₁ due to secondary raw material such as nuts and alinond. On the basis of the results of this study and previous studies, Monte-Carlo simulation is conducted to estimate the contamination level of AFM₁ in domestic market milk. To consider uncertainty and variability fitting procedure was passed through. And we used beta distribution to estimate the prevalence and triangular distribution to estimate the concentration level of AFM₁ in milk. As a result, the 5%, 50% and 95% points of the distribution of the probability of AFM₁ contamination level in milk is 0.0214, 0.0946 and 0.1888 ppb, respectively. Also we estimate that AFM₁ in almost milk was low more than 0.5 ppb that is American acceptable level but 80.4% exceeded far 0.05 ppb that is European standard.

Predictive food microbiology(PFM) is an emerging area of food microbiology since the later 1980's. It does apply mathematical models to predict the responses of microorganism to specified environmental variables. Although, at present, PFM models do not completely developed, models can provide very useful information for microbiological responses in HACCP(Hazard Analysis Critical Control Point) system and Risk Assessment. This study illustrates the possible use of PFM models(PMP: Pathogen Modeling Program win5.1) with milk in several elements in the HACCP system, such as conduction of hazard analysis and determination of CCP(Critical Control Points) and CL(Critical Limits). The factors likely to affect the growth of the pathogens in milk involved storage temperature, pH, Aw and NaCl content. The variable factor was storage temperature at the range of 4-15℃ and the fixed factors were pH 6.7, Aw 0.993 and NaCl 1.3%. PMPwin5.1 calculated generation time, lag phase duration, time to level of infective dose for pathogens across a range of storage temperature. The levels of safety associated with milk which were defined based on various storage temperature as affecting microbial growth according to PMPwin5.1 were classified in $quot;safe temperature zone$quot;, $quot;caution temperature zone$quot; and $quot;danger temperature zone$quot;, respectively. These zone ranges were determined by the lag phase duration and time to level of infective dose based on shelf life of milk, which is required 5 days in domestic legal. These results can be used to conduct a hazard analysis and set the criteria for CCP or CL. Though PFM contains limitation in the use, PFM models can be useful instrument to support of guarantee of food safety.