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Predictive Modeling for Microbial Risk Assessment (MRA) from the Literature Experimental Data
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  • Predictive Modeling for Microbial Risk Assessment (MRA) from the Literature Experimental Data
  • Predictive Modeling for Microbial Risk Assessment (MRA) from the Literature Experimental Data
저자명
Bahk. Gyung-Jin
간행물명
Food science and biotechnology
권/호정보
2009년|18권 1호|pp.137-142 (6 pages)
발행정보
한국식품과학회
파일정보
정기간행물|ENG|
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기타
이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

One of the most important aspects of conducting this microbial risk assessment (MRA) is determining the model in microbial behaviors in food systems. However, to fully these modeling, large expenditures or newly laboratory experiments will be spent to do it. To overcome these problems, it has to be considered to develop the new strategies that can be used data in the published literatures. This study is to show whether or not the data set from the published experimental data has more value for modeling for MRA. To illustrate this suggestion, as example of data set, 4 published Salmonella survival in Cheddar cheese reports were used. Finally, using the GInaFiT tool, survival was modeled by nonlinear polynomial regression model describing the effect of temperature on Weibull model parameters. This model used data in the literatures is useful in describing behavior of Salmonella during different time and temperature conditions of cheese ripening.