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Development of User-Friendly Modeling Software and Its Application in Processed Meat Products

  • Lee, Heeyoung (Risk Analysis Research Center, Sookmyung Women's University) ;
  • Lee, Panho (TNH) ;
  • Lee, Soomin (Risk Analysis Research Center, Sookmyung Women's University) ;
  • Kim, Sejeong (Risk Analysis Research Center, Sookmyung Women's University) ;
  • Lee, Jeeyeon (Risk Analysis Research Center, Sookmyung Women's University) ;
  • Ha, Jimyeong (Risk Analysis Research Center, Sookmyung Women's University) ;
  • Choi, Yukyung (Risk Analysis Research Center, Sookmyung Women's University) ;
  • Oh, Hyemin (Risk Analysis Research Center, Sookmyung Women's University) ;
  • Yoon, Yohan (Risk Analysis Research Center, Sookmyung Women's University)
  • Received : 2018.04.13
  • Accepted : 2018.05.31
  • Published : 2018.06.30

Abstract

The objective of this study was to develop software to predict the kinetic behavior and the probability of foodborne bacterial growth on processed meat products. It is designed for rapid application by non-specialists in predictive microbiology. The software, named Foodborne bacteria Animal product Modeling Equipment (FAME), was developed using Javascript and HTML. FAME consists of a kinetic model and a probabilistic model, and it can be used to predict bacterial growth pattern and probability. In addition, validation and editing of model equation are available in FAME. The data used by the software were constructed with 5,400 frankfurter samples for the kinetic model and 345,600 samples for the probabilistic model using a variety of combinations including atmospheric conditions, temperature, NaCl concentrations and $NaNO_2$ concentrations. Using FAME, users can select the concentrations of NaCl and $NaNO_2$ meat products as well as storage conditions (atmosphere and temperature). The software displays bacterial growth patterns and growth probabilities, which facilitate the determination of optimal safety conditions for meat products. FAME is useful in predicting bacterial kinetic behavior and growth probability, especially for quick application, and is designed for use by non-specialists in predictive microbiology.

본 연구에서는 육제품의 다양한 조건(포장, 저장온도, 염농도, 아질산염농도)에서의 식중독세균의 생장을 예측하는소프트웨어를 예측미생물학에 대한 지식이 부족한 비전문가도 손쉽게 이용할 수 있도록 개발하였다. 육제품에서의 식중독세균예측소프트웨어(FAME: Foodborne bacteria Animal product Modeling Equipment)는 Javascript와 HTML을 이용하여 개발하였으며, 육제품에 대한 카이네틱모델과 확률모델을 포함하고있다. FAME에서는 검증(validation) 기능을 포함하고 있으며, FAME에 탑재 되어있는 예측모델의 수식을 자유롭게 수정할 수 있도록 고안 하였다. FAME에는 포장조건, 온도, 염농도, 아질산염농도 조합에 따라 실험한 데이터를 카이네틱모델(5,400 데이터)과 확률모델(345,600 데이터)에 탑재하였다. 사용자가 FAME을 이용하여 육제품의 제조 조건을 소프트웨어에 입력하면, 시간에 따른 식중독세균의 생장패턴과 생장확률이 즉시 계산 되어진다. 따라서 예측 미생물학에 대한 전문 지식이 없는 비전문가라고 하더라도 FAME을 이용하여 직접 실험을 하지 않고도 육제품에서의 식중독세균의 생장을 쉽고 빠르게 예측할 수 있어, 육가공분야에서 매우 유용하게 사용되어 질 수 있을 것으로 판단된다.

Keywords

References

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