• 급식외식위생학회지
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• 제2권2호
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• pp.91-96
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• 2021

This study was performed to provide fundamental data on hygiene and quality control of ready-to-eat sandwiches. Predictive models were developed to the kinetics of Staphylococcus aureus growth in these sandwiches as a function of temperature (10, 15, 25, and 35℃). The result of the primary model that used the Gompertz equation showed that the lag phase duration (LPD) and generation time (GT) decreased and the exponential growth rate (EGR) increased with increasing storage temperature. The secondary model showed an R² for M and B of 0.9967 and 09916, respectively. A predictive growth model of the growth degree as a function of temperature was developed. L(t)=A+Cexp(-exp(-B(t-M))) (A=Initial contamination level, C=MPD-A, B=0.473166-0.045040*Temp-0.001718*Temp*Temp, M=19.924824-0.627442*Temp-0.004493*Temp*Temp, t=time, Temp=temperature). This model showed an R² value of 0.9288. All the models developed in this study showed a good fit.

• Food Science and Biotechnology
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• 제15권5호
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• pp.715-720
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• 2006

This study was performed to develop a predictive model for the growth kinetics of Listeria monocytogenes in tryptic soy broth (TSB) using a response surface model with a combination of potassium lactate (PL), temperature, and pH. The growth parameters, specific growth rate (SGR), and lag time (LT) were obtained by fitting the data into the Gompertz equation and showed high fitness with a correlation coefficient of $R^2{\geq}0.9192$. The polynomial model was identified as an appropriate secondary model for SGR and LT based on the coefficient of determination for the developed model ($R^2\;=\;0.97$ for SGR and $R^2\;=\;0.86$ for LT). The induced values that were calculated using the developed secondary model indicated that the growth kinetics of L. monocytogenes were dependent on storage temperature, pH, and PL. Finally, the predicted model was validated using statistical indicators, such as coefficient of determination, mean square error, bias factor, and accuracy factor. Validation of the model demonstrates that the overall prediction agreed well with the observed data. However, the model developed for SGR showed better predictive ability than the model developed for LT, which can be seen from its statistical validation indices, with the exception of the bias factor ($B_f$ was 0.6 for SGR and 0.97 for LT).

• 한국축산식품학회지
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• 제30권3호
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• pp.487-494
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• 2010

본 연구는 수육에 쉽게 오염될 수 있는 S. aureus에 대한 성장 예측모델을 적용하고, 이를 비교하여 수육을 안전하게 관리하기 위한 적절한 모델을 제시하고자 하였다. 온도에 따른 S. aureus의 성장곡선은 5, 15, $25^{\circ}C$의 보관온도에서 측정하였다. 수육에 오염된 S. aureus의 성장결과를 기초로 온도에 따라 Baranyi model과 Gompertz model을 이용하여 SGR와 LT를 산출하였다. 두 모델에 대하여 R2과 RMSE를 산출하여 통계적인 적합성을 비교하였으며 그 결과 Baranyi model에서는 각각 0.98, 0.27, Gompertz model에서는 각각 0.84, 0.84로 나타나 Baranyi model이 온도변화에 따라 S. aureus 생육을 예측하기 위한 이차모델의 변수 값으로 사용하는데 더 적합하였다. RSM을 이용한 2차 모델에서는 $R^2$이 5, 15, $25^{\circ}C$에서 각각 0.88, 0.99, 0.99로 나타나 실험값과 예측값의 상관관계가 높았다. 또한 RMSE는 온도별로 각각 0.11, 0.24, 0.10로 나타났고, $B_f$는 각각1.12, 1.02, 1.03로, $A_f$는 각각 1.17, 1.03, 1.03로 나타나 통계적 적합성이 높다고 할 수 있다. 따라서 개발된 모델을 이용할 경우 수육의 다양한 조리환경과 온도에 따른 S. auresus 성장을 추정할 수 있으며, 이를 위해 평가에서 충분히 활용할 수 있을 것으로 보인다.

• 한국식품과학회지
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• 제37권6호
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• pp.1012-1017
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• 2005

게맛살로부터 분리한 주요 부패세균은 내열성 포자를 형성하는 Bacillus subtilis와 Bacillus licheniformis로 동정되었다. 게맛살의 제조 공정상 가열 처리 과정에서 B. subtilis와 B. Licheniformis 등 내열성 포자를 형성하는 균을 완전히 사멸시키기는 어려우며, 살아남은 포자는 유통과정 중, 적정 온도와 시간이 경과함에 따라, 영향 세포로 발아하여 게맛살의 부패에 영향을 미친다. 이러한 부패세균의 증식에 있어서 초기균수와 온도의 영향을 조사한 결과, 초기균수에 따른 최대증식속도상수(k)와 유도기(LT), 세대시간(GT)은 유의적인 차이가 없었으며, 온도의 영향이 지배적인 것으로 나타났다. 또한 본 실험에서 유도기(LT)와 온도의 관계는 $L(hr)=2.5219e^{-0.2467{\cdot}T}$의 관계가 성립하며, square root model과 polynomial model을 이용, 온도와 초기균수에 대한 최대증식속도상수(k)를 정량화한 정량평가모델을 개발하였으며, 그 식은 다음과 같다. $$Square\;root\;model:\;{\sqrt{k}}=0.0267\;(T-3.5089)$$ $$Polynomial model:\;k=-0.2160+0.0241T-0.01999A_0$$ 온도와 초기균수에 대한 최대증식속도상수(k)의 정량평가모델로부터 특정온도와 초기 균수에서 최대증식속도상수(k)를 계산할 수 있으며, 계산된 최대증식속도상수(k)를 균의 기본 증식 모델인 Gomperz model에 적용하여 균의 성장을 예측할 수 있었다.

• 한국식품과학회지
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• 제36권2호
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• pp.349-354
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• 2004

수산식품에서 문제가 되는 식중독 균인 V. parahaemolyticus를 대상으로 온도, pH 및 초기균수에 따른 균의 성장 실험 결과를 데이터베이스화하여 이를 바탕으로 균의 성장을 정량적으로 평가할 수 있는 수학적 모델을 개발하였다. $1.0{\times}10^{2},\;1.0{\times}10^{3},\;1.0{\times}10^{4}\;CFU/mL$의 각 초기균수 조건에서 실험치와 예측치의 상관계수는 각각 0.966, 0.979, 0.965으로 나타났다. 또한, 초기균수를 고려하지 않은 모델식은 상관계수가 0.966으로 다음과 같이 나타났다. Polynomial model: $$k=1.10{\cdot}\exp(-0.5(((T-34.14)/9.09)^{2}+((pH-6.59)/4.68)^{2}))$$ 균의 증식 지표치인 최대증식속도상수 k는 온도에 지배적인 영향을 받았으며, pH 및 초기균수에 따른 유의적인 차이는 없었으므로 (P>0.05), k와 온도와의 관계식인 square root model로 나타내었다. Square root model: $${\sqrt{k}\;0.06(T-9.55)[1-\exp(0.07(T-49.98))]$$ V. parahaemolyticus의 경우, square root model에 의한 실험치와 예측치의 상관계수는 0.977로 polynomial model보다 높은 적용성을 나타내었다.

• 한국축산식품학회지
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• 제30권1호
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• pp.49-54
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• 2010

The aim of this research was to estimate the effect of temperature and develop predictive models for the growth of total viable cells (TVC) and Escherichia coli (EC) on chicken breast under aerobic and various temperature conditions. The primary models were determined by Baranyi model. The secondary models for the specific growth rate (SGR) and lag time (LT), as a function of storage temperature, were developed by the polynomial model. The initial contamination level of chicken breasts was around 4.3 Log CFU/g of TVC and 1.0 Log CFU/g of E. coli. During 216 h of storage, SGR of TVC showed 0.05, 0.15, and 0.54 Log CFU/g/h at 5, 15, and $25^{\circ}C$. Also, the growth tendency of EC was similar to those of TVC. As storage temperature increased, the values of SGR of microorganisms increased dramatically and the values of LT decreased inversely. The predicted growth models with experimental data were evaluated by $B_f$, $A_f$, RMSE, and $R^2$. These values indicated that these developed models were reliable to express the growth of TVC and EC on chicken breasts. The temperature changes of distribution and showcase in markets might affect the growth of microorganisms and spoilage of chicken breast mainly.

• 한국축산식품학회지
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• 제35권1호
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• pp.108-113
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• 2015

The purpose of this study was to develop predictive models for the growth of Listeria monocytogenes in pork Bulgogi at various storage temperatures. A two-strain mixture of L. monocytogenes (ATCC 15313 and isolated from pork Bulgogi) was inoculated on pork Bulgogi at 3 Log CFU/g. L. monocytogenes strains were enumerated using general plating method on Listeria selective medium. The inoculated samples were stored at 5, 15, and $25^{\circ}C$ for primary models. Primary models were developed using the Baranyi model equations, and the maximum specific growth rate was shown to be dependent on storage temperature. A secondary model of growth rate as a function of storage temperature was also developed. As the storage temperature increased, the lag time (LT) values decreased dramatically and the specific growth rate of L. monocytogenes increased. The mathematically predicted growth parameters were evaluated based on the modified bias factor ($B_f$), accuracy factor ($A_f$), root mean square error (RMSE), coefficient of determination ($R^2$), and relative errors (RE). These values indicated that the developed models were reliably able to predict the growth of L. monocytogenes in pork Bulgogi. Hence, the predictive models may be used to assess microbiological hygiene in the meat supply chain as a function of storage temperature.

• 한국축산식품학회지
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• 제29권3호
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• pp.289-295
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• 2009

The aim of this research was to develop predictive models for the growth of spoilage bacteria (total viable cells, Pseudomonas spp., and lactic acid bacteria) on frankfurters and to estimate the shelf-life of frankfurters under aerobic conditions at various storage temperatures (5, 15, and $25^{\circ}C$). The primary models were determined using the Baranyi model equation. The secondary models for maximum specific growth rate and lag time as functions of temperature were developed by the polynomial model equation. During 21 d of storage under various temperature conditions, lactic acid bacteria showed the longest lag time and the slowest growth rate among spoilage bacteria. The growth patterns of total viable cells and Pseudomonas spp. were similar each other. These data suggest that Pseudomonas spp. might be the dominant spoilage bacteria on frankfurters. As storage temperature increased, the growth rate of spoilage bacteria also increased and the lag time decreased. Furthermore, the shelf-life of frankfurters decreased from 7.0 to 4.3 and 1.9 (d) under increased temperature conditions. These results indicate that the most significant factor for spoilage bacteria growth is storage temperature. The values of $B_f$, $A_f$, RMSE, and $R^2$ indicate that these models were reliable for identifying the point of microbiological hazard for spoilage bacteria in frankfurters.

• 동아시아식생활학회지
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• 제22권4호
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• pp.558-565
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• 2012

The objective of this study was to develop a predictive growth model for Bacillus cereus in nutrient broth and validate the developed growth model in blanched vegetables. After inoculating B. cereus into nutrient broth, growth of B. cereus was investigated at 13, 17, 24, 30 and $35^{\circ}C$. Lag time (LT) decreased while specific growth rate (SGR) increased with an increase in storage temperature. Growth of B. cereus was not observed at temperatures lower than $12^{\circ}C$. Secondary growth models were developed to describe primary model parameters, including LT and SGR. Model performance was evaluated based on bias factor ($B_f$) and accuracy factor ($A_f$). In addition, we inoculated B. cereus into blanched vegetables stored at 13, 24, $35^{\circ}C$ and observed the growth kinetics of B. cereus in five different blanched vegetables. Growth of B. cereus was most delayed on Doraji at $13^{\circ}C$ and was not observed on Gosari at $13^{\circ}C$. Growth of B. cereus at $35^{\circ}C$ was significantly (p<0.05) slower on Gosari than on other blanched vegetables. The developed secondary LT model for broth in this study was suitable to predict growth of B. cereus on Doraji and Gosari, whereas the SGR model was only suitable for predicting the growth of B. cereus on mung bean sprout.

• 한국정보처리학회논문지
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• 제7권7호
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• pp.2076-2085
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• 2000

Almost all existing software reliability models are based on the assumptions of he software usage and software failure process. There, therefore, is no universally applicable software reliability model. To develop a universal software reliability model this paper suggests the predictive filter as a general software reliability prediction model for time domain failure data. Its usefulness is empirically verified by analyzing the failure datasets obtained from 14 different software projects. Based on the average relative prediction error, the suggested predictive filter is compared with other well-known neural network models and statistical software reliability growth models. Experimental results show that the predictive filter generally results in a simple model and adapts well across different software projects.