• 한국식품위생안전성학회지
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• 제26권1호
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• pp.25-30
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• 2011

본 연구에서는 식중독 예방과 식품의 안전성 확보 및 정량적 미생물 위해평가에 활용하기위하여, Gompertz model과 Baranyi model을 이용하여 샐러드용 신선채소에서 L. monocytogenes의 SGR에 관한 성징예측모델(SGR by Gompertz equation=-0.1606+$0.0574^*Temp$+$0.0009^*Temp^*Temp$, SGR by Baranyi equation=0.3502-$0.0496^*Temp$+$0.0022^*Temp^*Temp$)을 개발하였다. 개발된 모델의 적합성 평가를 위해 MSE, Bf, 및 Af factor를 산출하였다. 샐러드용 신선 채소의 MSE, Bf, Af는 Gompertz model식을 적용한 경우 0.002718, 1.050084, 1.160767, Baranyi model 식을 적용한 경우 0.055186, 1.931472, 2.137181으로 나타나 Gompertz model식을 적용하여 개발한 예측모델이 Baranyi model 식을 이용하여 개발한 예측모델에 비해 적합성이 높은 것으로 나타났다. Gompertz model식을 활용하여 본 연구에서 개발된 샐러드용 신선 채소에서의 L. monocytogenes 성장 예측모델은 신선 채소류를 생산, 가공, 보관 및 판매하는 산업체에서 널리 활용 가능할 것으로 판단되며, 더욱 정확한 예측모델 개발을 위해서는 pH 및 수분활성도 등 다양한 변수에 따른 미생물의 성장패턴 변화 등에 관한 연구가 추가적으로 시행되어야 할 것으로 생각되어 진다.

• Journal of Preventive Veterinary Medicine
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• 제42권4호
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• pp.143-147
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• 2018

The present study aimed to develop growth prediction models of Listeria monocytogenes in processed meat products, such as mixed pressed hams, to perform accurate microbial risk assessments. Considering cold storage temperatures and the amount of time in the stages of consumption after opening, the growth of L. monocytogenes was determined as a function of temperature at 0, 5, 10, and $15^{\circ}C$, and time at 0, 1, 3, 6, 8, 10, 15, 20, 25, and 30 days. Based on the results of these measurements, a Baranyi model using the primary model was developed. The input parameters of the Baranyi equation in the variable temperature for polynomial regression as a secondary model were developed: $SGR=0.1715+0.0199T+0.0012T^2$, $LT=5.5730-0.3215T+0.0051T^2$ with $R^2$ values 0.9972 and 0.9772, respectively. The RMSE (Root mean squared error), $B_f$ (bias factor), and $A_f$ (accuracy factor) on the growth prediction model were determined to be 0.30, 0.72, and 1.50 in SGR (specific growth rate), and 0.10, 0.84, and 1.35 in LT (lag time), respectively. Therefore, the model developed in this study can be used to determine microorganism growth in the stages of consumption of mixed pressed hams and has potential in microbial risk assessments (MRAs).

• Food Science and Biotechnology
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• 제17권6호
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• pp.1352-1356
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• 2008

Bootstrap method, a computer-intensive statistical technique to estimate the distribution of a statistic was applied to deal with uncertainty and variability of the experimental data in stochastic prediction modeling of microbial growth on a chill-stored food. Three different bootstrapping methods for the curve-fitting to the microbial count data were compared in determining the parameters of Baranyi and Roberts growth model: nonlinear regression to static version function with resampling residuals onto all the experimental microbial count data; static version regression onto mean counts at sampling times; dynamic version fitting of differential equations onto the bootstrapped mean counts. All the methods outputted almost same mean values of the parameters with difference in their distribution. Parameter search according to the dynamic form of differential equations resulted in the largest distribution of the model parameters but produced the confidence interval of the predicted microbial count close to those of nonlinear regression of static equation.

• 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.

• Asian-Australasian Journal of Animal Sciences
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• 제27권7호
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• pp.1013-1018
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• 2014

In this study, we developed kinetic models to predict the growth of pathogenic Escherichia coli on cheeses during storage at constant and changing temperatures. A five-strain mixture of pathogenic E. coli was inoculated onto natural cheeses (Brie and Camembert) and processed cheeses (sliced Mozzarella and sliced Cheddar) at 3 to 4 log CFU/g. The inoculated cheeses were stored at 4, 10, 15, 25, and $30^{\circ}C$ for 1 to 320 h, with a different storage time being used for each temperature. Total bacteria and E. coli cells were enumerated on tryptic soy agar and MacConkey sorbitol agar, respectively. E. coli growth data were fitted to the Baranyi model to calculate the maximum specific growth rate (${\mu}_{max}$; log CFU/g/h), lag phase duration (LPD; h), lower asymptote (log CFU/g), and upper asymptote (log CFU/g). The kinetic parameters were then analyzed as a function of storage temperature, using the square root model, polynomial equation, and linear equation. A dynamic model was also developed for varying temperature. The model performance was evaluated against observed data, and the root mean square error (RMSE) was calculated. At $4^{\circ}C$, E. coli cell growth was not observed on any cheese. However, E. coli growth was observed at $10{\circ}C$ to $30^{\circ}C$C with a ${\mu}_{max}$ of 0.01 to 1.03 log CFU/g/h, depending on the cheese. The ${\mu}_{max}$ values increased as temperature increased, while LPD values decreased, and ${\mu}_{max}$ and LPD values were different among the four types of cheese. The developed models showed adequate performance (RMSE = 0.176-0.337), indicating that these models should be useful for describing the growth kinetics of E. coli on various cheeses.

• 한국식품위생안전성학회지
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• 제32권3호
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• pp.206-210
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• 2017

본 연구에서는 축산식품인 편육을 대상으로 황색포도상구균의 성장예측모델을 개발하였다. 편육에서 황색포도상구균의 성장패턴은 4, 10, 20, $37^{\circ}C$의 보관온도에서 측정되었으며, 황색포도상구균은 각각의 저장 온도에서 모두 증가하는 것으로 나타났다. 편육에 오염된 황색포도상구균의 생육결과를 토대로 Baranyi model을 이용하여 유도기(LPD)와 최대성장률(${\mu}_{max}$)을 산출한 결과, 유도기는 4, 10, 20, $37^{\circ}C$에서 212.81, 79.67, 3.12, 2.21 h으로 온도에 반비례한 것으로 나타났고 최대성장률은 같은 보관온도에서 0.004, 0.009, 0.130, 0.568 log CFU/g/h으로 온도에 비례한 것으로 조사되었다. 2차 모델은 ${\mu}_{max}$의 경우, square root model, LPD는 polynomial equation을 사용하여 산출하였고, 개발한 모델의 적합성을 평가하기 위해 통계적 지표인 RMSE 값을 계산한 결과, 비교적 0에 가까운 0.42로 도출되어 모델이 적합한 것으로 확인되었다. 따라서 개발된 모델이 편육에 대한 황색포도상구균의 성장 예측모델로 사용 가능하다고 판단되어지며, 편육에서의 식중독을 예방하고 미생물학적 위생관리기준을 설정하는데 기초자료로 활용될 수 있을 것으로 사료된다.

• Asian-Australasian Journal of Animal Sciences
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• 제24권5호
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• pp.713-722
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• 2011

The aim of this study was to confirm Pseudomonas spp. as the specific spoilage organism (SSO) of chilled beef during aerobic storage and to establish a model to predict the shelf life of beef. Naturally contaminated beef was stored at $4^{\circ}C$, and the spoilage limit of Pseudomonas organisms was determined by measuring several quality indicators during storage, including the number of Pseudomonas organisms, total number of bacteria, total volatile basic nitrogen (TVBN) values, L value color scale scores and sensory evaluation scores. The beef was then stored at 0, 4, 7, 10, 15 or $20^{\circ}C$ for varying amounts of time, and the number of Pseudomonas organisms were counted, allowing a corresponding growth model to be established. The results showed that the presence of Pseudomonas spp. was significantly correlated to each quality characteristic (p<0.01), demonstrating that Pseudomonas spp. are the SSO of chilled beef and that the spoilage limit was $10^{8.20}$ cfu/g. The Baranyi and Roberts equation can predict the growth of Pseudomonas spp. in beef, and the $R^2$ value of each model was greater than 0.95. The square root model was used as follows, and the absolute values of the residuals were less than ${0.05:\;{\mu_{max}}^{1/2}$ = 0.15604 [T+(-0.08472)] (p<0.01), $R^2$ = 0.98, $\lambda^{-1/2}$ = 0.0649+0.0242T (p<0.01, $R^2$ = 0.94). The model presented here describes the impact of different temperatures on the growth of Pseudomonas spp., thereby establishing a model for the prediction of the shelf life of beef stored between 0 to $20^{\circ}C$.

• 대한영양사협회학술지
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• 제25권1호
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• pp.30-43
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• 2019

This study compared the predictive models for the growth kinetics of Staphylococcus aureus in ham rice balls. In addition, a semi-quantitative risk assessment of S. aureus on ham rice balls was conducted using FDA-iRISK 4.0. The rice was rounded with chopped ham, which was mixed with mayonnaise (SHM), soy sauce (SHS), or gochujang (SHG), and was contaminated artificially with approximately $2.5{\log}\;CFU{\cdot}g^{-1}$ of S. aureus. The inoculated rice balls were then stored at $7^{\circ}C$, $15^{\circ}C$, and $25^{\circ}C$, and the number of viable S. aureus was counted. The lag phases duration (LPD) and maximum specific growth rate (SGR) were calculated using a Baranyi model as a primary model. The growth parameters were analyzed using the polynomial equation as a function of temperature. The LPD values of S. aureus decreased with increasing temperature in SHS and SHG. On the other hand, those in SHM did not show any trend with increasing temperature. The SGR positively correlated with temperature. Equations for LPD and SGR were developed and validated using $R^2$ values, which ranged from 0.9929 to 0.9999. In addition, the total DALYs (disability adjusted life years) per year in the ham rice balls with soy sauce and gochujang was greater than mayonnaise. These results could be used to calculate the expected number of illnesses, and set the hazard management method taking the DALY value for public health into account.

• 한국축산식품학회지
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• 제40권6호
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• pp.938-945
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• 2020

A dynamic model was developed to predict the Escherichia coli cell counts in pig trotters at changing temperatures. Five-strain mixture of pathogenic E. coli at 4 Log CFU/g were inoculated to cooked pig trotter samples. The samples were stored at 10℃, 20℃, and 25℃. The cell count data was analyzed with the Baranyi model to compute the maximum specific growth rate (μ_max) (Log CFU/g/h) and lag phase duration (LPD) (h). The kinetic parameters were analyzed using a polynomial equation, and a dynamic model was developed using the kinetic models. The model performance was evaluated using the accuracy factor (A_f), bias factor (B_f), and root mean square error (RMSE). E. coli cell counts increased (p<0.05) in pig trotter samples at all storage temperatures (10℃-25℃). LPD decreased (p<0.05) and μ_max increased (p<0.05) as storage temperature increased. In addition, the value of h₀ was similar at 10℃ and 20℃, implying that the physiological state was similar between 10℃ and 20℃. The secondary models used were appropriate to evaluate the effect of storage temperature on LPD and μ_max. The developed kinetic models showed good performance with RMSE of 0.618, B_f of 1.02, and A_f of 1.08. Also, performance of the dynamic model was appropriate. Thus, the developed dynamic model in this study can be applied to describe the kinetic behavior of E. coli in cooked pig trotters during storage.