• 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.

• 한국축산식품학회지
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• 제29권1호
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• pp.47-54
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• 2009

Predictive modeling was applied to study the growth of microorganisms related to spoilage in frankfurter sausage containing various levels of dietary fiber (0, 1, 2, and 3%) from rice bran and to estimate its shelf-life. Using the Baranyi model, total viable cells, anaerobic and psychrotrophic bacteria were measured during 35 days of cold storage ($<4{\pm}1^{\circ}C$). The lag times (LT) demonstrated by control and treatment groups were 6.28, 623, 6.24, and 6.25 days, respectively. The growth rate of total viable cells in each group were 0.95, 0.91, 0.92, and 0.91 (Log CFU/g/day), respectively. The anaerobic and psychrotrophic bacteria had lower initial ($y_0$) and maximal bacterial counts ($y_{max}$) than total viable cells. Also, the anaerobic and psychrotrophic bacteria possessed lower growth rate and longer lag time than total viable cells. The estimated shelf-life of frankfurter containing rice bran fiber by the growth rate of total viable cells was 7.8, 7.9, 7.9, and 7.7 days, respectively. There were no significant differences in shelf-life as a function of fiber content. In other words, the addition of dietary fiber in sausage did not show the critically hazardous results in growth of microorganism. The 12 predictive models were then characterized by high $R^2$, and small RMSE. Furthermore, $B_f$ and $A_f$ values showed a very close relationship between the predictive and observed data.

• Preventive Nutrition and Food Science
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• 제13권2호
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• pp.104-111
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• 2008

As a way to account for the variability of the primary model parameters in the secondary modeling of microbial growth, three different regression approaches were compared in determining the confidence interval of the temperature-dependent primary model parameters and the estimated microbial growth during storage: bootstrapped regression with all the individual primary model parameter values; bootstrapped regression with average values at each temperature; and simple regression with regression lines of 2.5% and 97.5% percentile values. Temperature dependences of converted parameters (log $q_o$, ${\mu}_{max}^{1/2}$, log $N_{max}$) of hypothetical initial physiological state, maximum specific growth rate, and maximum cell density in Baranyi's model were subjected to the regression by quadratic, linear, and linear function, respectively. With an advantage of extracting the primary model parameters instantaneously at any temperature by using mathematical functions, regression lines of 2.5% and 97.5% percentile values were capable of accounting for variation in experimental data of microbial growth under constant and fluctuating temperature conditions.

• 급식외식위생학회지
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• 제2권2호
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• pp.84-90
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• 2021

This study was performed to assess the microbiological quality and safety of microgreen sampled from harvesting farms and food processing plant in Korea. The samples were analyzed for total viable counts, coliforms, Enterobacteriaceae, Escherichia coli, Salmonella spp., Listeria monocytogenes, Vibrio parahaemolyticus, Bacillus cereus, and Staphylococcus aureus. Total viable counts were highly contaminated in samples collected from farms (7.7~8.2 log CFU/g) and the final products (5.8~7.8 log CFU/g), respectively. B. cereus was detected less than 100 CFU/g, which was satisfied with Korean standards (<1,000 CFU/g) of fresh-cut produce. A predictive model was developed for the changes of total viable counts in microgreens during storage at 5~35℃. The predictive models were developed using the Baranyi model for the primary model and the square root model for the secondary model. The results obtained in this study can be useful to develop the safety management options along the food chain, including fresh-cut produce storage and distribution.

• Food Science and Biotechnology
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• 제27권6호
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• pp.1865-1869
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• 2018

This study examined the effects of alfalfa seed germination on growth of Salmonella enterica. We investigated the population changes of S. enterica during early sprout development. We found that the population density of S. enterica, which was inoculated on alfalfa seeds was increased during sprout development under all experimental temperatures, whereas a significant reduction was observed when S. enterica was inoculated on fully germinated sprouts. To establish a model for predicting S. enterica growth during alfalfa sprout development, the kinetic growth data under isothermal conditions were collected and evaluated based on Baranyi model as a primary model for growth data. To elucidate the influence of temperature on S. enterica growth rates, three secondary models were compared and found that the Arrhenius-type model was more suitable than others. We believe that our model can be utilized to predict S. enterica behavior in alfalfa sprout and to conduct microbial risk assessments.

• 한국축산식품학회지
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• 제28권2호
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• pp.232-239
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• 2008

본 연구는 미강 추출 식이섬유 혼합물을 첨가한 떡갈비의 미생물학적 안전성 확보와 유통기한 산출을 위하여 예측미생물학을 이용하였다. 이를 위해 미강 추출 식이섬유 혼합물을 0, 1, 2, 3%의 비율로 첨가하였으며 냉장($4{\pm}1^{\circ}C$) 조건에서 15일간 저장하며 일반세균수, 혐기성균, 저온균, 내열성균, 대장균군의 미생물학적 변화를 관찰하였다. 일반세균수의 초기 균수(저장 0일)의 경우 3.23-3.85 log CFU/g을 나타내었으며 혐기성균, 저온균의 경우 비슷한 초기균수를 나타내었다. 일반세균수와 혐기성균의 경우 저장 3-5일차에서 균수의 급격한 증가를 나타내었다. 저장기간 동안 측정된 균수를 Baranyi function을 바탕으로 성장예측곡선과 생육 지표를 예측하였으며, 성장예측곡선의 적합성을 검증한 결과 일반세균수, 혐기성균, 저온균의 경우 처리구간 모두 0.923 이상의 높은 $R^2$값을 나타내었으며 $B_f$, $A_f$의 경우 역시 이상적인 값인 1에 가까운 값은 나타내었다. RSME 값 역시 모두 0.65 이하를 나타내어 실측치와 예측치 간의 높은 정확성을 나타내었다. Baranyi function 식 (1), (2)을 이용하여 계산된 균수가 5 log CFU/g이 되는 시점인 predicted shelf-life의 경우 control, T1, T2, T3의 경우 각각 2.5, 3.5, 3.5, 3.6일로 예측되었으며 안전계수(1/1.5)를 고려한 estimated shelf-life의 경우 1.7, 2.3, 2.3, 2.4일로 측정되었다 미강 식이섬유 혼합물이 첨가된 처리구가 control보다 약 0.6-0.7일 긴 유통기한을 가지는 것으로 측정되었고 식이섬유가 3% 함유된 떡갈비가 2.4일로 가장 긴 유통기한을 갖는 것으로 나타났다. 본 연구를 통해, 미강 추출 식이섬유 혼합물이 첨가된 떡갈비의 유통기한을 예측미생물학을 적용하여 측정할 수 있었으며, 식품산업에 있어서 이러한 예측미생물학은 식품의 제조 가공 판매 등을 결정할 때 미생물의 정량적 위험성을 판단하는 도구로서 사용될 수 있을 것으로 기대된다.

• Preventive Nutrition and Food Science
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• 제13권4호
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• pp.348-353
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• 2008

Aerobic bacterial growth on Korean pan.fried meat patties as a primary quality deterioration factor was modeled as a function of temperature to estimate microbial spoilage on a real.time basis under dynamic storage conditions. Bacteria counts in the stretch.wrapped foods held at constant temperatures of 0, 5, 10 and $15^{\circ}C$ were measured throughout storage. The bootstrapping method was applied to generate many resampled data sets of mean microbial counts, which were then used to estimate the parameters of the microbial growth model of Baranyi & Roberts in the form of differential equations. The temperature functions of the primary model parameters were set up with confidence limits. Incorporating the temperature dependent parameters into the differential equations of bacterial growth could produce predictions closely representing the experimental data under constant and fluctuating temperature conditions.

• 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 $20^{\circ}C$.

• 한국축산식품학회지
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• 제38권3호
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• pp.442-450
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• 2018

Liquid egg products can be contaminated with Salmonella spp. during processing. A predictive model for the growth of Salmonella spp. in unpasteurized liquid eggs was developed and validated. Liquid whole egg, liquid yolk, and liquid egg white samples were prepared and inoculated with Salmonella mixture (approximately 3 Log CFU/mL) containing five serovars (S. Bareilly, S. Richmond, S. Typhimurium monophasic, S. Enteritidis, and S. Gallinarum). Salmonella growth data at isothermal temperatures (5, 10, 15, 20, 25, 30, 35, and $40^{\circ}C$) was collected by 960 h. The population of Salmonella in liquid whole egg and egg yolk increased at above $10^{\circ}C$, while Salmonella in egg white did not proliferate at all temperature. These results demonstrate that there is a difference in the growth of Salmonella depending on the types of liquid eggs (egg yolk, egg white, liquid whole egg) and storage temperature. To fit the growth data of Salmonella in liquid whole egg and egg yolk, Baranyi model was used as the primary model and the maximum growth rate and lag phase duration for each temperature were determined. A secondary model was developed with maximum growth rate as a function of temperature. The model performance measures, bias factor ($B_f$, 0.96-0.99) and $r^2$ (0.96-0.99) indicated good fit for both primary and secondary models. In conclusion, it is thought that the growth model can be used usefully to predict Salmonella spp. growth in various types of unpasteurized liquid eggs when those are exposed to various temperature and time conditions during the processing.

• 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.