• Food Science of Animal Resources
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• v.28 no.2
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• pp.232-239
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• 2008

The objective of this study is to estimate the shelf-life of Tteokgalbi containing dietary fiber extracted from rice bran by using the predictive microbiology. This Tteokgalbi was made with 0%, 1%, 2%, and 3% dietary fiber. The number of total viable cells, anaerobic, psychrotrophic, and heat-stable bacteria and coliforms was calculated during 15 days of storage under $4{\pm}1^{\circ}C$ and the obtained data was applied to Baranyi function. The evaluation of fitness between predicted and observed data showed that these were matched in a satisfactory way. Heat-stable bacteria was detected lower than <1 log CFU/g and coliforms were not detected during the storage. The changes of total viable cells and psychrotrophic bacteria in Tteokgalbi were increased gradually, but dramatically increased after 3 days of storage. The models of total viable cells and anaerobic bacteria showed very similar growth trends and values of growth parameters each other. The estimated shelf-life of each Tteokgalbi was calculated from the predictive model of total viable cells and the estimated shelf-life was 1.7, 2.3, 2.3, and 2.4 days, respectively. The results suggested that the prediction of bacteria growth could be used to evaluate the microbiological safety and determine the shelf-life of Tteokgalbi as ready-to-eat food in the local market.

• Journal of Food Hygiene and Safety
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• v.32 no.3
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• pp.206-210
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• 2017

This study was performed to develope mathematical models for predicting growth kinetics of Staphylococcus aureus in the processed meat product, pyeonyuk. Growth patterns of S. aureus in pyeonyuk were determined at the storage temperatures of 4, 10, 20, and $37^{\circ}C$ respectively. The number of S. aureus in pyeonyuk increased at all the storage temperatures. The maximum specific growth rate (${\mu}_{max}$) and lag phase duration (LPD) values were calculated by Baranyi model. The ${\mu}_{max}$ values went up, while the LPD values decreased as the storage temperature increased from $4^{\circ}C$ to $37^{\circ}C$. Square root model and polynomial model were used to develop the secondary models for ${\mu}_{max}$ and LPD, respectively. Root Mean Square Error (RMSE) was used to evaluate the developed model and the fitness was determind to be 0.42. Therefore the developed predictive model was useful to predict the growth of S. aureus in pyeonyuk and it will help to prevent food-born disease by expanding for microbial sanitary management guide.

• Food Science and Preservation
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• v.24 no.6
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• pp.778-785
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• 2017

This study was conducted to develop a predictive model for the growth of Escherichia coli strain RC-4-D isolated from red kohlrabi sprout seeds. We collected E. coli kinetic growth data during red kohlrabi seed sprouting under isothermal conditions (10, 15, 20, 25, and $30^{\circ}C$). Baranyi model was used as a primary order model for growth data. The maximum growth rate (${\mu}max$) and lag-phase duration (LPD) for each temperature (except for $10^{\circ}C$ LPD) were determined. Three kinds of secondary models (suboptimal Ratkowsky square-root, Huang model, and Arrhenius-type model) were compared to elucidate the influence of temperature on E. coli growth rate. The model performance measures for three secondary models showed that the suboptimal Huang square-root model was more suitable in the accuracy (1.223) and the suboptimal Ratkowsky square-root model was less in the bias (0.999), respectively. Among three secondary order model used in this study, the suboptimal Ratkowsky square-root model showed best fit for the secondary model for describing the effect of temperature. This model can be utilized to predict E. coli behavior in red kohlrabi sprout production and to conduct microbial risk assessments.

• Preventive Nutrition and Food Science
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• v.13 no.4
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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.

• Preventive Nutrition and Food Science
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• v.13 no.2
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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.

• Food Science and Biotechnology
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• v.27 no.6
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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.

• Journal of the FoodService Safety
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• v.2 no.2
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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.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.7
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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.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.5
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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 $20^{\circ}C$.

• Food Science of Animal Resources
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• v.38 no.3
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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.