• Korean Chemical Engineering Research
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• v.51 no.1
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• pp.87-92
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• 2013

Microalgae have been suggested as a promising feedstock for producing biofuel because of their potential of lipid production. In this study, a first principles ODE model for microalgae growth and neutral lipid synthesis proposed by Surisetty et al. (2010) is investigated for the purpose of maximizing the rate of microalgae growth and the amount of neutral lipid. The model has 6 states and 12 parameters and follows the assumption of Droop model which explains the growth as a two-step phenomenon; the uptake of nutrients is first occurred in the cell, and then use of intra-cellular nutrient to support cells growth. In this study, optimal input design using D-optimality criterion is performed to compute the system input profile and sensitivity analysis is also performed to determine which parameters have a negligible effect on the model predictions. Furthermore, model predictive control based on successive linearization is implemented to maximize the amount of neutral lipid contents.

• Korean Journal of Organic Agriculture
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• v.25 no.2
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• pp.279-293
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• 2017

This study suggests the yield forecast models for autumn chinese cabbage and radish using crop growth and development information. For this, we construct 24 alternative yield forecast models and compare the predictive power using root mean square percentage errors. The results shows that the predictive power of model including crop growth and development informations is better than model which does not include those informations. But the forecast errors of best forecast models exceeds 5%. Thus it is important to establish reliable data and improve forecast models.

• Journal of the Korea Computer Graphics Society
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• v.23 no.4
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• pp.21-29
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• 2017

Previous visual analytics researches has focused on reducing the uncertainty of predicted results using a variety of interactive visual data exploration techniques. The main purpose of the interactive search technique is to reduce the quality difference of the predicted results according to the level of the decision maker by understanding the relationship between the variables and choosing the appropriate model to predict the unknown variables. However, it is difficult to create a predictive model which forecast time series data whose overall trends is unknown such as youth physical growth data. In this paper, we pro pose a novel predictive analysis technique to forecast the physical growth value in small pieces of time series data with un certain trends. This model estimates the distribution of data at a particular point in time. We also propose a visual analytics system that minimizes the possible uncertainties in predictive modeling process.

• 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.

• Journal of the Korean Dietetic Association
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• v.25 no.1
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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.

• Food Science of Animal Resources
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• v.29 no.1
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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.

• Food Science and Biotechnology
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• v.17 no.3
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• pp.642-650
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• 2008

A mathematical model was developed for predicting the growth rate of Enterobacter sakazakii in tryptic soy broth medium as a function of the combined effects of temperature (5, 10, 20, 30, and $40^{\circ}C$), pH (4, 5, 6, 7, 8, 9, and 10), and the NaCl concentration (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10%). With all experimental variables, the primary models showed a good fit ($R^2=0.8965$ to 0.9994) to a modified Gompertz equation to obtain growth rates. The secondary model was 'In specific growth $rate=-0.38116+(0.01281^*Temp)+(0.07993^*pH)+(0.00618^*NaCl)+(-0.00018^*Temp^2)+(-0.00551^*pH^2)+(-0.00093^*NaCl^2)+(0.00013^*Temp*pH)+(-0.00038^*Temp*NaCl)+(-0.00023^*pH^*NaCl)$'. This model is thought to be appropriate for predicting growth rates on the basis of a correlation coefficient (r) 0.9579, a coefficient of determination ($R^2$) 0.91, a mean square error 0.026, a bias factor 1.03, and an accuracy factor 1.13. Our secondary model provided reliable predictions of growth rates for E. sakazakii in broth with the combined effects of temperature, NaCl concentration, and pH.

• Korean Journal of Food Science and Technology
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• v.37 no.5
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• pp.850-855
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• 2005

Model was developed to predict the growth of Listeria monocytogenes in raw pork. Experiment condition for model development was full 5-by-7 factorial arrangements of temperature (0, 5, 10, 15, and $20^{\circ}C$) and time (0, 1, 2, 3, 18, 48, and 120 hr). Gompertz values A, C, B, and M, and growth kinetics, exponential growth rate (EGR), generation time (GT), lag phase duration (LPD), and maximum population density (MPD) were calculated based on growth increased data. GT and LPD values gradually decreased, whereas EGR value gradually increased with increasing temperature. Response surface analysis (RSA) was carried out using Gompertz B and M values, to formulate equation with temperature being main control factor. This equation was applied to Gompertz equation. Experimental and predictive values for GT, LPD, and EGR, compared using the model, showed no significant differences (p<0.01). Proposed model could be used to predict growth of microorganisms for exposure assessment of MRA, thereby allowing more informed decision-making on potential regulatory actions of microorganisms in raw pork.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.13 no.6
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• pp.284-289
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• 2003

The electric furnace, inside which the desired temperature is kept by the generated heat, is known to be a difficult system to control and model exactly because system parameters and response delayed time are varied as the temperature and positions are changed. In this study, the GPCEW (generalized predictive control with exponential weight), which always guarantees the stability of the closed loop system and can be effectively applied to the internally unstable system, was introduced to the ceramic drying electric furnace and was verified by showing its temperature tracking performance experimentally.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.53 no.5
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• pp.699-706
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• 2020

Predictive models in food microbiology are used for predicting microbial growth or death rates using mathematical and statistical tools considering the intrinsic and extrinsic factors of food. This study developed predictive growth models for Bacillus cereus on dried laver Pyropia pseudolinearis stored at different temperatures (5, 10, 15, 20, and 25℃). Primary models developed for specific growth rate (SGR), lag time (LT), and maximum population density (MPD) indicated a good fit (R²≥0.98) with the Gompertz equation. The SGR values were 0.03, 0.08, and 0.12, and the LT values were 12.64, 4.01, and 2.17 h, at the storage temperatures of 15, 20, and 25℃, respectively. Secondary models for the same parameters were determined via nonlinear regression as follows: SGR=0.0228-0.0069*T₁+0.0005*T₁²; LT=113.0685-9.6256*T₁+0.2079*T₁²; MPD=1.6630+0.4284*T₁-0.0080*T₁² (where T₁ is the storage temperature). The appropriateness of the secondary models was validated using statistical indices, such as mean squared error (MSE<0.01), bias factor (0.99≤B_f≤1.07), and accuracy factor (1.01≤A_f≤1.14). External validation was performed at three random temperatures, and the results were consistent with each other. Thus, these models may be useful for predicting the growth of B. cereus on dried laver.