• Food Science and Preservation
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• v.12 no.6
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• pp.608-612
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• 2005

Fourier transform-near infrared (FT-NIR) spectroscopy is a simple, rapid, non-destructive technique which can be used to make quantitative analysis of chemical composition in grain. An interest in total dietary fiber (TDF) of grain such as rice has been increased due to its beneficial effects for health. Since measuring methods for TDF content were highly depending on experimental technique and time consumptions, the application of FT-NIR spectroscopy to determine TDF content in milled rice. Results of enzymatic-gravimetric method were $1.17-1.92\%$ Partial least square (PLS) regression on raw NIR spectra to predict TDF content was developed Accuracy of prediction model for TDF content was certified for regression coefficient (r), standard error of estimation (SEE) and standard error of prediction (SEP). The r, SEE and SEP were 0.9705, 0.0464, and 0.0604, respectively. The results indicated that FT-NIR techniques could be very useful in the food industry and rice processing complex for determination of TDF in milled rice on real time analysis.

• Nutrition Research and Practice
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• v.9 no.1
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• pp.71-78
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• 2015

BACKGROUND/OBJECTIVES: The purposes of this study were to compare total energy expenditure (including PAL and RMR) of Korean farmers between the farming season and off farming season and to assess the accuracy of estimated energy requirement (EER) prediction equation reported in KDRIs. SUBJECTS/METHODS: Subjects were 72 Korean farmers (males 23, females 49) aged 30-64 years. Total energy expenditure was calculated by multiplying measured RMR by PAL. EER was calculated by using the prediction equation suggested in KDRIs 2010. RESULTS: The physical activity level (PAL) was significantly higher (P < 0.05) in the farming season (male $1.77{\pm}0.22$, female $1.69{\pm}0.24$) than the off farming season (male $1.53{\pm}0.32$, female $1.52{\pm}0.19$). But resting metabolic rate was significantly higher (P < 0.05) in the off farming season (male $1,890{\pm}233kcal/day$, female $1,446{\pm}140kcal/day$) compared to the farming season (male $1,727{\pm}163kcal/day$, female $1,356{\pm}164kcal/day$). TEE ($2,304{\pm}497kcal/day$) of females was significantly higher in the farming season than that ($2,183{\pm}389kcal/day$) of the off farming season, but in males, there was no significant difference between two seasons in TEE. On the other hand, EER of male and female ($2,825{\pm}354kcal/day$ and $2,115{\pm}293kcal/day$) of the farming season was significantly higher (P < 0.05) than those ($2,562{\pm}339kcal/day$ and $1,994{\pm}224kcal/day$) of the off farming season. CONCLUSIONS: This study indicates that there is a significant difference in PAL and TEE of farmers between farming and off farming seasons. And EER prediction equation proposed by KDRI 2010 underestimated TEE, thus EER prediction equation for farmers should be reviewed.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.347-353
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• 2022

In the restaurant industry, start-ups are active due to high demand from consumers and low entry barriers. However, the restaurant industry has a high closure rate, and in the case of franchises, there is a large deviation in sales within the same brand. Thus, research is needed to prevent the closure of food franchises. Therefore, this study examines the factors affecting franchise sales and uses machine learning techniques to predict the success and failure of franchises. Various factors that affect franchise sales are extracted by using Point of Sale (PoS) data of food franchise and public data in Gangnam-gu, Seoul. And for more valid variable selection, multicollinearity is removed by using Variance Inflation Factor (VIF). Finally, classification models are used to predict the success and failure of food franchise stores. Through this method, we propose success and failure prediction model for food franchise stores with the accuracy of 0.92.

• Food Science and Preservation
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• v.4 no.2
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• pp.123-130
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• 1997

To develop the prediction program for quality change of Citrus unshiu during marketing, we examined the quality characteristics of Citrus unshiu stored at experimental refrigerator set to 4, 8, 12 and 16$^{\circ}C$ for 2 months. According to the storage temperature the changes of quality characteristics were different respectively, but it was most severe during 16$^{\circ}C$ storage. Activation energy and Q10 value were 6683.16 cal/mol K and 1.53 respectively. The determination coefficient of regression equation of pH, acidity and vitamin C by surface response analysis were over 0.85. Using these regression equation, we developed the prediction program for the change of pH, acidity and vitamin C contents. The calculated values and experimental values of pH, acidity and vitamin C contents for short-term storage of Citrus unshiu were coincided well.

• Journal of Microbiology
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• v.44 no.2
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• pp.137-144
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• 2006

The problems associated with gene identification and the prediction of gene structure in DNA sequences have been the focus of increased attention over the past few years with the recent acquisition by large-scale sequencing projects of an immense amount of genome data. A variety of prediction programs have been developed in order to address these problems. This paper presents a review of the computational approaches and gene-finders used commonly for gene prediction in eukaryotic genomes. Two approaches, in general, have been adopted for this purpose: similarity-based and ab initio techniques. The information gleaned from these methods is then combined via a variety of algorithms, including Dynamic Programming (DP) or the Hidden Markov Model (HMM), and then used for gene prediction from the genomic sequences.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.207-209
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• 2022

In the IoT environment, it is possible to collect life pattern data by recognizing human physical activity from smart devices. In this paper, the proposed model consists of a prediction stage and a recommendation stage. The prediction stage predicts the scale of anxiety and depression by using logistic regression and k-nearest neighbor algorithm through machine learning on the dataset collected from life pattern data. In the recommendation step, if the symptoms of anxiety and depression are classified, the principal component analysis algorithm is applied to recommend food and light exercise that can improve them. It is expected that the proposed anxiety/depression prediction and food/exercise recommendations will have a ripple effect on improving the quality of life of individuals.

• Nutrition Research and Practice
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• v.11 no.1
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• pp.51-56
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• 2017

BACKGROUND/OBJECTIVES: The purpose of this study was to assess the accuracy of a dietary reference intake (DRI) predictive equation for estimated energy requirements (EER) in female college tennis athletes and non-athlete students using doubly labeled water (DLW) as a reference method. MATERIALS/METHODS: Fifteen female college students, including eight tennis athletes and seven non-athlete subjects (aged between 19 to 24 years), were involved in the study. Subjects' total energy expenditure (TEE) was measured by the DLW method, and EER were calculated using the DRI predictive equation. The accuracy of this equation was assessed by comparing the EER calculated using the DRI predictive equation ($EER_{DRI}$) and TEE measured by the DLW method ($TEE_{DLW}$) based on calculation of percentage difference mean and percentage of accurate prediction. The agreement between the two methods was assessed by the Bland-Altman method. RESULTS: The percentage difference mean between the methods was -1.1% in athletes and 1.8% in non-athlete subjects, whereas the percentage of accurate prediction was 37.5% and 85.7%, respectively. In the case of athletic subjects, the DRI predictive equation showed a clear bias negatively proportional to the subjects' TEE. CONCLUSIONS: The results from this study suggest that the DRI predictive equation could be used to obtain EER in non-athlete female college students at a group level. However, this equation would be difficult to use in the case of athletes at the group and individual levels. The development of a new and more appropriate equation for the prediction of energy expenditure in athletes is proposed.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.21 no.6
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• pp.361-365
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• 1988

Thermal diffusivities of white muscled fish meat paste products were measured and an experimental equation for prediction of the thermal diffusivity was suggested. The thermal diffusivities of products with water contents of 43.03 to $82.49\%$ and lipid contents of 0.50 to $14.88\%$ could be deduced as following equations ; $$\alpha_{80.39^{\circ}C}=0.0832{\cdot}10^{-6}{\cdot}X_w+0.0797{\cdot}10^{-6},\;m^2{\cdot}s^{-1}$$ $$\alpha_{100.63^{\circ}C}=0.0873{\cdot}10^{-6}{\cdot}X_w+0.0830{\cdot}10^{-6},\;m^2{\cdot}s^{-1}$$ $$\alpha_{120.09^{\circ}C}=0.0842{\cdot}10^{-6}{\cdot}X_w+0.0901{\cdot}10^{-6},\;m^2{\cdot}s^{-1}$$ From these equations, an experimental equation was derived for the prediction of thermal diffusivities of white muscled fish meat paste products ; $$\alpha=(1.308+0.1324{\cdot}X_w){\cdot}\alpha_w-0.0626{\cdot}10^{-6}{\cdot}X_w-0.1355{\cdot}10^{-6},\;m^2{\cdot}s^{-1}$$ The errors of the thermal diffusivities predicted with this equation were less than ${\pm}\;0.30\%$ compared with those measured.

• Korean Journal of Food Science and Technology
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• v.35 no.2
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• pp.211-216
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• 2003

Absorption characteristics of hot air-. vacuum-, and freeze-dried ginger powder were investigated. Monolayer moisture content as determined by GAB equation was $0.257{\sim}0.540\;H_2O/g$, showing higher significance than BET equation. Absorption enthalpy was calculated based on different drying methods and water activities. Absorption energy decreased with increasing water activity but was not affected by drying method. Isotherm curves showed a typical sigmoid form. Among models applied for predicting equilibrium moisture content, Caurie model was the best fit model for ginger powder, showing the lowest prediction deviation of $1.2{\sim}5.4%$, followed by Henderson then Bradley models. The prediction model equations for the moisture content were established by in(time), water activity, and temperature.

• Korean Journal of Food Science and Technology
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• v.23 no.5
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• pp.605-609
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• 1991

The physical properties which must be considered as engineering factors affecting on the evaporation process of fruit juices are boiling point rise, density, viscosity, thermal conductivity and specific heat. These factors are varied with food ingredients, soluble solids, pressure and temperature. In the reserch, it has been worked to obtain the data and to develop prediction model for the boiling point rise as a faction of soluble solid and pressure by the regression of SPSS package program. For the prediction model of density, it was developed as a fuction of soluble solid content on apple and pear juices. For the viscosity model, it was establised by the factors of temperature and content of soluble solid through the optimization program.