• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.spc
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• pp.108-114
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• 2008

Near infrared spectroscopy (NIRS) is a rapid and accurate analytical method for determining the composition of agricultural products and feeds. The applicability of near infrared reflectance spectroscopic method was tested to determine the color value (L, a, b) of green tea. A total of 162 green tea calibration samples and 82 validation samples were used for NIRS equation development and validation, respectively. In the developed NIRS equation for analysis of the color value (L, a, b), the most accurate equation for L value was obtained at 2, 8, 6, 1 (2nd derivative, 8 nm gap, 6 points smoothing, and 1pointsecond smoothing), and for a, and b value were obtained at 1, 4, 4, 1 (1st derivative, 4 nm gap, 4points smoothing, and 1 point second smoothing) math treatment condition with SNVD (Standard Normal Variate and Detrend) scatter correction method and entire spectrum ($400{\sim}2,500\;nm$) by using MPLS (Modified Partial Least Squares) regression. Validation results of these NIRS equations showed very low bias (L: 0.005%, a: 0.003%, b: -0.013%) and standard error of prediction (SEP, L: 0.361%, a: 0.141%, b: 0.306%) as well as high coefficient of determination ($R^2$, L: 0.905, a: 0.986, b: 0.931). Therefore, these NIRS equations can be applicable and reliable for determination of color value (L, a, b) of green tea, and NIRS method could be used as a mass screening technique for breeding programs and quality control in the green tea industry.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.6
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• pp.522-528
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• 2009

This study focused on establishing prediction models using visible-near infrared spectrum to simultaneously detect multiple components of soils and enhancing the performance quality by suitably transformed input spectra and classification of soil spectral types for prediction model input. The continuum-removed spectra showed significant result for all cases in terms of soil properties and classified or bulk predictions. The prediction model using classified soil spectra at an absorption peak area around 500nm and 950nm efficiently indicating soil color showed slightly better performance. Especially, Ca and CEC were well estimated by the classified prediction model at $R^{2}$ > 0.8. For organic carbon, both classified and bulk prediction model had a good performance with $R^{2}$ > 0.8 and RPD> 2. This prediction model may be applied in global soil mapping, soil classification, and remote sensing data analysis.

• Journal of Animal Science and Technology
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• v.45 no.6
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• pp.885-890
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• 2003

This study was conducted to estimate the growth curve parameters of 253 heads of F2 population produced by inter-crossing F1 from Korean Native boars and Landrace sows, and to estimate the effects of Leptin receptor gene(LEPR) on their growth characteristics. Growth curve parameters were estimated from nonlinear regression using Gompertz model individually. Average mature weight and average maturing rate estimated were 179.69${\pm}$4.40kg and 0.3103${\pm}$0.0043, respectively. The effect of sex was insignificant for all the parameters estimated from Gempertz model(p〉.05), and the effect of calving group was significant for mature weight and maximum growth rate at inflection point (p〈.05). The effect of LEPR genotype were significant for all the growth curve parameters(p〈.05). According from the results of the least squares means of growth curve parameters by LEPR genotypes, mature weight and point of inflection were highest in genotype AA in which the maturing rate was the lowest, and were lowest in genotype DD in which maturing rate was the highest, reversely.

• Journal of the Korean Geographical Society
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• v.51 no.4
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• pp.509-523
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• 2016

The soil prediction can provide quantitative soil information for sustainable mountainous ecosystem management. Visible near infrared spectroscopy, one of soil prediction methods, has been applied to predict several soil properties with effective costs, rapid and nondesctructive analysis, and satisfactory accuracy. Spectral preprocessing is a essential procedure to correct noisy spectra for visible near infrared spectroscopy. However, there are no attempts to evaluate various spectral preprocessing methods. We tested 5 different pretreatments, namely continuum removal, Savitzky-Golay filter, discrete wavelet transform, 1st derivative, and 2nd derivative to predict soil carbon(C) and nitrogen(N). Partial least squares regression was used for the prediction method. The total of 153 soil samples was split into 122 samples for calibration and 31 samples for validation. In the all range, absorption was increased with increasing C contents. Specifically, the visible region (650nm and 700nm) showed high values of the correlation coefficient with soil C and N contents. For spectral preprocessing methods, continuum removal had the highest prediction accuracy(Root Mean Square Error) for C(9.53mg/g) and N(0.79mg/g). Therefore, continuum removal was selected as the best preprocessing method. Additionally, there were no distinct differences between Savitzky-Golay filter and discrete wavelet transform for visual assessment and the methods showed similar validation results. According to the results, we also recommended Savitzky-Golay filter that is a simple pre-treatment with continuum removal.

• KDI Journal of Economic Policy
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• v.28 no.2
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• pp.131-177
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• 2006

This paper examines a simultaneous determination of corporate control mechanisms, and its effects on firm performance. The corporate control mechanisms considered include the following; insider shareholding, institutional shareholding, the board of directors, dividend policy, and capital structure. This paper applies a simultaneous equation methodology and investigates the interdependence among the corporate control mechanisms. In the first part, the paper finds that firm-level variations of control mechanisms are large across time although average variations are relatively small. These variations are related to one another, which is confirmed by Granger causality test based on dynamic panel autoregression model. More specifically insider shareholding, institutional shareholding and outside director ratio cause each other. With regard to interdependence among the control mechanisms, 2SLS(two stage least squares) regression results show that insider shareholding and institutional shareholding are substitutes while institutional shareholding acts as complements to the ratio of outside members in the board of directors. Then in the second part, the paper examines the relationship between firm performance and corporate governance. Firm performance, measured by Tobin's Q, has a positive association with leverage ratio while that has a negative relation to outside director ratio. This suggests that there may be a room for reforming corporate governance in Korea. Specifically it is necessary to enhance the independence of the outside directors.

• Journal of Korean Society of Transportation
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• v.25 no.6
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• pp.141-157
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• 2007

This paper evaluates the effects of privatization and deregulation on the firm-specific efficiency and total factor productivity (TFP) growth in the Korean and Japanese railways. Using a stochastic frontier approach and a generalized translog functional form, the paper specifies the equation system consisting of a multiproduct variable cost function and input share equations which is estimated with Zellner's iterative seemingly unrelated regression and the corrected least squares method. The Korean and Japanese railway firms are assumed to produce three outputs (Shinkansen passenger-kilometers, incumbent railway passenger-kilometers, ton-kilometers of freight) using three input factors (labor, fuel, maintenance and rolling stock). A monetary value of the ways and fixed installations held by the railroad firm is also included as a quasi-fixed input. The empirical results indicate that the average estimate of cost inefficiency is 2.57% for the total sample and on the average, JNR and JR Kyushu are found to be worst efficient while the most efficient railway firm in the sample is JR West. Also the cost efficiency levels of seven JRs have been improved after the reform and privatization of JNR. The findings also indicate that TFP growth of the privately-owned JRs are higher than those of the government-owned KNR and JNR. Three-island JRs and JR Freight have slightly higher TFP growth than Honshu JRs as well. Thus, the results suggest that managerial autonomy and increased competition via deregulation have improved efficiency and TFP growth.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1527-1527
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• 2001

Glucose, fructose and sucrose being the main sugars that can be found in natural fruit juice. Many instrumental methods, such as GC, LC, electrochemical or spectrometric methods provide information about both the total content of sugars and the specific concentration of each carbohydrate[1]. The simplicity of sample handling and measurement in the near IR(NIR) wavelength region, which allows the use of long pathlength, optical glass cells and optical fibers, makes NIR a good alternative for sugar determination [2]. In the present study, six varieties of persian grapes were harvested at intervals through august to october and analysed for sugars by NIR. The results were processed by principal component regression (PCR) and partial least squares (PLS) analysis. Sample juice was prepared by squeezing through gauze from crashed grape. This solution was treated by zinc ferrocyanide prior to analysis in order to eliminate colored compounds and all optically active nonsugar substances. For glucose and fructose the most characteristic wavelengths were 1456nm corresponding to the first harmonic O-H stretching and the second at 2062nm corresponding to O-H stretching and deformation; secondary characteristic combination bands were also seen at 2265 nm (O-H and C-C stretching) and at 2240 nm (C-H and C-C stretching). However these spectra were taken over a wavelength range from 1100-2500nm at room temperature of 25-$30^{\circ}C$. To test the accuracy of the described procedure, samples of six varieties of grape were analysed by the proposed NIR and a standard method[2]. Good agreement were found between these two sets of the results. To perform the recovery studies , samples of grape juices previously analysed by the proposed method, were spiked with known amounts of each individual sugars and then analysed again. Relative standard deviations varied from 1.4 to 1.8% for six independent measurements of individual and total sugar concentration. In the analysis of real and synthetic samples, precise and accurate results were obtained , providing accuracy errors lower than 1.9% in all cases. Average recoveries of ${97}{\pm}{4%}$ for total sugar and between ${95}{\pm}{5%}$ and ${99}{\pm}{2%}$ for sing1e sugars demonstrate the applicability of the methodology developed to the direct analysis of grape Juice.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1121-1121
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• 2001

A previous study (Berzaghi et al., 2001) evaluated the performance of 3 calibration methods, modified partial least squares (MPLS), local PLS (LOCAL) and artificial neural networks (ANN) on the prediction of the chemical composition of forages, using a large NIR database. The study used forage samples (n=25,977) from Australia, Europe (Belgium, Germany, Italy and Sweden) and North America (Canada and U.S.A) with reference values for moisture, crude protein and neutral detergent fibre content. The spectra of the samples were collected using 10 different Foss NIR Systems instruments, only some of which had been standardized to one master instrument. The aim of the present study was to evaluate the behaviour of these different calibration methods when predicting the same samples measured on different instruments. Twenty-two sealed samples of different kind of forages were measured in duplicate on seven instruments (one master and six slaves). Three sets of near infrared spectra (1100 to 2500nm) were created. The first set consisted of the spectra in their original form (unstandardized); the second set was created using a single sample standardization (Clone1); the third was created using a multiple sample procedure (Clone6). WinISI software (Infrasoft International Inc., Port Mathilda, PA, USA) was used to perform both types of standardization, Clone1 is just a photometric offset between a “master” instrument and the “slave” instrument. Clone6 modifies both the X-axis through a wavelength adjustment and the Y-axis through a simple regression wavelength by wavelength. The Clone1 procedure used one sample spectrally close to the centre of the population. The six samples used in Clone 6 were selected to cover the range of spectral variation in the sample set. The remaining fifteen samples were used to evaluate the performances of the different models. The predicted values for dry matter, protein and neutral detergent fibre from the master Instrument were considered as “reference Y values” when computing the statistics RMSEP, SEPC, R, Bias, Slope, mean GH (global Mahalanobis distance) and mean NH (neighbourhood Mahalanobis distance) for the 6 slave instruments. From the results we conclude that i) all the calibration techniques gave satisfactory results after standardization. Without standardization the predicted data from the slaves would have required slope and bias correction to produce acceptable statistics. ii) Standardization reduced the errors for all calibration methods and parameters tested, reducing not only systematic biases but also random errors. iii) Standardization removed slope effects that were significantly different from 1.0 in most of the cases. iv) Clone1 and Clone6 gave similar results except for NDF where Clone6 gave better RMSEP values than Clone1. v) GH and NH were reduced by half even with very large data sets including unstandardized spectra.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.15 no.1
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• pp.74-82
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• 1982

In the previous study, we have reported the effect of lowering water activity of sodium chloride, sugars and polyols. In this study, water activity($a_w$)of various model Preparations of Kamaboko was measured and ability of lowering $a_w$ of starch, glycine and sodium lactate was discussed. In addition, the aw measurements were also compared to the predicted values of au which were derived from the equation of Raoult's law and the linear slope method by Sloan ana Labuza. The effect of starch on lowering $a_w$ was very low, and glycine was more effective than starch. Sodium lactate was shown to be the most effective among humectants examined. When it was added by $7.5\%$, the $a_w$ of model Kamaboko A was reduced to 0.93-0.94. Generally, prediction by the equation of Raoult's law turned out to be useful for determining $a_w$ lowering effect of humectants, excepting sodium chloride and sodium lactate. The equation of regression derived from the least squares could be applied to Predicting au in the model Kamaboko.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.233-242
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• 2018

This study explored the richness patterns of vascular plant species and evaluated the effects of the climatic and habitat variables on the observed patterns along elevational gradients on the Mt. Baekhwa, South Korea. Plant data were recorded from 70 plots and a total of 187 plant species with 78 woody and 109 herbaceous species were recorded along two study transects, the Banyasa and Bohyunsa transects, on the Mt. Baekhwa. A total of 154 plant species with 66 woody and 88 herbaceous species and 131 plant species with 58 woody and 73 herbaceous species were recorded along the Banyasa and Bohyunsa transects, respectively. We used simple ordinary least squares regression model, multi-model inference and variation partitioning to analyze the relative contribution of climatic and habitat variables on the elevational richness patterns. Species richness pattern for vascular plants along the Banyasa transect monotonically decreased with elevation, whereas plant species richness showed reversed hump-shaped pattern along the Bohyunsa transect. Although the elevational patterns of species richness for vascular plants were different between the both transects, habitat variables are more important predictors than climatic variables for the elevational patterns of plant species richness along our study transects on the Mt. Baekhwa. These results indicate that elevational diversity patterns of vascular plants may be different even between nearby elevational transects in a mountain ecosystem but the diversity patterns may be controlled by same drivers.