• Journal of Biosystems Engineering
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• v.33 no.1
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• pp.63-68
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• 2008

This study was carried out to find a useful method to predict hardness of tomato using optical spectrum data. Optical spectrum of reflectance and transmittance data were collected processed by 9 kind of preprocessing methods-normalizations of mean, maximum and range, SNV (standard normal variate), MSC (multiplicative scatter correction), the first derivative and second derivative of Savitzky-Golay and Norris-Gap. With the preprocessed and non-processed original spectrum data, prediction models of hardness of tomato were developed using analytical tools of PLS (partial least squares) and MLR (multiple linear regression) and tested for their validation. The test of validation resulted that the analytical tools of PLS and MLR output similar performances while the transmittance spectra showed much better result than the reflectance spectra.

• Journal of environmental and Sanitary engineering
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• v.17 no.4
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• pp.61-67
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• 2002

NIRS(Near infrared spectroscopy) scanning from 1300nm to 2400nm was appl ied for the DEHP(di-(2 ethylhexyl)phthalate) in PVC(polyvinyl chloride_packing material. All samples were devided into calibration group and validation group. As a result of conduction the multiple regression analysis on the correlation between the NIR spectrum data and chemical assay value obtained by the Korea Food Sanitation Act. The validation model for measuring the DEHP content had R of 0.997, SEC of 0.132, SEP of 0.176 by MLR and R of 0.996, SEC of 0.142, SEP of 0.198 by PLS and the detection limit was 0.1%. The obtained results indicate that the NIR procedure can potentially be used as a nondestructive analysis method for the purpose of rapid and simple measurement of DEHP in PVC packing material.

• The Journal of Industrial Distribution & Business
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• v.10 no.12
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• pp.7-14
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• 2019

Purpose: In Korea, win-win growth policy has been successfully implemented in supply chain and logistics management. In the policy, it is recommended to support supply chain partners with various mechanisms including financial and technical aids. This study attempts to scientifically analyze the effects of direct and indirect win-win growth policy factors on supply chain and logistics management performance through partnership factors. Research design, data and methodology: This study builds a structural equation model reflecting the relationship between the win-win growth policy, partnership and performance factors. The proposed model is verified with the PLS (Partial Least Squares regression) methodology. Data from shipper and logistics companies were collected and analyzed by the PLS model. Results: The analysis showed that both direct and indirect policy factors are meaningful to improve supply chain and logistics performance. Indirect support factors including R&D, management innovation, human resources development and educational supports have positive impacts on partnership factors. Direct support factors including financial aids and fairness also have positive impacts on the performance. Conclusions: This study is meaningful in that it suggests a turning point in which supply chain Win-win growth and partnership efforts are perceived as new value-creating mechanism rather than unilateral cost reduction for logistics industry.

• Journal of Biosystems Engineering
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• v.40 no.4
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• pp.335-344
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• 2015

Purpose: Soybean growth is adversely affected by environmental stresses such as drought, extreme temperatures, and nutrient deficiency. The objective of this study was to develop a method for rapid measurement of drought stress in soybean plants using a hyperspectral fluorescence imaging technique. Methods: Hyperspectral fluorescence images were obtained using UV-A light with 365 nm excitation. Two soybean cultivars under drought stress were analyzed. A partial least square regression (PLSR) model was used to predict drought stress in soybeans. Results: Partial least square (PLS) images were obtained for the two soybean cultivars using the results of the developed model during the period of drought stress treatment. Analysis of the PLS images showed that the accuracy of drought stress discrimination in the two cultivars was 0.973 for an 8-day treatment group and 0.969 for a 6-day treatment group. Conclusions: These results validate the use of hyperspectral fluorescence images for assessing drought stress in soybeans.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.51-61
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• 2020

The study aims to compare whether using Earnings Response Coefficient (ERC) is better than using the new concept of Accounting Earnings Response Coefficient (AERC) in determining the earnings quality response coefficient value. Also, the study seeks to explain the effect of company characteristics and corporate governance on AERC through voluntary disclosure and information asymmetry. Research samples include 69 manufacturing companies listed on the Indonesian Stock Exchange over the period 2014-2017. The data come from annual reports, stock market prices, CSPI, EPS, stock returns and market returns. The research model is tested using the structural equation model (SEM) with partial least square (PLS). The results showed the value of the earnings response coefficient produced by AERC and ERC was different. Earnings quality resulting from AERC regression by adding CFO values better reflects the actual earnings quality. These results are consistent with the concept built from the proposition about earnings quality at AERC, that quality earnings are informative accounting earnings. The theoretical findings of this study provide an explanation that operational cash flow plays a role in evaluating earnings quality, while providing reinforcement that the ERC regression model fails to detect stock market reactions to information relevant to the aggregated values of accounting earnings.

• Transactions of the Korean hydrogen and new energy society
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• v.25 no.5
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• pp.491-499
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• 2014

It is important to accurately measure and control the relative humidity of humidified gas entering a PEM (polymer electrolyte membrane) fuel cell stack because the level of humidification strongly affects the performance and durability of the stack. Humidity measurement devices can be used to directly measure the relative humidity, but they cost much to be equipped and occupy spaces in a fuel cell system. We present soft sensors for predicting the relative humidity without actual humidity measuring devices. By combining FIR (finite impulse response) model with PLS (partial least square) and SVM (support vector machine) regression models, DPLS (dynamic PLS) and DSVM (dynamic SVM) soft sensors were developed to correctly estimate the relative humidity of humidified gases exiting a planar-type membrane humidifier. The DSVM soft sensor showed a better prediction performance than the DPLS one because it is able to capture nonlinear correlations between the relative humidity and the input data of the soft sensors. Without actual humidity sensors, the soft sensors presented in this work can be used to monitor and control the humidity in operation of PEM fuel cell systems.

• YAKHAK HOEJI
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• v.49 no.2
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• pp.115-121
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• 2005

In this study, the performance of a portable NIR system and a FT NIR spectrometer were compared to determine water content of hairless mouse skin. The stratum corneum parts wer e separated from the epidermal tissues by trypsin solution. NIR diffuse reflectance spectra of hairless mouse skin were acquired using a fiber optic probe. In the near infrared, water molecules show two clear absorption bands at 1450 nm from first overtone of O-H stretching and 1940 nm from the combination involving O-H stretching and O-H deformation. It was found that the variations of O-H absorption band according to water content. Partial least squares regression (PLSR) was applied to develop a calibration model. The PLS model showed a good correlation between NIR predicted value and the absolute water content of separated hairless mouse skin, in vitro. For both the portable and the FT NIR spectrometer, These studies showed the possibility of a rapid and nondestructive skin moisture measurement using NIR spectroscopy. The portable NIR spectrometer with a photodiode arrays-microsensor could be more rapidly applied for the determination of water content with comparable accuracy with the performance of a FT spectrometer .

• Advances in environmental research
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• v.1 no.4
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• pp.257-276
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• 2012

QSAR models for chlorinated alkenes were developed between $E_{HOMO}$ and their chlorine and carbon content. The aim is to provide valid QSAR model which is statistically validated for $E_{LUMO}$ prediction. Different molecular descriptors, $N_{Cl}$, $N_C$ and $E_{HOMO}$ have been used to take into account relevant information provided by molecular features and physicochemical properties. The best model were selected using Partial Least Square (PLS) and Multiple Linear Regression (MLR) led to models with satisfactory predictive ability for a data set of 15 chlorinated alkene compounds.

• Journal of Biosystems Engineering
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• v.41 no.3
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• pp.273-280
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• 2016

Purpose: This experiment was conducted to detect water stress in terms of the moisture content of cucumber seedlings under water stress condition using a hyperspectral image acquisition system, linear regression analysis, and partial least square regression (PLSR) to achieve a non-destructive measurement procedure. Methods: Changes in the reflectance spectrum of cucumber seedlings under water stress were measured using hyperspectral imaging techniques. A model for estimating moisture content of cucumber seedlings was constructed through a linear regression analysis that used the moisture content of cucumber seedlings and a normalized difference vegetation index (NDVI). A model using PLSR that used the moisture content of cucumber seedlings and reflectance spectrum was also created. Results: In the early stages of water stress, cucumber seedlings recovered completely when sub-irrigation was applied. However, the seedlings suffering from initial wilting did not recover when more than 42 h passed without irrigation. The reflectance spectrum of seedlings under water stress decreased gradually, but increased when irrigation was provided, except for the seedlings that had permanently wilted. From the results of the linear regression analysis using the NDVI, the model excluding wilted seedlings with less than 20% (n=97) moisture content showed a precision ($R^2$ and $R^2_{\alpha}$) of 0.573 and 0.568, respectively, and accuracy (RE) of 4.138% and 4.138%, which was higher than that for models including all seedlings (n=100). For PLS regression analysis using the reflectance spectrum, both models were found to have strong precision ($R^2$) with a rating of 0.822, but accuracy (RMSE and RE) was higher in the model excluding wilted seedlings as 5.544% and 13.65% respectively. Conclusions: The estimation model of the moisture content of cucumber seedlings showed better results in the PLSR analysis using reflectance spectrum than the linear regression analysis using NDVI.

• Journal of the Korean Wood Science and Technology
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• v.43 no.3
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• pp.311-319
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• 2015

Near infrared (NIR) reflectance spectroscopy was employed to develop moisture content prediction model of pitch pine (Pinus rigida), red pine (Pinus densiflora), Korean pine (Pinus koraiensis), yellow poplar (Liriodendron tulipifera) wood below fiber saturation point. NIR reflectance spectra of specimens ranging from 1000 nm to 2400 nm were acquired after humidifying specimens to reach several equilibrium moisture contents. To determine the optimal moisture contents prediction model, 5 mathematical preprocessing methods (moving average (smoothing point: 3), baseline, standard normal variate (SNV), mean normalization, Savitzky-Golay $2^{nd}$ derivatives (polynomial order: 3, smoothing point: 11)) were applied to reflectance spectra of each specimen as 8 combinations. After finishing mathematical preprocessings, partial least squares (PLS) regression analysis was performed to each modified spectra. Consequently, the mathematical preprocessing methods deriving optimal moisture content prediction were 1) moving average/SNV for pitch pine and red pine, 2) moving average/SNV/Savitzky-golay $2^{nd}$ derivatives for Korean pine and yellow poplar. Every model contained three principal components.