• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.6
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• pp.3931-3943
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• 2014

Domestic industrial complexes have more loss competitiveness due to their deterioration length and environmental problems. Therefore, it is necessary to suggest the direction of realignment as advanced industrial estates and establish new alternative plans for improving the quality of public and environmental designs as well as reinforcing the competitive power. This study examined the design planning factors affecting the service users' satisfaction in the Seoul Digital Industrial Complex through a PLS regression model. The research result showed that 12 crucial design planning factors out of a total 31 planning factors have a more than 1.0 VIP. In addition, 8 comparatively important planning aspects that were measured between 0.9 and 1.0 were also investigated. These factors were the strategic design planning factors estimating the quantitative priority while enforcing the design improvement project and they should be considered as a useful material for strengthening the competitive power of the Seoul Digital Industrial Complex.

• Journal of Environmental Science International
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• v.33 no.3
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• pp.193-203
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• 2024

This study aims to determine the factors affecting the level of satisfaction with the Cell Broadcast Service (CBS) among citizens in Incheon. Partial least squares (PLS) regression, instead of multiple regression, was used for the analysis because it can solve multicollinearity and sample size issues. The analysis results are as follows: The factor with the greatest effect on satisfaction with CBS among Incheon citizens, was the elimination of redundancies (VIP=1.185). Therefore, local governments, government agencies, and public organizations must coordinate their ideas and collectively create guidelines to eliminate redundancies. The second most influential factor was the expansion in the broadcast medium from legal, institutional, and policy aspects (VIP=1.087). This is because differences in generation, age, gender, and personal characteristics were not considered. Therefore, it is necessary to devise a customized messaging tool through the expansion of broadcast media. The broadcast criteria of the legal, institutional, and policy perspectives comprised the third most influential factor, with a high VIP value of 1.053. Consequently, it is essential to devise a plan to avoid distributing unnecessary cell broadcast services, by establishing criteria for areas and sections, time, and the direct and indirect impact zones of a disaster. In the future, this study could be used as base data to develop policies, guidelines, and response measures for Incheon CBS. Given the lack of research on the diverse characteristics of each social class and the city traits of each region, and a lack of concrete empirical research on each factor, continuous and in-depth studies are required in the future.

• Korean Chemical Engineering Research
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• v.53 no.2
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• pp.236-242
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• 2015

We present two data-driven modeling methods, partial least square (PLS) and artificial neural network (ANN), to predict the major operating and performance variables of a polymer electrolyte membrane (PEM) fuel cell stack. PLS and ANN models were constructed using the experimental data obtained from the testing of a 30 kW-class PEM fuel cell stack, and then were compared with each other in terms of their prediction and computational performances. To reduce the complexity of the models, we combined a variables importance on PLS projection (VIP) as a variable selection method into the modeling procedure in which the predictor variables are selected from a set of input operation variables. The modeling results showed that the ANN models outperformed the PLS models in predicting the average cell voltage and cathode outlet temperature of the fuel cell stack. However, the PLS models also offered satisfactory prediction performances although they can only capture linear correlations between the predictor and output variables. Depending on the degree of modeling accuracy and speed, both ANN and PLS models can be employed for performance predictions, offline and online optimizations, controls, and fault diagnoses in the field of PEM fuel cell designs and operations.

• Food Science of Animal Resources
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• v.34 no.5
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• pp.700-708
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• 2014

The volatile composition of veal has yet to be reported and is one of the important factors determining meat character and quality. To identify the most important aroma compounds in veal from Holstein bull calves fed one of three diets, samples were subjected to solid-phase microextraction (SPME) combined with gas chromatography-quadrupole mass spectrometry (GC-MS). Most of the important odorants were aldehydes and alcohols. For group A (veal calves fed entirely on milk for 90 d before slaughter), the most abundant compound class was the aldehydes (52.231%), while that was alcohols (26.260%) in group C (veal calves fed starter diet for at least 60 d before slaughter). In both classes the absolute percentages of the volatile compounds in veal were different indicating that the veal diet significantly (p<0.05) affected headspace volatile composition in veal as determined by principal component analysis (PCA). Twenty three volatile compounds showed significance by using a partial least-squared discriminate analysis (PLS-DA) (VIP>1). The establishment of the global volatile signature of veal may be a useful tool to define the beef diet that improves the organoleptic characteristics of the meat and consequently impacts both its taste and economic value.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.6
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• pp.175-182
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• 2013

Lately, customers think highly of the emotional satisfaction and as a result, issues on odor are matters of concern. The cases are odor of interior material and air-conditioner of vehicles. In particualar, with respect to the odor of air-conditioner, customers strongly claimed defects with provocative comments : "It smells like something rotten," "It smells like a foot odor," "It stinks like a rag." Generally, it is known that mold of evaporator core in the air-conditioning system decays and this produce VOCs which causes the odor to occur. In this study, partial least squares regression model is applied to predict the strength of the odor and select of important VOCs which affect car air conditioning smell. The PLS method is basically a particular multilinear regression algorithm which can handle correlated inputs and limited data. The number of latent variable is determined by the point which is stabilized mean absolute deviations of VOCs data. Also multiple linear regression is carried out to confirm the validity of PLS method.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.44-44
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• 2017

The objective of this study was to evaluate firmness, and sweetness index (SI) of tomatoes (Lycopersicum esculentum) by using hyperspectral imaging (HSI) in the range of 1000-1400 nm. The mean spectra of the 95 matured tomato samples were extracted from the hyperspectral images, and the reference firmness and sweetness index of the same sample were measured and calibrated with their corresponding spectral data by partial least squares (PLS) regression with different preprocessing method. The results showed that the regression model developed by PLS regression based on Savitzky-Golay (S-G) second-derivative preprocessed spectra resulted in better performance for firmness, and SI of tomatoes compared to models developed by other preprocessing methods, with correlation coefficients (rpred) of 0.82, and 0.74 with standard error of prediction (SEP) of 0.86 N, and 0.63 respectively. Then, the feature wavelengths were identified using model-based variable selection method, i.e., variable important in projection (VIP), resulting from the PLS regression analyses and finally chemical images were derived by applying the respective regression coefficient on the spectral image in a pixel-wise manner. The resulting chemical images provided detailed information on firmness, and sweetness index (SI) of tomatoes. Therefore, these research demonstrated that HIS technique has a potential for rapid and non-destructive evaluation of the firmness and sweetness index of tomatoes.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.257-271
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• 2019

The use of food grade hexane (FGH) for edible oil extraction is responsible for the presence of benzene in the crude oil. Benzene is a Group 1 carcinogen and could pose a serious threat to the health of consumer. However, its detection still depends on classical methods using chromatography which requires a rapid non-destructive detection method. Hence, the aim of this study was to investigate the feasibility of using Fourier transform infrared (FTIR) spectroscopy combined with multivariate analysis to detect and quantify the benzene residue in edible oil (sesame and cottonseed oil). Oil samples were adulterated with varying quantities of benzene, and their FTIR spectra were acquired with an attenuated total reflectance (ATR) method. Optimal variables for a partial least-squares regression (PLSR) model were selected using the variable importance in projection (VIP) and the selectivity ratio (SR) methods. The developed PLS models with whole variables and the VIP- and SR-selected variables were validated against an independent data set which resulted in $R^2$ values of 0.95, 0.96, and 0.95 and standard error of prediction (SEP) values of 38.5, 33.7, and 41.7 mg/L, respectively. The proposed technique of FTIR combined with multivariate analysis and variable selection methods can detect benzene residuals in edible oils with the advantages of being fast and simple and thus, can replace the conventional methods used for the same purpose.

• Journal of Microbiology and Biotechnology
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• v.28 no.2
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• pp.199-209
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• 2018

Volatile organic compounds (VOCs) are increasingly been recognized as the chemical mediators of mold interactions, shaping their community dynamics, growth, and metabolism. Herein, we selectively examined the time-correlated (0 D-11 D, where D = incubation days) effects of intraspecies VOC-mediated interactions (VMI) on Aspergillus oryzae KCCM 60345 (S1), following co-cultivation with partner strain A. oryzae KACC 44967 (S2), in a specially designed twin plate assembly. The comparative evaluation of $S1_{VMI}$ (S1 subjected to VMI with S2) and its control ($S1_{Con}$) showed a notable disparity in their radial growth ($S1_{VMI}$ < $S1_{Con}$) at 5 D, protease activity ($S1_{VMI}$ > $S1_{Con}$) at 3-5 D, amylase activity ($S1_{VMI}$ < $S1_{Con}$) at 3-5 D, and antioxidant levels ($S1_{VMI}$ > $S1_{Con}$) at 3 D. Furthermore, we observed a distinct clustering pattern for gas chromatography-time of flight-mass spectrometry datasets from 5 D extracts of $S1_{VMI}$ and $S1_{Con}$ in principle component analysis (PC1: 30.85%; PC2: 10.31%) and partial least squares discriminant analysis (PLS-DA) (PLS1: 30.77; PLS2: 10.15%). Overall, 43 significantly discriminant metabolites were determined for engendering the metabolic variance based on the PLS-DA model (VIP > 0.7, p < 0.05). In general, a marked disparity in the relative abundance of amino acids ($S1_{VMI}$ > $S1_{Con}$) at 5 D, organic acids ($S1_{VMI}$ > $S1_{Con}$) at 5 D, and kojic acid ($S1_{VMI}$ < $S1_{Con}$) at 5-7 D were observed. Examining the headspace VOCs shared between S1 and S2 in the twin plate for 5 D incubated samples, we observed the relatively higher abundance of C-8 VOCs (1-octen-3-ol, (5Z)-octa-1,5-dien-3-ol, 3-octanone, 1-octen-3-ol acetate) having known semiochemical functions. The present study potentially illuminates the effects of VMI on commercially important A. oryzae's growth and biochemical phenotypes with subtle details of altered metabolomes.

• Korean Chemical Engineering Research
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• v.54 no.5
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• pp.621-629
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• 2016

Recently, the researches on quantitative structure activity relationship (QSAR) for describing toxicities or activities of chemicals based on chemical structural characteristics have been widely carried out in order to estimate the toxicity of chemicals in multiuse facilities. Because the toxicity of chemicals are explained by various kinds of molecular descriptors, an important step for QSAR model development is how to select significant molecular descriptors. This research proposes a statistical selection of significant molecular descriptors and a new QSAR model based on partial least square (PLS). The proposed QSAR model is applied to estimate the logarithm of partition coefficients (log P) of 130 polychlorinated biphenyls (PCBs) and lethal concentration ($LC_{50}$) of 14 PCBs, where the prediction accuracies of the proposed QSAR model are compared to a conventional QSAR model provided by OECD QSAR toolbox. For the selection of significant molecular descriptors that have high correlation with molecular descriptors and activity information of the chemicals of interest, correlation coefficient (r) and variable importance of projection (VIP) are applied and then PLS model of the selected molecular descriptors and activity information is used to predict toxicities and activity information of chemicals. In the prediction results of coefficient of regression ($R^2$) and prediction residual error sum of square (PRESS), the proposed QSAR model showed improved prediction performances of log P and $LC_{50}$ by 26% and 91% than the conventional QSAR model, respectively. The proposed QSAR method based on computational toxicology can improve the prediction performance of the toxicities and the activity information of chemicals, which can contribute to the health and environmental risk assessment of toxic chemicals.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.316-328
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• 2021

In this study, the leaf Na content prediction model for spring potato was established using 400-1000 nm hyperspectral sensor to develop the multispectral sensor for the salinity monitoring in reclaimed land. The irrigation conditions were standard, drought, and salinity (2, 4, 8 dS/m), and the irrigation amount was calculated based on the amount of evaporation. The leaves' Na contents were measured 1st and 2nd weeks after starting irrigation in the vegetative, tuber formative, and tuber growing periods, respectively. The reflectance of the leaves was converted from 5 nm to 10 nm, 25 nm, and 50 nm of FWHM (full width at half maximum) based on the 10 nm wavelength intervals. Using the variance importance in projections of partial least square regression(PLSR-VIP), ten band ratios were selected as the variables to predict salinity damage levels with Na content of spring potato leaves. The MLR(Multiple linear regression) models were estimated by removing the band ratios one by one in the order of the lowest weight among the ten band ratios. The performance of models was compared by not only R2, MAPE but also the number of band ratios, optimal FWHM to develop the compact multispectral sensor. It was an advantage to use 25 nm of FWHM to predict the amount of Na in leaves for spring potatoes during the 1st and 2nd weeks vegetative and tuber formative periods and 2 weeks tuber growing periods. The selected bandpass filters were 15 bands and mainly in red and red-edge regions such as 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm.