• 대한화학회지
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• 제45권5호
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• pp.422-428
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• 2001

기체 크로마토그래피에서 PAH와 그것의 유도체들의 상대적 용리시간을 인공신경망분석과 다변량 선형 회귀분석을 사용하여 학습한 후, 시험세트들의 상대적 용리시간을 예측하였다. QSRR에서 PAH와 그것의 유도체의 주요한 설명인자는 분자량의 제곱근, 분자의 연결지수($^1{\chi}_v$), 분자 쌍극자모멘트 및 분자의 길이와 폭의 비율(L/B)이었다. 다변량선형회귀분석에 의하면 큰 분자일수록 용리시간은 길어지며 또한 L/B의 값이 커지면 용리시간이 증가하는 것으로 보아 슬롯이론을 따르고 있음을 알 수 있었다. 반면에 설명인자 사이의 선형 독립성에 영향을 받지 않는 인공신경망 분석결과에 의하면 분자량과 분자 쌍극자 모멘트가 주요한 인자로 작용하고 있었다. 시험세트의 예측 정확도를 나타내는 분산은 선형회귀분석에서는 1.860, 인공신경망분석법에서 0.206으로서 인공신경망 분석법이 다변량회귀분석보다 더 좋은 예측방법임을 알 수 있었다.

• 한국환경보건학회지
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• 제42권4호
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• pp.280-292
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• 2016

Objectives: The objective of this study was to build COD regression models for the Han River and evaluate water quality. Methods: Water quality data sets for the dry season (as of January) during a four-year period (2012-2015) were collected from the database of the Han River automatic water quality monitoring stations. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR) were used to build five-descriptor COD models. Multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA) are useful tools for extracting meaningful information. Results: The $r^2$ of the best COD models provided significant high values (> 0.8) between 2012 and 2015. Total organic carbon (TOC) was a surrogate indicator for COD (as COD/TOC) with high reliability ($r^2=0.63$ in 2012, $r^2=0.75$ for 2013, $r^2=0.79$ for 2014 and $r^2=0.85$ for 2015). The ratios of COD/TOC were calculated as 2.08 in 2012, 1.79 in 2013, 1.52 and 1.45 in 2015, indicating that biodegradability in the water body of the Han River was being sustained, thereby further improving water quality. The BOD/COD ratio supported these findings. The cluster analysis revealed higher annual levels of microorganisms and phosphorous at stations along the Hangang-Seoul and Hantangang areas. Nevertheless, the overall water quality over the last four years showed an observable trend toward continuous improvement. These findings also suggest that non-point pollution control strategies should consider the influence of upstreams and downstreams to protect water quality in the Han River. Conclusion: This data analysis procedure provided an efficient and comprehensive tool to interpret complex water quality data matrices. Results from a trend analysis provided much important information about sources and parameters for Han River water quality management.

• 대한화학회지
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• 제58권6호
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• pp.543-552
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• 2014

A quantitative structure activity relationship (QSAR) study is performed for modeling and prediction of oral bioavailabilities of 216 diverse set of drugs. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM) and random forest (RF) techniques. Comparison between statistical parameters of these models indicates the suitability of SVM over other models. The root mean square errors of SVM model were 5.933 and 4.934 for training and test sets, respectively. Robustness and reliability of the developed SVM model was evaluated by performing of leave many out cross validation test, which produces the statistic of $Q^2_{SVM}=0.603$ and SPRESS = 7.902. Moreover, the chemical applicability domains of model were determined via leverage approach. The results of this study revealed the applicability of QSAR approach by using SVM in prediction of oral bioavailability of drugs.

• 한국수자원학회논문집
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• 제51권2호
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• pp.151-163
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• 2018

본 연구에서는 미계측 유역에서 오염부하량 모의를 위해 LOADEST (LOAD Estimator) 기반 회귀모형의 최적 매개변수를 추정하고, 다중회귀분석 기법을 이용하여 유역특성에 따른 회귀 모의 모형의 매개변수 추정 방법을 개발하였으며, 개발된 모형의 적용성을 평가하였다. 오염부하량 모의모형으로, T-N (Total-Nitrogen)은 LOADEST의 5번 회귀모형을, T-P (Total-Phosphorous)는 3번 회귀모형을 선택하였다. 모의결과, T-N, T-P 모두 선택된 회귀모형이 실측치를 잘 반영하였으나, 두 물질 모두 오염부하량이 과소 모의되어 실측치와 편의가 발생하는 것으로 나타나, 분위사상법을 이용하여 모의치의 편의보정을 실시하였다. 보정결과, 모형의 정확도는 크게 변하지 않았으나, 오염부하량이 과소 모의 되는 경향이 감소하는 것으로 나타났다. 다중회귀분석을 이용하여 회귀모형 매개변수와 유역특성간의 회귀식을 개발하였으며, 개발된 식을 평가한 결과, 실측치를 잘 반영하여 모의할 수 있는 것으로 나타났으며, 기존 매개변수에 의한 모의치와 유사한 모의능력을 갖는 것으로 나타났다. 본 연구에서 개발된 매개변수 추정방법은 실측자료가 확보되지 않은 소유역에 대한 오염부하량 모의와 정책결정을 위한 스크린 모델로서 활용할 수 있을 것으로 사료된다.

• Bulletin of the Korean Chemical Society
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• 제22권4호
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• pp.388-394
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• 2001

Quantitative Structure-Activity Relationship (QSAR) have been established of 57 fluorovinyloxyacetamides compounds to correlate and predict EC50 values. Genetic algorithm (GA) and multiple linear regression analysis were used to select the descriptors and to generate the equations that relate the structural features to the biological activities. This equation consists of three descriptors calculated from the molecular structures with molecular mechanics and quantum-chemical methods. The results of MLR and GA show that dipole moment of z-axis, radius of gyration and logP play an important role in growth inhibition of barnyard grass.

• Bulletin of the Korean Chemical Society
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• 제34권6호
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• pp.1643-1650
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• 2013

A non linear QSAR model was constructed on a series of 57 xanthone and curcuminoide derivatives as ${\alpha}$-glucosidase inhibitors by back-propagation neural network method. The neural network architecture was optimized to obtain a three-layer neural network, composed of five descriptors, nine hidden neurons and one output neuron. A good predictive determination coefficient was obtained (${R^2}_{Pset}$ = 86.7%), the statistical results being better than those obtained with the same data set using a multiple regression analysis (MLR). As in the MLR model, the descriptor MATS7v weighted by Van der Waals volume was found as the most important independent variable on the ${\alpha}$-glucosidase inhibitory.

• Molecular & Cellular Toxicology
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• 제4권2호
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• pp.170-176
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• 2008

The quantitative structure-activity relationship (QSAR) of a set of 35 flavonoid compounds presenting antioxidant activity was established by means of Genetic Algorithm-Multiple Linear Regression (GA-MLR) technique. Four-parametric models for two sets of data, the 1,1-diphenyl-2-picryl hydrazyl (DPPH) radical scavenging activity $(R^2=0.788,\;Q^2_{cv}=0.699\;and\;Q^2_{ext}=0.577)$ and scavenging activity of reactive oxgen species (ROS) induced by $H_2O_2 (R^=0.829,\;Q^2_{cv}=0.754\;and\;Q^2_{ext}=0.573)$ were obtained with low external predictive ability on a mass basis, respectively. Each model gave some different mechanistic aspects of the flavonoid compounds tested in terms of the radical scavenging activity. Topological charge, H-bonding complex and deprotonation processes were likely to be involved in the radical scavenging activity.

• Near Infrared Analysis
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• 제1권1호
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• pp.27-30
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• 2000

In this study, we investigated the feasibility of non-destructive determination of internal quality factors of Fuji apple fruit using near infrared(NIR) reflectance spectroscopy and developed the calibration models. As the reference methods, refractometer, titration and texture analyzer for sugar content, acidity and firmness were used, respectively. Samples were scanned from 1100∼2500nm with InfraAlyzer 500C spectrometer and SESAME software was used for data analysis. A multiple linear regression(MLR) analysis was performed to develop the calibrations. The correlation coefficient(R) and standard error of prediction(SEP) were as follows; 0.91, 0.41$^{\circ}$Brix for sugar content, 0.90, 0.04% for acidity and 0.84, 0.094 kg for firmness, respectively. This study shows that NIR spectroscopy can be used to evaluate the sugar content acidity and firmness of apple fruit with acceptable accuracy.

• Journal of Biosystems Engineering
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• 제33권1호
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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.

• 대한화학회지
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• 제59권6호
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• pp.483-487
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• 2015

The different calculated quantum chemical descriptors by DFT method were used for prediction of some sulphanilamide Schiff bases inhibitor activity as a binding constant (log K). Multiple linear regression (MLR) and artificial neural network (ANN) were employed for developing the useful quantitative structure activity relationship (QSAR) model. The obtained results presented superiority of ANN model over the MLR one. The offering QSAR model is very easy to computation and Physico-Chemically interpretable. Sensitivity analysis was used to determine the relative importance of each descriptor in ANN model. The order of importance of each descriptor according to this analysis is: molecular volume, molecular weight and dipole moment, respectively. These descriptors appear good information related to different structure of sulphanilamide Schiff bases can participate in their inhibitor activity.