Quantitative structure–affinity relationship study of azo dyes for cellulose fibers by multiple linear regression and artificial neural network

Wang, Xiaojun; Sun, Yongyuan; Wu, Lei; Gu, Shaojin; Liu, Ruina; Liu, Li; Liu, Xin; Xu, Jie

doi:10.1016/j.chemolab.2014.03.001

Cited by 17 publications

(3 citation statements)

References 37 publications

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“…Y-randomizationtestswereperformedtotestifythepossible existenceofchancecorrelationofthemodel. 40,41 Inthetests,the dependentvariable(lga)wasrandomlyscrambledandusedto buildandinvestigatethePLSmodel.Theresultmodelwiththe randomizedvaluesshouldhavedramaticallylower r 2 and q 2 than theoriginalone,becausetherelationshipbetweenthestructure andtheoriginaldependentvariableisbroken.35randomization runswereconductedinthepresentstudy. Fig.8showstheresults ofthe Y-randomizationtests.Fromthefigure,itcanbeseenthat allrandomizedmodelshavebadqualitywhencomparedwiththe originalmodel.The r 2 valueisabout0.2-0.5andall q 2 valuesare negative,whichmeansthatthemodelshavenopredictiveability.…”

Section: Qsprmodelfortheseparationfactorsmentioning

confidence: 99%

Predicting Retention and Separation Factors of Chiral Diarylmethane Derivates by QSPR Models

Hu¹,

Luo²,

Pan³

et al. 2015

Acta Physico-Chimica Sinica

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Section: Qsprmodelfortheseparationfactorsmentioning

confidence: 99%

Predicting Retention and Separation Factors of Chiral Diarylmethane Derivates by QSPR Models

Hu¹,

Luo²,

Pan³

et al. 2015

Acta Physico-Chimica Sinica

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“…Another study by Muthukumar et.al [13] describes the effect of active groups on some dyes on COD values using UV/H2O2 modeled by statistical tests. To determine the effect of the dye strength on the fabric have been modeled with MLR also by Timofei et.al [14] and Wang et.al [15]. The MLR application is also used by Yao et.al [16] to find out how much the molecular structure affects molecular activity.…”

Section: Introductionmentioning

confidence: 99%

Multiple linear regression for the analysis of the parameters used in dyes decolourisation by ozonation techniques

Fatimah

Wiharto

2018

MATEC Web Conf.

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The ozonation process of dye Acid Orange 7 (AO7), Acid Yellow 19 (AY19), and Acid Black 1 (AB1) have been performed. The experimental results predicted the magnitude of the influence of each variable by using Multiple Linear Regression (MLR). This process produces predictive modeling of the variables studied. The variables studied in this ozonation process are independent variables consisting of ozone concentration (mg/L), concentration of dye (mg/L), pH and temperature (°C). The dependent variables studied were the percentage of dye decolorization. The feasibility of the prediction model used has also been tested using the t-test. Based on the prediction model, R2 values for AO7, AY19, and AB1 dyes are 0.84. 0.87, and 0.93 respectively. The analysis of the influence of the independent variables on the percentage of dye decolorization predicted that the concentration of ozone, dyestuff concentration and pH, significantly influenced the ozonation process of AO7, AY19, and AB1. In the ozonation process the temperature variables have a significant effect on AY19 and AB1, but stronger influence on AO7 ozonation process. Based on the predictions of the influence of each variable, the process in the laboratory can be optimized more effectively and efficiently, so that the cost and time factor can be reduced.

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“…The QSPR method is based on the assumption that the variation in the behavior of compounds, as expressed by any measured physicochemical properties, can be correlated with numerical changes in structural features of all compounds, termed "molecular descriptors" [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. The advantage of this method lies in the fact that it requires only knowledge of the chemical structure and is not dependent on any experimental properties.…”

Section: Introductionmentioning

confidence: 99%

A simple 2D-QSPR model for the prediction of Setschenow constants of organic compounds

Fan

2016

Maced. J. Chem. Chem. Eng.

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A quantitative structure-property relationship (QSPR) analysis of the Setschenow constants (K salt ) of organic compounds in a sodium chloride solution was carried out using only two-dimensional (2D) descriptors as input parameters. The whole set of 101 compounds was split into a training set of 71 compounds and a validation set of 30 compounds by means of the Kennard and Stones algorithm. A general four-parameter equation, with correlation coefficient (R) of 0.887 and standard error of estimation (s) of 0.031, was obtained by stepwise multilinear regression analysis (MLRA) on the training set. The reliability and robustness of the present model was verified with leave-one-out cross-validation, randomization tests, and the external validation set. All of the descriptors contained in this model are calculated directly from the molecular 2D structures; thus, this model can be used to easily predict the K salt of other compounds not involved in the present dataset.Keywords: QSPR; Setschenow constants; 2D descriptor; multilinear regression analysis ЕДНОСТАВЕН 2D-QSPR-МОДЕЛ ЗА ПРЕДВИДУВАЊЕ НА КОНСТАНТИТЕ НА SETSCHENOW ЗА ОРГАНСКИТЕ СОЕДИНЕНИЈААнализата на квантитативната зависност на структурата и својствата (QSPR) на константите на Setschenow (K salt ) на органските соединенија во раствор од натриум хлорид е извршена користејќи само дводимензионални (2D) дескриптори како влезни параметри. Целото множество од 101 соединение беше поделено во множество за подготовка од 71 соединение и множество за валидација од 30 соединенија според алгоритамот на Kennard и Stones. Од множеството за подготовка со мултилинеарна регресиона анализа MLRA е добиена општа четирипараметарска равенка со коефициент на корелација R = 0,887 и стандардна грешка на процената s = 0,031. Веродостојноста и робусноста на овој модел беше верифицирана со повеќе тестови: вкрстена валидација со испуштање на еден параметар, тест на рандомизација, како и екстерно множество за валидација. Сите дескриптори содржат модел што се пресметува директно од молекулските 2D-структури, и така овој модел може да се користи за едноставно предвидување на K salt на други соединенија што не се вклучени во ова множество на податоци.

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Quantitative structure–affinity relationship study of azo dyes for cellulose fibers by multiple linear regression and artificial neural network

Cited by 17 publications

References 37 publications

Predicting Retention and Separation Factors of Chiral Diarylmethane Derivates by QSPR Models

Predicting Retention and Separation Factors of Chiral Diarylmethane Derivates by QSPR Models

Multiple linear regression for the analysis of the parameters used in dyes decolourisation by ozonation techniques

A simple 2D-QSPR model for the prediction of Setschenow constants of organic compounds

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