QSPRs for the prediction of photodegradation half-life of PCBs in<i>n</i>-hexane

Niu, Junfeng; Zhang, Yang; Shen, Zhenyao; Wang, L L

doi:10.1080/10659360600636170

Cited by 20 publications

(15 citation statements)

References 32 publications

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“…In 1984, based on principal component analysis (PCA) and ordinary least squares regression (OLS), partial leastsquares regression (PLS) was proposed by Wold et al (Wold et al, 1984), and it was one of the most effective methods for QSPR (Chen et al, 2003;Niu et al, 2005;Niu et al, 2006a;Niu et al, 2006b). Along with the development of intelligent algorithms, artificial neural network (ANN) was used to fit the nonlinearity continuous functions, and the capability of prediction was improved (Tabaraki et al, 2006;Arab Chamjangali et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

A comparative QSPR study on aqueous solubility of polycyclic aromatic hydrocarbons by GA-SVM, GA-RBFNN and GA-PLS

Wei

Sun

et al. 2011

Front. Earth Sci.

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A novel method to develop quantitative structure-property relationship (QSPR) models of organic contaminants was proposed based on genetic algorithm (GA) and support vector machine (SVM). GA was used to perform the variable selection and SVM was used to construct QSPR models. In this study, GA-SVM was applied to develop the QSPR model for aqueous solubility (S w , mol$L -1 ) of polycyclic aromatic hydrocarbons (PAHs). The R 2 (0.98) of the model developed by GA-SVM indicated a good predictive precision for lg S w values of PAHs. According to leave-one-out (LOO) cross validation, the results of GA-SVM were compared with those of genetic algorithm-radial based function neural network (GA-RBFNN) and genetic algorithm-partial leastsquares (GA-PLS) regression. The comparisons showed that the cross validation correlation coefficient ðQ 2 LOO ¼ 0:92Þ and root mean square error of LOO cross validation (RMSE LOO = 0.49) of GA-SVM were the highest and lowest, respectively, which illustrated that GA-SVM was more suitable to develop QSPR model for the lg S w values of PAHs than GA-RBFNN and GA-PLS.

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Section: Introductionmentioning

confidence: 99%

A comparative QSPR study on aqueous solubility of polycyclic aromatic hydrocarbons by GA-SVM, GA-RBFNN and GA-PLS

Wei

Sun

et al. 2011

Front. Earth Sci.

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“…Many investigations on the photodegradation of PCBs have been reported in recent decades [14][15][16][17][18][19][20]. PCBs are sensitive to ultraviolet (UV) irradiation in aqueous and organic solutions [14][15][16][17]19,21]. Photochemical behaviors of PCBs in organic solutions have been reported by many researchers, for example, in cyclohexane [22], in n-hexane [19,23,24] and in alkaline 2-propanol [25].…”

Section: Introductionmentioning

confidence: 99%

Application of quantum chemical descriptors into quantitative structure-property relationship models for prediction of the photolysis half-life of PCBs in water

Bao

Huang

Wang

et al. 2011

Front. Environ. Sci. Eng. China

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Quantitative structure-property relationship (QSPR) models were developed for prediction of photolysis half-life (t 1/2 ) of polychlorinated biphenyls (PCBs) in water under ultraviolet (UV) radiation. Quantum chemical descriptors computed by the PM3 Hamiltonian software were used as independent variables. The cross-validated Q 2 cum value for the optimal QSPR model is 0.966, indicating good prediction capability for lg t 1/2 values of PCBs in water. The QSPR results show that the largest negative atomic charge on a carbon atom (Q -C ) and the standard heat of formation (ΔH f ) have a dominant effect on t 1/2 values of PCBs. Higher Q -C values or lower ΔH f values of the PCBs leads to higher lg t 1/2 values. In addition, the lg t 1/2 values of PCBs increase with the increase in the energy of the highest occupied molecular orbital values. Increasing the largest positive atomic charge on a chlorine atom and the most positive net atomic charge on a hydrogen atom in PCBs leads to the decrease of lg t 1/2 values.

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“…Niu and coworkers conducted a QSPR study on photodegradation half-lives of 22 individual PCBs in n-hexane solution under UV irradiation [23]. Establishment of their model was implemented based on the calculation of quantum chemical descriptors and PLS regression.…”

Section: Introductionmentioning

confidence: 99%

“…In a similar way, Niu et al[23] used QSPR approaches to estimate the half-lives of PCB molecules. They used five quantum chemical descriptors in their PLS-based model and reported statistics of Q 2 cum ¼ 0:589, r 2 ¼ 0:659 and SE ¼ 0:357.…”

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confidence: 99%

QSPR-based estimation of the half-lives for polychlorinated biphenyl congeners

Fatemi

Chahi

2012

SAR and QSAR in Environmental Research

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In this study, the depuration half-lives of 62 polychlorinated biphenyl (PCB) congeners in juvenile rainbow trout (Oncorhynchus mykiss) were estimated from their structural features based on QSPR methodology. A genetic algorithm (GA) was applied as a variable subset selection strategy and QSPR models established upon multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and support vector regression (SVR) procedures. Robustness and predictive stability of the constructed models were evaluated through internal and external validation methods. The high numerical values of [Formula: see text] and [Formula: see text], and low RMSE in the case of the MLP NN model, confirm the supremacy of this model as well as nonlinear dependency of molecular structural features to the PCB congeners half-lives. In the best developed QSPR model the following four descriptors are used; lopping centric index (Lop), mean topological charge index of order 1 (JGI1), Geary autocorrelation lag-8/weighted by atomic Sanderson electronegativities (GATS8e) and highest eigenvalue of Burden matrix/weighted by atomic masses (BEHm3). Analysis of the descriptors involved in these models revealed that 2D molecular structural features, compactness and electronegativities are the main factors contributing to the half-lives of PCBs. The structural information presented in this work can be used for further evaluation of half-lives of PCBs and other similar structural compounds in the environment.

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QSPRs for the prediction of photodegradation half-life of PCBs inn-hexane

Cited by 20 publications

References 32 publications

A comparative QSPR study on aqueous solubility of polycyclic aromatic hydrocarbons by GA-SVM, GA-RBFNN and GA-PLS

A comparative QSPR study on aqueous solubility of polycyclic aromatic hydrocarbons by GA-SVM, GA-RBFNN and GA-PLS

Application of quantum chemical descriptors into quantitative structure-property relationship models for prediction of the photolysis half-life of PCBs in water

QSPR-based estimation of the half-lives for polychlorinated biphenyl congeners

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