Support Vector Machine Applied to Study on Quantitative Structure–Retention Relationships of Polybrominated Diphenyl Ether Congeners

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• 作者：Xiaotong Zhang ; Xin Zhang ; Qiang Li ; Zhaolin Sun ; Lijuan Song ; Ting Sun
• 关键词：Support vector machine (SVM) ; Genetic algorithm (GA) ; Gas chromatography ; Quantitative structure–retention relationships (QSRR) ; Polybrominated diphenyl ether congeners (PBDE)
• 刊名：Chromatographia
• 出版年：2014
• 出版时间：October 2014
• 年：2014
• 卷：77
• 期：19-20
• 页码：1387-1398
• 全文大小：1,622 KB
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• 作者单位：Xiaotong Zhang (1) (2)
  Xin Zhang (2)
  Qiang Li (2) (3)
  Zhaolin Sun (2)
  Lijuan Song (2)
  Ting Sun (1)
  
  1. College of Sciences, Northeastern University, Shenyang, Liaoning, China
  2. Liaoning Key Laboratory of Petrochemical Engineering, Liaoning Shihua University, Fushun, Liaoning, China
  3. College of Chemical Engineering, Beijing University of Chemical Technology, Beijing, China
  
• ISSN：1612-1112

文摘

Quantitative structure–retention relationships (QSRR) models were constructed for the GC relative retention times (RRTs) of 126 polybrominated diphenyl ether (PBDE) congeners. First, a number of topological and connectivity indices descriptors were derived from E-dragon software. In a further step, six molecular descriptors were extracted by genetic algorithm (GA) coupled with multiple linear regression (MLR) method. The QSRR model was established using a support vector machine (SVM) algorithm as regression tool. High training sets correlation coefficients R 2 indicated that >99.6?% (except for stationary phase CP-Sil 19) of the total variation in the predicted RRTs is explained by the fitted models. It showed that we provided a more accurate model that was subsequently used to predict the RRTs of validation sets. The excellent statistical parameters Q 2 loo (correlation coefficient of leave-one-out cross validation) and validation sets correlation coefficients R 2?>?99.0?% reveal that the models are robust and have high internal and external predictive capability. According to sum of ranking differences (SRD) validation values, we concluded that DB-1 and DB-5 are the best two models.