Prediction of ozone tropospheric degradation rate constants by projection pursuit regression

Yueying Ren, Huanxiang Liu, Xiaojun Yao, Mancang Liu

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)


Quantitative structure-property relationship (QSPR) models were developed to predict degradation rate constants of ozone tropospheric and to study the degradation reactivity mechanism of 116 diverse compounds. DUPLEX algorithm was utilized to design the training and test sets. Seven molecular descriptors selected by the heuristic method (HM) were used as inputs to perform multiple linear regression (MLR), support vector machine (SVM) and projection pursuit regression (PPR) studies. The PPR model performs best both in the fitness and in the prediction capacity. For the test set, it gave a predictive correlation coefficient (R) of 0.955, root mean square error (RMSE) of 1.041 and absolute average relative deviation (AARD, %) of 4.663, respectively. The results proved that PPR is a useful tool that can be used to solve the nonlinear problems in QSPR. In addition, methods used in this paper are simple, practical and effective for chemists to predict the ozone degradation rate constants of compounds in troposphere.

Original languageEnglish
Pages (from-to)150-158
Number of pages9
JournalAnalytica Chimica Acta
Issue number1
Publication statusPublished - 18 Apr 2007
Externally publishedYes


  • Heuristic method
  • Ozone tropospheric degradation rate constants
  • Projection pursuit regression
  • Quantitative structure-property relationship
  • Support vector machine


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