A QSPR study of GC/MS Retention Data of 85 Volatile Organic Compounds as Air Pollutant Materials by Multivariate Methods

A quantitative structure-property relationship (QSPR) study is suggested for the prediction of retention times of volatile organic compounds. Various kinds of molecular descriptors were calculated to represent the molecular structure of compounds. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and artificial neural network (ANN). The stepwise regression was used for the selection of the variables which gives the best-fitted models. After variable selection ANN, MLR methods were used with leave-one-out cross validation for building the regression models. This provided a new and effective method for predicting the chromatography retention index for the volatile organic compounds.