QSPR study on the flash point of organic binary mixtures by using electrotopological state index

Abstract

The quantitative structure property relationship (QSPR) for the flash point of 288 organic binary mixtures was investigated. The electrotopological state (E-state) index of the components in each mixture was calculated and weight summed to generate the quantitative descriptor of the investigated mixtures. Multivariable linear regression (MLR), stepwise regression and radial basis function artificial neural network (RBF-ANN) was respectively used to build the calibration model. The weight summed E-state index was used as the independent variable of the established models. The prediction performance of the developed models were assessed with external test validation, k-fold cross validation and Monte Carlo cross validation (MCCV). The results of the three validations demonstrate that the RBF-ANN model which includes five input variables is the best one among the developed models. The prediction root mean square relative error (RMSRE) of the external test validation, k-fold cross validation and MCCV is 1.86, 1.11 and 1.07 respectively for this model. It is demonstrated that there is a quantitative relationship between the E-state index and flash point of the investigated mixtures. MLR, stepwise regression and RBF-ANN are all practicable for modeling this relationship. The developed RBF-ANN model involving five input variables is the most promising method for predicting the flash point of organic binary mixtures.