The study of the relationship between the new topological index Am and the gas chromatographic retention indices of hydrocarbons by artificial neural networks

Abstract

The newly developed topological indices A m1– A m3 and the molecular connectivity indices m X were applied to multivariate analysis in structure–property correlation studies. The topological indices calculated from the chemical structures of some hydrocarbons were used to represent the molecular structures. The prediction of the retention indices of the hydrocarbons on three different kinds of stationary phase in gas chromatography can be achieved applying artificial neural networks and multiple linear regression models. The results from the artificial neural networks approach were compared with those of multiple linear regression models. It is shown that the predictive ability of artificial neural networks is superior to that of multiple linear regression method under the experimental conditions in this paper. Both the topological indices 2 X and A m1 can improve the predicted results of the retention indices of the hydrocarbons on the stationary phase studied.