Application of artificial neural networks for prediction of the retention indices of alkylbenzenes

Artificial neural networks (ANN) with extended delta–bar–delta (EDBD) learning algorithms were used to predict the retention indices of alkylbenzenes. The data used in this paper include 96 retention indices of 32 alkylbenzenes on three different stationary phases. Four parameters: temperature, boiling point, molar volume and the kind of stationary phase, were used as input parameters. These three stationary phases are: PEG, SE-30, SQ. The 96 group data were randomly divided into two sets: a training set (including 64 group data) and a testing set (including 32 group data). The structures of networks and the learning times were optimized. The best network structure is 4–7–1. The optimum number of learning time is about 20 000. It is shown that the maximum relative error is no more than 3%. The result illustrated that the prediction performance of ANN in the field of investigating the retention behaviors of alkylbenzenes is very satisfactory.