Quantitative study of the structure–retention index relationship in the imine family

The Kovلts retention index is one of the most popular descriptors of the performance of organic compounds in gas chromatography (GC). The mathematical modeling of this index is an interesting and open problem in analytical chemistry. In this paper, two models for the prediction of the Kovلts retention index are presented. Topologic, topographic and quantum-chemical descriptors were used as structural descriptors. Multiple linear regression (MLR) analysis provides the first model using the forward stepwise procedure for the variable selection. For the second one, an ensemble of artificial neural network (ANN) was constructed using the pruning algorithm. Both methods were validated by an external set of compounds, by the Golbraikh and Tropsha method and by the leave-one-out (LOO) and the leave many out (LMO) procedures. The R2, RMScv and Q2, values for the training sets were 0.884, 0.589 and 0.830 for NN and 0.974, 0.417 and 0.970 for MLR models, respectively. The robustness of both models was demonstrated. Both portrait the chromatographic performance of the sample but in this case, the results of MLR equation are better than the NN ones. The MLR model is recommended because of its simplicity.