Quantitative structure-retention relationship study of some phenol derivatives in gas chromatography.

A quantitative structure-retention relationship (QSRR) model has been developed for the gas chromatographic retention times of 37 phenolic derivatives in a DB-5 non-polar column (95% dimethyl and 5% diphenyl-polysiloxane). As a first step, multiple linear regression (MLR) was employed to gain informative descriptors that can predict the retention times of these compounds. Descriptors appearing in the MLR model are categorized as topological and geometric parameters that comply with the applied column. Furthermore, each molecular descriptor in this model was examined to unfold the relationship between molecular structures and their retention times. Then, a 4-4-1 neural network was developed using the descriptors selected by the MLR model. The comparison of the standard errors and correlation coefficients reveals the superiority of artificial neural networks (ANN) over the MLR model. This refers to the fact that the retention behaviors of molecules display non-linear characteristics. The consistency and reliability of ANN model was investigated using the L4O cross-validation technique. The obtained results are closely in compliance with the experiment. Moreover, the mean effect of descriptors shows that Kier symmetry index is the most important factor affecting the retention behavior of molecules.