QSRR Model for predicting retention indices of Satureja kitaibelii Wierzb. ex Heuff. essential oil composition

Abstract

A prediction model of retention indices of compounds from the aboveground parts of Satureja kitaibelii essential oil, obtained by hydrodistillation and analysed by Gas Chromatography coupled with Mass Spectrometry (GC-MS), was the aim of this study. The quantitative structure–retention relationship was employed to predict the retention time using five molecular descriptors selected by a genetic algorithm. The selected descriptors were used as inputs of an artificial neural network. Total of 53 experimentally obtained retention indices (log RI) were used to build a prediction model. The selected descriptors were used as inputs of an artificial neural network model, to build a prediction time predictive quantitative structure-retention relationship model. The coefficient of determination for the training cycle was 0.962, indicating that this model could be used for prediction of retention indices for S. kitaibelii essential oil compounds.