QSRR model for predicting retention indices of geraniol chemotype of Thymus serpyllum essential oil

Abstract

A total of 26 experimentally obtained retention indices on a logarithmic scale (log RI) from Thymus serpyllum essential oil were used to build a robust predictive model. The selected descriptors were used as inputs of an artificial neural network model to build a predictive quantitative structure-retention time relationship model. The coefficient of determination for the training cycle was 0.977, indicating that this model could be used for the prediction of retention indices for T. serpyllum essential oil compounds. These 26 compounds comprise about 99.8 % of the total oil, but among them only 6 compounds had the average relative concentration over 1 percent: geraniol (63.4 %), nerol (or cis-geraniol) (18.9 %), geranyl acetate (4.7 %), transcaryophyllene (4.6 %), β-bisabolene (2.0 %) and geranial (1.2 %). According to these results, it can be concluded that T. serpyllum from village Sesalac (Serbia) belongs to geraniol chemotype, in total 82.3 % in both, trans and cis forms (63.4 % and 18.9 %, respectively).