QSRR Models to Predict Retention Indices of Cyclic Compounds of Essential Oils

Abstract

Genetic algorithm and multiple linear regression (GA-MLR), partial least square (GA-PLS) and Levenberg–Marquardt artificial neural network (L–M ANN) techniques were used to investigate the correlation between retention indices (RI) and descriptors for 40 cyclic compounds in rosemary and sage essential oil. Descriptors of the GA-MLR model were selected as inputs in the L–M ANN model. The R2 and RMSE for calibration, prediction and validation sets are (0.985, 0.985, 980), (0.013, 0.009, 0.010), respectively. This indicates that L–M ANN can be used as an alternative modeling tool for quantitative structure–property/retention relationship (QSPR/QSRR) studies. This is the first research on the QSRR of the essential oil compounds against the RI using the L–M ANN model.