Genetic algorithm and multiple linear regression (GA‐MLR), partial least square (GA‐PLS), kernel PLS (GA‐KPLS) and Levenberg‐Marquardt artificial neural network (L‐M ANN) technique were used to investigate the correlation between retention index (RI) and descriptors for diverse compounds in essential oils. The correlation coefficient cross validation (Q2) between experimental and predicted retention index for training and test sets by GA‐MLR, GA‐PLS, GA‐KPLS and L‐M ANN was 0.948, 0.924, 0.958 and 0.980 (for training set), 0.917, 0.890, 0.915 and 0.954 (for test set), respectively. The L‐M ANN model with the final optimum network architecture of [5‐2‐1] gave a significantly better performance than the other models. This indicates that L‐M ANN can be used as an alternative modeling tool for quantitative structure‐property/retention relationship (QSPR/QSRR) studies.

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Genetic algorithm and partial least square (GA-PLS) and kernel PLS (GA-KPLS) techniques were used to investigate the correlation between retention indices (RI) and descriptors for 117 diverse compounds in essential oils from 5 Pimpinella species gathered from central Turkey which were obtained by gas chromatography and gas chromatography-mass spectrometry. The square correlation coefficient leave-group-out cross validation (LGO-CV) (Q 2 ) between experimental and predicted RI for training set by GA-PLS and GA-KPLS was 0.940 and 0.963, respectively. This indicates that GA-KPLS can be used as an alternative modeling tool for quantitative structure-retention relationship (QSRR) studies.

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