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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A new spectrophotometric method is reported for the determination of nanomolar level of malachite green in surface water samples. The method is based on the catalytic effect of silver nanoparticles on the oxidation of malachite green by hexacyanoferrate (III) in acetate-acetic acid medium. The absorbance is measured at 610 nm with the fixed-time method. Under the optimum conditions, the linear range was 8.0 × 10(-9)-2.0 × 10(-7) mol L(-1) malachite green with a correlation coefficient of 0.996. The limit of detection (S/N = 3) was 2.0 × 10(-9) mol L(-1). Relative standard deviation for ten replicate determinations of 1.0 × 10(-8) mol L(-1) malachite green was 1.86%. The method is featured with good accuracy and reproducibility for malachite green determination in surface water samples without any pre-concentration and separation step.

Genetic algorithm (GA) and partial least squares (PLS) and kernel PLS (KPLS) techniques were used to investigate the correlation between immobilized liposome chromatography partitioning (log Ks) and descriptors for 65 drug compounds. The models were validated using leave-group-out cross validation LGO-CV. The results indicate that GA-KPLS can be used as an alternative modelling tool for quantitative structure-property relationship (QSPR) studies.

The essential oils are widely used in pharmaceutical, cosmetic and perfume industry, and for flavouring and preservation of several food products. GC and GC‐MS is one of the most powerful tools in analytical volatile compound (such as essential oils). 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) techniques were used to investigate the correlation between retention index (RI) and descriptors for 113 diverse compounds in essential oils of four Teucrium species which obtained by GC and GC‐MS. Five simple one‐ and two‐dimensional descriptors were selected by GA‐KPLS and considered as input for developing L‐M ANN. The applied internal (leave‐group‐out cross validation (LGO‐CV)) and external (test set) validation methods were used for the predictive power of four models. The correlation coefficient LGO‐CV (Q2) between experimental and predicted RI for training and test sets by GA‐MLR, GA‐PLS, GA‐KPLS and L‐M ANN was 0.91, 0.92, 0.96 and 0.99 (for 88 compounds), 0.88, 0.91, 0.94 and 0.97 (for 25 compounds), 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 GA‐KPLS and L‐M ANN.