QSRR Study of Linear Retention Indices for Volatile Compounds using Statistical Methods

Abstract

ACD/ChemSketch, MarvinSketch and ChemOffice programs were used to calculate several molecular descriptors of 138 volatile compounds (32 hydrocarbons, 29 ketones, 28 aldehydes, 23 alcohols, 7 carboxylic acids, 6 halogenated compounds, 4 furans, 2 pyrazines, 1 ester, 1 sulphur compounds, 1 pyridine, 1 amine and three other compounds). The best descriptors were selected to establish the quantitative structure retention relationship (QSRR) of linear retention indices of volatile compounds using multiple linear regressions (MLR), multiple non-linear regressions (MNLR) and artificial neural network (ANN) methods. We propose quantitative models according to these analyses. The models were used to predict the linear retention indices of the test set compounds and agreement between the experimental and predicted values was verified. The descriptors showed by QSRR study were used for study and designing of new compounds. The statistical results indicate that the predicted values are in good agreement with the experimental results. To validate the predictive power of the resulting models, external validation multiple correlation coefficient was calculated and has both in addition to a performance prediction power, a favorable estimation of stability.