Quantitative structure–property relationship study for estimation of quantitative calibration factors of some organic compounds in gas chromatography Original Research Article

Quantitative structure–property relationship (QSPR) models have been used to predict and explain gas chromatographic data of quantitative calibration factors (fM). This method allows for the prediction of quantitative calibration factors in a variety of organic compounds based on their structures alone. Stepwise multiple linear regression (MLR) and non-linear radial basis function neural network (RBFNN) were performed to build the models. The statistical characteristics provided by multiple linear model (R2 = 0.927, RMS = 0.073; AARD = 6.34% for test set) indicated satisfactory stability and predictive ability, while the predictive ability of RBFNN model is somewhat superior (R2 = 0.959; RMS = 0.0648; AARD = 4.85% for test set). This QSPR approach can contribute to a better understanding of structural factors of the compounds responsible for quantitative analysis by gas chromatography, and can be useful in predicting the quantitative calibration factors of other compounds.