Quantitative structure-property relationship study for estimation of quantitative calibration factors of some organic compounds in gas chromatography

摘要

Quantitative structure-property relationship(QSPR)models have been used to predict and explain gas chromatographic data of quantitative calibration factors(f_M).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.