title: Quantitative structure-property relationship study for estimation of quantitative calibration factors of some organic compounds in gas chromatography.
abstract: 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.
authors: Department of Applied Chemistry, Yantai University, Yantai 264005, PR China. fluan@sina.com
authors: Feng
authors: F
authors: Luan
authors: Hui Tao
authors: HT
authors: Liu
authors: Yingying
authors: Y
authors: Wen
authors: Xiaoyun
authors: X
authors: Zhang
mesh_terms: D002138:Calibration; D002849:Chromatography, Gas / Q000379:methods*; D008956:Models, Chemical; D009930:Organic Chemicals / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship
chemical_list: D009930:Organic Chemicals