The logarithms of retention factors normalized to a hypothetical pure water eluent(log k w) were determined on a reversed phase high performance liquid chromatography(RP HPLC) column (Li Chrosorb RP 18 column...The logarithms of retention factors normalized to a hypothetical pure water eluent(log k w) were determined on a reversed phase high performance liquid chromatography(RP HPLC) column (Li Chrosorb RP 18 column) for 20 new α\|branched phenylsulfonyl acetates. The atomic charge method was applied to develop quantitative structure retention relationships(QSRRs). Among the available geometric and electronic descriptors, surface area (S), ovality (O), and the charge of carboxyl group(Q OC ) are significant. In the model, the contribution of surface area (S) is the greatest. The molecular mechanism of retention was demonstrated through the model. With the correlation coefficient ( r 2 adj , adjusted for degrees of freedom) of 0.964, the standard error of 0.164 and the F value of 170.39, the model has good predictive capacity.展开更多
Chlorinated paraffins（CPs） are potential persistent organic pollutants（POPs）, which threat the safety of environment and organisms. However, the analysis of CPs is a difficult task due to their complex composition...Chlorinated paraffins（CPs） are potential persistent organic pollutants（POPs）, which threat the safety of environment and organisms. However, the analysis of CPs is a difficult task due to their complex composition containing thousands of congeners. In the present work, quantitative structure retention relationship（QSRR） of CPs was studied. A total of 470 molecular descriptors were generated, for describing the structures of 28 CPs and 12 descriptors relevant to retention time of the CPs were selected by stepwise regression. Then, QSRR models between retention time on the one hand and the selected descriptors on the other hand were established by multiple linear regres- sion（MLR）, partial least squares（PLS） and least square support vector regression（LS-SVR）. The result shows that PLS model is better than MLR and LS-SVR, obtaining a squared correlation coefficient（r2） of 0.9996 and a root mean squared error（RMSE） of 0.015. The PLS model was then used to predict the retention time of 49 C10-CPs. Three of them were investigated by gas chromatography coupled with mass spectrometry（GC-MS）. A well-defined correlation was found between the measured retention time and the predicted value.展开更多
基金TheNationalNaturalScienceFoundationofChina (No .2 9837180 )
文摘The logarithms of retention factors normalized to a hypothetical pure water eluent(log k w) were determined on a reversed phase high performance liquid chromatography(RP HPLC) column (Li Chrosorb RP 18 column) for 20 new α\|branched phenylsulfonyl acetates. The atomic charge method was applied to develop quantitative structure retention relationships(QSRRs). Among the available geometric and electronic descriptors, surface area (S), ovality (O), and the charge of carboxyl group(Q OC ) are significant. In the model, the contribution of surface area (S) is the greatest. The molecular mechanism of retention was demonstrated through the model. With the correlation coefficient ( r 2 adj , adjusted for degrees of freedom) of 0.964, the standard error of 0.164 and the F value of 170.39, the model has good predictive capacity.
基金Supported by the National Natural Science Foundation of China（No.21175074）.
文摘Chlorinated paraffins（CPs） are potential persistent organic pollutants（POPs）, which threat the safety of environment and organisms. However, the analysis of CPs is a difficult task due to their complex composition containing thousands of congeners. In the present work, quantitative structure retention relationship（QSRR） of CPs was studied. A total of 470 molecular descriptors were generated, for describing the structures of 28 CPs and 12 descriptors relevant to retention time of the CPs were selected by stepwise regression. Then, QSRR models between retention time on the one hand and the selected descriptors on the other hand were established by multiple linear regres- sion（MLR）, partial least squares（PLS） and least square support vector regression（LS-SVR）. The result shows that PLS model is better than MLR and LS-SVR, obtaining a squared correlation coefficient（r2） of 0.9996 and a root mean squared error（RMSE） of 0.015. The PLS model was then used to predict the retention time of 49 C10-CPs. Three of them were investigated by gas chromatography coupled with mass spectrometry（GC-MS）. A well-defined correlation was found between the measured retention time and the predicted value.
