A new molecular structural characterization (MSC) method was constructed in this paper. The structure descriptors were used to describe the structures of 149 compounds. Through multiple linear regression (MLR) and stepwise multiple regression (SMR), a quantitative structure-retention relationship (QSRR) model with 6 variables was obtained. The correlation coefficient (R) of the model was 0.944. Through partial least-squares regression (PLS), another QSRR model with 5 principal components was obtained. The correlation coefficient (R) of the model was 0.941. The estimation stability and prediction ability of the two models was strictly analyzed by both internal and external validations. For the internal validation, the Cross-Validation (CV) correlation coefficients (RCV) for Leave-One-Out (LOO) were 0.931 and 0.932, respectively. For the external validation, the correlation coefficients (Rtest) of the two models were 0.907 and 0.932. The results suggested good stability and predictability of the model. The prediction results are in very good agreement with the experimental values. This paper provided a new and effective method for predicting the chromatography retention time.
A new molecular structural characterization (MSC) method was constructed in this paper. The structure descriptors were used to describe the structures of 149 compounds. Through multiple linear regression (MLR) and stepwise multiple regression (SMR), a quantitative structure-retention relationship (QSRR) model with 6 variables was obtained. The correlation coefficient (R) of the model was 0.944. Through partial least-squares regression (PLS), another QSRR model with 5 principal components was obtained. The correlation coefficient (R) of the model was 0.941. The estimation stability and prediction ability of the two models was strictly analyzed by both internal and external validations. For the internal validation, the Cross-Validation (CV) correlation coefficients (RCV) for Leave-One-Out (LOO) were 0.931 and 0.932, respectively. For the external validation, the correlation coefficients (Rtest) of the two models were 0.907 and 0.932. The results suggested good stability and predictability of the model. The prediction results are in very good agreement with the experimental values. This paper provided a new and effective method for predicting the chromatography retention time.
覃松;李建凤;廖立敏. Estimation and Prediction of Retention Time for a Variety of Volatile Organic Compounds[J]. , 2012, 31(5): 665-672.
QIN Song;LI Jian-Feng;LIAO Li-Min. Estimation and Prediction of Retention Time for a Variety of Volatile Organic Compounds. , 2012, 31(5): 665-672.
2012, Vol. 31 
