Generalization of Reference System for Calculating the Second Dimension Retention Index in GC×GC-MS

Using C_(4)-C_(25)fatty acid methyl esters(C_(4)-C_(25)FAMEs)as a sample reference series,a method was developed to generalize the reference system for calculating the second dimension retention index(2I)of compounds analyzed by comprehensive two-dimensional gas chromatography-mass spectrometry(GC×GC-MS).The second dimension elution temperature(2Te),second dimension unadjusted retention time(2tR),and the linear retention index(IT)of C_(4)-C_(25)FAMEs were used to form a second dimension retention index surface(2IS)via a three-dimensional surface fitting model.The 2I of an analyte analyzed by GC×GC-MS was then calculated from the 2IS based on its 2tR and 2Te.The developed method was validated by calculat-ing the 2I of n-alkanes,80 compounds,and two commercially available mixtures(MegaMix A and MegaMix B).Compared to the conventional method,the developed method keeps the 2I in n-alkane retention index scale,and enables using any compounds as references to obtain a much increased separation space in the second dimension GC.

Chemometrics to chemical modeling: novel molecular distance-edge vector (λ) in alkanes and retention index of gas chromatography

Systematic studies are made on application of chemometrics to chemical modeling and/or molecular modeling as well as the regularity of retention index for gas chromatography (GC). A set of novel molecular graph theoretical parameters, called the molecular distance-edge (MDE) vector (λ), is proposed ad found to be excellently correlated to retention index of GC for alkanes. The MDE parameters were tested by the multiple linear regression (MLR) estimation and production of the retention index of GC, and the results obtained are satisfactory.

A QSRR Study on the Chromatographic Retention Indices of Hydroxylated Polychlorinated Biphenyls

Hydroxylated Polychlorinated Biphenyls （HO-PCBs） are the metabolite of polychlorinated biphenyls and have drawn much attention because they have hazard on human health and ecosystems. Molecular connectivity index calculation has been performed for 19 HO-PCB compounds. A number of statistically based parameters have been extracted. Linear relationship between chromatographic retention index （RI） and the molecular connectivity index of 15 compounds in the training set has been established by multiple linear regression method. The other 4 HO-PCBs are used as the external test set. The result shows that the parameters can be well used to express the quantitative structure-retention relationship （QSRR） of HO-PCBs. Good stability and predictive ability have been demonstrated by leave-one-out cross-validation and the external test set.

Study of Quantitative Structure-retention Relationship for Substituted Phenols

Quantum chemistry parameters of 20 substituted phenols were computed at the 6- 31G＊ level using DFT method. Based on theoretical linear solvation energy theory, the correlation equation that can predict the retention index （Ri） was developed using structural parameters of 15 substituted phenols with experimental data as theoretical descriptors, and the conventional correlation coefficient （R^2） is 0.9885. The correlation degree of each independent variable in the model was validated by variance inflation factors （VIF） and t-test. Cross-validation indicates that the model possesses high predicting ability. The correlation and predicting ability of the Ri equation are both more advantageous than those based on orientating group index, connectivity index, and topological index method. In addition, RI values of 5 compounds without experimental data were predicted with the model.

Estimation and Prediction of Gas Chromatography Retention Indices of Hydrocarbons in Straight-run Gasoline by Using Artificial Neural Network and Structural Coding Method

The molecular structures of hydrocarbons in straight run gasoline were numerically coded. The nonlinear quantitative relationship(QSRR) between gas chromatography(GC) retention indices of the hydrocarbons and their molecular structures were established by using an error back propagation(BP) algorithm. The GC retention indices of 150 hydrocarbons were then predicted by removing 15 compounds(as a test set) and using the 135 remained molecules as a calibration set. Through this procedure, all the compounds in the whole data set were then predicted in groups of 15 compounds. The results obtained by BP with the correlation coefficient and the standard deviation 0 993 4 and 16 54, are satisfied.

Quantitative Structure-retention Relationship Study of Volatile Components from Rosa Banksiae Ait

In the present study,（QSRR） study had been carried out for volatile components from Rosa banksiae Ait.based on various quantum-chemical and physicochemical descriptors derived by B3LYP method.To build QSRR models,a multiple linear regression （MLR） stepwise method was used.The generated models have good predictive ability and are of high statistical significance with good correlation coefficients （R2≥0.734） and p values far less than 0.05.Preliminary results indicated that the application of the models,especially the prediction of GC retention time and linear retention index of volatile components from Rosa banksiae Ait.,will be helpful.The models contribute also to the identification of important quantum-chemical and physicochemical descriptors responsible for the retention time and linear retention index.It was found that the shape attribute （ShpA） and logP value play a vital role in determining component’s GC retention time and linear retention index which increase with the lipophilicity of volatile components.The larger the shape attribute of analyte is,the larger the deformability is,the stronger the interaction between analyte and stationary phase is,and the longer the GC retention time is,the larger the linear retention index is.The importance of E HOMO,q＋,and SEV is also embodied in models,but they are not dominant.