A QSRR Study on the Relative Retention Time of Halogenated Methyl-phenyl Ethers

Halogenated methyl-phenyl ethers （anisoles） are ubiquitous organic compounds in the environment. In the present study, geometrical optimization and electrostatic potential calculations have been performed for 42 halogenated anisoles at the HF/6-31 G^＊ level. A number of statistically based parameters have been obtained. By multiple regression method, linear relationships between the gas-chromatographic relative retention time （RRT） and structural descriptors have been established for the training set of 32 halogenated anisoles. The result showed that the parameters derived from electrostatic potentials （ESPs） together with the molecular volume （Vmc） could be well used to express the quantitative structure-RRT relationships of halogenated anisoles. The best two-variable regression model gives a correlation coefficient of 0.980 and a standard deviation of 0.07, and the leave-one-out cross-validated correlation coefficient is 0.975. The goodness of the model has been further validated through exploring the predictive power for the testing set of 10 halogenated anisoles.

Predicting the Relative Retention Time (RRT) of Polybrominated Diphenyl Ethers (PBDEs)

Using the molecular electronegativity distance vector descriptors derived directly from the molecular topological structures, the relative retention time （RRT） of polybrominated diphenyl ethers （PBDEs） were predicted. A four-variable regression model （M30） with the correlation coefficient of 0.9816 and the root mean square errors of 0.061 was developed using a training set including 30 PBDEs. The correlation coefficient of 0.9841 and the root mean square errors of 0.054 between the values of RRT predicted by M30 and the RRT observed for 16 external PBDEs show a good predictive potential of M30. The descriptors included in the M30 represent four interactions between four pairs of atom types, i.e., atom -C= and -C=, -C= and 〉C=, 〉C= and 〉C=, -C= and -Br.

Quantitative structure retention relationship studies for predicting relative retention times of chlorinated phenols on gas chromatography

A new method of quantitative structure retention relationship(QSRR) studies was reported for predicting gas chromatography(GC) relative retention times(RRTs) of chlorinated phenols (CPs) using a DB 5 column. Chemical descriptors were calculated from the molecular structure of CPs and related to their gas chromatographic RRTs by using multiple linear regression analysis. The proposed model had a multiple square correlation coefficient R 2=0.970, standard error SE =0.0472, and significant level P =0.0000. The QSRR model also reveals that the gas chromatographic relative retention times of CPs are associated with physicochemical property interactions with the stationary phase,and influenced by the number of chlorine and oxygen in the CP melecules.

New Modes for the Prediction of Gas Chromatographic Relative Retention Times of Polybrominated Diphenyl Ethers

A series of quantitative structure-retention relationship models were developed to predict gas chromatographic relative retention times （GC-RRTs） for 209 polybrominated diphenyl ether （PBDE） congeners on 10 stationary phases. A genetic algorithm with twofold leave-multiple-out cross validation （LMOCV） was used to select optimal subsets from large-size molecular descriptors. Overall multiple-linear regression fitting correlation coefficients （R2） are greater than 0.988, except for the CP-Sil 19 colunm, in which Q^uocv （correlation coefficient of LMOCV）, Q^oocv （correlation coefficient of leave-one-out cross validation, LOOCV）, and Rp2re （correlation coefficients of prediction set） are larger than 0.98. The excellent statistical parameters reveal that the models are robust and have high internal and external predictive capability. According to the descriptors for constructing the models, the GC-RRTs in various stationary phases are highly dependent on distances among atoms, branches of molecules, and molecular properties. PBDE congeners with 1, 9, and 10 bromines are major outliers based on the results of the application domain.