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Halogenated methyl-phenyl ethers (anisoles) are ubiquitous organic compounds in the environment. In the present study, geometrical optimization and electrostatic potential calcu- lations have been performed for 42 halogenated anisoles at the HF/6-31G* level. A number of sta- tistically 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.
Halogenated methyl-phenyl ethers (anisoles) are ubiquitous organic compounds in the environment. In the present study, geometrical optimization and electrostatic potential calcu lations have been performed for 42 halogenated anisoles at the HF / 6-31G * level. A number of sta 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 t hrough exploring the predictive power for the testing set of 10 halogenated anisoles.