Modeling the Capacity Factor of Analytes in MicellarElectrokinetic Chromatography Using Computational Descriptors

A multiple linear regression model is proposed to calculate capacity factor ofanalytes using structural features. the chemical descriptors of analytes werecomputed using hyperchem software and regressed against the experimentalcapacity factors of analytes collected from the literature. the absolute averagepercentage deviation (aard) and individual percentage deviation (ipd) werecalculated as accuracy criteria. the accuracy of the proposed method was comparedwith that of a previously reported linear solvation energy relationship (lser).the proposed method was tested on ten experimental data sets and mean ± standarddeviation of aards were 48.5 ± 20.4 and 130.1 ± 79.7, respectively, for theproposed and lser models in which the mean difference was statistically significant(p<0.01). the distribution of ipds sorted in three subgroups, i.e. ≤45%, 45%-90%, and >90 %, shows the superiority of the proposed model over the lser. asignificant improvement in capacity factor modeling was achieved and theimprovement factor is about 2.7. the descriptors could be easily computed and thecalculations are straightforward. therefore, the model is suggested to be employedin practice, however, the efforts should be continued until providing more accuratemodels