Reversed phase chromatographic separations are optimized for analytes containing ionizable groups by adjustment of pH of mobile phases. As it seems the pKa values of compounds affect their retention because of the variety in their solvation. However,it is of stressful need to predict their behavior taking into account also a series of other parameters. This work focuses on the development of ten different models,using partial least squares regression,which will identify and quantify the impact of several factors in the chromatographic behavior of 104 analytes. The combined effect of their numerous characteristics is obvious since along with pH ( at 2. 3 and 6. 2),factors such as lipophilicity,molecular volume,polar surface area and the presence of spe-cific moieties in their structures are not diminished. On the contrary,they work increasing or counterbalancing several effects on the retention time. The models compiled can be applied to predict with reliability(R2>0. 865 and Q2>0. 777)the behavior of unknown drugs.
参考文献
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