An Efficient Strategy for Optimization of Isocratic Mobile Phase Conditions for HPLC Separation of a Complex Mixture

Abstract

Chromatographic resolution of a complex mixture is often a trial and error process. An efficient strategy for the optimization of previously reported isocratic mobile phase conditions utilizes a factorial design and multivariate regression equations to model the dependence of elution time of each component on chromatographic parameters. Several parameters are varied simultaneously, with extreme and central values to allow interpolation in the modeling. Run order is scrambled to minimize time dependent errors. Reported here is the optimization of the separation of monoamine neurotransmitters and metabolites using isocratic reverse phase HPLC chromatography with serial oxidation and reduction electrochemical detection. The predicted retention times closely follow the experimental retention times for a set of conditions not included in the calculation of the model. The results enable the rational adjustment of parameters to meet subsequent chromatographic needs.