Chemometric modeling of neurotransmitter amino acid separation in normal and reversed migration micellar electrokinetic chromatography

Abstract A chemometric experimental design has been applied for the optimization of neurotransmitter amino acid separation in capillary electrophoresis. The optimizations were carried out for normal micellar electrokinetic chromatography (N-MEKC) and reversed migration micellar electrokinetic chromatography (RM-MEKC). In order to optimize three separation factors and study the interaction between factors, a response function was optimized via searching its optimum (minimum/maximum). For this purpose a central composite design with multivariate linear regression (MLR) analysis was utilized. Modeling with good regression coefficients from the MLR adequately described the interaction of factors such as background electrolyte and sodium dodecylsulfate concentrations which had a large impact on selectivity and migration behaviors. Similar optimal conditions regarding resolution and number of theoretical plates but different retention behaviors as a function of background electrolyte and micellar concentrations were observed for N-MEKC and RM-MEKC. Improved overall performance from the RM-MEKC separation of five neurotransmitter acids, superior to N-MEKC, is demonstrated in terms of repeatability, peak symmetry, sensitivity, and in particular, impurity determination in an overloaded separation system.