An assisted approach of the global optimization for the experimental conditions in capillary electrophoresis

Experimental conditions have effect on the separation of capillary electrophoresis (CE) directly. In this work, a set of index to describe the separation in CE was established properly. Based on a combination of genetic algorithm and least square support vector machine, an assisted approach of global optimization for experimental conditions was proposed for the first time, and it was applied to the separation of four synthetic compounds by CE in nonaqueous system. Under the optimum conditions obtained by this approach, the result of the experiment was satisfactory and proved that this novel approach was effective. Furthermore, we investigated the most important conditions that mainly affect the separation effectiveness of CE by partial least squares regression analysis. Because of the generalization of this new approach proposed, it can be applied to the optimization of other experimental processes.