粒子群算法优化中药化学成分的毛细管电泳分离条件

建立了一种基于粒子群优化算法的毛细管电泳条件辅助优化方法。以丹参为研究对象,将改良的色谱指数方程用于评价酚酸类成分的电泳分离性能,用粒子群优化算法对分离条件进行全局寻优,获得最佳的区带电泳分离条件(5.0 mmol/L硼砂,18.5 mmol/L磷酸二氢钠,6.1%乙腈,运行电压18.2 kV)。为进一步改善分离,在所获优化条件下添加50.0 mmol/L SDS,在胶束电动毛细管色谱分离模式下使酚酸类成分(原儿茶醛、丹参素、丹酚酸B等)得到更好分离。本方法准确可靠,可推广应用于其他复杂化学体系的毛细管电泳分离条件优化。

An Approach to Optimizing the Separation of Chinese Medicines by Capillary Electrophoresis Using Particle Swarm Optimization

A novel method based on particle swarm optimization(PSO) algorithm for the optimization of the separation of phenolic acids in salvia miltiorrhiza by capillary electrophoresis(CE) was proposed.The modified chromatographic exponential function(MCEF) was used to evaluate the quality of the CE separation.The optimized condition(5.0 mmol/L borate,18.5 mmol/L phosphate,6.1% acetonitrile,applied voltage(18.2 kV)) provided the best separation by capillary zone electrophoresis(CZE).To improve the separation further,micellar electrokinetic capillary chromatography(MECC) was employed and 50.0 mmol/L sodium dodecyl sulfonate was added into the optimized running buffer.It was shown that PSO was an effective(approach) for the optimization of the running condition of CE in the separation of complex matrices.