Frías-García Sergio, Sánchez M Jesús, Rodríguez- Delgado Miguel Angel
Department of Analytical Chemistry, Nutrition and Food Science, University of La Laguna, La Laguna, Tenerife, Spain.
Electrophoresis. 2004 Apr;25(7-8):1042-50. doi: 10.1002/elps.200305781.
The micellar electrokinetic chromatography separation of a group of triazine compounds was optimized using a combination of experimental design (ED) and artificial neural network (ANN). Different variables affecting separation were selected and used as input in the ANN. A chromatographic exponential function (CEF) combining resolution and separation time was used as output to obtain optimal separation conditions. An optimized buffer (19.3 mM sodium borate, 15.4 mM disodium hydrogen phosphate, 28.4 mM SDS, pH 9.45, and 7.5% 1-propanol) provides the best separation with regard to resolution and separation time. Besides, an analysis of variance (ANOVA) approach of the MEKC separation, using the same variables, was developed, and the best capability of the combination of ED-ANN for the optimization of the analytical methodology was demonstrated by comparing the results obtained from both approaches. In order to validate the proposed method, the different analytical parameters as repeatability and day-to-day precision were calculated. Finally, the optimized method was applied to the determination of these compounds in spiked and nonspiked ground water samples.
采用实验设计（ED）和人工神经网络（ANN）相结合的方法，对一组三嗪化合物的胶束电动色谱分离进行了优化。选择影响分离的不同变量并将其用作ANN的输入。使用结合了分离度和分离时间的色谱指数函数（CEF）作为输出，以获得最佳分离条件。一种优化的缓冲液（19.3 mM硼酸钠、15.4 mM磷酸氢二钠、28.4 mM十二烷基硫酸钠、pH 9.45和7.5%正丙醇）在分离度和分离时间方面提供了最佳分离效果。此外，开发了一种使用相同变量的胶束电动色谱分离的方差分析（ANOVA）方法，并通过比较两种方法获得的结果，证明了ED-ANN组合在优化分析方法方面的最佳能力。为了验证所提出的方法，计算了不同的分析参数，如重复性和日常精密度。最后，将优化后的方法应用于加标和未加标地下水样品中这些化合物的测定。
