Modeling of the relationship between electroosmotic flow and separation parameters in capillary zone electrophoresis using artificial neural networks and experimental design

• Published in: Talanta (Oxford) Vol. 65; no. 4; pp. 853 - 860
• Main Authors: Zhang, Ya Xiong, Li, Hua, Havel, Josef
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 28.02.2005; Oxford Elsevier
• Subjects: Analytical chemistry; Artificial neural networks; Chemistry; Chromatographic methods and physical methods associated with chromatography; Electroosmotic flow; EOF prediction; Exact sciences and technology; Other chromatographic methods; Principal component analysis; Standard normal distribution simulation; Electroosmotic flow; Standard normal distribution simulation; EOF prediction; Artificial neural networks; Principal component analysis; Performance evaluation; Zone electrophoresis; Capillary electrophoresis; Marker; Neural network; Modeling; Simulation; Ionic strength; Experimental design; Electroosmosis
• ISSN: 0039-9140; 1873-3573
• DOI: 10.1016/j.talanta.2004.08.016

The prediction of migration time of electroosmotic flow (EOF) marker was achieved by applying artificial neural networks (ANN) model based on principal component analysis (PCA) and standard normal distribution simulation to the input variables. The voltage of performance, the temperature in the capillary, the pH and the ionic strength of background electrolytes (BGE) were applied as the input variables to ANN. The range of the performance voltage studied was from 15 to 27 kV, and that of the temperature in the capillary was from 20 to 30 °C. For the pH values studied, the range was from 5.15 to 8.04. The range of the ionic strength investigated in this paper was from 0.040 to 0.097. The prediction abilities of ANN with different pre-processing procedure to the input variables were compared. Under the same performance conditions, the average prediction error of the migration time of the EOF marker was 5.46% with RSD = 1.76% according to 10 parallel runs of the optimized ANN structure by the proposed approach, and that of the 10 parallel predictions of the optimal ANN structure for the different performance conditions was 12.95% with RSD = 2.29% according to the proposed approach. The study showed that the proposed method could give better predicted results than other approaches discussed.