Zhang Ya Xiong, Li Hua, Havel Josef
Institute of Analytical Science, Northwest University, Xi'an, 710069, China.
Talanta. 2005 Feb 28;65(4):853-60. 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 27kV, and that of the temperature in the capillary was from 20 to 30 degrees 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.
通过将基于主成分分析（PCA）和标准正态分布模拟的人工神经网络（ANN）模型应用于输入变量，实现了电渗流（EOF）标记物迁移时间的预测。将电泳性能电压、毛细管温度、pH值以及背景电解质（BGE）的离子强度作为ANN的输入变量。所研究的电泳性能电压范围为15至27kV，毛细管温度范围为20至30摄氏度。所研究的pH值范围为5.15至8.04。本文研究的离子强度范围为0.040至0.097。比较了不同预处理程序的ANN对输入变量的预测能力。在所提出的方法下，在相同的电泳性能条件下，根据优化后的ANN结构进行10次平行运行，EOF标记物迁移时间的平均预测误差为5.46%，相对标准偏差（RSD）为1.76%；对于不同的电泳性能条件，根据所提出的方法，最优ANN结构的10次平行预测的平均预测误差为12.95%，RSD为2.29%。研究表明，所提出的方法比所讨论的其他方法能给出更好的预测结果。
