人工神经元网络法提高毛细管电泳药物控制分析准确度的研究

研究了人工神经元网络法在毛细管电泳定量测定memantine中提高测定准确度 的可行性。在毛细管电泳法定量测定memantine的过程中,其浓度与峰高或峰面积 以及与二者和内标的比值均没有良好的线性关系。人工神经元网络具有很强的非线 性校正能力,其最大优点是无须对分离体系及组分的迁移行为预先予以了解。人工 神经元网络的输为memantine的峰高和峰面积,输出为memantine的浓度。通过实验 确定的网络结构为2：1：1型。由于人工神经元网络的通用性,该法也可用于毛细 管电泳在其他药物控制分析中改善定量分析的准确度。

Enhancement of Precision in the Analysis of Medicines by Capillary Electrophoresis Using Artificial Neural Networks

Possibility of increasing the precision of quantitative determination of memantine by capillary electrophoresis (CE) was studied using artificial neural networks (ANNs). ANNs show their unique merits in the calibration of non-linear models. In the analysis of memantine by CE, there is no obvious linear relationships between the concentrations of the memantine and its peak areas or heights, or even their ratios to those of the corresponding internal standards, respectively. The greatest advantage of ANNs is that no prior knowledge of migration behavior and separation system is needed. The inputs of the ANNs were the peak areas and heights of memantine, and the outputs were the concentrations of the memantine. The 2:2:1 ANNs gave excellent results. The ANNs approach can also be used to improve the precision of determination of other kinds of analytes for its general validity.