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Acta Chimica Sinica ›› 2004, Vol. 62 ›› Issue (19): 1917-1921. Previous Articles     Next Articles

毛细管电泳径向基神经网络校正法定量分析核苷

毛利锋1, 沈朋2, 程翼宇1   

  1. 1. 浙江大学药物信息学研究所, 杭州, 310027;
    2. 浙江大学医学院附属第一医院, 杭州, 310027
  • 投稿日期:2004-02-17 修回日期:2004-06-09 发布日期:2014-02-17
  • 通讯作者: 程翼宇,Email:chengyy@zju.edu.cn E-mail:chengyy@zju.edu.cn
  • 基金资助:
    国家863计划(No.2003AA222002)及浙江省科技计划(No.2004C33026)资助项目.

Calibration Modeling of Capillary Electrophoresis for Quantitative Analysis of Guanosine Based on Radial Basis Function Network

MAO Li-Feng1, SHEN Peng2, CHENG Yi-Yu1   

  1. 1. Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhenjing University, Hangzhou 310027;
    2. The First Hospital Attached to College of Medicine, Zhejiang University, Hangzhou 310003
  • Received:2004-02-17 Revised:2004-06-09 Published:2014-02-17

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A new method for calibration modeling of capillary electrophoresis (CE) is a nonlinear regression based on radial basis function network (RBFN). The RBFN was trained with a set of known concentration data and CE peak area of guanosine and internal standards to establish the calibration model between the concentration of guanosine and its peak area. Subsequently, the RBFN model established can predict the concentration of guanosine by the peak area of guanosine and internal standards. For comparison, the calibration modeling of CE was carried out using a linear regression equation, BP artificial neural network (BP-ANN) and RBFN approaches. The results of predicting the concentration of guanosine showed that the RBFN model had a lower mean relative error of prediction (0.86%) than those of linear regression equation (2.60%) and BP-ANN model (1.07%). The method is easy to be used and can effectively improve the accuracy of CE quantitative analysis.

Key words: capillary electrophoresis, quantitative analysis of nucleoside, radial basis function network(RBFN), calibration method