预测毛细管区带电泳有效淌度的支持向量回归建模方法  

A Method for Building Calibration Model of Prediction of Electrophoretic Mobilities in Capillary Zone Electrophoresis Using Support Vector Regression

作　　者：康宇飞[1] 瞿海斌[1] 沈朋[2] 程翼宇[1] 

机构地区：[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310003

出　　处：《分析化学》2004年第9期1151-1155,共5页Chinese Journal of Analytical Chemistry

基　　金：国家自然科学基金重大研究计划 (No .90 2 0 90 0 5 );浙江省科技计划资助项目 (No .2 0 0 4C3 3 0 2 6)

摘　　要：提出预测毛细管电泳迁移行为的支持向量回归建模方法。以核苷为实际研究对象 ,利用正交试验获得的数据 ,结合二标记物技术 ,用支持向量回归算法建立毛细管区带电泳的柱温、电压、缓冲液浓度和pH值与 3种核苷的有效淌度之间的相关模型。将其与偏最小二乘回归和人工神经网络方法相比较 ,结果表明所建模型的预测准确性优于后两者 ,适宜用于毛细管电泳迁移行为的预测。A novel method for building calibration model of prediction of electrophoretic mobilities in capillary zone electrophoresis using support vector regression (SVR) was proposed. Nucleotide was studied as an example and orthogonal design was applied to arrange the experiments. Support vector regression was used to build calibration models to predict the electrophoretic mobilities of nucleotide from separation conditions of capillary zone electorphresis, i.e. column temperature, voltage, buffer concentration and pH. The proposed method was compared with partial least square regression (PLSR) and artificial neural network (ANN) modeling methods. The results show that the predictive accuracy of calibration models built by SVR is better than that of the models built by PLSR and ANN. Therefore, the method is appropriate to predict electrophoretic mobilities in capillary zone electorphresis.

关 键 词：毛细管区带电泳 有效淌度 支持向量回归算法 核苷 

分 类 号：O658.9[理学—分析化学]