一种新的线性神经网络多组分分析法及其在VC银翘片NIR定量分析中的应用  

A New Linear Neural Network Multi-Component Analysis Methodand Its Application in the Analysis of VC Yinqiao Tablets Quantitative Analysis

作　　者：白英奎[1] 申铉国[2] 冯毅[2] 张铁强[2] 黄芳[2] 

机构地区：[1]吉林大学通信工程学院,吉林长春130025 [2]吉林大学物理学院,吉林长春130025

出　　处：《光谱学与光谱分析》2005年第6期898-901,共4页Spectroscopy and Spectral Analysis

基　　金：教育部高等学校骨干教师资助计划(2000931)资助

摘　　要：用红外光谱仪测量了VC银翘片的近红外谱图,然后将主成分分析法(PCA)和线性神经网络结合,分析VC银翘片中的对乙酰氨基酚和维生素C的含量。讨论了主成分数的选择及影响神经网络的各参数。为了比较算法的性能,作者又分别采用了偏最小二乘法、主成分分析结合BP神经网络进行数据处理。实验及数据处理结果表明,在3种多组分分析方法中,主成分分析结合线性神经网络的方法具有最高的预测精度。We measured NIR spectrum of VC Yinqiao tablets with spectral instrument, analyzed the contents of acetaminophen and vitamin C in the VC Yinqiao tablets with principal component analysis(PCA) and Linear Neural Network, and discussed the choice of principal component number and ANN's parameters affecting the network. To compare arithmetic performance, the authors also processed the spectral data with partial least squares and PCA-BP neural network. Compared with other two data process methods, the experiment and the result of data process showed that the PCA-linear neural network possess the best forecasting precision.

关 键 词：主成分分析 线性神经网络 近红外光谱 VC银翘片 

分 类 号：O657.3[理学—分析化学]