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

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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。

关键词：主成分分析;线性神经网络;近红外光谱;VC银翘片

Abstract：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.

Key words：Principal component analysis(PCA);Linear neural network;NIR spectrum;VC yinqiao tablet

收稿日期: 2003-11-28 修订日期: 2004-04-15

中图分类号:

O657.3

通讯作者: 白英奎

引用本文:

白英奎¹，申铉国²，冯毅²，张铁强²，黄芳² . 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[J]. 光谱学与光谱分析, 2005, 25(06): 898-901.
BAI Ying-kui¹, SHEN Xuan-guo², FENG Yi², ZHANG Tie-qiang², HUANG Fang² . A New Linear Neural Network Multi-Component Analysis Method and Its Application in the Analysis of VC Yinqiao Tablets Quantitative Analysis . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2005, 25(06): 898-901.

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https://www.gpxygpfx.com/CN/Y2005/V25/I06/898