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.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
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
关键词
主成分分析
线性神经网络
近红外光谱
VC银翘片
principal component analysis(PCA)
linear neural network
NIR spectrum
VC yinqiao tablet