Discriminant analysis of Chinese patent medicines based on near-infrared spectroscopy and principal component discriminant transformation
Discriminant analysis of Chinese patent medicines based on near-infrared spectroscopy and principal component discriminant transformation
复制标题
基于近红外光谱和主成分判别变换的中成药判别分析
DOI：
10.1016/j.saa.2015.05.030
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发表时间：
2015-10-05
影响因子：
4.4
通讯作者：
Shao, Xueguang
中科院分区：
文献类型：
--
作者：
Xu, Zhihong;Liu, Yan;Shao, Xueguang
采用主成分判别变换对不同中成药进行近红外光谱分析。在该方法中，通过最大化Fisher判别函数，设计了一组突出不同类别近红外光谱差异的最优正交判别向量。因此，可以利用不同类别的近红外光谱之间的微小差异来获得区分类别和其他类别的模型。此外，由于近红外光谱包含大量的冗余信息，采用主成分分析(PCA)对其进行降维。另一方面，采用连续小波变换(CWT)作为去除变化背景的预处理方法。对不同药品和不同厂家生产的同一药品进行了鉴定。结果表明，所有模型都能提供100%的识别率。(C)2015爱思唯尔B.V.保留所有权利。
Principal component discriminant transformation was applied for discrimination of different Chinese patent medicines based on near-infrared (NIR) spectroscopy. In the method, an optimal set of orthogonal discriminant vectors, which highlight the differences between the NIR spectra of different classes, is designed by maximizing Fisher's discriminant function. Therefore, a model for discriminating a class and the others can be obtained with the tiny differences between the NIR spectra of different classes. Furthermore, because NIR spectra contain a large amount of redundant information, principal component analysis (PCA) is employed to reduce the dimension. On the other hand, continuous wavelet transform (CWT) is taken as the pretreatment method to remove the variant background. For identifying the method, different medicines and the same medicine from different manufactures were studied. The results show that all the models can provide 100% discrimination. (C) 2015 Elsevier B.V. All rights reserved.