KPCA-聚类分析法和用便携式拉曼仪快速鉴别降糖药

对不同种类的降糖药片进行拉曼光谱的核主成分分析(KPCA)-聚类分析，实现快速、简便的鉴别。KPCA可以有效地避免主成分分析(PCA)只能处理线性问题和降维效果不明显的弊端。它通过一个非线性变换，首先将原变量空间映射到高维特征空间，然后在这个高维特征空间中进行线性主成分分析。采集得到的药片拉曼光谱的KPCA-聚类分析结果表明，采用KPCA提取特征变量的聚类结果比采用PCA提取特征变量后进行聚类分析的效果好，并且未经刮除表面包膜的降糖药片识别准确率为96.5%，经过刮除表面包膜处理的降糖药片的识别准确率为100%。便携式拉曼光谱仪结合该方法以其检测速度快、准确率高、使用简便、无样品前处理等显著优势，为药品的快速检验技术提供一种新的有效的鉴别手段。

Rapid Determination of Hypoglycemic Tablets by Handheld Raman Spectrometer and KPCA-Clustering Analysis

In the present paper, five different kinds of hypoglycemic tablets were identified using kernel principal component analysis (KPCA)-clustering analysis of their Raman spectra. KPCA was used to compress thousands of spectral data into several variables and to describe the body of the spectra before clustering analysis was chosen as further research method. The results showed that hypoglycemic tablets could be quickly classified using KPCA-clustering analysis. A disadvantage of Raman spectros-copy for this type of analysis is that it is primarily a surface technique. As a consequence, the spectra of the tablet core and its coating might differ. However, the KPCA-clustering analysis turned out to be a sufficiently reliable discrimination, i. e., 96% of the hypoglycemic tablets with coating and 100% of the hypoglycemic tablets without coating were predicted correctly. Over-all, the Raman spectroscopic method in the present paper plays a good role in the identification and offers a new approach to the rapid discrimination of different kinds of hypoglycemic tablets.