拉曼光谱的荧光背景扣除及其用于药物聚类分析

拉曼光谱分析中,由于有机分子或样品中污染物的荧光影响,常会使拉曼光谱产生高背景信号,以致其拉曼光谱吸收信号被淹没。利用自行开发的软件包baselineWavelet,本文对醋酸泼尼松片和格列本脲片的拉曼光谱进行了荧光背景扣除研究,采用主成分分析和随机森林算法对它们进行聚类分析,得到了较好的结果。通过这2种药物的拉曼光谱聚类分析结果,检验了该背景扣除算法的有效性和准确性,并讨论了荧光背景对拉曼光谱聚类分析的影响。结果说明,荧光背景对拉曼光谱聚类分析影响很大,在分析前必须预先扣除。

Raman spectroscopy fluorescence background correction and its application in clustering analysis of medicines

During Raman spectroscopy analysis, the organic molecules and contaminations will obscure or swamp Raman signals. The present study starts from Raman spectra of prednisone acetate tablets and glibenclamide tables, which are acquired from the BWTek i-Raman spectrometer. The background is corrected by R package baselineWavelet. Then principle component analysis and random forests are used to perform clustering analysis. Through analyzing the Raman spectra of two medicines, the accurate and validity of this background-correction algorithm is checked and the influences of fluorescence background on Raman spectra clustering analysis is discussed. Thus, it is concluded that it is important to correct fluorescence background for further analysis, and an effective background correction solution is provided for clustering or other analysis.