The Institute of Optics, University of Rochester, Rochester, NY 14627, USA.
Analyst. 2009 Jun;134(6):1198-202. doi: 10.1039/b821856k. Epub 2009 Mar 10.
The use of Raman spectroscopy for biomedical applications requires overcoming the obstacle of the broad background that is also generated by biological samples. This background, which is often largely attributed to fluorescence, is frequently orders of magnitude greater than the Raman signal and needs to be removed in order to use Raman spectra in sample analysis. Several methods have been proposed for removing fluorescent signal, both instrumental and computational. Of the computational methods, polynomial fitting has become increasingly popular. Typically, a polynomial of approximately fifth order is used in the fitting. This method alone is not always capable of fitting some more tightly featured spectra that may be present in data, potentially coming from a contaminant in the sample itself or from the experimental design. If this signal is present in varying amounts, the polynomial background removal method can leave the residual spectra with non-uniform artifacts that hinder classification results. If a reference spectrum can be obtained for this interfering signal, however, it can be incorporated into the polynomial fit and removed separately. An automated method for the removal of broad and/or moderately featured background signal is described. In addition to simulations, the method has been applied to spectra from biofilms of Streptococcus mutans.
拉曼光谱在生物医学应用中需要克服由生物样本产生的宽背景的障碍。这种背景通常主要归因于荧光，其强度通常比拉曼信号大几个数量级，因此需要将其去除，才能在样品分析中使用拉曼光谱。已经提出了几种用于去除荧光信号的方法，包括仪器和计算方法。在计算方法中，多项式拟合变得越来越流行。通常，在拟合中使用大约五阶的多项式。然而，这种方法本身并不总是能够拟合可能存在于数据中的更紧密特征的光谱，这些光谱可能来自样品本身的污染物或实验设计。如果这种信号以不同的量存在，多项式背景去除方法可能会使剩余光谱带有不均匀的伪影，从而阻碍分类结果。然而，如果可以获得该干扰信号的参考光谱，则可以将其合并到多项式拟合中并单独去除。本文描述了一种用于去除宽谱和/或中等特征背景信号的自动方法。除了模拟，该方法还应用于变形链球菌生物膜的光谱。
