Coordinación para la Innovación y la Aplicación de la Ciencia y la Tecnología, Universidad Autónoma de San Luis Potosí, México.
Appl Spectrosc. 2012 Jun;66(6):650-5. doi: 10.1366/11-06495.
In this work principal component analysis (PCA), a multivariate pattern recognition technique, is used to characterize the noise contribution of the experimental apparatus and two commonly used methods for fluorescence removal used in biomedical Raman spectroscopy measurements. These two methods are a fifth degree polynomial fitting and an iterative variation of it commonly known as the Vancouver method. The results show that the noise in Raman spectroscopy measurements is related to the spectral resolution of the measurement equipment, the intrinsic variability of the biological measurements, and the fluorescence removal algorithm used.
在这项工作中，主成分分析（PCA），一种多元模式识别技术，用于描述实验仪器的噪声贡献以及生物医学拉曼光谱测量中常用的两种荧光去除方法。这两种方法是五阶多项式拟合和一种常用的迭代变化，称为温哥华方法。结果表明，拉曼光谱测量中的噪声与测量设备的光谱分辨率、生物测量的固有可变性以及所使用的荧光去除算法有关。
