Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, SLP, México.
Laboratorio Nacional CI3M, Facultad de Ciencias & CICSaB, Universidad Autónoma de San Luis Potosí, San Luis Potosí, SLP, México.
Appl Spectrosc. 2019 Dec;73(12):1436-1450. doi: 10.1177/0003702819860121. Epub 2019 Aug 14.
A novel method based on the Vancouver Raman algorithm (VRA) and empirical mode decomposition (EMD) for denoising Raman spectra of biological samples is presented. The VRA is one of the most used methods for denoising Raman spectroscopy and is composed of two main steps: signal filtering and polynomial fitting. However, the signal filtering step consists in a simple mean filter that could eliminate spectrum peaks with small intensities or merge relatively close spectrum peaks into one single peak. Thus, the result is often sensitive to the order of the mean filter, so the user must choose it carefully to obtain the expected result; this introduces subjectivity in the process. To overcome these disadvantages, we propose a new algorithm, namely the modified-VRA (mVRA) with the following improvements: (1) to replace the mean filter step by EMD as an adaptive parameter-free signal processing method; and (2) to automate the selection of polynomial degree. The denoising capabilities of VRA, EMD, and mVRA were compared in Raman spectra of artificial data based on Teflon material, synthetic material obtained from vitamin E and paracetamol, and biological material of human nails and mouse brain. The correlation coefficient (ρ) was used to compare the performance of the methods. For the artificial Raman spectra, the denoised signal obtained by mVRA () outperforms VRA () for moderate to high noise levels whereas mVRA outperformed EMD () for high noise levels. On the other hand, when it comes to modeling the underlying fluorescence signal of the samples (i.e., the baseline trend), the proposed method mVRA showed consistent results (. For Raman spectra of synthetic material, good performance of the three methods ( for VRA, for EMD, and for mVRA) was obtained. Finally, in the biological material, mVRA and VRA showed similar results ( for VRA, for EMD, and for mVRA); however, mVRA retains valuable information corresponding to relevant Raman peaks with small amplitude. Thus, the application of EMD as a filter in the VRA method provides a good alternative for denoising biological Raman spectra, since the information of the Raman peaks is conserved and parameter tuning is not required. Simultaneously, EMD allows the baseline correction to be automated.
提出了一种基于温哥华拉曼算法（VRA）和经验模态分解（EMD）的生物样本拉曼光谱去噪新方法。VRA 是拉曼光谱去噪最常用的方法之一，由两个主要步骤组成：信号滤波和多项式拟合。然而，信号滤波步骤由一个简单的均值滤波器组成，该滤波器可能会消除强度较小的光谱峰或将相对接近的光谱峰合并为一个单独的峰。因此，结果通常对均值滤波器的阶数很敏感，因此用户必须仔细选择以获得预期的结果；这在过程中引入了主观性。为了克服这些缺点，我们提出了一种新的算法，即改进的-VRA（mVRA），具有以下改进：（1）用 EMD 代替均值滤波器作为自适应无参数信号处理方法；（2）自动选择多项式阶数。在基于特氟隆材料的人工数据、维生素 E 和扑热息痛合成材料以及人指甲和鼠脑的生物材料的拉曼光谱中，比较了 VRA、EMD 和 mVRA 的去噪能力。使用相关系数（ρ）来比较方法的性能。对于人工拉曼光谱，mVRA 得到的去噪信号（）在中等至高强度噪声水平下优于 VRA（），而 mVRA 在高强度噪声水平下优于 EMD（）。另一方面，当涉及到模拟样品的潜在荧光信号（即基线趋势）时，所提出的方法 mVRA 显示出一致的结果（。对于合成材料的拉曼光谱，三种方法（VRA 为，EMD 为，mVRA 为）都表现出良好的性能。最后，在生物材料中，mVRA 和 VRA 表现出相似的结果（VRA 为，EMD 为，mVRA 为）；然而，mVRA 保留了具有小振幅的相关拉曼峰的有价值信息。因此，将 EMD 作为 VRA 方法中的滤波器应用提供了一种很好的生物拉曼光谱去噪替代方法，因为保留了拉曼峰的信息，并且不需要调整参数。同时，EMD 允许自动进行基线校正。
