College of Electrical and Information, Heilongjiang Bayi Agricultural University, Daqing 163319, China.
College of Electrical and Information, Heilongjiang Bayi Agricultural University, Daqing 163319, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Apr 5;250:119374. doi: 10.1016/j.saa.2020.119374. Epub 2020 Dec 22.
Raman spectroscopy is a non-destructive technique utilizing lasers to observe scattered light in order to determine things such as vibrational modes in the molecular system. A major problem inherent to this technique is that due to their short exposure time and the low power of the excitation laser, Raman signals are very weak. They tend to be much weaker than the noise and can even be drowned out. Conventional denoising methods are currently unable to extract Raman peaks with precision so it is necessary to specifically study Raman signal extraction methods that involve a low signal-to-noise ratio (SNR). In this study, a denoising method for Raman spectra with low SNR based on feature extraction was proposed. Based on the Hilbert Vibration Decomposition (HVD) method, the Raman spectra was decomposed into two components. The peaks were located in the first component and compensated by those in the second component. Then based on the position and height of the peaks, their full widths at half maximum (FWHM) are calculated. Finally, based on the position, height and FWHM of the peaks, Gaussian signals are used to reconstruct the Raman peaks from strong noise and baseline. In the data simulation experiment, the denoising method used improved the SNR from 3.5316 to 130.6386 and the mean square error (MSE) was reduced from 213.8635 to 14.0404. In the actual experiment, this method successfully extracted the characteristic peaks of melamine despite the noise from employing a low excitation laser (10 mW). The characteristics such as the amplitude and position of the peaks were identical to those obtained under a high excitation laser (150 mW). The error of the FWHM under different excitation laser powers (10 and 150 mW) was less than the spectral resolution. Using the method proposed in this paper, the Raman signal of biological samples such as rice leaves were extracted from the raw spectrum, and information on the spectral peak position, amplitude and FWHM were obtained with clarity. The characteristic peaks of the carotene molecule, protein amide I, protein phenylalanine, nucleic acid cytosine, cellulose, DNA phosphodiester, RNA phosphodiester, D-glucose, α-D glucose, chlorophyll, lignin and cellulose were all accurate as well. The results from the simulation data and actual experiments show that a method based on feature extraction can effectively extract Raman peaks even when they are submerged in background noise. It should be noted that the practicality of this method lies in the fact that it requires few parameters and is simple to operate and implement.
拉曼光谱是一种利用激光观察散射光的无损技术，用于确定分子系统中的振动模式等。该技术的一个主要问题是，由于其曝光时间短且激发激光的功率低，拉曼信号非常弱。它们往往比噪声弱得多，甚至可能被淹没。目前，常规的去噪方法无法精确提取拉曼峰，因此有必要专门研究涉及低信噪比 (SNR) 的拉曼信号提取方法。在这项研究中，提出了一种基于特征提取的低 SNR 拉曼光谱去噪方法。基于 Hilbert 振动分解 (HVD) 方法，将拉曼光谱分解为两个分量。在第一分量中定位峰，并由第二分量中的峰进行补偿。然后基于峰的位置和高度，计算其半峰全宽 (FWHM)。最后，基于峰的位置、高度和 FWHM，使用高斯信号从强噪声和基线中重建拉曼峰。在数据模拟实验中，该去噪方法将 SNR 从 3.5316 提高到 130.6386，均方误差 (MSE) 从 213.8635 降低到 14.0404。在实际实验中，尽管采用低激发激光 (10 mW)，该方法仍成功提取了三聚氰胺的特征峰。在不同激发激光功率 (10 和 150 mW) 下，峰的幅度和位置的特征与高激发激光 (150 mW) 下获得的特征相同。不同激发激光功率 (10 和 150 mW) 下 FWHM 的误差小于光谱分辨率。使用本文提出的方法，从原始光谱中提取了水稻叶片等生物样品的拉曼信号，并清晰地获得了光谱峰位置、幅度和 FWHM 的信息。胡萝卜分子、蛋白质酰胺 I、蛋白质苯丙氨酸、核酸胞嘧啶、纤维素、DNA 磷酸二酯、RNA 磷酸二酯、D-葡萄糖、α-D 葡萄糖、叶绿素、木质素和纤维素的特征峰也都准确无误。模拟数据和实际实验的结果表明，基于特征提取的方法即使在淹没在背景噪声中时也能有效地提取拉曼峰。需要注意的是，该方法的实用性在于它需要的参数较少，操作和实现简单。
