Denoising method for Raman spectra with low signal-to-noise ratio based on feature extraction

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.