Multi-scale wavelet thresholding denoising algorithm of Raman spectrum

摘要

Raman spectroscopy provides information about the structure, functional groups and environment of the molecules in thesamples, and is widely used in various application areas including chemical analysis, biological processes, environmentaland food sciences etc., because of its features of rapidness and non-destruction. The processing and analysis of Ramanspectrum is required to extract useful information from original spectrum. For each individual spectrum, a multitude ofpreprocessing algorithms are required to eliminate effects of unwanted signals such as fluorescence, Mie scattering,detector noise, calibration errors, cosmic rays, laser power fluctuations, and other distortions. Among common methods,Moving Window Average, Moving Window Median and Savitzky-Golay (SG) filter require to set the length of thewindow, Wavelet based method requires to choose the appropriate Wavelet family, thresholds, and scales, thus themethods mentioned above is not applicable for fully automated data processing and qualitative analysis of handheldRaman spectroscopy. This paper proposes a multi-scale wavelet thresholding denoising algorithm (MWTD). The Ramansignal is decomposed into different scales (multi resolution), each scale (resolution) gives different frequency-relatedinformation contained in the Raman signal. As noise (high frequency) related frequencies are different compared withgenuine Raman bands (mid frequency), at an optimum resolution appropriate thresholds can be applied to eliminatenoise. After thresholding (removing) the noise, the corrected Raman signal can be obtained by the Inverse WaveletTransform. Both simulated and experimental data are used to evaluate the performance of the MWTD algorithm. Theresults demonstrate that the proposed MWTD method is superior to the hard/soft threshold and Savitzky-Golay (SG)methods in improving SNR, and can effectively eliminate the spectral noise and retain important detail features in thesignal. When processing large datasets, a fully automated algorithm such as MWTD would be desirable as it is notrequired to set any parameters. Thus, the proposed MWTD method is more suitable for the preprocessing before thespectral data modeling and has a better application in the spectroscopic analysis.