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Raman spectroscopy provides information about the structure, functional groups and environment of the molecules in the samples, and is widely used in various application areas including chemical analysis, biological processes, environmental and food sciences etc., because of its features of rapidness and non-destruction. The processing and analysis of Raman spectrum is required to extract useful information from original spectrum. For each individual spectrum, a multitude of preprocessing 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 the window, Wavelet based method requires to choose the appropriate Wavelet family, thresholds, and scales, thus the methods mentioned above is not applicable for fully automated data processing and qualitative analysis of handheld Raman spectroscopy. This paper proposes a multi-scale wavelet thresholding denoising algorithm (MWTD). The Raman signal is decomposed into different scales (multi resolution), each scale (resolution) gives different frequency-related information contained in the Raman signal. As noise (high frequency) related frequencies are different compared with genuine Raman bands (mid frequency), at an optimum resolution appropriate thresholds can be applied to eliminate noise. After thresholding (removing) the noise, the corrected Raman signal can be obtained by the Inverse Wavelet Transform. Both simulated and experimental data are used to evaluate the performance of the MWTD algorithm. The results 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 the signal. When processing large datasets, a fully automated algorithm such as MWTD would be desirable as it is not required to set any parameters. Thus, the proposed MWTD method is more suitable for the preprocessing before the spectral data modeling and has a better application in the spectroscopic analysis.
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