A Novel Pre-Processing Algorithm Based on the Wavelet Transform for Raman Spectrum

Abstract

Noise and fluorescent background are two major problems for acquiring Raman spectra from samples, which blur Raman spectra and make Raman detection or imaging difficult. In this paper, a novel algorithm based on wavelet transform that contains denoising and baseline correction is presented to automatically extract Raman signals. For the denoising section, the improved conventional-scale correlation denoising method is proposed. The baseline correction section, which is performed after denoising, basically consists of five aspects: (1) detection of the peak position; (2) approximate second derivative calculation based on continuous wavelet transform is performed using the Haar wavelet function to find peaks and background areas; (3) the threshold is estimated from the peak intensive area for identification of peaks; (4) correction of endpoints, spectral peaks, and peak position; and (5) determine the endpoints of the peak after subtracting the background. We tested this algorithm for simulated and experimental Raman spectra, and a satisfactory denoising effect and a good capability to correct background are observed. It is noteworthy that this algorithm requires few human interventions, which enables automatic denoising and background removal.