Denoising of Raman spectroscopy for biological samples based on empirical mode decomposition

Raman spectroscopy of biological samples presents undesirable noise and fluorescence generated by the biomolecular excitation. The reduction of these types of noise is a fundamental task to obtain the valuable information of the sample under analysis. This paper proposes the application of the empirical mode decomposition (EMD) for noise elimination. EMD is a parameter-free and adaptive signal processing method useful for the analysis of nonstationary signals. EMD performance was compared with the commonly used Vancouver algorithm (VRA) through artificial data (Teflon), synthetic (Vitamin E and paracetamol) and biological (Mouse brain and human nails) Raman spectra. The correlation coefficient (ρ) was used as performance measure. Results on synthetic data showed a better performance of EMD (ρ=0.52) at high noise levels compared with VRA (ρ=0.19). The methods with simulated fluorescence added to artificial material exhibited a similar shape of fluorescence in both cases (ρ=0.95 for VRA and ρ=0.93 for EMD). For synthetic data, Raman spectra of vitamin E were used and the results showed a good performance comparing both methods (ρ=0.95 for EMD and ρ=0.99 for VRA). Finally, in biological data, EMD and VRA displayed a similar behavior (ρ=0.85 for EMD and ρ=0.96 for VRA), but with the advantage that EMD maintains small amplitude Raman peaks. The results suggest that EMD could be an effective method for denoising biological Raman spectra, EMD is able to retain information and correctly eliminates the fluorescence without parameter tuning.