Improved Vancouver Raman Algorithm Based on Empirical Mode Decomposition for Denoising Biological Samples. Issue 12 (December 2019)

• Record Type: Journal Article
• Authors: León-Bejarano, Fabiola; Méndez, Martin O.; Ramírez-Elías, Miguel G.; Alba, Alfonso
• Is Part Of: Applied spectroscopy. Volume 73:Issue 12(2019)
• Page Start: 1436
• Page End: 1450
• Publication Date: 2019-12
• Subjects: Background removal -- autofluorescence -- signal denoising -- time frequency analysis -- Raman spectroscopy -- polynomial fitting; Spectrum analysis -- Periodicals; 543.505
• DOI: 10.1177/0003702819860121
• Languages: English
• ISSNs: 0003-7028

Abstract

A novel method based on the Vancouver Raman algorithm (VRA) and empirical mode decomposition (EMD) for denoising Raman spectra of biological samples is presented. The VRA is one of the most used methods for denoising Raman spectroscopy and is composed of two main steps: signal filtering and polynomial fitting. However, the signal filtering step consists in a simple mean filter that could eliminate spectrum peaks with small intensities or merge relatively close spectrum peaks into one single peak. Thus, the result is often sensitive to the order of the mean filter, so the user must choose it carefully to obtain the expected result; this introduces subjectivity in the process. To overcome these disadvantages, we propose a new algorithm, namely the modified-VRA (mVRA) with the following improvements: (1) to replace the mean filter step by EMD as an adaptive parameter-free signal processing method; and (2) to automate the selection of polynomial degree. The denoising capabilities of VRA, EMD, and mVRA were compared in Raman spectra of artificial data based on Teflon material, synthetic material obtained from vitamin E and paracetamol, and biological material of human nails and mouse brain. The correlation coefficient (ρ) was used to compare the performance of the methods. For the artificial Raman spectra, the denoised signal obtained by mVRA (ρ> 0 . 91 ) outperforms VRA (ρ> 0 . 86 ) for moderate to high noise levels whereas mVRA outperformed EMD (ρ> 0 . 90 ) for high noise levels. On the other hand, when it comes to modeling the underlying fluorescence signal of the samples (i.e., the baseline trend), the proposed method mVRA showed consistent results (ρ> 0 . 94 ) . For Raman spectra of synthetic material, good performance of the three methods (ρ = 0 . 99 for VRA, ρ = 0 . 93 for EMD, andρ = 0 . 99 for mVRA) was obtained. Finally, in the biological material, mVRA and VRA showed similar results (ρ = 0 . 96 for VRA, ρ = 0 . 85 for EMD, andρ = 0 . 91 for mVRA); however, mVRA retains valuable information corresponding to relevant Raman peaks with small amplitude. Thus, the application of EMD as a filter in the VRA method provides a good alternative for denoising biological Raman spectra, since the information of the Raman peaks is conserved and parameter tuning is not required. Simultaneously, EMD allows the baseline correction to be automated.