Improved Vancouver Raman Algorithm Based on Empirical Mode Decomposition for Denoising Biological Samples
| Autor: | Martin O. Mendez, Alfonso Alba, Miguel G. Ramírez-Elías, Fabiola León-Bejarano |
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| Rok vydání: | 2019 |
| Předmět: |
Materials science
Noise reduction 02 engineering and technology Spectrum Analysis Raman 01 natural sciences Hilbert–Huang transform Specimen Handling Mice symbols.namesake Animals Humans Vitamin E Polytetrafluoroethylene Instrumentation Spectroscopy Acetaminophen 010401 analytical chemistry Brain Signal Processing Computer-Assisted 021001 nanoscience & nanotechnology 0104 chemical sciences Nails symbols 0210 nano-technology Raman spectroscopy Algorithm Algorithms |
| Zdroj: | Applied Spectroscopy. 73:1436-1450 |
| ISSN: | 1943-3530 0003-7028 |
| Popis: | 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 ([Formula: see text]) outperforms VRA ([Formula: see text]) for moderate to high noise levels whereas mVRA outperformed EMD ([Formula: see text]) 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 ([Formula: see text]. For Raman spectra of synthetic material, good performance of the three methods ([Formula: see text] for VRA, [Formula: see text] for EMD, and [Formula: see text] for mVRA) was obtained. Finally, in the biological material, mVRA and VRA showed similar results ([Formula: see text] for VRA, [Formula: see text] for EMD, and [Formula: see text] 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. |
| Databáze: | OpenAIRE |
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