Improved Vancouver Raman Algorithm Based on Empirical Mode Decomposition for Denoising Biological Samples

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 ([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.

Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre‐diabetes and diabetes

“…The modified Vancouver Raman algorithm (mVRA) is a novel method to remove fluorescence in biological Raman spectra, proposed by Le on-Bejarano et al [20] The mVRA showed good results with respect to other methods with improvements such as the automatization of baseline correction using Empirical Mode Decomposition, and the denoising of Raman spectra by a nonlinear filter based on Empirical Mode Decomposition; thus, the method does not require adjustment of parameters to correct the baseline preserving the inherent variability on the signal. The results of mVRA suggest that the technique is a good alternative for processing complex Raman spectra, especially for biological samples.…”

Multispectral Raman Differentiation of Malignant Skin Neoplasms In Vitro: Search for Specific Biomarkers and Optimal Wavelengths

“…The most commonly used method in biomedical skin tissue measurements is the Vancouver Raman Algorithm, which combines peak removal The spectra obtained with excitation at 532 nm contained a broad, exponentially decreasing fluorescence curve with prominent Raman peaks, so the most important step in Raman signal processing is the removal of the fluorescence background that is superimposed on the Raman signal. The most commonly used method in biomedical skin tissue measurements is the Vancouver Raman Algorithm, which combines peak removal with a modified polynomial fitting [28][29][30][31][32]. An example of the fluorescence background calculated by the Vancouver Raman Algorithm and the final Raman spectra smoothed by the Savitzky-Golay method are shown in Figure 3.…”

“…The measuring of Raman/PL spectra at 1064 nm has been widely used for the investigation of various human tissues, including tumors, without destruction (thermal damage, photo damage) [12,30,[32][33][34][35][36], because this wavelength is non-destructive and non-mutagenic. The Raman/PL spectra of normal skin, BCC and SCC were registered at 1064 nm laser excitation in the 900-3100 cm -1 range.…”

Evaluating Noise Reduction Methods for Raman Spectroscopy in Transmission and Reflection Configurations

“…Boxcar averaging (BA) was used for high-frequency modulation of Raman noise [13]. Vancouver Raman algorithm (VRA), which is simple and effective, is a technique used to reduce fluorescence noise [14]. Three parameters of the boxcar averaging technique consisting of boxcar width order, integration time, and scan average, were used to find the optimum condition that provided good signals.…”