Adaptive noise removal for biological Raman spectra with low SNR

Abstract

Raman data with low signal-to-noise ratio (SNR) are often obtained in the process of rapid or low-power acquisition for biological samples. The noise removal out of a biological Raman spectrum is laborious owing to numerous and superimposed Raman peaks, and it often takes a lot of time to find the optimal preproceed parameters. In this study, an adaptive denoising method for biological Raman data has been proposed, using three comparisons and a quantitative prediction model to show its simplicity and effectiveness. In this work, we decomposed Raman spectra of human skin and rice leaf on Hilbert vibration decomposition (HVD) method, to obtain a series of initial values, for example as peak position, intensity and full width at half maximum (FWHM), then fitted Raman spectrum according the Vogit function until the indicator function F reached the minimum value. The SNR and the root mean square error（RMSE) of HVD denoised spectrum were 57.5864 dB and 0.0108, and were both better than those of preproceed data (35.1826 dB, 0.0337; 32.4120 dB, 0.0352; 38.0685 dB, 0.0226) by Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Local Complementary Ensemble Empirical Mode Decomposition (LCEMD), compared with adaptive denoising methods, EMD, EEMD and LCEEMD, the improved algorithm had the advantages of no modal aliasing, no noise residue and independence of normalized permutation entropy. Then HVD denoised data of rice leaf samples were compressed by principal component analysis (PCA) and used for chlorophyll content prediction model. The correlation coefficient and standard error for prediction set of HVD +PCA model were 0.856 and 5.38, and were also better than those of non-adaptive preproceed data models (Mobing Average Smoothing, Gaussian Filter Smoothing, Median Filter Smoothing, Savitzky-Golay Smoothing and Wavelet Transform Denoising). The final experimental comparison showed that the HVD denoising method can also be applied to the Raman spectrum noise removal of inorganic samples, but better performances had been shown in the noise removal of complex biological Raman spectra. Accurate denoising of biological Raman spectrum is of great significance for accurate quantitative prediction and qualitative analysis, and no prior setting of the preproceed parameters makes the HVD denoising method easy to operate.