SNR Enhancement and Deconvolution of Raman Spectra Using a Two-Point Entropy Regularization Method

Abstract

A new method for Raman signal recovery, the two-point maximum entropy method (TPMEM), based on a regularization method using two-point entropy is presented. The method can be used for signal-to-noise ratio (SNR) enhancement in very low SNR measurements or for deconvolution, in order to remove the effects of the instrumental line shape on the measured spectrum. Unlike most SNR enhancement schemes, TPMEM requires no filter parameters and no a priori knowledge of the expected signal. A rigorous test on a randomly produced set of convolved and/or noise-corrupted simulated Raman spectra is presented in order to validate the method and compare it to Savitzky-Golay filtering and the maximum entropy method. The method is evaluated on the basis of the root mean square (rms) error and correlation coefficients of the recovered data with the original data, as well as on the basis of SNR improvement, and showed significant improvements in both performance and speed over conventional methods. The method is demonstrated in an application involving fiber-optic-linked Raman and resonance Raman spectroscopy.

Discrimination between UV radiation‐induced and thermally induced spectral changes in AT‐paired DNA oligomers using UV resonance Raman spectroscopy

“…All spectral intensities were normalized to the 1049 cm 1 peak of the NO 3 internal standard. In instances where smoothing was applied, the 18-mer spectra were smoothed using a two-point maximum-entropy smoothing algorithm implemented in HiQ script (National Instruments, Austin, TX), 23 and the 12-mer spectra were smoothed using a 13-point, 3rd order Savitzky-Golay filter. 24 Spectra for the 12-mer samples were obtained in triplicate and averaged before further processing.…”

Development and Integration of Block Operations for Data Invariant Automation of Digital Preprocessing and Analysis of Biological and Biomedical Raman Spectra

“…79 In contrast, if the baseline is flattened and has a ''zero'' mean, TPMEM is quite effective in SNR improvement, especially for low-SNR spectra. [78][79][80] The effect of a baseline is evident in Fig. 5D, where a spectrum is shown when smoothed without baseline flattening.…”

“…The success of noise reduction is reflected using an increase in the signal-to-noise ratio (SNR) of the processed measurement relative to the original, and a number of automated 82,83 and fully automated algorithms [76][77][78] exist that could be employed to this end. Figure 5 shows a typical result from the baselineflattened and despiked demonstration dataset of live human embryonic stem cells before (Fig.…”

“…We have developed a number of methods for fully automated smoothing. [76][77][78]80 All of them use the v 2 goodness-of-fit test where a moderately good fit is provided with the commonly used stopping criterion of v 2 = N, with N being the number of points in the spectrum. 124 Consequently, some built-in safeguard exists against severe smoothing-induced spectral distortions.…”

Investigation of a two-point maximum entropy regularization method for signal enhancement applied to magnetoencephalography data

“…The balance between these two functions is mediated by a Lagrange multiplier (l) which can be used to determine the overall smoothness of the reconstructed data. TPMEM for signal recovery is a relatively new method first introduced by Greek et al [4] to smooth Raman spectroscopic data and is based on the more established Maximum Entropy Method (MEM) [5,6].…”