A Model-Free, Fully Automated Baseline-Removal Method for Raman Spectra

Abstract

We present here a fully automated spectral baseline-removal procedure. The method uses a large-window moving average to estimate the baseline; thus, it is a model-free approach with a peak-stripping method to remove spectral peaks. After processing, the baseline-corrected spectrum should yield a flat baseline and this endpoint can be verified with the χ(2)-statistic. The approach provides for multiple passes or iterations, based on a given χ(2)-statistic for convergence. If the baseline is acceptably flat given the χ(2)-statistic after the first pass at correction, the problem is solved. If not, the non-flat baseline (i.e., after the first effort or first pass at correction) should provide an indication of where the first pass caused too much or too little baseline to be subtracted. The second pass thus permits one to compensate for the errors incurred on the first pass. Thus, one can use a very large window so as to avoid affecting spectral peaks--even if the window is so large that the baseline is inaccurately removed--because baseline-correction errors can be assessed and compensated for on subsequent passes. We start with the largest possible window and gradually reduce it until acceptable baseline correction based on the χ(2) statistic is achieved. Results, obtained on both simulated and measured Raman data, are presented and discussed.

Raman Imaging of Lignocellulosic Feedstock

“…For removing background coming from the measured material (fluorescence) or signal from the substrate different methods have been developed that are capable of handling irregularly shaped baselines [125][126][127][128]. Baseline correction of Raman spectra is especially important prior to multivariate methods and different solutions to improve baseline correction methods have been developed [125,129,130].…”

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

“…Savitzky-Golay filters are quite useful for full automation because there are few parameters to specify, and the filters can be viewed as model-free because the baseline is typically estimated locally. 40,42 Even so, some parameters need to be specified (filter order, window size, increment step size), and this can be done using the lowest possible order and either the largest possible or smallest possible window. The window size is then, respectively, either decreased or increased with the smallest possible step size until the stopping criteria are triggered.…”

An overview of signal processing issues in chemical sensing

“…1-left) in a signal is generally an ill-posed posed problem, despite its apparent simplicity. The need for almost automatic methods is still present, after many attempts using leastsquare fits, wavelet preprocessing, robust or asymmetric error regression or factor analysis methods [30,31,32,33,34].…”