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 χ2-statistic. The approach provides for multiple passes or iterations, based on a given χ2-statistic for convergence. If the baseline is acceptably flat given the χ2-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 χ2-statistic is achieved. Results, obtained on both simulated and measured Raman data, are presented and discussed.