Statistical estimation of physical spectra is central to many laboratory processes, including, prominently, atom probe tomography, electromagnetic radiation characterization, gas phase spectroscopy, vibrational spectroscopy, chromatography, and NMR experiments. The baselines of measured spectra often include spectra noise, drift, and distortion, all of which obscure spectral lines, peaks, shoulders, and other spectral features of main experimental interest. Indeed, baseline distortions can be greater than and potentially fully obscure even peak spectral intensities.
In common practice, spectral baseline noise and distortion are identified and removed manually by a laboratory analyst. This is time-intensive and judgment-dependent, and the analyst’s subjective intervention greatly complicates the possibility of a subsequent rigorous uncertainty analysis. Ideally, an effective, robust automatic baseline detection/correction procedure would be available that permits statistical uncertainty estimation. A variety of such (semi-)automatic procedures have been proposed for baseline spectrum estimation, based variously on wavelets, penalized least squares, smoothers, and iteratively reweighted quantile regression. The challenge remains to adapt one or a combination of these procedures or to devise new procedures, perhaps machine learning-based, that (1) applies broadly to different physical spectral data without user intervention, (2) capably recognizes different forms of baseline corruption, and (3) allows a well-founded uncertainty analysis. Progress on these problems is an exciting technical challenge with, potentially, significant benefit to many laboratory sciences.
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Spectral estimation; Baseline estimation; Uncertainty analysis; Automatic statistical procedure; Machine learning;