期刊
APPLIED SPECTROSCOPY
卷 59, 期 5, 页码 545-574出版社
SOC APPLIED SPECTROSCOPY
DOI: 10.1366/0003702053945985
关键词
automated baseline determination; baseline correction; baseline removal; noise median; first derivative; peak picking; peak stripping; artificial neural networks; local regression; spectral shifting; maximum entropy; Fourier transforms; wavelet transforms
Observed spectra normally contain spurious features along with those of interest and it is common practice to employ one of several available algorithms to remove the unwanted components. Low frequency spurious components are often referred to as 'baseline', 'background', and/or 'background noise'. Here we examine a cross-section of non-instrumental methods designed to remove background features from spectra; the particular methods considered here represent approaches with different theoretical underpinnings. We compare and evaluate their relative performance based on synthetic data sets designed to exemplify vibrational spectroscopic signals in realistic contexts and thereby assess their suitability for computer automation. Each method is presented in a modular format with a concise review of the underlying theory, along with a comparison and discussion of their strengths, weaknesses, and amenability to automation, in order to facilitate the selection of methods best suited to particular applications.
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