|Title||Investigation of selected baseline removal techniques as candidates for automated implementation|
|Publication Type||Journal Article|
|Year of Publication||2005|
|Authors||Schulze, G, Jirasek, A, Yu, MML, Lim, A, Turner, RFB, Blades, MW|
|Type of Article||Review|
|Keywords||artificial neural networks, automated baseline determination, BACKGROUND SUBTRACTION, baseline, baseline correction, DERIVATIVE SPECTROSCOPY, DIMENSIONAL NMR-SPECTRA, first derivative, Fourier transforms, GAMMA-RAY SPECTRA, GAS-CHROMATOGRAPHY, HIGH-RESOLUTION, INFRARED SPECTROMETRY, local regression, maximum entropy, noise median, peak picking, peak stripping, QUANTITATIVE-ANALYSIS, RAMAN-SPECTROSCOPY, REMOVAL, SHIFTED, spectral shifting, TIME-DOMAIN DATA, 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.
|URL||<Go to ISI>://000228982500001|