title: Investigation of selected baseline removal techniques as candidates for automated implementation.
abstract: 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.
authors.affiliation: Michael Smith Laboratories, The University of British Columbia, 301-2185 East Mall, Vancouver, BC, Canada, V6T 1Z4.
authors.forename: Georg
authors.initials: G
authors.lastname: Schulze
authors.forename: Andrew
authors.initials: A
authors.lastname: Jirasek
authors.forename: Marcia M L
authors.initials: MM
authors.lastname: Yu
authors.forename: Arnel
authors.initials: A
authors.lastname: Lim
authors.forename: Robin F B
authors.initials: RF
authors.lastname: Turner
authors.forename: Michael W
authors.initials: MW
authors.lastname: Blades
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013057:Spectrum Analysis / Q000379:methods*