title: Mixture models for two-dimensional baseline correction, applied to artifact elimination in time-resolved spectroscopy.
abstract: Baseline correction and artifact removal are important pre-processing steps in analytical chemistry. We propose a correction algorithm using a mixture model in combination with penalized regression. The model is an extension of a method recently introduced for baseline estimation in the case of one-dimensional data. The data are modeled as a smooth surface using tensor product P-splines. The weights of the P-splines regression model are computed from a mixture model where a datapoint is either allocated to the noise around the baseline, or to the artifact component. The method is broadly applicable for anisotropic smoothing of two-way data such as two-dimensional gel electrophoresis and two-dimensional chromatography data. We focus here on the application of the approach in femtosecond time-resolved spectroscopy, to eliminate strong artifact signals from the solvent.
authors.affiliation: Department of Bioinformatics, Erasmus Medical Center, Rotterdam, The Netherlands.
authors.forename: Johan J
authors.initials: JJ
authors.lastname: de Rooi
authors.forename: Olivier
authors.initials: O
authors.lastname: Devos
authors.forename: Michel
authors.initials: M
authors.lastname: Sliwa
authors.forename: Cyril
authors.initials: C
authors.lastname: Ruckebusch
authors.forename: Paul H C
authors.initials: PH
authors.lastname: Eilers
mesh_terms: D016477:Artifacts; D015233:Models, Statistical; D012044:Regression Analysis; D013057:Spectrum Analysis / Q000379:methods*; D013997:Time Factors