title: Leveraging probabilistic peak detection to estimate baseline drift in complex chromatographic samples.
abstract: Accurate analysis of chromatographic data often requires the removal of baseline drift. A frequently employed strategy strives to determine asymmetric weights in order to fit a baseline model by regression. Unfortunately, chromatograms characterized by a very high peak saturation pose a significant challenge to such algorithms. In addition, a low signal-to-noise ratio (i.e. s/n<40) also adversely affects accurate baseline correction by asymmetrically weighted regression. We present a baseline estimation method that leverages a probabilistic peak detection algorithm. A posterior probability of being affected by a peak is computed for each point in the chromatogram, leading to a set of weights that allow non-iterative calculation of a baseline estimate. For extremely saturated chromatograms, the peak weighted (PW) method demonstrates notable improvement compared to the other methods examined. However, in chromatograms characterized by low-noise and well-resolved peaks, the asymmetric least squares (ALS) and the more sophisticated Mixture Model (MM) approaches achieve superior results in significantly less time. We evaluate the performance of these three baseline correction methods over a range of chromatographic conditions to demonstrate the cases in which each method is most appropriate.
authors.affiliation: Korteweg-de Vries Institute for Mathematics, University of Amsterdam, Postbus 94248, 1090 GE Amsterdam, The Netherlands; Netherlands Forensic Institute, Postbus 24044, 2490 AA Den Haag, The Netherlands. Electronic address: m.lopatka@uva.nl.
authors.forename: Martin
authors.initials: M
authors.lastname: Lopatka
authors.affiliation: Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Postbus 94248, 1090 GE Amsterdam, The Netherlands. Electronic address: a.barcaru@uva.nl.
authors.forename: Andrei
authors.initials: A
authors.lastname: Barcaru
authors.affiliation: Korteweg-de Vries Institute for Mathematics, University of Amsterdam, Postbus 94248, 1090 GE Amsterdam, The Netherlands; Netherlands Forensic Institute, Postbus 24044, 2490 AA Den Haag, The Netherlands. Electronic address: m.j.sjerps@uva.nl.
authors.forename: Marjan J
authors.initials: MJ
authors.lastname: Sjerps
authors.affiliation: Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Postbus 94248, 1090 GE Amsterdam, The Netherlands. Electronic address: g.vivotruyols@uva.nl.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Vivó-Truyols
mesh_terms: D000465:Algorithms; D002845:Chromatography / Q000379:methods*; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D059629:Signal-To-Noise Ratio
keywords: Asymmetric least squares; Baseline correction; Chemometrics; Chromatography; Data preprocessing; Peak detection