title: Probabilistic peak detection for first-order chromatographic data.
abstract: We present a novel algorithm for probabilistic peak detection in first-order chromatographic data. Unlike conventional methods that deliver a binary answer pertaining to the expected presence or absence of a chromatographic peak, our method calculates the probability of a point being affected by such a peak. The algorithm makes use of chromatographic information (i.e. the expected width of a single peak and the standard deviation of baseline noise). As prior information of the existence of a peak in a chromatographic run, we make use of the statistical overlap theory. We formulate an exhaustive set of mutually exclusive hypotheses concerning presence or absence of different peak configurations. These models are evaluated by fitting a segment of chromatographic data by least-squares. The evaluation of these competing hypotheses can be performed as a Bayesian inferential task. We outline the potential advantages of adopting this approach for peak detection and provide several examples of both improved performance and increased flexibility afforded by our approach.
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: M
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: g.vivotruyols@uva.nl.
authors.forename: G
authors.initials: G
authors.lastname: Vivó-Truyols
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: M J
authors.initials: MJ
authors.lastname: Sjerps
keywords: Bayesian statistics; Chemometrics; Chromatography; Peak detection