title: An online peak extraction algorithm for ion mobility spectrometry data.
abstract: Ion mobility (IM) spectrometry (IMS), coupled with multi-capillary columns (MCCs), has been gaining importance for biotechnological and medical applications because of its ability to detect and quantify volatile organic compounds (VOC) at low concentrations in the air or in exhaled breath at ambient pressure and temperature. Ongoing miniaturization of spectrometers creates the need for reliable data analysis on-the-fly in small embedded low-power devices. We present the first fully automated online peak extraction method for MCC/IMS measurements consisting of several thousand individual spectra. Each individual spectrum is processed as it arrives, removing the need to store the measurement before starting the analysis, as is currently the state of the art. Thus the analysis device can be an inexpensive low-power system such as the Raspberry Pi. The key idea is to extract one-dimensional peak models (with four parameters) from each spectrum and then merge these into peak chains and finally two-dimensional peak models. We describe the different algorithmic steps in detail and evaluate the online method against state-of-the-art peak extraction methods.
authors.affiliation: Bioinformatics for High-Throughput Technologies, Computer Science XI, and Collaborative Research Center SFB 876, TU Dortmund, Dortmund, Germany.
authors.forename: Dominik
authors.initials: D
authors.lastname: Kopczynski
authors.affiliation: Bioinformatics for High-Throughput Technologies, Computer Science XI, and Collaborative Research Center SFB 876, TU Dortmund, Dortmund, Germany ; Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
authors.forename: Sven
authors.initials: S
authors.lastname: Rahmann
keywords: Automated data analysis; Ion mobility spectrometry; Online analysis; Peak detection