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dc.contributor.authorBedia, Carmenes_ES
dc.contributor.authorTauler, Romàes_ES
dc.contributor.authorJaumot, Joaquimes_ES
dc.identifier.citationJournal of Chemometrics 30 (10): 575-588 (2016)es_ES
dc.description.abstractApplication of chemometric methods to mass spectrometry imaging (MSI) data faces a bottleneck concerning the vast size of the experimental data sets. This drawback is critical when considering high-resolution mass spectrometry data, which provide several thousand points for each considered pixel. In this work, different approaches have been tested to reduce the size of the analyzed data with the aim to allow the subsequent application of typical chemometric methods for image analysis. The standard approach for MSI data compression consists in binning mass spectra for each pixel to reduce the number of m/z values. In this work, a method is proposed to handle the huge size of MSI data based on the adaptation of a liquid chromatography-mass spectrometry data compression method by the detection of regions of interest. Results showed that both approaches achieved high compression rates, although the proposed regions of interest–based method attains this reduction requiring lower computational requirements and keeping utter spectral information. For instance, typical compression rate reached values higher than 90% without loss of information in images and spectra. Copyright © 2016 John Wiley & Sons, Ltd.es_ES
dc.description.sponsorshipThe research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 320737. Also, recognition from the Catalan government (grant 2014 SGR 1106) is acknowledged.es_ES
dc.publisherJohn Wiley & Sonses_ES
dc.subjectData compressiones_ES
dc.subjectMass spectrometry imaginges_ES
dc.titleCompression strategies for the chemometric analysis of mass spectrometry imaging dataes_ES
dc.description.peerreviewedPeer reviewedes_ES
dc.contributor.funderEuropean Research Counciles_ES
oprm.item.hasRevisionno ko 0 false*
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