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Campo DC | Valor | Lengua/Idioma |
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dc.contributor.author | Navarro-Reig, Meritxell | es_ES |
dc.contributor.author | Bedia, Carmen | es_ES |
dc.contributor.author | Tauler, Romà | es_ES |
dc.contributor.author | Jaumot, Joaquim | es_ES |
dc.date.accessioned | 2018-07-31T13:35:18Z | - |
dc.date.available | 2018-07-31T13:35:18Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Proteomics 2018 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10261/168078 | - |
dc.description.abstract | The increasing complexity of omics research has encouraged the development of new instrumental technologies able to deal with these challenging samples. In this way, the rise of multidimensional separations should be highlighted due to the massive amounts of information that provide with an enhanced analyte determination. Both proteomics and metabolomics benefit from this higher separation capacity achieved when different chromatographic dimensions are combined, either in LC or GC. However, this vast quantity of experimental information requires the application of chemometric data analysis strategies to retrieve this hidden knowledge, especially in the case of nontargeted studies. In this work, the most common chemometric tools and approaches for the analysis of this multidimensional chromatographic data are reviewed. First, different options for data preprocessing and enhancement of the instrumental signal are introduced. Next, the most used chemometric methods for the detection of chromatographic peaks and the resolution of chromatographic and spectral contributions (profiling) are presented. The description of these data analysis approaches is complemented with enlightening examples from omics fields that demonstrate the exceptional potential of the combination of multidimensional separation techniques and chemometric tools of data analysis. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. | es_ES |
dc.description.sponsorship | The research leading to these results has received funding from the Euro- pean Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement no. 320737. Also, the authors acknowledge support from both the Ministry of Economy, In- dustry and Competitiveness (Grant CTQ2017-82598-P) and the Catalan Agency for Management of University and Research Grants (AGAUR) (2017SGR753). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Wiley-Blackwell | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/FP7/320737 | es_ES |
dc.relation.isversionof | Postprint | es_ES |
dc.rights | openAccess | en_EN |
dc.subject | Chemometrics | es_ES |
dc.subject | Comprehensive chromatography | es_ES |
dc.subject | Multidimensional chromatography | es_ES |
dc.subject | Peak detection | es_ES |
dc.subject | Peak resolution | es_ES |
dc.title | Chemometric Strategies for Peak Detection and Profiling from Multidimensional Chromatography | es_ES |
dc.type | artículo | es_ES |
dc.identifier.doi | 10.1002/pmic.201700327 | - |
dc.description.peerreviewed | Peer reviewed | es_ES |
dc.relation.publisherversion | https://doi.org/10.1002/pmic.201700327 | es_ES |
dc.embargo.terms | 2019-04-03 | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.relation.csic | Sí | es_ES |
oprm.item.hasRevision | no ko 0 false | * |
dc.identifier.funder | http://dx.doi.org/10.13039/501100000780 | es_ES |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | es_ES |
item.openairetype | artículo | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en | - |
Aparece en las colecciones: | (IDAEA) Artículos |
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Chemometrics strategies for peak detection and profiling from multidimensional chromatography.docx | 2,91 MB | Microsoft Word XML | Visualizar/Abrir |
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