dc.contributor.author | Mishra, Puneet | |
dc.contributor.author | Passos, Dário | |
dc.contributor.author | Marini, Federico | |
dc.contributor.author | Xu, Junli | |
dc.contributor.author | Amigo Rubio, José Manuel | |
dc.contributor.author | Gowen, Aoife A. | |
dc.contributor.author | Jansen, Jeroen J. | |
dc.contributor.author | Biancolillo, Alessandra | |
dc.contributor.author | Roger, Jean Michel | |
dc.contributor.author | Rutledge, Douglas N. | |
dc.contributor.author | Nordon, Alison | |
dc.date.accessioned | 2023-01-20T18:05:31Z | |
dc.date.available | 2023-01-20T18:05:31Z | |
dc.date.issued | 2022-12 | |
dc.identifier.citation | TrAC Trends in Analytical Chemistry 157 : (2022) // Article ID 116804 | es_ES |
dc.identifier.issn | 0165-9936 | |
dc.identifier.issn | 1879-3142 | |
dc.identifier.uri | http://hdl.handle.net/10810/59397 | |
dc.description.abstract | Deep learning (DL) is emerging as a new tool to model spectral data acquired in analytical experiments. Although applications are flourishing, there is also much interest currently observed in the scientific community on the use of DL for spectral data modelling. This paper provides a critical and compre-hensive review of the major benefits, and potential pitfalls, of current DL tecnhiques used for spectral data modelling. Although this work focuses on DL for the modelling of near-infrared (NIR) spectral data in chemometric tasks, many of the findings can be expanded to cover other spectral techniques. Finally, empirical guidelines on the best practice for the use of DL for the modelling of spectral data are provided. | es_ES |
dc.description.sponsorship | Junli Xu and Aoife Gowen acknowledge funding from Science Foundation Ireland (SFI) under the investigators programme Proposal ID 15/IA/2984-HyperMicroMacro. Dário Passos acknowledges FCT - Fundação para a Ciência e a Tecnologia, Portugal, for funding CEOT project UIDB/00631/2020 CEOT BASE and UIDP/00631/2020 CEOT PROGRAMÁTICO. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | artificial intelligence | es_ES |
dc.subject | neural networks | es_ES |
dc.subject | NIR | es_ES |
dc.subject | near-infrared | es_ES |
dc.subject | spectroscopy | es_ES |
dc.subject | chemometrics | es_ES |
dc.title | Deep learning for near-infrared spectral data modelling: Hypes and benefits | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0165993622002874?via%3Dihub | es_ES |
dc.identifier.doi | 10.1016/j.trac.2022.116804 | |
dc.departamentoes | Química analítica | es_ES |
dc.departamentoeu | Kimika analitikoa | es_ES |