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Deep learning for near-infrared spectral data modelling: Hypes and benefits

2022Journal article Open access
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dc.contributor.authorMishra, Puneet
dc.contributor.authorPassos, Dário
dc.contributor.authorMarini, Federico
dc.contributor.authorXu, Junli
dc.contributor.authorAmigo, Jose M.
dc.contributor.authorGowen, Aoife A.
dc.contributor.authorJansen, Jeroen J.
dc.contributor.authorBiancolillo, Alessandra
dc.contributor.authorRoger, Jean Michel
dc.contributor.authorRutledge, Douglas N.
dc.contributor.authorNordon, Alison
dc.date.accessioned2023-02-10T14:17:02Z
dc.date.available2023-02-10T14:17:02Z
dc.date.issued2022
dc.description.abstractDeep 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.pt_PT
dc.description.sponsorship15/IA/2984 HyperMicroMacro
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.trac.2022.116804pt_PT
dc.identifier.issn0165-9936
dc.identifier.urihttp://hdl.handle.net/10400.1/19047
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationCenter for Electronics, Optoelectronics and Telecommunications
dc.relationCenter for Electronics, Optoelectronics and Telecommunications
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectArtificial intelligencept_PT
dc.subjectNeural networkspt_PT
dc.subjectNIRpt_PT
dc.subjectNear-infraredpt_PT
dc.subjectSpectroscopypt_PT
dc.subjectChemometricspt_PT
dc.titleDeep learning for near-infrared spectral data modelling: Hypes and benefitspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCenter for Electronics, Optoelectronics and Telecommunications
oaire.awardTitleCenter for Electronics, Optoelectronics and Telecommunications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00631%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00631%2F2020/PT
oaire.citation.startPage116804pt_PT
oaire.citation.titleTrAC Trends in Analytical Chemistrypt_PT
oaire.citation.volume157pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNamePassos
person.givenNameDário
person.identifier324764
person.identifier.ciencia-id3D13-C289-0595
person.identifier.orcid0000-0002-5345-5119
person.identifier.scopus-author-id21743737200
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication30c8500a-c12b-47c3-845e-9b64957cf233
relation.isAuthorOfPublication.latestForDiscovery30c8500a-c12b-47c3-845e-9b64957cf233
relation.isProjectOfPublication6c1217d9-1340-45e8-91d8-e75348854f62
relation.isProjectOfPublication77b70459-1e8c-4a6c-9856-58860aaddb6b
relation.isProjectOfPublication.latestForDiscovery6c1217d9-1340-45e8-91d8-e75348854f62

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