The canonical partial least squares approach to analysing multiway datasets—N-CPLS
dc.contributor.author | Liland, Kristian Hovde | |
dc.contributor.author | Indahl, Ulf Geir | |
dc.contributor.author | Skogholt, Joakim | |
dc.contributor.author | Mishra, Puneet | |
dc.date.accessioned | 2023-03-22T13:34:15Z | |
dc.date.available | 2023-03-22T13:34:15Z | |
dc.date.created | 2022-09-27T09:35:18Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Journal of Chemometrics. 2022, 36 (7), . | |
dc.identifier.issn | 0886-9383 | |
dc.identifier.uri | https://hdl.handle.net/11250/3059896 | |
dc.language.iso | eng | |
dc.title | The canonical partial least squares approach to analysing multiway datasets—N-CPLS | |
dc.title.alternative | The canonical partial least squares approach to analysing multiway datasets—N-CPLS | |
dc.type | Peer reviewed | |
dc.type | Journal article | |
dc.description.version | publishedVersion | |
dc.source.pagenumber | 14 | |
dc.source.volume | 36 | |
dc.source.journal | Journal of Chemometrics | |
dc.source.issue | 7 | |
dc.identifier.doi | 10.1002/cem.3432 | |
dc.identifier.cristin | 2055738 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 |