Publication
High-throughput plant phenotyping: a role for metabolomics?
| dc.contributor.author | Hall, Robert D. | |
| dc.contributor.author | D'Auria, John C. | |
| dc.contributor.author | Ferreira, António C. Silva | |
| dc.contributor.author | Gibon, Yves | |
| dc.contributor.author | Kruszka, Dariusz | |
| dc.contributor.author | Mishra, Puneet | |
| dc.contributor.author | Zedde, Rick van de | |
| dc.date.accessioned | 2022-03-16T15:31:50Z | |
| dc.date.available | 2022-03-16T15:31:50Z | |
| dc.date.issued | 2022-06 | |
| dc.description.abstract | High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype–phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1016/j.tplants.2022.02.001 | pt_PT |
| dc.identifier.eid | 85125649738 | |
| dc.identifier.issn | 1360-1385 | |
| dc.identifier.pmid | 35248492 | |
| dc.identifier.uri | http://hdl.handle.net/10400.14/37056 | |
| dc.identifier.wos | 000804394900007 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.relation | Sensors and daTA tRaininG towards high-performance Agri-food sysTEms | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Data integration | pt_PT |
| dc.subject | Metabolomics | pt_PT |
| dc.subject | Multimodal sensing | pt_PT |
| dc.subject | Phenomics | pt_PT |
| dc.subject | Plant phenotyping | pt_PT |
| dc.title | High-throughput plant phenotyping: a role for metabolomics? | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Sensors and daTA tRaininG towards high-performance Agri-food sysTEms | |
| oaire.awardURI | info:eu-repo/grantAgreement/EC/H2020/952330/EU | |
| oaire.citation.endPage | 563 | |
| oaire.citation.issue | 6 | |
| oaire.citation.startPage | 549 | |
| oaire.citation.title | Trends in Plant Science | pt_PT |
| oaire.citation.volume | 27 | |
| oaire.fundingStream | H2020 | |
| project.funder.identifier | http://doi.org/10.13039/501100008530 | |
| project.funder.name | European Commission | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isProjectOfPublication | e3cc2490-091c-412e-8c92-3feead5a72e6 | |
| relation.isProjectOfPublication.latestForDiscovery | e3cc2490-091c-412e-8c92-3feead5a72e6 |
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