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dc.contributor.authorLow, Dorrain Yanwen
dc.contributor.authorKoistinen, Ville Mikael
dc.contributor.authorHanhineva, Kati
dc.contributor.authorRodriguez-Mateos, Ana
dc.contributor.authorAbrankó, László
dc.contributor.authorda Silva, Andreia Bento
dc.contributor.authorvan Poucke, Christof
dc.contributor.authorAlmeida, Conceição
dc.contributor.authorAndres-Lacueva, Cristina
dc.contributor.authorRai, Dilip K.
dc.contributor.authorCapanoglu, Esra
dc.contributor.authorBarberán, Francisco A. Tomás
dc.contributor.authorMattivi, Fulvio
dc.contributor.authorSchmidt, Gesine
dc.contributor.authorGürdeniz, Gözde
dc.contributor.authorValentová, Kateřina
dc.contributor.authorBresciani, Letizia
dc.contributor.authorPetrásková, Lucie
dc.contributor.authorDragsted, Lars Ove
dc.contributor.authorPhilo, Mark
dc.contributor.authorUlaszewska, Marynka
dc.contributor.authorMena, Pedro
dc.contributor.authorGonzález-Domínguez, Raúl
dc.contributor.authorKamiloglu, Senem
dc.contributor.authorde Pascual-Teresa, Sonia
dc.contributor.authorDurand, Stéphanie
dc.contributor.authorWiczkowski, Wieslaw
dc.contributor.authorBronze, Maria Rosario
dc.contributor.authorStanstrup, Jan
dc.contributor.authorManach, Claudine
dc.date.accessioned2021-04-21T08:22:31Z
dc.date.available2021-04-21T08:22:31Z
dc.date.created2021-04-19T10:18:09Z
dc.date.issued2021
dc.identifier.citationFood Chemistry. 2021, 357 .
dc.identifier.issn0308-8146
dc.identifier.urihttps://hdl.handle.net/11250/2738780
dc.description.abstractPrediction of retention times (RTs) is increasingly considered in untargeted metabolomics to complement MS/MS matching for annotation of unidentified peaks. We tested the performance of PredRet (http://predret.org/) to predict RTs for plant food bioactive metabolites in a data sharing initiative containing entry sets of 29–103 compounds (totalling 467 compounds, >30 families) across 24 chromatographic systems (CSs). Between 27 and 667 predictions were obtained with a median prediction error of 0.03–0.76 min and interval width of 0.33–8.78 min. An external validation test of eight CSs showed high prediction accuracy. RT prediction was dependent on shape and type of LC gradient, and number of commonly measured compounds. Our study highlights PredRet’s accuracy and ability to transpose RT data acquired from one CS to another CS. We recommend extensive RT data sharing in PredRet by the community interested in plant food bioactive metabolites to achieve a powerful community-driven open-access tool for metabolomics annotation.
dc.language.isoeng
dc.subjectMetabolomikk
dc.subjectMetabolomics
dc.subjectMetabolites
dc.subjectMetabolites
dc.subjectData sharing
dc.subjectData sharing
dc.subjectPlant food bioactive compounds
dc.subjectPlant food bioactive compounds
dc.subjectPredicted retention time
dc.subjectPredicted retention time
dc.titleData sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionpublishedVersion
dc.source.pagenumber10
dc.source.volume357
dc.source.journalFood Chemistry
dc.identifier.doi10.1016/j.foodchem.2021.129757
dc.identifier.cristin1905008
dc.relation.projectEC/H2020/609398
dc.relation.projectNorges forskningsråd: 262300
dc.relation.projectNofima AS: 201701
dc.relation.projectEC/H2020/754412
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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