Počet záznamů: 1
Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds
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SYSNO ASEP 0543397 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds Tvůrce(i) Low, D. Y. (FR)
Micheau, P. (FR)
Koistinen, V. M. (FI)
Hanhineva, K. (FI)
Abranko, L. (HU)
Rodriguez-Mateos, A. (GB)
da Silva, A. B. (PT)
van Poucke, C. (BE)
Almeida, C. (PT)
Andres-Lacueva, C. (PT)
Rai, D. K. (IE)
Capanoglu, E. (TR)
Barberan, F. A. T. (ES)
Mattivi, F. (IT)
Schmidt, G. (NO)
Gurdeniz, G. (DK)
Valentová, Kateřina (MBU-M) RID, ORCID
Bresciani, L. (IT)
Petrásková, Lucie (MBU-M) ORCID
Dragsted, L.O. (DK)
Philo, M. (GB)
Ulaszewska, M. (IT)
Mena, P. (IT)
Gonzalez-Dominguez, R. (ES)
Garcia-Villalba, R. (ES)
Kamiloglu, S. (IE)
de Pascual-Teresa, S. (ES)
Durand, S. (FR)
Wiczkowski, W. (PL)
Bronze, M. R. (PT)
Stanstrup, J. (DK)
Manach, C. (FR)Číslo článku 129757 Zdroj.dok. Food Chemistry. - : Elsevier - ISSN 0308-8146
Roč. 357, SEP 30 2021 (2021)Poč.str. 10 s. Jazyk dok. eng - angličtina Země vyd. GB - Velká Británie Klíč. slova Predicted retention time ; Metabolomics ; Plant food bioactive compounds ; Metabolites ; Data sharing ; uhplc Vědní obor RIV CB - Analytická chemie, separace Obor OECD Analytical chemistry CEP GA19-00043S GA ČR - Grantová agentura ČR Způsob publikování Open access Institucionální podpora MBU-M - RVO:61388971 UT WOS 000655533400011 EID SCOPUS 85104344822 DOI 10.1016/j.foodchem.2021.129757 Anotace Prediction 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 & nbsp.predict RTs for plant food bioactive metabolites in a data sharing initiative containing entry sets of 29 & ndash,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 & ndash,0.76 min and interval width of 0.33 & ndash,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 & rsquo, 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. Pracoviště Mikrobiologický ústav Kontakt Eliška Spurná, eliska.spurna@biomed.cas.cz, Tel.: 241 062 231 Rok sběru 2022 Elektronická adresa https://www.sciencedirect.com/science/article/pii/S0308814621007639?via%3Dihub
Počet záznamů: 1