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Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds
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SYSNO ASEP 0543397 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds Author(s) 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)Article number 129757 Source Title Food Chemistry. - : Elsevier - ISSN 0308-8146
Roč. 357, SEP 30 2021 (2021)Number of pages 10 s. Language eng - English Country GB - United Kingdom Keywords Predicted retention time ; Metabolomics ; Plant food bioactive compounds ; Metabolites ; Data sharing ; uhplc Subject RIV CB - Analytical Chemistry, Separation OECD category Analytical chemistry R&D Projects GA19-00043S GA ČR - Czech Science Foundation (CSF) Method of publishing Open access Institutional support MBU-M - RVO:61388971 UT WOS 000655533400011 EID SCOPUS 85104344822 DOI 10.1016/j.foodchem.2021.129757 Annotation 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. Workplace Institute of Microbiology Contact Eliška Spurná, eliska.spurna@biomed.cas.cz, Tel.: 241 062 231 Year of Publishing 2022 Electronic address https://www.sciencedirect.com/science/article/pii/S0308814621007639?via%3Dihub
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