Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds

0543397 - MBÚ 2022 RIV GB eng J - Journal Article
Low, D. Y. - Micheau, P. - Koistinen, V. M. - Hanhineva, K. - Abranko, L. - Rodriguez-Mateos, A. - da Silva, A. B. - van Poucke, C. - Almeida, C. - Andres-Lacueva, C. - Rai, D. K. - Capanoglu, E. - Barberan, F. A. T. - Mattivi, F. - Schmidt, G. - Gurdeniz, G. - Valentová, Kateřina - Bresciani, L. - Petrásková, Lucie - Dragsted, L.O. - Philo, M. - Ulaszewska, M. - Mena, P. - Gonzalez-Dominguez, R. - Garcia-Villalba, R. - Kamiloglu, S. - de Pascual-Teresa, S. - Durand, S. - Wiczkowski, W. - Bronze, M. R. - Stanstrup, J. - Manach, C.
Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds.
Food Chemistry. Roč. 357, SEP 30 2021 (2021), č. článku 129757. ISSN 0308-8146. E-ISSN 1873-7072
R&D Projects: GA ČR(CZ) GA19-00043S
Institutional support: RVO:61388971
Keywords : Predicted retention time * Metabolomics * Plant food bioactive compounds * Metabolites * Data sharing * uhplc
OECD category: Analytical chemistry
Impact factor: 9.231, year: 2021
Method of publishing: Open access
https://www.sciencedirect.com/science/article/pii/S0308814621007639?via%3Dihub

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.
Permanent Link: http://hdl.handle.net/11104/0320613