A1 Journal article – refereed

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

List of Authors: Low, D.Y., Micheau, P., Koistinen, V.M., Hanhineva, K., Abrankó, L., Rodriguez-Mateos, A., da Silva, A.B., van Poucke, C., Almeida, C., Andres-Lacueva, C., Rai, D.K., Capanoglu, E., Tomás Barberán, F.A., Mattivi, F., Schmidt, G., Gürdeniz, G., Valentová, K., Bresciani, L., Petrásková, L., Dragsted, L.O., Philo, M., Ulaszewska, M., Mena, P., González-Domínguez, R., Garcia-Villalba, R., Kamiloglu, S., de Pascual-Teresa, S., Durand, S., Wiczkowski, W., Bronze, M.R., Stanstrup, J., Manach, C.

Publisher: Elsevier Ltd

Publication year: 2021

Journal: Food Chemistry

Journal name in source: Food Chemistry

Volume number: 357

eISSN: 1873-7072

DOI: http://dx.doi.org/10.1016/j.foodchem.2021.129757

URL: https://doi.org/10.1016/j.foodchem.2021.129757


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 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.

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Last updated on 2021-24-06 at 08:18