Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds. (30th September 2021)

• Record Type: Journal Article
• Authors: Low, Dorrain Yanwen; Micheau, Pierre; Koistinen, Ville Mikael; Hanhineva, Kati; Abrankó, László; Rodriguez-Mateos, Ana; da Silva, Andreia Bento; van Poucke, Christof; Almeida, Conceição; Andres-Lacueva, Cristina; Rai, Dilip K.; Capanoglu, Esra; Tomás Barberán, Francisco A.; Mattivi, Fulvio; Schmidt, Gesine; Gürdeniz, Gözde; Valentová, Kateřina; Bresciani, Letizia; Petrásková, Lucie; Dragsted, Lars Ove; Philo, Mark; Ulaszewska, Marynka; Mena, Pedro; González-Domínguez, Raúl; Garcia-Villalba, Rocío; Kamiloglu, Senem; de Pascual-Teresa, Sonia; Durand, Stéphanie; Wiczkowski, Wieslaw; Bronze, Maria Rosário; Stanstrup, Jan; Manach, Claudine;
• Is Part Of: Food chemistry. Volume 357(2021)
• Publication Date: 2021-09-30
• Subjects: Predicted retention time -- Metabolomics -- Plant food bioactive compounds -- Metabolites -- Data sharing -- UHPLC; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2021.129757
• Languages: English
• ISSNs: 0308-8146

Abstract

Graphical abstract: Highlights: Identifying food bioactive compounds in untargeted metabolomics is challenging. Predicted retention time is valuable towards effort in metabolite identification. 24 Chromatographic systems obtained predicted retention times from PredRet database. High accuracy and coverage of retention time predictions for new compounds obtained. We recommend extensive retention time data sharing in open access PredRet database. Abstract: 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.