A UHPLC-HRMS based metabolomics and chemoinformatics approach to chemically distinguish 'super foods' from a variety of plant-based foods. (30th May 2020)

• Record Type: Journal Article
• Authors: Di Ottavio, Francesca; Gauglitz, Julia M.; Ernst, Madeleine; Panitchpakdi, Morgan W.; Fanti, Federico; Compagnone, Dario; Dorrestein, Pieter C.; Sergi, Manuel
• Is Part Of: Food chemistry. Volume 313(2020)
• Publication Date: 2020-05-30
• Subjects: Untargeted MS -- Food -- Superfood -- Metabolomics -- LC–MS/MS; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2019.126071
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Study of molecular composition of superfood analyzing the metabolomic pattern. Untargeted mass spectrometry and molecular network to obtain an overview of all features. Multivariate analysis to highlight special characteristics of superfood. Twenty-one superfoods were analyzed against a broad number of raw fruits. Propagation annotation of bioactive compound was performed in superfood subnetworks. Abstract: The aim of this study was to investigate if the declared benefits associated with superfoods are related to a specific molecular composition. For this purpose, untargeted metabolomics and molecular networking were used to obtain an overview of all features, focusing on compounds with anti-inflammatory, antioxidant or antimicrobial properties. 565 plant-based food samples were analyzed using UHPLC-HRMS and advanced data analysis tools. The molecular networking of the whole dataset allowed identification of a greater diversity of molecules, in particular, prenol lipids, isoflavonoids and isoquinolines in superfoods, when compared with non-superfood species belonging to the same botanical family. Furthermore, in silico tools were used to expand our chemical knowledge of compounds observed in superfood samples.