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Updated: Feb 16, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
Published on: September 20, 2022
Metabolomics fingerprint of coffee species determined by untargeted-profiling study using LC-HRMS
Florence Souard1, Cédric Delporte2, Piet Stoffelen3
1Univ. Grenoble Alpes, CNRS, DPM, 38000 Grenoble, France; Laboratoire de Pharmacognosie, de Bromatologie et de Nutrition Humaine, Faculté de Pharmacie, Université Libre de Bruxelles, Campus Plaine, CP 205/09, 1050 Brussels, Belgium.
Abstract:
Coffee bean extracts are consumed all over the world as beverage and there is a growing interest in coffee leaf extracts as food supplements. The wild diversity in Coffea (Rubiaceae) genus is large and could offer new opportunities and challenges. In the present work, a metabolomics approach was implemented to examine leaf chemical composition of 9 Coffea species grown in the same environmental conditions. Leaves were analyzed by LC-HRMS and a comprehensive statistical workflow was designed. It served for univariate hypothesis testing and multivariate modeling by PCA and partial PLS-DA on the Workflow4Metabolomics infrastructure. The first two axes of PCA and PLS-DA describes more than 40% of variances with good values of explained variances. This strategy permitted to investigate the metabolomics data and their relation with botanic and genetic informations. Finally, the identification of several key metabolites for the discrimination between species was further characterized.
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