Metabolite profiles of green leaves and coffee beans as predictors of coffee sensory quality in Robusta (Coffea canephora) germplasm from the Democratic Republic of the Congo

Research on the chemical composition of coffee beans and its correlation with sensory quality is advancing, but the metabolites in coffee leaves have often been overlooked. This study investigated the metabolite profiles of roasted coffee, green beans, and coffee leaves from 39 C. canephora genotype...

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Main Authors: Robrecht Bollen, Olga Rojo-Poveda, Lauren Verleysen, Rachel Ndezu, Ebele Aaron Tshimi, Hélène Mavar, Tom Ruttink, Olivier Honnay, Piet Stoffelen, Caroline Stévigny, Florence Souard, Cédric Delporte
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Applied Food Research
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Online Access:http://www.sciencedirect.com/science/article/pii/S2772502224001707
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author Robrecht Bollen
Olga Rojo-Poveda
Lauren Verleysen
Rachel Ndezu
Ebele Aaron Tshimi
Hélène Mavar
Tom Ruttink
Olivier Honnay
Piet Stoffelen
Caroline Stévigny
Florence Souard
Cédric Delporte
author_facet Robrecht Bollen
Olga Rojo-Poveda
Lauren Verleysen
Rachel Ndezu
Ebele Aaron Tshimi
Hélène Mavar
Tom Ruttink
Olivier Honnay
Piet Stoffelen
Caroline Stévigny
Florence Souard
Cédric Delporte
author_sort Robrecht Bollen
collection DOAJ
description Research on the chemical composition of coffee beans and its correlation with sensory quality is advancing, but the metabolites in coffee leaves have often been overlooked. This study investigated the metabolite profiles of roasted coffee, green beans, and coffee leaves from 39 C. canephora genotypes through LC–HRMS with an untargeted metabolomics approach. Our results showed that metabolite profiles of roasted coffee, green beans, and coffee leaves can be discriminated based on the coffee's sensory quality. The highest predictive power of coffee sensory quality was achieved with the metabolite profiles of the coffee leaves. Genotypes with varying genetic backgrounds could only be discriminated by the metabolite profiles of the coffee leaves. Metabolite marker compounds predictive of sensory quality were a putative quercetin derivate in green beans and likely physagulin E and argophyllin in coffee leaves. Estimated levels of caffeoylquinic acids, dicaffeoylquinic acids, and caffeoylquinic acid lactones were higher in roasted coffee with a lower sensory quality. These differences were not apparent in their respective green beans and coffee leaves. Overall, our study revealed the promising potential of the metabolite profile of coffee leaves as a predictive tool for coffee sensory quality and the genetic background of C. canephora.
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spelling doaj-art-37da4acea5404701bdc6ca09f3683e612025-08-20T02:48:57ZengElsevierApplied Food Research2772-50222024-12-014210056010.1016/j.afres.2024.100560Metabolite profiles of green leaves and coffee beans as predictors of coffee sensory quality in Robusta (Coffea canephora) germplasm from the Democratic Republic of the CongoRobrecht Bollen0Olga Rojo-Poveda1Lauren Verleysen2Rachel Ndezu3Ebele Aaron Tshimi4Hélène Mavar5Tom Ruttink6Olivier Honnay7Piet Stoffelen8Caroline Stévigny9Florence Souard10Cédric Delporte11Meise Botanic Garden, 1860 Meise, Belgium; Plant Conservation and Population Biology, Department of Biology, University of Leuven, 3001 Leuven, Belgium; Corresponding author.Department of Research in Drug Development (RD3), Pharmacognosy, Bioanalysis and Drug Discovery Unit, Faculty of Pharmacy, Université libre de Bruxelles, 1050 Brussels, BelgiumMeise Botanic Garden, 1860 Meise, Belgium; Plant Conservation and Population Biology, Department of Biology, University of Leuven, 3001 Leuven, Belgium; Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), 9090 Melle, BelgiumUniversity of Kisangani, Kisangani, Democratic Republic of the CongoInstitut Nationale pour l'Etude et la Recherche Agronomiques (INERA), Yangambi, Democratic Republic of the CongoUniversity of Kisangani, Kisangani, Democratic Republic of the CongoFlanders Research Institute for Agriculture, Fisheries and Food (ILVO), 9090 Melle, Belgium; Department of Plant Biotechnology and Bioinformatics, Faculty of Sciences, Ghent University, 9052 Ghent, BelgiumPlant Conservation and Population Biology, Department of Biology, University of Leuven, 3001 Leuven, BelgiumMeise Botanic Garden, 1860 Meise, BelgiumDepartment of Research in Drug Development (RD3), Pharmacognosy, Bioanalysis and Drug Discovery Unit, Faculty of Pharmacy, Université libre de Bruxelles, 1050 Brussels, BelgiumDepartment of Pharmacotherapy and Pharmaceutics (DPP), Pharmacology, Pharmacotherapy and Pharmaceutical care Unit, Faculty of Pharmacy, Université libre de Bruxelles, 1050 Brussels, BelgiumDepartment of Research in Drug Development (RD3), Pharmacognosy, Bioanalysis and Drug Discovery Unit, Faculty of Pharmacy, Université libre de Bruxelles, 1050 Brussels, Belgium; Analytical Platform of the Faculty of Pharmacy (APFP), Faculty of Pharmacy, Université libre de Bruxelles, 1050 Brussels, BelgiumResearch on the chemical composition of coffee beans and its correlation with sensory quality is advancing, but the metabolites in coffee leaves have often been overlooked. This study investigated the metabolite profiles of roasted coffee, green beans, and coffee leaves from 39 C. canephora genotypes through LC–HRMS with an untargeted metabolomics approach. Our results showed that metabolite profiles of roasted coffee, green beans, and coffee leaves can be discriminated based on the coffee's sensory quality. The highest predictive power of coffee sensory quality was achieved with the metabolite profiles of the coffee leaves. Genotypes with varying genetic backgrounds could only be discriminated by the metabolite profiles of the coffee leaves. Metabolite marker compounds predictive of sensory quality were a putative quercetin derivate in green beans and likely physagulin E and argophyllin in coffee leaves. Estimated levels of caffeoylquinic acids, dicaffeoylquinic acids, and caffeoylquinic acid lactones were higher in roasted coffee with a lower sensory quality. These differences were not apparent in their respective green beans and coffee leaves. Overall, our study revealed the promising potential of the metabolite profile of coffee leaves as a predictive tool for coffee sensory quality and the genetic background of C. canephora.http://www.sciencedirect.com/science/article/pii/S2772502224001707Coffea canephoraRobustaCoffee beansCoffee leavesLC-HRMSPLS-DA
spellingShingle Robrecht Bollen
Olga Rojo-Poveda
Lauren Verleysen
Rachel Ndezu
Ebele Aaron Tshimi
Hélène Mavar
Tom Ruttink
Olivier Honnay
Piet Stoffelen
Caroline Stévigny
Florence Souard
Cédric Delporte
Metabolite profiles of green leaves and coffee beans as predictors of coffee sensory quality in Robusta (Coffea canephora) germplasm from the Democratic Republic of the Congo
Applied Food Research
Coffea canephora
Robusta
Coffee beans
Coffee leaves
LC-HRMS
PLS-DA
title Metabolite profiles of green leaves and coffee beans as predictors of coffee sensory quality in Robusta (Coffea canephora) germplasm from the Democratic Republic of the Congo
title_full Metabolite profiles of green leaves and coffee beans as predictors of coffee sensory quality in Robusta (Coffea canephora) germplasm from the Democratic Republic of the Congo
title_fullStr Metabolite profiles of green leaves and coffee beans as predictors of coffee sensory quality in Robusta (Coffea canephora) germplasm from the Democratic Republic of the Congo
title_full_unstemmed Metabolite profiles of green leaves and coffee beans as predictors of coffee sensory quality in Robusta (Coffea canephora) germplasm from the Democratic Republic of the Congo
title_short Metabolite profiles of green leaves and coffee beans as predictors of coffee sensory quality in Robusta (Coffea canephora) germplasm from the Democratic Republic of the Congo
title_sort metabolite profiles of green leaves and coffee beans as predictors of coffee sensory quality in robusta coffea canephora germplasm from the democratic republic of the congo
topic Coffea canephora
Robusta
Coffee beans
Coffee leaves
LC-HRMS
PLS-DA
url http://www.sciencedirect.com/science/article/pii/S2772502224001707
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