Metabolomics fingerprint of coffee species determined by untargeted-profiling study using LC-HRMS

被引：59

作者：

Souard, Florence ^{[1
,2
]}

Delporte, Cedric ^{[3
,4
]}

Stoffelen, Piet ^{[5
]}

Thevenot, Etienne A. ^{[6
]}

Noret, Nausicaa ^{[7
]}

Dauvergne, Bastien ^{[2
,8
]}

Kauffmann, Jean-Michel ^{[8
]}

Van Antwerpen, Pierre ^{[3
,4
]}

Stevigny, Caroline ^{[2
]}

机构：

[1] Univ Grenoble Alpes, CNRS, DPM, F-38000 Grenoble, France

[2] Univ Libre Bruxelles, Fac Pharm, Lab Pharmacognosie Bromatol & Nutr Humaine, Campus Plaine,CP 205-09, B-1050 Brussels, Belgium

[3] Univ Libre Bruxelles, Fac Pharm, Plateforme Analyt, Campus Plaine,CP 205-5, B-1050 Brussels, Belgium

[4] Univ Libre Bruxelles, Fac Pharm, Lab Chim Pharmaceut Organ, Campus Plaine,CP 205-5, B-1050 Brussels, Belgium

[5] Bot Garden Meise, Nieuwelaan 38, B-1860 Meise, Belgium

[6] MetaboHUB Gif sur Yvette, CEA, LIST, Lab Data Anal & Syst Intelligence, Gif Sur Yvette, France

[7] Univ Libre Bruxelles, Lab Ecol Vegetale & Biogeochim, Campus Plaine,CP 244, B-1050 Brussels, Belgium

[8] Univ Libre Bruxelles, Fac Pharm, Lab Chim Analyt & Instrumentale & Bioelecrochem, Campus Plaine,CP 205-09, B-1050 Brussels, Belgium

来源：

FOOD CHEMISTRY | 2018年 / 245卷

关键词：

Plant metabolomics; Coffea; LC-(HR) MS; Workflow4metabolomics; Caffeine; Ent-kaurane diterpenoid; MASS-SPECTROMETRY; CAFFEINE; ARABICA; CHROMATOGRAPHY; BIOSYNTHESIS; DISCRIMINATION; RUBIACEAE; LEAVES; GENES;

D O I：

10.1016/j.foodchem.2017.10.022

中图分类号：

O69 [应用化学];

学科分类号：

081704 ;

摘要：

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.

引用

页码：603 / 612

页数：10

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