Literature DB >> 35492526

UHPLC-MS metabolome based classification of umbelliferous fruit taxa: a prospect for phyto-equivalency of its different accessions and in response to roasting.

Ahmed Serag1, Mostafa H Baky2, Stefanie Döll3, Mohamed A Farag4,5.   

Abstract

Herbs of the Umbelliferae family are popular spices valued worldwide for their many nutritional and health benefits. Herein, five chief umbelliferous fruits viz., cumin, fennel, anise, coriander and caraway were assessed for its secondary metabolites diversity along with compositional changes incurring upon roasting as analyzed via ultra-high performance liquid chromatography coupled to photodiode array and electrospray ionization mass detectors UHPLC-qToF/MS. A total of 186 metabolites were annotated, according to metabolomics society guidelines, belonging mainly to flavonoids, fatty acids and phenolic acids. Multivariate models viz., PCA, HCA and OPLS-DA were further employed to assess fruits' heterogeneity in an untargeted manner and determine mechanistic changes in bioactive makeup post roasting viz., glycosidic cleavage, lipid degradation and Maillard reaction. Finally, the fruits' antioxidant activity showed decline upon roasting and in correlation with its total phenolic content. This study presents the first complete map of umbelliferous fruit metabolome, compositional differences and its roasting effect. This journal is © The Royal Society of Chemistry.

Entities:  

Year:  2019        PMID: 35492526      PMCID: PMC9047984          DOI: 10.1039/c9ra07841j

Source DB:  PubMed          Journal:  RSC Adv        ISSN: 2046-2069            Impact factor:   3.361


  18 in total

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2.  A non-targeted metabolomic approach based on reversed-phase liquid chromatography-mass spectrometry to evaluate coffee roasting process.

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4.  The blood pressure-lowering effect and safety of chlorogenic acid from green coffee bean extract in essential hypertension.

Authors:  Takuya Watanabe; Yoichi Arai; Yuki Mitsui; Tatsuya Kusaura; Wataru Okawa; Yasushi Kajihara; Ikuo Saito
Journal:  Clin Exp Hypertens       Date:  2006-07       Impact factor: 1.749

5.  UHPLC/MS2-based approach for the comprehensive metabolite profiling of bean (Vicia faba L.) by-products: A promising source of bioactive constituents.

Authors:  Ibrahim M Abu-Reidah; David Arráez-Román; Ismail Warad; Alberto Fernández-Gutiérrez; Antonio Segura-Carretero
Journal:  Food Res Int       Date:  2017-01-20       Impact factor: 6.475

6.  Metabolomics driven analysis of 11 Portulaca leaf taxa as analysed via UPLC-ESI-MS/MS and chemometrics.

Authors:  Mohamed A Farag; Zeinab T Abdel Shakour
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7.  Variation in Ceratonia siliqua pod metabolome in context of its different geographical origin, ripening stage and roasting process.

Authors:  Mohamed A Farag; Dina M El-Kersh; Anja Ehrlich; Mouchira A Choucry; Hesham El-Seedi; Andrej Frolov; Ludger A Wessjohann
Journal:  Food Chem       Date:  2019-01-14       Impact factor: 7.514

8.  Treatment of rheumatoid arthritis with gammalinolenic acid.

Authors:  L J Leventhal; E G Boyce; R B Zurier
Journal:  Ann Intern Med       Date:  1993-11-01       Impact factor: 25.391

9.  New Antiglycative Compounds from Cumin (Cuminum cyminum) Spice.

Authors:  Yan Zhang; Hang Ma; Weixi Liu; Tao Yuan; Navindra P Seeram
Journal:  J Agric Food Chem       Date:  2015-11-17       Impact factor: 5.279

10.  UPLC-MS metabolome based classification of Lupinus and Lens seeds: A prospect for phyto-equivalency of its different accessions.

Authors:  Mohamed A Farag; Amira R Khattab; Amal A Maamoun; Matthias Kropf; Andreas G Heiss
Journal:  Food Res Int       Date:  2018-11-04       Impact factor: 6.475

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  2 in total

1.  Comparative Metabolite Fingerprinting of Four Different Cinnamon Species Analyzed via UPLC-MS and GC-MS and Chemometric Tools.

Authors:  Mohamed A Farag; Eman M Kabbash; Ahmed Mediani; Stefanie Döll; Tuba Esatbeyoglu; Sherif M Afifi
Journal:  Molecules       Date:  2022-05-04       Impact factor: 4.927

2.  Rapidly detecting fennel origin of the near-infrared spectroscopy based on extreme learning machine.

Authors:  Enguang Zuo; Lei Sun; Junyi Yan; Cheng Chen; Chen Chen; Xiaoyi Lv
Journal:  Sci Rep       Date:  2022-08-10       Impact factor: 4.996

  2 in total

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