Literature DB >> 32517906

Metabolomics fingerprint of Philippine coffee by SPME-GC-MS for geographical and varietal classification.

Emelda A Ongo1, Giuseppe Montevecchi2, Andrea Antonelli2, Veronica Sberveglieri3, Fortunato Sevilla Iii4.   

Abstract

Volatile metabolites of Philippine Arabica and Robusta coffee beans in both forms standard (not-eaten by the Asian palm civet) and civet coffee grown in different Philippine regions were identified using the hyphenated technique headspace-solid phase microextraction-gas chromatography-mass spectrometry. A great number of volatile metabolites with a wide variety of functional groups were extracted and forty-seven prominent compounds were identified. The volatile metabolomics (volatilomics) fingerprint of Arabica coffees considerably differed from Robusta coffee and geographical origin slightly altered the fingerprint profile of coffee samples. Chemometric analysis such as principal component analysis (PCA) displayed a good classification between Arabica and Robusta coffee samples. Although Arabica coffee samples from different geographical origins were clustered separately from each other, the proximity of clusters between Arabica coffee samples which could be classified into one large group, indicated their close similarity of headspace metabolites. The distinction between Arabica samples and Robusta coffees was attributed through the PCA to several key volatile metabolites, in particular, higher quantities of acetic acid, furfural, 5-methylfurfural, 2-formylpyrrole and maltol and lower concentrations of 4-ethylguaiacol and phenol. These discriminating metabolites could represent useful quality markers to differentiate Arabica from Robusta coffee. Results revealed that the headspace metabolites in coffee provide significant information on its inherent aroma quality. Also, the findings suggested that the overall quality of Philippine coffee is variety and region-specific.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Arabica; Asian palm civet; Civet coffee; Discriminant markers; Geographical origin; HS-SPME-GC-MS; Robusta; Volatile metabolites; Volatilomics

Mesh:

Year:  2020        PMID: 32517906     DOI: 10.1016/j.foodres.2020.109227

Source DB:  PubMed          Journal:  Food Res Int        ISSN: 0963-9969            Impact factor:   6.475


  5 in total

1.  Identification of VOCs in essential oils extracted using ultrasound- and microwave-assisted methods from sweet cherry flower.

Authors:  Huimin Zhang; Hongguang Yan; Quan Li; Hui Lin; Xiaopeng Wen
Journal:  Sci Rep       Date:  2021-01-13       Impact factor: 4.379

2.  Authenticity Assessment and Fraud Quantitation of Coffee Adulterated with Chicory, Barley, and Flours by Untargeted HPLC-UV-FLD Fingerprinting and Chemometrics.

Authors:  Nerea Núñez; Javier Saurina; Oscar Núñez
Journal:  Foods       Date:  2021-04-12

Review 3.  Aroma Clouds of Foods: A Step Forward to Unveil Food Aroma Complexity Using GC × GC.

Authors:  Sílvia M Rocha; Carina Pedrosa Costa; Cátia Martins
Journal:  Front Chem       Date:  2022-03-01       Impact factor: 5.221

Review 4.  Characterization of the Aroma Profile and Main Key Odorants of Espresso Coffee.

Authors:  Simone Angeloni; Ahmed M Mustafa; Doaa Abouelenein; Laura Alessandroni; Laura Acquaticci; Franks Kamgang Nzekoue; Riccardo Petrelli; Gianni Sagratini; Sauro Vittori; Elisabetta Torregiani; Giovanni Caprioli
Journal:  Molecules       Date:  2021-06-24       Impact factor: 4.411

Review 5.  Metabolomics-Based Approach for Coffee Beverage Improvement in the Context of Processing, Brewing Methods, and Quality Attributes.

Authors:  Mohamed A Farag; Ahmed Zayed; Ibrahim E Sallam; Amr Abdelwareth; Ludger A Wessjohann
Journal:  Foods       Date:  2022-03-18
  5 in total

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