Literature DB >> 19836541

Coffee aroma--statistical analysis of compositional data.

M Korhonová1, K Hron, D Klimcíková, L Müller, P Bednár, P Barták.   

Abstract

Solid-phase microextraction in headspace mode coupled with gas chromatography-mass spectrometry was applied to the determination of volatile compounds in 30 commercially available coffee samples. In order to differentiate and characterize Arabica and Robusta coffee, six major volatile compounds (acetic acid, 2-methylpyrazine, furfural, 2-furfuryl alcohol, 2,6-dimethylpyrazine, 5-methylfurfural) were chosen as the most relevant markers. Cluster analysis and principal component analysis (PCA) were applied to the raw chromatographic data and data processed by centred logratio transformation.

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Year:  2009        PMID: 19836541     DOI: 10.1016/j.talanta.2009.07.054

Source DB:  PubMed          Journal:  Talanta        ISSN: 0039-9140            Impact factor:   6.057


  3 in total

1.  Characterization of Arabica and Robusta volatile coffees composition by reverse carrier gas headspace gas chromatography-mass spectrometry based on a statistical approach.

Authors:  Giuseppe Procida; Corrado Lagazio; Francesca Cateni; Marina Zacchigna; Angelo Cichelli
Journal:  Food Sci Biotechnol       Date:  2020-06-04       Impact factor: 2.391

2.  Identification of Volatile Compounds and Selection of Discriminant Markers for Elephant Dung Coffee Using Static Headspace Gas Chromatography-Mass Spectrometry and Chemometrics.

Authors:  Poowadol Thammarat; Chadin Kulsing; Kanet Wongravee; Natchanun Leepipatpiboon; Thumnoon Nhujak
Journal:  Molecules       Date:  2018-07-31       Impact factor: 4.411

3.  Volatile Compound Characterization of Coffee (Coffea arabica) Processed at Different Fermentation Times Using SPME-GC-MS.

Authors:  Gustavo Galarza; Jorge G Figueroa
Journal:  Molecules       Date:  2022-03-21       Impact factor: 4.411

  3 in total

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