Profiling of phenolic compounds using UPLC–MS for determining the geographical origin of green coffee beans from Ethiopia

•Chlorogenic acids determined in various Ethiopian green coffee beans.•Chlorogenic acid profiles used for chemometric classification of green beans.•3-CQA, 3,4-diCQA and 4,5-diCQA identified as discriminating compounds.•4,5-diCQA was found to be a suitable quantitative marker for coffee from the Eas...

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Bibliographic Details
Published in:Journal of food composition and analysis 2016-02, Vol.45, p.16-25
Main Authors: Mehari, Bewketu, Redi-Abshiro, Mesfin, Chandravanshi, Bhagwan Singh, Combrinck, Sandra, Atlabachew, Minaleshewa, McCrindle, Rob
Format: Article
Language:English
Subjects:
Authentication
Coffee
Ethiopia
Food analysis
Food composition
Geographical origin
LDA
PCA
Phenolic compounds
UPLC–MS
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Summary:•Chlorogenic acids determined in various Ethiopian green coffee beans.•Chlorogenic acid profiles used for chemometric classification of green beans.•3-CQA, 3,4-diCQA and 4,5-diCQA identified as discriminating compounds.•4,5-diCQA was found to be a suitable quantitative marker for coffee from the East. A total of 100 samples of green coffee (Coffea arabica L.) beans from the major producing regions of Ethiopia were studied using ultra performance liquid chromatography–mass spectroscopy to determine if the phenolic content could be linked to their geographical origins for authentication purposes. Principal component analysis allowed the most discriminating compounds to be identified. Based on their concentrations, 3-O-caffeoylquinic and 4,5-O-dicaffeoylquinic acids were found to be characteristic markers for Northwest and East (Harar) region coffees, respectively. Sub-regional coffee types from West, except Jimma B, could be distinguished by their 3,5-O-dicaffeoylquinic to 4,5-O-dicaffeoylquinic acid concentration ratios, while Yirgachefe coffees from South could be distinguished by their 4,5-O-dicaffeoylquinic to 3,4-O-dicaffeoylquinic acid concentration ratios. Linear discriminant analysis provided a classification model with recognition and prediction abilities of 91% and 90%, respectively, at regional level, and 89% and 86%, respectively, at sub-regional level. This is important for the detection of fraud, including the selling of inferior Ethiopian coffees under the label of the more expensive Harar coffees.
ISSN:0889-1575
1096-0481
DOI:10.1016/j.jfca.2015.09.006