Classification of Coffee Beans by GC-C-IRMS, GC-MS, and H-1-NMR

Victoria Andrea Arana; Jessica Medina; Pierre Esseiva; Diego Pazos; Julien Wist

doi:10.1155/2016/8564584

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Classification of Coffee Beans by GC-C-IRMS, GC-MS, and H-1-NMR

Journal article

Open access

Peer reviewed

Classification of Coffee Beans by GC-C-IRMS, GC-MS, and H-1-NMR

Victoria Andrea Arana, Jessica Medina, Pierre Esseiva, Diego Pazos and Julien Wist

Journal of analytical methods in chemistry, 8564584

2016

DOI: https://doi.org/10.1155/2016/8564584

PMID: 27516919

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Abstract

Chemistry

Chemistry, Analytical

Physical Sciences

Science & Technology

In a previous work using H-1-NMR we reported encouraging steps towards the construction of a robust expert system for the discrimination of coffees from Colombia versus nearby countries (Brazil and Peru), to assist the recent protected geographical indication granted to Colombian coffee in 2007. This system relies on fingerprints acquired on a 400 MHz magnet and is thus well suited for small scale random screening of samples obtained at resellers or coffee shops. However, this approach cannot easily be implemented at harbour's installations, due to the elevated operational costs of cryogenic magnets. This limitation implies shipping the samples to the NMR laboratory, making the overall approach slower and thereby more expensive and less attractive for large scale screening at harbours. In this work, we report on our attempt to obtain comparable classification results using alternative techniques that have been reported promising as an alternative to NMR: GC-MS and GC-C-IRMS. Although statistically significant information could be obtained by all three methods, the results show that the quality of the classifiers depends mainly on the number of variables included in the analysis; hence NMR provides an advantage since more molecules are detected to obtain a model with better predictions.

Details

Title: Classification of Coffee Beans by GC-C-IRMS, GC-MS, and H-1-NMR
Authors/Creators: Victoria Andrea Arana - University of Atlántico
Jessica Medina - Universidad del Valle
Pierre Esseiva - University of Lausanne
Diego Pazos - University of Lausanne
Julien Wist - Universidad del Valle
Publication Details: Journal of analytical methods in chemistry, 8564584
Publisher: Hindawi Publishing Group
Number of pages: 11
Identifiers: 991005616457807891
Murdoch Affiliation: Centre for Computational and Systems Medicine
Language: English
Resource Type: Journal article

UN Sustainable Development Goals (SDGs)

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#12 Responsible Consumption & Production

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37 Record Views

13 Times Cited - Web of Science

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 1 Clinical & Life Sciences; 1.287 Dietary Stimulants; 1.287.1516 Coffee and Caffeine
Web Of Science research areas: Chemistry, Analytical
ESI research areas: Chemistry