Classification of Coffee Beans by GC-C-IRMS, GC-MS, and 1H-NMR
In a previous work using 1H-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...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Journal of Analytical Methods in Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2016/8564584 |
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| author | Victoria Andrea Arana Jessica Medina Pierre Esseiva Diego Pazos Julien Wist |
| author_facet | Victoria Andrea Arana Jessica Medina Pierre Esseiva Diego Pazos Julien Wist |
| author_sort | Victoria Andrea Arana |
| collection | DOAJ |
| description | In a previous work using 1H-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. |
| format | Article |
| id | doaj-art-fdc4f260de904c71b30cadfaa89215e3 |
| institution | Kabale University |
| issn | 2090-8865 2090-8873 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Analytical Methods in Chemistry |
| spelling | doaj-art-fdc4f260de904c71b30cadfaa89215e32025-02-03T01:31:04ZengWileyJournal of Analytical Methods in Chemistry2090-88652090-88732016-01-01201610.1155/2016/85645848564584Classification of Coffee Beans by GC-C-IRMS, GC-MS, and 1H-NMRVictoria Andrea Arana0Jessica Medina1Pierre Esseiva2Diego Pazos3Julien Wist4Grupo de Investigación Ciencias, Educación y Tecnología (CETIC), Programa de Química, Facultad de Ciencias Básicas, Universidad del Atlántico, km 7 Antigua Vía Puerto Colombia, Barranquilla, Atlántico, ColombiaChemistry Department, Universidad del Valle, A.A. 25360, Cali, ColombiaInstitut de Police Scientifique, École des Sciences Criminelles, Université de Lausanne, 1015 Lausanne, SwitzerlandInstitut de Police Scientifique, École des Sciences Criminelles, Université de Lausanne, 1015 Lausanne, SwitzerlandChemistry Department, Universidad del Valle, A.A. 25360, Cali, ColombiaIn a previous work using 1H-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.http://dx.doi.org/10.1155/2016/8564584 |
| spellingShingle | Victoria Andrea Arana Jessica Medina Pierre Esseiva Diego Pazos Julien Wist Classification of Coffee Beans by GC-C-IRMS, GC-MS, and 1H-NMR Journal of Analytical Methods in Chemistry |
| title | Classification of Coffee Beans by GC-C-IRMS, GC-MS, and 1H-NMR |
| title_full | Classification of Coffee Beans by GC-C-IRMS, GC-MS, and 1H-NMR |
| title_fullStr | Classification of Coffee Beans by GC-C-IRMS, GC-MS, and 1H-NMR |
| title_full_unstemmed | Classification of Coffee Beans by GC-C-IRMS, GC-MS, and 1H-NMR |
| title_short | Classification of Coffee Beans by GC-C-IRMS, GC-MS, and 1H-NMR |
| title_sort | classification of coffee beans by gc c irms gc ms and 1h nmr |
| url | http://dx.doi.org/10.1155/2016/8564584 |
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