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Variability of single bean coffee volatile compounds of Arabica and robusta roasted coffees analysed by SPME-GC-MS
We report on the analysis of volatile compounds by SPME-GC-MS for individual roasted coffee beans. The aim was to understand the relative abundance and variability of volatile compounds between individual roasted coffee beans at constant roasting conditions. Twenty-five batches of Arabica and robust...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published on behalf of the Canadian Institute of Food Science and Technology by Elsevier Applied Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5960070/ https://www.ncbi.nlm.nih.gov/pubmed/29735099 http://dx.doi.org/10.1016/j.foodres.2018.03.077 |
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author | Caporaso, Nicola Whitworth, Martin B. Cui, Chenhao Fisk, Ian D. |
author_facet | Caporaso, Nicola Whitworth, Martin B. Cui, Chenhao Fisk, Ian D. |
author_sort | Caporaso, Nicola |
collection | PubMed |
description | We report on the analysis of volatile compounds by SPME-GC-MS for individual roasted coffee beans. The aim was to understand the relative abundance and variability of volatile compounds between individual roasted coffee beans at constant roasting conditions. Twenty-five batches of Arabica and robusta species were sampled from 13 countries, and 10 single coffee beans randomly selected from each batch were individually roasted in a fluidised-bed roaster at 210 °C for 3 min. High variability (CV = 14.0–53.3%) of 50 volatile compounds in roasted coffee was obtained within batches (10 beans per batch). Phenols and heterocyclic nitrogen compounds generally had higher intra-batch variation, while ketones were the most uniform compounds (CV < 20%). The variation between batches was much higher, with the CV ranging from 15.6 to 179.3%. The highest variation was observed for 2,3-butanediol, 3-ethylpyridine and hexanal. It was also possible to build classification models based on geographical origin, obtaining 99.5% and 90.8% accuracy using LDA or MLR classifiers respectively, and classification between Arabica and robusta beans. These results give further insight into natural variation of coffee aroma and could be used to obtain higher quality and more consistent final products. Our results suggest that coffee volatile concentration is also influenced by other factors than simply the roasting degree, especially green coffee composition, which is in turn influenced by the coffee species, geographical origin, ripening stage and pre- and post-harvest processing. |
format | Online Article Text |
id | pubmed-5960070 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Published on behalf of the Canadian Institute of Food Science and Technology by Elsevier Applied Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-59600702018-06-01 Variability of single bean coffee volatile compounds of Arabica and robusta roasted coffees analysed by SPME-GC-MS Caporaso, Nicola Whitworth, Martin B. Cui, Chenhao Fisk, Ian D. Food Res Int Article We report on the analysis of volatile compounds by SPME-GC-MS for individual roasted coffee beans. The aim was to understand the relative abundance and variability of volatile compounds between individual roasted coffee beans at constant roasting conditions. Twenty-five batches of Arabica and robusta species were sampled from 13 countries, and 10 single coffee beans randomly selected from each batch were individually roasted in a fluidised-bed roaster at 210 °C for 3 min. High variability (CV = 14.0–53.3%) of 50 volatile compounds in roasted coffee was obtained within batches (10 beans per batch). Phenols and heterocyclic nitrogen compounds generally had higher intra-batch variation, while ketones were the most uniform compounds (CV < 20%). The variation between batches was much higher, with the CV ranging from 15.6 to 179.3%. The highest variation was observed for 2,3-butanediol, 3-ethylpyridine and hexanal. It was also possible to build classification models based on geographical origin, obtaining 99.5% and 90.8% accuracy using LDA or MLR classifiers respectively, and classification between Arabica and robusta beans. These results give further insight into natural variation of coffee aroma and could be used to obtain higher quality and more consistent final products. Our results suggest that coffee volatile concentration is also influenced by other factors than simply the roasting degree, especially green coffee composition, which is in turn influenced by the coffee species, geographical origin, ripening stage and pre- and post-harvest processing. Published on behalf of the Canadian Institute of Food Science and Technology by Elsevier Applied Science 2018-06 /pmc/articles/PMC5960070/ /pubmed/29735099 http://dx.doi.org/10.1016/j.foodres.2018.03.077 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Caporaso, Nicola Whitworth, Martin B. Cui, Chenhao Fisk, Ian D. Variability of single bean coffee volatile compounds of Arabica and robusta roasted coffees analysed by SPME-GC-MS |
title | Variability of single bean coffee volatile compounds of Arabica and robusta roasted coffees analysed by SPME-GC-MS |
title_full | Variability of single bean coffee volatile compounds of Arabica and robusta roasted coffees analysed by SPME-GC-MS |
title_fullStr | Variability of single bean coffee volatile compounds of Arabica and robusta roasted coffees analysed by SPME-GC-MS |
title_full_unstemmed | Variability of single bean coffee volatile compounds of Arabica and robusta roasted coffees analysed by SPME-GC-MS |
title_short | Variability of single bean coffee volatile compounds of Arabica and robusta roasted coffees analysed by SPME-GC-MS |
title_sort | variability of single bean coffee volatile compounds of arabica and robusta roasted coffees analysed by spme-gc-ms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5960070/ https://www.ncbi.nlm.nih.gov/pubmed/29735099 http://dx.doi.org/10.1016/j.foodres.2018.03.077 |
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