Cluster analysis of coffee blends for some sensory properties: a comparative approach to the ABIC’s classification criteria
DOI:
https://doi.org/10.5433/1679-0375.2021v42n2p145Keywords:
Grouping, Specialty coffees, Dendrogram, Cutoff score, Taster,Abstract
In Brazil, coffee beverage quality is classified according to technical recommendations of the Associação Brasileira da Indústria de Café (ABIC), which determines cutoff points to discriminate from non-recommended to gourmet coffees. Accordingly, this study aimed to propose the use of cluster analysis to evaluate coffee blends composed of coffees with different qualities and of different varieties regarding a few sensory properties, using continuous and binary scales obtained by a cutoff, which defines whether the coffee is recommendable or not according to the ABIC criteria. It is believed, therefore, that this technique can be used to analyze coffee beverage quality as it is easily accessible and implemented by researchers. In conclusion, a qualitative cluster analysis using the minimum cutoff value of the ABIC had more promising results. This is because blends whose composition contained high and moderate proportions of specialty coffees were more homogeneous in the clustering.Downloads
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References
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CHARRAD, M.; GHAZZALI, N.; BOITEAU, V.; NIKNAFS, A. NbClust: an R package for determining the relevant number of clusters in a data set. Journal of Statistical Software, [California], v. 61, n. 6, p. 1-36, 2014.
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MONTEIRO, M. C.; TRUGO, L. C. Determinação de compostos bioativos em amostras comerciais de café torrado. Química Nova, São Paulo, v. 28, n. 4, p. 637-641, 2005.
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PAULINO, A. L. B.; CIRILLO, M.A.; RIBEIRO, D. E.; BORÉM, F. M.; MATIAS, G. C. A mixed model applied to joint analysis in experiments with coffee blends using the least squares method. Revista Ciência Agronômica, v. 50, n. 3, p. 345-352, 2019.
RIBEIRO, M. V. M.; BORALLE, N.; PEZZA, H. R.; PEZZA, L.; TOCI, A. T. Authenticity of roasted coffee using 1H NMR spectroscopy. Journal of Food Composition and Analysis, San Diego, v. 57, p. 24-30, 2017.
SANTOS, P. M.; CIRILLO, M. Â.; GUIMARÃES, E. R. Specialty coffee in Brazil: transition among consumers’ constructs using structural equation modeling. British Food Journal, Bingley, v. 123, n. 5, p. 1913-1930, 2021.
SCA – SPECIALTY COFFE ASSOCIATION. SCA protocols: Cupping specialty coffee. Califórnia: SCA, 2018. Retrieved from: http://www.scaa.org/PDF/resources/cupping-protocols .pdf. Access in: Aug 25, 2021.
SUNARHARUM, W. B.; WILLIAMS, D. J.; SMYTH, H. E. Complexity of coffee flavor: a compositional and sensory perspective. Food Research International, Ottawa, v. 62, p. 315-325, 2014.
TOLEDO, P. R. A. B.; PEZZA, L.; PEZZA, H. P.; TOCI, A. T. Relationship between the different aspects related to coffee quality and their volatile compounds. Comprehensive reviews in Food Science and Food Safety, Chicago, v. 15, n. 4, p. 705–719, 2016. DOI: https://doi.org/10.1111/1541-4337.12205.
TOTTI, R.; VENCOVSKY, R.; BATISTA, L. A. R. Utiliza-ção de métodos de agrupamentos hierárquicos em acessosde Paspalum (Graminea (Poaceae). Semina: Ciências Exa-tas e Tecnológicas, Londrina, v. 22, n. 1, p. 25-35, 2001.
ALESSANDRINI, L.; ROMANI, S.; PINNAVAIA, G.; ROSA, M. D. Near infrared spectroscopy: an analytical to tool predicted coffee roasting degree. Analytica Chemical Acta, [s. l.], v. 625, n. 1, p. 95-102, 2008. DOI: 10.1016/j.aca.2008.07.013.
ALVARADO, R. A.; LINNEMANN, A. R. The predictive value of a small consumer panel for coffee-cupper judgment. British Food Journal, Bingley, v. 112, n. 9, p. 1023–1032, 2010. DOI: https:// doi.org/10.1108/00070701011074372.
BAQUETA, M. R.; COQUEIRO, A.; VALDERRAMA, P. Brazilian Coffee Blends: a simple and fast method by near-infrared spectroscopy for the determination of the sensory attributes elicited in professional coffee cupping. Journal of Food Science, Champaign, v. 84, n. 6, p. 1247- 1255, 2019. DOI 10.1111/1750-3841.14617.
BORÉM, F. M.; CIRILLO, M. A.; ALVES, A. P. C.; SANTOS, C. M.; LISKA, G. R.; RAMOS, M. F. LIMA, R. R. Coffee sensory quality study based on spatial distribution in the mantiqueira mountain region of brazil. Journal of Sensory Studies, Westport, v. 35, n. 2, p. e12552, 2019.
BOTELHO, F. M.; CORRÊA, P. C.; BOTELHO, S. C. C.; VARGAS-ELÍAS, G. A.; DINIZ, M. D. M. S.; OLIVEIRA, G. H. H. Propriedades físicas de frutos de café robusta durante a secagem: Determinação e modelagem. Coffee Science, Lavras, v. 11, p. 65-75, 2016.
CARVALHO, P. R.; MUNITA, C. S.; LAPOLLI, A. L. Validity studies among hierarchical methods of cluster analysis using cophenetic correlation coefficient. Brazilian Journal of Radiation Sciences, Rio de Janeiro, v. 7, n. 2A, p. 1-14, 2019.
CHARRAD, M.; GHAZZALI, N.; BOITEAU, V.; NIKNAFS, A. NbClust: an R package for determining the relevant number of clusters in a data set. Journal of Statistical Software, [California], v. 61, n. 6, p. 1-36, 2014.
CIRILLO, M. A.; RAMOS, M. F.; BORÉM, F. M.; MIRANDA, F. M.; RIBEIRO, D. E.; MENEZES, F.S. Statistical procedure for the composition of a sensory panel of blends of coffee with different qualities using the distribution of the extremes of the highest scores. Acta Scientiarum. Agronomy, Maringá, v. 41, p. e39332, 2019. DOI: 10.4025/actasciagron.v41i1.39323.
CONAB - COMPANHIA NACIONAL DE ABASTECIMENTO. Acompanhamento da safra brasileira: café: segundo levantamento. Brasília: Conab, 2021. (Safra 2021, v. 8, n. 2).
COSTA, A. S.; RESENDE, M.; NAKANO, E. Y.; CIRILLO, M. A; BORÉM, F. M.; RIBEIRO, D. E. Proposal of a metric selection index for correspondence analysis: an application in the sensory evaluation of coffee blends. Semina: Ciências Agrárias, Londrina, v. 41, n. 2, p. 479-492, 2020. DOI: 10.5433/1679-0359.2020v41n2p479.
COSTA, B. R. Brazilian specialty coffee scenario. In: ALMEIDA, Luciana Florêncio; SPERS, Eduardo Eugênio (ed.). Coffee consumption and industry strategies in Brazil. Cambridge: Woodhead Publishing, 2020. p. 51-64.
FERRÃO, R. G.; FONSECA, A. F. A.; FERRÃO, M. A. G.; MUNER, L. H. Conilon coffee: the coffea canephora produced in Brazil. 3rd ed. Vitória: Incaper, 2019.
FERREIRA, D. F. Estatística multivariada. Lavras: Ed. UFLA, 2018.
LIMA FILHO, T.; LUCIA, S. M. D.; SARAIVA, S. H.; LIMA, R. M. Physical and chemical characteristics of espresso coffee beverages prepared from Arabica and Robusta coffee blends. Revista Ceres, Viçosa, v. 62, n. 4, p. 333-339, 2015. DOI: 10.1590/0034-737X201562040001.
JOHNSON, R. A.; WICHERN, D. W. Applied multivariate statistical analysis. London, UK: Pearson, 2014.
MONTEIRO, M. C.; TRUGO, L. C. Determinação de compostos bioativos em amostras comerciais de café torrado. Química Nova, São Paulo, v. 28, n. 4, p. 637-641, 2005.
MOURA, S. C. S. R.; GERMER, S. P. M; ANJOS, V. D. A.; Mori, E. E. M.; MATTOSO, L. H. C.; FIRMINO, A.; NASCIMENTO, C. J. F. Physical, chemical and sensorial evaluations of Arabica coffee blends with Canephora (Robusta) coffee blends. Brazilian Journal of Food Technology, Campinas, v. 10, n. 4, p. 271-277, 2007.
PAULINO, A. L. B.; CIRILLO, M.A.; RIBEIRO, D. E.; BORÉM, F. M.; MATIAS, G. C. A mixed model applied to joint analysis in experiments with coffee blends using the least squares method. Revista Ciência Agronômica, v. 50, n. 3, p. 345-352, 2019.
RIBEIRO, M. V. M.; BORALLE, N.; PEZZA, H. R.; PEZZA, L.; TOCI, A. T. Authenticity of roasted coffee using 1H NMR spectroscopy. Journal of Food Composition and Analysis, San Diego, v. 57, p. 24-30, 2017.
SANTOS, P. M.; CIRILLO, M. Â.; GUIMARÃES, E. R. Specialty coffee in Brazil: transition among consumers’ constructs using structural equation modeling. British Food Journal, Bingley, v. 123, n. 5, p. 1913-1930, 2021.
SCA – SPECIALTY COFFE ASSOCIATION. SCA protocols: Cupping specialty coffee. Califórnia: SCA, 2018. Retrieved from: http://www.scaa.org/PDF/resources/cupping-protocols .pdf. Access in: Aug 25, 2021.
SUNARHARUM, W. B.; WILLIAMS, D. J.; SMYTH, H. E. Complexity of coffee flavor: a compositional and sensory perspective. Food Research International, Ottawa, v. 62, p. 315-325, 2014.
TOLEDO, P. R. A. B.; PEZZA, L.; PEZZA, H. P.; TOCI, A. T. Relationship between the different aspects related to coffee quality and their volatile compounds. Comprehensive reviews in Food Science and Food Safety, Chicago, v. 15, n. 4, p. 705–719, 2016. DOI: https://doi.org/10.1111/1541-4337.12205.
TOTTI, R.; VENCOVSKY, R.; BATISTA, L. A. R. Utiliza-ção de métodos de agrupamentos hierárquicos em acessosde Paspalum (Graminea (Poaceae). Semina: Ciências Exa-tas e Tecnológicas, Londrina, v. 22, n. 1, p. 25-35, 2001.
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Published
2021-11-03
How to Cite
Gonçalves, D. de O., Resende, M., Martins, N. da S., Borém, F. M., & Cirillo, M. A. (2021). Cluster analysis of coffee blends for some sensory properties: a comparative approach to the ABIC’s classification criteria. Semina: Ciências Exatas E Tecnológicas, 42(2), 145–152. https://doi.org/10.5433/1679-0375.2021v42n2p145
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