Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/30466
metadata.artigo.dc.title: Selecting a probabilistic model applied to the sensory analysis of specialty coffees performed with consumer
metadata.artigo.dc.creator: Ferreira, Haiany Aparecida
Liska, Gilberto Rodrigues
Cirillo, Marcelo Angelo
Borem, Flavio Meira
Ribeiro, Diego Egídio
Cortez, Ricardo Miguel
Guiraldeli, Carlos Henrique Cardeal
metadata.artigo.dc.publisher: IEEE Xplore
metadata.artigo.dc.date.issued: Mar-2016
metadata.artigo.dc.identifier.citation: FERREIRA, H. A. et al. Selecting a probabilistic model applied to the sensory analysis of specialty coffees performed with consumer. Revista IEEE America Latina, [S.l.], v. 14, n. 3, Mar. 2016.
metadata.artigo.dc.description.abstract: Sensory analysis of cafes assumes that a sensory panel is formed by trained panelists according to recommendations of the American Specialty Coffee Association. However, the choice that determines the preference of a coffee is routinely done through experimentation with consumers, in which largely presents no particular skill in terms of sensory characteristics. Upon this fact, this study aimed to conduct a study considering several probabilistic distributions belonging to the class of generalized extreme value, considering a sensory analysis applied to evaluation of four specialty coffees produced with different processes and at different altitudes in the mountain region of the Mantiqueira state of Minas Gerais. For this analysis, we considered a sensory panel trained to untrained consumers. It was found that the extreme value distribution was the best fit and the final note that the odds of a consumer to submit a maximum score was 9.0 points lower. Therefore, there is evidence to conclude that an efficient identification of specialty coffees produced in this region made by consumers requires more intensive training.
metadata.artigo.dc.identifier.uri: https://ieeexplore.ieee.org/document/7459642/
http://repositorio.ufla.br/jspui/handle/1/30466
metadata.artigo.dc.language: en_US
Appears in Collections:DEG - Artigos publicados em periódicos
DES - Artigos publicados em periódicos

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