Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/63073
Type: Artigo de periódico
Title: Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy
Author: Ribeiro, JS
Ferreira, MMC
Salva, TJG
Abstract: Mathematical models based on chemometric analyses of the coffee beverage sensory data and NIR spectra of 51 Arabica roasted coffee samples were generated aiming to predict the scores of acidity, bitterness, flavour, cleanliness, body and overall quality of coffee beverage. Partial least squares (PIS) were used to construct the models. The ordered predictor selection COPS) algorithm was applied to select the wavelengths for the regression model of each sensory attribute in order to take only significant regions into account. The regions of the spectrum defined as important for sensory quality were closely related to the NIR spectra of pure caffeine, trigonelline,5-caffeoylquinic acid, cellulose, coffee lipids, sucrose and casein. The NIR analyses sustained that the relationship between the sensory characteristics of the beverage and the chemical composition of the roasted grain were as listed below: 1 - the lipids and proteins were closely related to the attribute body: 2 - the caffeine and chlorogenic acids were related to bitterness; 3 - the chlorogenic acids were related to acidity and flavour; 4 - the cleanliness and overall quality were related to caffeine, trigonelline, chlorogenic acid, polysaccharides, sucrose and protein. (C) 2010 Elsevier B.V. All rights reserved.
Subject: Chemometrics
Near infrared diffuse reflectance
Partial least squares
Arabica coffee
Sensory analysis
Country: Holanda
Editor: Elsevier Science Bv
Rights: fechado
Identifier DOI: 10.1016/j.talanta.2010.11.001
Date Issue: 2011
Appears in Collections:Unicamp - Artigos e Outros Documentos

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