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Type: Artigo de periódico
Title: Prediction models for Arabica coffee beverage quality based on aroma analyses and chemometrics
Author: Ribeiro, JS
Augusto, F
Salva, TJG
Ferreira, MMC
Abstract: In this work, soft modeling based on chemometric analyses of coffee beverage sensory data and the chromatographic profiles of volatile roasted coffee compounds is proposed to predict the scores of acidity, bitterness, flavor, cleanliness, body, and overall quality of the coffee beverage. A partial least squares (PLS) regression method was used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the compounds for the regression model of each sensory attribute in order to take only significant chromatographic peaks into account. The prediction errors of these models, using 4 or 5 latent variables, were equal to 0.28, 0.33, 0.35, 0.33, 0.34 and 0.41, for each of the attributes and compatible with the errors of the mean scores of the experts. Thus, the results proved the feasibility of using a similar methodology in on-line or routine applications to predict the sensory quality of Brazilian Arabica coffee. (C) 2012 Elsevier B.V. All rights reserved.
Subject: Chemometrics
Sensorial data
Overall quality
Country: Holanda
Editor: Elsevier Science Bv
Rights: fechado
Identifier DOI: 10.1016/j.talanta.2012.09.022
Date Issue: 2012
Appears in Collections:Unicamp - Artigos e Outros Documentos

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