Prediction models for Arabica coffee beverage quality based on aroma analyses and chemometrics

TALANTA

卷:101

Article

Ribeiro, J. S.¹  Augusto, F.²  Salva, T. J. G.³  Ferreira, M. M. C.⁴ 

[1] CET, Fed Inst Espirito Santo, BR-29106010 Vila Velha, ES, Brazil

[2] Univ Estadual Campinas, Inst Chem, Gas Chromatog Lab, BR-13083970 Campinas, SP, Brazil

[3] Agron Inst Campinas Coffee Ctr, BR-13012970 Campinas, SP, Brazil

[4] Univ Estadual Campinas, Inst Chem, Theoret & Appl Chemometr Lab, BR-13083970 Campinas, SP, Brazil

关键词: Chemometrics;    Sensorial data;    Overall quality;    Flavor;    Bitterness;    SPME;   

DOI  :  10.1016/j.talanta.2012.09.022

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free