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

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. ► Analyses of coffee volatiles are of great interest for quality control. ► Use of the chromatographic fingerprint of roasted coffees instead of peak areas is a significant innovation. ► Prediction of six sensorial attributes of Brazilian Arabica coffee quality. ► The results demonstrated that SPME-CG coupled with chemometrics is an effective technique for prediction of Arabica coffee quality.