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 (PLS) were used to construct the models. The ordered predictor selection (OPS) 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.

No 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.

In this work, the potential of mid‐infrared diffuse reflectance spectroscopy with Fourier transform for discrimination of 29 commercial Brazilian coffee samples with different industrial processing, i.e., caffeine extraction and roasting degree, was evaluated. The statistical treatments applied to pre‐treated spectral data were principal component analysis and partial least squares – discriminant analysis (PLS‐DA). The ordered predictors selection method was used for variable selection. The chemometric analyses of the mid‐infra‐red spectra allowed inferring on the lower carbohydrate, caffeine and chlorogenic acid concentration as well as on the higher water content in the decaffeinated coffee. The technique also allowed speculation on the higher lipid and lower water content in the dark roasted coffee compared with traditional roasted coffee. A clear discrimination of decaffeinated from medium and dark roasted coffees was observed in PC1. PLS‐DA was used for the discrimination between medium and dark roasted coffees. A model with one latent variable correctly classified 100% of the external validation and prediction samples according to their roasting degree. PRACTICAL APPLICATIONS Diffuse reflectance mid infrared spectroscopy (DRIFTS), principal component analysis and partial least squares were successfully applied to discriminate decaffeinated coffees from nondecaffeinated coffees and to discriminate roasted coffees by their roasting degree. This study have shown that DRIFTS coupled with chemometrics consists in a simple and straightforward analytical method for monitoring the roasted coffee authenticity, and the results could help in developing an alternative and inexpensive method for quality control of coffee products.