Prediction of sensory properties of Brazilian Arabica roasted coffees by headspace solid phase microextraction-gas chromatography and partial least squares.

Volatile compounds in fifty-eight Arabica roasted coffee samples from Brazil were analyzed by SPME-GC-FID and SPME-GC-MS, and the results were compared with those from sensory evaluation. The main purpose was to investigate the relationships between the volatile compounds from roasted coffees and certain sensory attributes, including body, flavor, cleanliness and overall quality. Calibration models for each sensory attribute based on chromatographic profiles were developed by using partial least squares (PLS) regression. Discrimination of samples with different overall qualities was done by using partial least squares-discriminant analysis (PLS-DA). The alignment of chromatograms was performed by the correlation optimized warping (COW) algorithm. Selection of peaks for each regression model was performed by applying the ordered predictors selection (OPS) algorithm in order to take into account only significant compounds. The results provided by the calibration models are promising and demonstrate the feasibility of using this methodology in on-line or routine applications to predict the sensory quality of unknown Brazilian Arabica coffee samples. According to the PLS-DA on chromatographic profiles of different quality samples, compounds 3-methypropanal, 2-methylfuran, furfural, furfuryl formate, 5-methyl-2-furancarboxyaldehyde, 4-ethylguaiacol, 3-methylthiophene, 2-furanmethanol acetate, 2-ethyl-3,6-dimethylpyrazine, 1-(2-furanyl)-2-butanone and three others not identified compounds can be considered as possible markers for the coffee beverage overall quality.