Characterization of espresso coffee aroma by static headspace GC-MS and sensory flavor profile

The aromas of three espresso coffee (EC) samples from different botanical varieties and types of roast (Arabica coffee, Robusta natural blend, and Robusta Torrefacto blend (special roast by adding sugar)) were studied by static headspace GC-MS and sensory flavor profile analysis. Seventy-seven compounds were identified in all of the EC samples. Among them, 13 key odorants have been quantified and correlated with their flavor notes by applying multivariate statistical methods. Some correlations have been found in the EC samples: some aldehydes with fruity flavors, diones with buttery flavors, and pyrazines with earthy/musty, roasty/burnt, and woody/papery flavors. By applying principal component analysis (PCA), Arabica and Robusta samples were separated successfully by principal component 1 (60.7% of variance), and Torrefacto and Natural Robusta EC samples were separated by principal component 2 (28.1% of total variance). With PCA, the aroma characterization of each EC sample could be observed. A very simple discriminant function using some key odorants was obtained by discriminant analysis, allowing the classification of each EC sample into its respective group with a success rate of 100%.