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%.

The final quality of espresso coffee (EC) depends upon certain technical conditions, such as the extraction temperature used in preparing it. The aim of this work was to investigate the effects of water temperature (88, 92, 96 and 98°C) on the final quality of three types of EC (Arabica, Robusta Natural blend and Robusta Torrefacto blend) in order to select the optimal temperature. Volatile compound (analysed by Static headspace gas chromatography/mass spectrometry) and sensory flavour profiles were the most relevant parameters, whereas physicochemical, taste and mouthfeel parameters were not very useful for selecting the water temperature. For Arabica and Robusta Natural blend ECs, 92°C was the optimal water temperature. For Robusta Torrefacto blend EC the overall acceptability might lead to the selection of 88°C as the ideal water temperature, but the high percentages of key odorants related to roasty and earthy/musty flavours and the 'not hot enough' perception dictated the selection of 92°C in this case as well.

Three espresso coffee (EC) samples of different botanical varieties and types of roast were prepared in standard conditions using an experimental EC prototype: Arabica coffee, Robusta Natural blend, and Robusta Torrefacto blend (a special roast by adding sugar). The ECs were characterized with regard to the physical parameters, amount of total solids, total solids on filtrate, lipids, caffeine, trigonelline, and chlorogenic acids by HPLC, and sensory descriptive analysis related to foam appearance, taste, and mouthfeel. Principal component analysis (PCA) was applied to differentiate the EC samples. Arabica and Robusta samples were separated successfully by principal component 1 (55.3% of variance) including physicochemical and sensory parameters related to foam and taste of ECs. Torrefacto and Robusta Natural EC samples were separated by principal component 2 (20.7% of total variance) including mouthfeel and other attributes of color foam. Some interesting correlations among sensory and physicochemical variables were found. A very simple discriminate function was obtained by discriminate analysis allowing the classification of each EC sample into its respective group with a success rate of 100%.

The volatile profiles of espresso and plunger (cafetière) coffees prepared from (1) an 80:20 (w/w) blend of natural roasted Robusta and Arabica (Robusta Natural blend), (2) a 40:40:20 (w/w/w) blend of Robusta Natural blend, Robusta torrefacto roast (850 g kg −1 Robusta, 150 g kg −1 sugar) and (3) natural roasted pure Arabica were established by headspace solid phase microextraction (SPME) after selection of the fibre coating (polyacrylate or polydimethylsiloxane) and the temperature and time of extraction. For the analysis of furans and indoles the polyacrylate coating proved to be more suitable; however, for the overall characterisation of the volatile composition of espresso and plunger coffees the polydimethylsiloxane coating was chosen. SPME/gas chromatography (GC)/mass spectrometry (MS) analyses allowed the identification of 37 compounds: four aldehydes, two ketones, 11 furans, 10 pyrazines, two pyridines, three phenolic compounds, two indoles, one lactone, one ester and one benzothiazine. The volatile composition was related more to the botanical variety (Arabica or Robusta) than to the method of preparation of the brew (espresso or plunger). Furthermore, use of the variability provided solely by the GC peak areas and respective retention times, combined with principal component analysis (PCA), yielded the information necessary for discrimination. The combined technique of headspace SPME/GC/PCA, as an alternative to conventional techniques based on GC/MS, is proposed as a lower-cost, fast and reliable technique for the screening and distinction of coffee brews.

Water pressure is one of the most important factors which influence the final quality of espresso coffee (EC). However, few studies dealing with this issue have been found. The aim of this work was to study the effect of water pressure on the final quality of Arabica ECs as well as to classify ECs prepared at different pressures (7, 9, and 11 atm) according to their physicochemical and sensory characteristics, key odorants, by means of multivariate analysis. Statistically, principal component 1 (PC1) separated ECs prepared at 7 and 9 atm from ECs prepared at 11 atm and included the main foam and taste characteristics as well as some key odorants and flavor compounds. ECs prepared at 7 and 9 atm were separated by principal component 2 (PC2). Coffees prepared at 9 atm showed consistency of foam and a high percentage of key odorants related to freshness and fruity, malty, and buttery flavors. A simple discriminate function was obtained by discriminate analysis, allowing the classification of ECs prepared at three pressures into their respective groups with a success rate of 100%.