Espresso coffee

Illy, A. and Vianni, R., Eds., Espresso Coffee The Chemistry of Quality, Academic Press, London, 1995. 

Fig. 3.2 Nosespace spectra during drinking of espresso coffee by an experienced coffee taster. The top-left frame shows the in-mouth temperature profile during drinking...

M. Petracco, Espresso coffee foam A Maillard-mediated phenomenon Description and analytical characterisation, in Melanoidins in Food and Health, J. M. Ames (ed), Vol. 2, European Communities, Luxembourg, 2001, 31—42. 

D Agostina, A., Boschin, G., Bacchini, F., and Amoldi, A. (2004). Investigations on the high molecular whight foaming fraction of espresso coffee. J. Agric. Food Chem. 52,7118-7125. 

Answer Trevor liked to start his mornings with a stop at the local coffee bar to have a double shot of espresso coffee. The amount of caffeine present in the coffee was sufficient... 

Illy A., Illy E., Macrae R., Petracco M., Sondahl M.R., Valussi S. and Viani R. (1995) Espresso coffee. Espresso Coffee. The Chemistry of Quality. Illy A. and Viani R. Eds, Academic Press, New York. 

Perez de Obanos et ai. (2005) anaiysed nine sampies of naturaiiy contaminated Vietnamese ooffee. Roasting for 5 min with air at 260 °C produoed reductions ranging from 12% to 93%. Preparation of espresso coffee from these roasted sampies produced a further reduction of 16-70% in oohratoxin A, whereas the production of mocha coffee caused reductions ranging from 17% to 55%, and the production of fiitered coffee caused reductions that ranged from oniy 1.2% to 25%. 

Espresso coffee is prepared in a machine that injects high-pressure steam through a cake of ground coffee. In a conventional machine, the user manually loads ground coffee into a metal filter cup, locks the cup under the steam head, and then opens the steam heater. 

Several of the major constituents of espresso coffee have been identified in the H-NMR spectrum. H-NMR data in combination with chemometrics... 

Dugas, V., Pineau, N. and Folmer, B. (2012). Evaluating whole cup experience in gourmet espresso coffee by using dynamic methods. Eurosens Symposium, Bern, Switzerland. 

PTR Monitoring preparation of Espresso coffee Sanchez-Lopez etal. [187]... 

Figure 4.8 Set up for sampling volatile organic compounds from the coffee flow. Volatiles were introduced into the dilution lance by a flow created with a vacuum pump and were then diluted 7.5 fold using dried compressed air containing a standard for mass calibration [187]. Reprinted with permission from Sinchez-Lopez, J.A., Zimmermann, R., Yeretzian, C. (2014) Insight into the Time-resolved Extraction of Aroma Compounds during Espresso Coffee Preparation Online Monitoring by PTR-ToE-MS. Anal. Chem. 86 11696 11704. Copyright (2014) American Chemical Society...

Sanchez-L6pez, J.A., Zimmermann, R., Yeretzian, C. (2014) Insight into the Time-resolved Extraction of Aroma Compounds during Espresso Coffee Preparation Online Monitoring by PTR-ToF-MS. Anal. Chem. 86 11696-11704. 

In other flavor perception studies, the PTR mass spectra of the headspace of seven different brands of mozzarella cheese held at 36 C have been first compared with the judge panel flavor profile [157]. A PCA of the mass spectral data was used to discriminate different cheese types. And a trained panel of sensory judge was employed to give qualitative and quantitative analysis of mozzarella cheese. It was found that there was an interesting and clear similarity between the classical sensory and the instrumental analysis. More recently, a robust and reproducible model was developed to predict the sensory profile of espresso coffee, and the model was derived from 11 different espresso coffees, which had been analyzed by a trained panel and PTR-MS, and further validated using eight additional espressos [158]. Flavor studies of whey [159], custard desserts [160], other types of cheeses [148,161], milk [162], wine [163,164], apples [165],olive oils [166],bread [167], and butter and butter oil [168,169] were also conducted by the PTR-MS system. 

Lindinger, C., Labbe, D., Pollien, P, Rytz, A., Juillerat, M.A.,Yeretzian,C.,Blank,I. (2008) When machine tastes coffee instrumental approach to predict the sensory profile of espresso coffee. Analytical Chemistry, 80, 1574-1581. 

Espresso coffee is prepared in a machine that pumps cold water at high pressure (commonly 10-20 bar) into a hot water boiler, which displaces near-boiling hot water. This... 

Fig. 11. A typical espresso machine (A) pressure vessel, (B) portafilter holding ground coffee, (C) on/off switch, with built in pressure indicator, (D) solenoid valve for espresso coffee, (E) cup holding leached espresso coffee.

Alves R.C., Casal S., Oliveira B.P.P. Factors influencing the norharman and harman contents in espresso coffee. Journal of Agricultural and Food Chemistry, 55 1832-1838 (2007). 

HPLC-DPPH Spices Tea (green, oolong, and white) Espresso coffees Vegetable oil Normal phase Murkovic (2002) Damasius et al. (2014) Zhang et al. (2013) Mnatsakanyan et al. (2010) Zhang et al. (2009)... 

Mnatsakanyan, M., T. A. Goodie, X. A. Conlan, P. S. Francis, G. P. McDermott, N. W. Barnett, D. Shock, F. Gritti, G. Guiochon, and R. A. Shalliker. 2010. High performance liquid chromatography with two simultaneous on-line antioxidant assays Evaluation and comparison of espresso coffees. Talanta 81(3) 837-842. 

Figure 6.1 Identification of the VOCs in the headspace above espresso coffee. These data were recorded using a CC-PTR-MS combination, (a) Shows the power of CC separation to identify VOCs with the same nominal m/z. For example at m/z III, two VOCs are contributing to the ion intensity, 2-acetylfuran and 5-methylfurfural, contributing 29% and 71% to the signal intensity, respectively, in a pure PTR-MS experiment, (b) Similar chromatographic data showing features at m/z values where only a single VOC dominates. Reprinted from Reference 14 with permission.

Seven types of espresso coffee were classified by Pardo and Sberveglieri with a system composed of an electronic nose and an SVM with polynomial and Gaussian RBF kernels.For each coffee type, 36 measurements were performed with an electronic nose equipped with five thin-film semiconductor sensors based on SnOi and Ti-Fe. The output signal from sensors was submitted to a PGA analysis whose principal components (between 2 and 5) represented the input data for the SVM classifier. The error surface corresponding to various kernel parameters and number of input principal components was investigated. 

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