Predicting the composition of red wine blends using an array of Multicomponent peptide-based sensors

Molecules

Volumn 20, Issue 5, 2015, Pages 9170-9182

  Ghanem, Eman ^a   Hopfer, Helene ^b   Navarro, Andrea ^c   Ritzer, Maxwell S ^c   Mahmood, Lina ^c   Fredell, Morgan ^c   Cubley, Ashley ^c   Bolen, Jessica ^c   Fattah, Rabia ^c   Teasdale, Katherine ^c   Lieu, Linh ^c   Chua, Tedmund ^c   Marini, Federico ^d   Heymann, Hildegarde ^b   Anslyn, Eric V ^a  

^a University of Texas at Austin   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^d SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

Blends; Differential sensing; Supramolecular sensors; Tannins; Wine

Indexed keywords

PEPTIDE; POLYPHENOL; TANNIN DERIVATIVE;

ANALYSIS; CHEMISTRY; GENETIC PROCEDURES; LEAST SQUARE ANALYSIS; ODOR; PEPTIDE MAPPING; PROCEDURES; TASTE; THEORETICAL MODEL; VITIS; WINE;

BIOSENSING TECHNIQUES; LEAST-SQUARES ANALYSIS; MODELS, THEORETICAL; ODORS; PEPTIDE MAPPING; PEPTIDES; POLYPHENOLS; SMELL; TANNINS; TASTE; VITIS; WINE;

EID: 84976601375     PISSN: None     EISSN: 14203049     Source Type: Journal    
DOI: 10.3390/molecules20059170     Document Type: Article

References (29)

1. Hopfer, H.; Ebeler, S.E.; Heymann, H. How Blending Affects the Sensory and Chemical Properties of Red Wine. Am. J. Enol. Vitic. 2012, 63, 313-324.
2. Hjelmeland, A.K.; King, E.S.; Ebeler, S.E.; Heymann, H. Characterizing the Chemical and Sensory Profiles of United States Cabernet Sauvignon Wines and Blends. Am. J. Enol. Vitic. 2013, 64, 169-179.
3. Dooley, L.M.; Threlfall, R.T.; Jean-Francois, M.; Howard, L.R. Compositional and Sensory Impacts from Blending Red Wine Varietals. Am. J. Enol. Vitic. 2012, 63, 241-250.
4. Frank, Mitch. Georges Duboeuf's Company Convicted of Fraud. Wine Spectator. 14 February 2015. Available online: http://www.winespectator.com/webfeature/show/id/Georges-Duboeufs-Company-Convicted-of-Fraud-3133 (accessed on 14 February 2015).
5. Agence France-Presse. Italian police foil counterfeit Tuscan red wine scam in biggest food fraud. The Guardian. Available online: http://www.theguardian.com/world/2014/sep/11/italian-policefoil-brunello-di-montalcino-wine-scam (accessed on 14 February 2015).
6. Codinachs, L.M.; Kloock, J.P.; Schoning, M.J.; Baldi, A.; Ipatov, A.; Bratova, A.; Jimenez-Jorquera, C. Electronic integrated multisensor tongue applied to grape juice and wine analysis. Analyst 2008, 133, 1440-1448.
7. Ciosek, P.; Wróblewski, W. Potentiometric electronic tongues for foodstuff and biosample recognition-An overview. Sensors 2011, 11, 4688-4701.
8. Tahara, Y.; Toko, K. Electronic tongues-A review. IEEE Sens. J. 2013, 13, 3001-3011.
9. Aleixandre, M.; Lozano, J.; Gutiérrez, J.; Sayago, I.; Fernandez, M.J.; Horrillo, M.C. Portable e-nose to classify different kinds of wine. Sens. Actuators B Chem. 2008, 131, 71-76.
10. López de Lerma, M.N.; Bellincontro, A.; García-Martínez, T.; Mencarelli, F.; Moreno, J.J. Feasibility of an electronic nose to differentiate commercial Spanish wines elaborated from the same grape variety. Food Res. Int. 2013, 51, 790-796.
11. Garcia, M.; Aleixandre, M.; Gutíerrez, J.; Horrillo, M.C. Electronic nose for wine discrimination. Sens. Actuators B Chem. 2006, 113, 911-916.
12. Garcia, M.; Fernandez, M.J.; Fontecha, J.L.; Lozano, J.; Santos, J.P.; Aleixandre, M.; Sayago, I.; Gutierrez, J.; Horrillo, M.C. Differentiation of red wines using an electronic nose based on surface acoustic wave devices. Talanta 2006, 68, 1162-1165.
13. Zhang, Y.; Chen, J.; Lei, Y.; Zhou, Q.; Sun, S.; Noda, I. Discrimination of different red wine by Fourier-Transform infrared and two-dimensional infrared correlation spectroscopy. J. Mol. Struct. 2010, 974, 144-150.
14. Fernandez, K.; Agosin, E. Quantitative analysis of red wine tannins using fourier-Transform mid-infrared spectrometry. J. Agric. Food Chem. 2007, 55, 7294-7300.
15. Weekley, A.J.; Bruins, P.; Sisto, M.; Augustine, M.P. Using NMR to study full intact wine bottles J. Magn. Reson. 2003, 161, 91-98.
16. Pereira, G.E.; Gaudillere, J.P.; Leeuwen, C.V.; Hilbert, G.; Lavialle, O.; Maucourt, M.; Deborde, C.; Moing, A.; Rolin, D. 1H-NMR and Chemometrics to Characterize Mature Grape Berries in Four Wine-Growing Areas in Bordeaux, France. J. Agric. Food Chem. 2005, 53, 6382-6389.
17. Lopez, R.; Aznar, M.; Cacho, J.; Ferreira, V. Determination of minor and trace volatile compounds in wine by solid-phase extraction and gas chromatography with mass spectrometric detection. J. Chromatogr. A 2002, 966, 167-177.
18. Umali, A.P.; Anslyn, E.V. A General Approach to Differential Sensing Using Synthetic Molecular Receptors. Curr. Opin. Chem. Biol. 2010, 14, 685-692.
19. Umali, A.P.; LeBoeuf, S.E.; Newberry, R.W.; Kim, S.; Tran, L.; Rome, W.A.; Tian, T.; Taing, D.; Hong, J.; Kwan, M.; et al. Discrimination of flavonoids and red wine varietals by arrays of differential peptidic sensors. Chem. Sci. 2011, 2, 439-445.
20. Gallagher, L.T.; Heo, J.H.; Lopez, M.A.; Ray, B.M.; Xiao, J.; Umali, A.P.; Zhang, A.; Dharmarajan, S.; Heymann, H.; Anslyn, E.V. Pattern-based discrimination of organic acids and red wine varietals by arrays of synthetic receptors. Supramol. Chem. 2012, 24, 143-148.
21. Nguyen, B.T.; Anslyn, E.V.; Indicator Displacement Assays. Coord. Chem. Rev. 2006, 250, 3118-3127.
22. Umali, A.P.; Ghanem, E.; Hopfer, H.; Hussain, A.; Kao, Y.; Zabanal, L.G.; Wilkins, B.J.; Hobza, C.; Quach, D.K.; Fredell, M.; et al. Grape and wine sensory attributes correlate with pattern-based discrimination of Cabernet Sauvignon wines by a peptidic sensor array. Tetrahedron 2014, 1-5.
23. Cala, O.; Pinaud, N.; Simon, C.; Fouquet, E.; Laguerre, M.; Dufourc, E.J.; Pianet, I. NMR and Molecular Modeling of Wine Tannins Binding to Saliva Proteins: Revisiting Astringency from Molecular and Colloidal Prospects. FASEB J. 2010, 24, 4281-4290.
24. Abdi, H. Partial Least Squares (PLS) Regression. In Encyclopedia of Social Sciences Research Methods; Lewis-Beck, M.S., Bryman, A., Liao, T.F., Eds.; Sage Publications: Thousand Oaks, CA, USA, 2003; Volumes 1-3.
25. Mattivi, F.; Vrhovesk, U.; Masuero, D.; Trainotti, D. Differences in the Amount and Structure of Extractable Skin and Seed Tannins amongst Red Grape Varieties. Aust. J. Grape Wine Res. 2009, 15, 27-35.
26. Vidal1, S.; Francis, L.; Guyot, S.; Marnet, N.; Kwiatkowski, M.; Gawel, R.; Cheynier, V.; Waters, E.J. The Mouth-feel Properties of Grape and Apple Proanthocyanidins in a Wine-like Medium. J. Sci. Food Agric. 2003, 83, 564-573.
27. Yamane, T.; Jeong, S.T.; Goto-Yamamoto, N.; Koshita, Y.; Kobayashi, S. Effects of Temperature on Anthocyanin Biosynthesis in Grape Berry Skins. Am. J. Enol. Vitic. 2006, 57, 54-59.
28. R: A Language and Environment for Statistical Computing; The R Foundation for Statistical Computing; R Development Core Team: Vienna, Austria, 2011. Available online: http://www.R-project.org/(accessed on 18 May 2015).
29. MATLAB 8.0 and Statistics Toolbox 8.1; The MathWorks, Inc.: Natick, MA, USA, 2012.