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Molecules 2015, 20(5), 9170-9182; doi:10.3390/molecules20059170

Predicting the Composition of Red Wine Blends Using an Array of Multicomponent Peptide-Based Sensors

1
Department of Chemistry, The University of Texas at Austin; 105 E 24th St. Mail Stop A5300, Austin, TX 78712-1224, USA
2
Department of Viticulture and Enology, University of California; One Shields Ave., Davis, CA 95616-5270, USA
3
Freshman Research Initiative, The University of Texas at Austin, 1 University Station, Mail Stop G2550, Austin, TX 78712, USA
4
Department of Chemistry, University of Rome "La Sapienza", P.le Aldo Moro 5, Rome I-00185, Italy
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editor: Derek J. McPhee
Received: 31 March 2015 / Revised: 9 May 2015 / Accepted: 12 May 2015 / Published: 20 May 2015
(This article belongs to the Collection Wine Chemistry)
View Full-Text   |   Download PDF [1523 KB, uploaded 22 May 2015]   |  

Abstract

Differential sensing using synthetic receptors as mimics of the mammalian senses of taste and smell is a powerful approach for the analysis of complex mixtures. Herein, we report on the effectiveness of a cross-reactive, supramolecular, peptide-based sensing array in differentiating and predicting the composition of red wine blends. Fifteen blends of Cabernet Sauvignon, Merlot and Cabernet Franc, in addition to the mono varietals, were used in this investigation. Linear Discriminant Analysis (LDA) showed a clear differentiation of blends based on tannin concentration and composition where certain mono varietals like Cabernet Sauvignon seemed to contribute less to the overall characteristics of the blend. Partial Least Squares (PLS) Regression and cross validation were used to build a predictive model for the responses of the receptors to eleven binary blends and the three mono varietals. The optimized model was later used to predict the percentage of each mono varietal in an independent test set composted of four tri-blends with a 15% average error. A partial least square regression model using the mouth-feel and taste descriptive sensory attributes of the wine blends revealed a strong correlation of the receptors to perceived astringency, which is indicative of selective binding to polyphenols in wine. View Full-Text
Keywords: differential sensing; supramolecular sensors; wine; blends; tannins differential sensing; supramolecular sensors; wine; blends; tannins
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ghanem, E.; Hopfer, H.; Navarro, A.; Ritzer, M.S.; Mahmood, L.; Fredell, M.; Cubley, A.; Bolen, J.; Fattah, R.; Teasdale, K.; Lieu, L.; Chua, T.; Marini, F.; Heymann, H.; Anslyn, E.V. Predicting the Composition of Red Wine Blends Using an Array of Multicomponent Peptide-Based Sensors. Molecules 2015, 20, 9170-9182.

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