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

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

doi:10.3390/molecules20059170

Molecules (May 2015)

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

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

Affiliations

Eman Ghanem: Department of Chemistry, The University of Texas at Austin; 105 E 24th St. Mail Stop A5300, Austin, TX 78712-1224, USA
Helene Hopfer: Department of Viticulture and Enology, University of California; One Shields Ave., Davis, CA 95616-5270, USA
Andrea Navarro: Freshman Research Initiative, The University of Texas at Austin, 1 University Station, Mail Stop G2550, Austin, TX 78712, USA
Maxwell S. Ritzer: Freshman Research Initiative, The University of Texas at Austin, 1 University Station, Mail Stop G2550, Austin, TX 78712, USA
Lina Mahmood: Freshman Research Initiative, The University of Texas at Austin, 1 University Station, Mail Stop G2550, Austin, TX 78712, USA
Morgan Fredell: Freshman Research Initiative, The University of Texas at Austin, 1 University Station, Mail Stop G2550, Austin, TX 78712, USA
Ashley Cubley: Freshman Research Initiative, The University of Texas at Austin, 1 University Station, Mail Stop G2550, Austin, TX 78712, USA
Jessica Bolen: Freshman Research Initiative, The University of Texas at Austin, 1 University Station, Mail Stop G2550, Austin, TX 78712, USA
Rabia Fattah: Freshman Research Initiative, The University of Texas at Austin, 1 University Station, Mail Stop G2550, Austin, TX 78712, USA
Katherine Teasdale: Freshman Research Initiative, The University of Texas at Austin, 1 University Station, Mail Stop G2550, Austin, TX 78712, USA
Linh Lieu: Freshman Research Initiative, The University of Texas at Austin, 1 University Station, Mail Stop G2550, Austin, TX 78712, USA
Tedmund Chua: Freshman Research Initiative, The University of Texas at Austin, 1 University Station, Mail Stop G2550, Austin, TX 78712, USA
Federico Marini: Department of Chemistry, University of Rome "La Sapienza", P.le Aldo Moro 5, Rome I-00185, Italy
Hildegarde Heymann: Department of Viticulture and Enology, University of California; One Shields Ave., Davis, CA 95616-5270, USA
Eric V. Anslyn: Department of Chemistry, The University of Texas at Austin; 105 E 24th St. Mail Stop A5300, Austin, TX 78712-1224, USA

DOI: https://doi.org/10.3390/molecules20059170
Journal volume & issue: Vol. 20, no. 5
pp. 9170 – 9182

Abstract

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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.

Published in Molecules

ISSN: 1420-3049 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science: Chemistry: Organic chemistry
Website: http://www.mdpi.com/journal/molecules

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