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

Molecules

Eman Ghanem¹  Helene Hopfer²  Andrea Navarro⁴  Maxwell S. Ritzer⁴  Lina Mahmood⁴  Morgan Fredell⁴  Ashley Cubley⁴  Jessica Bolen⁴  Rabia Fattah⁴  Katherine Teasdale⁴  Linh Lieu⁴  Tedmund Chua⁴  Federico Marini³  Hildegarde Heymann²  Eric V. Anslyn¹ 

[1] Department of Chemistry, The University of Texas at Austin; 105 E 24th St. Mail Stop A5300, Austin, TX 78712-1224, USA; E-Mail:;Department of Viticulture and Enology, University of California; One Shields Ave., Davis, CA 95616-5270, USA; E-Mail:;Department of Chemistry, University of Rome “La Sapienza”, P.le Aldo Moro 5, Rome I-00185, Italy;Freshman Research Initiative, The University of Texas at Austin, 1 University Station, Mail Stop G2550, Austin, TX 78712, USA; E-Mails:

关键词: differential sensing;    supramolecular sensors;    wine;    blends;    tannins;   

DOI  :  10.3390/molecules20059170

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.