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

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)9170-9182
Number of pages13
JournalMolecules
Volume20
Issue number5
DOIs
StatePublished - 2015

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Chemistry (miscellaneous)
  • Molecular Medicine
  • Pharmaceutical Science
  • Drug Discovery
  • Physical and Theoretical Chemistry
  • Organic Chemistry

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