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Predicting the Composition of Red Wine Blends Using an Array of Multicomponent Peptide-Based Sensors
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 t...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6272560/ https://www.ncbi.nlm.nih.gov/pubmed/26007178 http://dx.doi.org/10.3390/molecules20059170 |
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author | Ghanem, Eman Hopfer, Helene Navarro, Andrea Ritzer, Maxwell S. Mahmood, Lina Fredell, Morgan Cubley, Ashley Bolen, Jessica Fattah, Rabia Teasdale, Katherine Lieu, Linh Chua, Tedmund Marini, Federico Heymann, Hildegarde Anslyn, Eric V. |
author_facet | Ghanem, Eman Hopfer, Helene Navarro, Andrea Ritzer, Maxwell S. Mahmood, Lina Fredell, Morgan Cubley, Ashley Bolen, Jessica Fattah, Rabia Teasdale, Katherine Lieu, Linh Chua, Tedmund Marini, Federico Heymann, Hildegarde Anslyn, Eric V. |
author_sort | Ghanem, Eman |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6272560 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-62725602019-01-07 Predicting the Composition of Red Wine Blends Using an Array of Multicomponent Peptide-Based Sensors Ghanem, Eman Hopfer, Helene Navarro, Andrea Ritzer, Maxwell S. Mahmood, Lina Fredell, Morgan Cubley, Ashley Bolen, Jessica Fattah, Rabia Teasdale, Katherine Lieu, Linh Chua, Tedmund Marini, Federico Heymann, Hildegarde Anslyn, Eric V. Molecules Article 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. MDPI 2015-05-20 /pmc/articles/PMC6272560/ /pubmed/26007178 http://dx.doi.org/10.3390/molecules20059170 Text en © 2015 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ghanem, Eman Hopfer, Helene Navarro, Andrea Ritzer, Maxwell S. Mahmood, Lina Fredell, Morgan Cubley, Ashley Bolen, Jessica Fattah, Rabia Teasdale, Katherine Lieu, Linh Chua, Tedmund Marini, Federico Heymann, Hildegarde Anslyn, Eric V. Predicting the Composition of Red Wine Blends Using an Array of Multicomponent Peptide-Based Sensors |
title | Predicting the Composition of Red Wine Blends Using an Array of Multicomponent Peptide-Based Sensors |
title_full | Predicting the Composition of Red Wine Blends Using an Array of Multicomponent Peptide-Based Sensors |
title_fullStr | Predicting the Composition of Red Wine Blends Using an Array of Multicomponent Peptide-Based Sensors |
title_full_unstemmed | Predicting the Composition of Red Wine Blends Using an Array of Multicomponent Peptide-Based Sensors |
title_short | Predicting the Composition of Red Wine Blends Using an Array of Multicomponent Peptide-Based Sensors |
title_sort | predicting the composition of red wine blends using an array of multicomponent peptide-based sensors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6272560/ https://www.ncbi.nlm.nih.gov/pubmed/26007178 http://dx.doi.org/10.3390/molecules20059170 |
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