Modeling grape taste and mouthfeel from chemical composition
This research aims at predicting sensory properties generated by the phenolic fraction (PF) of grapes from chemical composition. Thirty-one grape extracts of different grape lots were obtained by maceration of grapes in hydroalcoholic solution; afterward they were submitted to solid phase extraction. The recovered PFs were reconstituted in a wine model. Subsequently the wine models, containing the PFs, were sensory (taste, mouthfeel) and chemically characterized. Significant sensory differences among the 31 PFs were identified. Sensory variables were predicted from chemical parameters by PLS-regression. Tannin activity and concentration along with mean degree of polymerization were found to be good predictors of dryness, while the concentration of large polymeric pigments seems to be involved in the “sticky” percept and flavonols in the “bitter” taste. Four fully validated PLS-models predicting sensory properties from chemical variables were obtained. Two out of the three sensory dimensions could be satisfactorily modeled. These results increase knowledge about grape properties and proposes the measurement of chemical variables to infer grape quality.
Main Authors: | , , , , , , , |
---|---|
Other Authors: | |
Format: | artículo biblioteca |
Published: |
Elsevier
2022-03-01
|
Subjects: | Sensory analysis, Sorting task, Rate-k-attributes, Astringency sub-qualities, Tannin activity, |
Online Access: | http://hdl.handle.net/10261/269991 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100009890 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
dig-icvv-es-10261-269991 |
---|---|
record_format |
koha |
spelling |
dig-icvv-es-10261-2699912022-05-20T02:21:04Z Modeling grape taste and mouthfeel from chemical composition Ferrero-del-Teso, Sara Suárez, Alejandro Ferreira, Vicente Perenzoni, Daniele Arapitsas, Panagiotis Mattivi, Fulvio Ferreira, Vicente Fernández-Zurbano, Purificación Sáenz-Navajas, María-Pilar Ministerio de Ciencia, Innovación y Universidades (España) Ministerio de Economía y Competitividad (España) Agencia Estatal de Investigación (España) Universidad de La Rioja Diputación General de Aragón European Commission Provincia Autonoma di Trento Sensory analysis Sorting task Rate-k-attributes Astringency sub-qualities Tannin activity This research aims at predicting sensory properties generated by the phenolic fraction (PF) of grapes from chemical composition. Thirty-one grape extracts of different grape lots were obtained by maceration of grapes in hydroalcoholic solution; afterward they were submitted to solid phase extraction. The recovered PFs were reconstituted in a wine model. Subsequently the wine models, containing the PFs, were sensory (taste, mouthfeel) and chemically characterized. Significant sensory differences among the 31 PFs were identified. Sensory variables were predicted from chemical parameters by PLS-regression. Tannin activity and concentration along with mean degree of polymerization were found to be good predictors of dryness, while the concentration of large polymeric pigments seems to be involved in the “sticky” percept and flavonols in the “bitter” taste. Four fully validated PLS-models predicting sensory properties from chemical variables were obtained. Two out of the three sensory dimensions could be satisfactorily modeled. These results increase knowledge about grape properties and proposes the measurement of chemical variables to infer grape quality. This project was funded by the Spanish Ministry of Economy and Competitiveness (MINECO) (project AGL-2017-87373-C3-3-R). S.F.T acknowledges the University of La Rioja for her predoctoral fellowship (UR-CAR-2018). LAAE acknowledges the continuous support of Diputación General de Aragón (T53), the European Social Fund, and the diligent participation of the wine experts from the Rioja region. MPSN acknowledges the Spanish National Research Agency, the Ministry of Science, Innovation, and Universities and the European Social Fund for her postdoctoral fellowship: Ramón y Cajal Program (RYC2019-027995-I/AEI/10.13039/501100011033). P.A., D.P., and F.M. acknowledge the financial support from AdP 2019 funded by the Autonomous Province of Trento (Italy). 2022-05-19T13:10:11Z 2022-05-19T13:10:11Z 2022-03-01 2022-05-19T13:10:11Z artículo http://purl.org/coar/resource_type/c_6501 doi: 10.1016/j.foodchem.2021.131168 issn: 0308-8146 Food Chemistry 371: 131168 (2022) http://hdl.handle.net/10261/269991 10.1016/j.foodchem.2021.131168 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100009890 #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2017-87373-C3-3-R/ES/POTENCIAL QUIMICO-SENSORIAL DE LA FRACCION FENOLICA (FF) DE LAS UVAS: CARACTERIZACION Y MEJORA EN LA ELABORACION DE VINOS TINTOS/ info:eu-repo/grantAgreement/AEI//RYC2019-027995-I Publisher's version http://dx.doi.org/10.1016/j.foodchem.2021.131168 Sí open Elsevier |
institution |
ICVV ES |
collection |
DSpace |
country |
España |
countrycode |
ES |
component |
Bibliográfico |
access |
En linea |
databasecode |
dig-icvv-es |
tag |
biblioteca |
region |
Europa del Sur |
libraryname |
Biblioteca del ICVV España |
topic |
Sensory analysis Sorting task Rate-k-attributes Astringency sub-qualities Tannin activity Sensory analysis Sorting task Rate-k-attributes Astringency sub-qualities Tannin activity |
spellingShingle |
Sensory analysis Sorting task Rate-k-attributes Astringency sub-qualities Tannin activity Sensory analysis Sorting task Rate-k-attributes Astringency sub-qualities Tannin activity Ferrero-del-Teso, Sara Suárez, Alejandro Ferreira, Vicente Perenzoni, Daniele Arapitsas, Panagiotis Mattivi, Fulvio Ferreira, Vicente Fernández-Zurbano, Purificación Sáenz-Navajas, María-Pilar Modeling grape taste and mouthfeel from chemical composition |
description |
This research aims at predicting sensory properties generated by the phenolic fraction (PF) of grapes from chemical composition. Thirty-one grape extracts of different grape lots were obtained by maceration of grapes in hydroalcoholic solution; afterward they were submitted to solid phase extraction. The recovered PFs were reconstituted in a wine model. Subsequently the wine models, containing the PFs, were sensory (taste, mouthfeel) and chemically characterized. Significant sensory differences among the 31 PFs were identified. Sensory variables were predicted from chemical parameters by PLS-regression. Tannin activity and concentration along with mean degree of polymerization were found to be good predictors of dryness, while the concentration of large polymeric pigments seems to be involved in the “sticky” percept and flavonols in the “bitter” taste. Four fully validated PLS-models predicting sensory properties from chemical variables were obtained. Two out of the three sensory dimensions could be satisfactorily modeled. These results increase knowledge about grape properties and proposes the measurement of chemical variables to infer grape quality. |
author2 |
Ministerio de Ciencia, Innovación y Universidades (España) |
author_facet |
Ministerio de Ciencia, Innovación y Universidades (España) Ferrero-del-Teso, Sara Suárez, Alejandro Ferreira, Vicente Perenzoni, Daniele Arapitsas, Panagiotis Mattivi, Fulvio Ferreira, Vicente Fernández-Zurbano, Purificación Sáenz-Navajas, María-Pilar |
format |
artículo |
topic_facet |
Sensory analysis Sorting task Rate-k-attributes Astringency sub-qualities Tannin activity |
author |
Ferrero-del-Teso, Sara Suárez, Alejandro Ferreira, Vicente Perenzoni, Daniele Arapitsas, Panagiotis Mattivi, Fulvio Ferreira, Vicente Fernández-Zurbano, Purificación Sáenz-Navajas, María-Pilar |
author_sort |
Ferrero-del-Teso, Sara |
title |
Modeling grape taste and mouthfeel from chemical composition |
title_short |
Modeling grape taste and mouthfeel from chemical composition |
title_full |
Modeling grape taste and mouthfeel from chemical composition |
title_fullStr |
Modeling grape taste and mouthfeel from chemical composition |
title_full_unstemmed |
Modeling grape taste and mouthfeel from chemical composition |
title_sort |
modeling grape taste and mouthfeel from chemical composition |
publisher |
Elsevier |
publishDate |
2022-03-01 |
url |
http://hdl.handle.net/10261/269991 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/501100009890 |
work_keys_str_mv |
AT ferrerodeltesosara modelinggrapetasteandmouthfeelfromchemicalcomposition AT suarezalejandro modelinggrapetasteandmouthfeelfromchemicalcomposition AT ferreiravicente modelinggrapetasteandmouthfeelfromchemicalcomposition AT perenzonidaniele modelinggrapetasteandmouthfeelfromchemicalcomposition AT arapitsaspanagiotis modelinggrapetasteandmouthfeelfromchemicalcomposition AT mattivifulvio modelinggrapetasteandmouthfeelfromchemicalcomposition AT ferreiravicente modelinggrapetasteandmouthfeelfromchemicalcomposition AT fernandezzurbanopurificacion modelinggrapetasteandmouthfeelfromchemicalcomposition AT saenznavajasmariapilar modelinggrapetasteandmouthfeelfromchemicalcomposition |
_version_ |
1777671039091736576 |