Modelling wine astringency from its chemical composition using machine learning algorithms
Sáenz-Navajas, M.P. (Universidad de Zaragoza) ; Ferrero-Del-Teso, S. ; Romero, M. ; Pascual, D. ; Diaz, D. ; Ferreira, V. (Universidad de Zaragoza) ; Fernández-Zurbano, P.
Resumen: Aims: The present work aims to predict sensory astringency from wine chemical composition using machine learning algorithms.
Material and results: Moristel grapes from different vineblocks and at different stages of ripening were collected. Eleven different wines were produced in 75 L tanks in triplicate, and further sensory factors were described by the rate-all-that-apply method with a trained panel of participants. The polyphenolic composition was characterised in wines by measuring the concentration and activity of tannins using UHPLC-UV/VIS, the mean degree of polymerisation (mDP. and the composition of tannins using thiolysis followed by UHPLC-MS. Conventional oenological parameters were analysed using FTIR and UV-Vis. Machine learning was applied to build models for predicting a wines astringency from its chemical composition. The best model was obtained using the support vector regressor (radial kernel) algorithm presenting a root-mean-square error (RMSE) value of 0.190.
Conclusions: The main variables of the astringency model were the % of procyanidins constituting tannins and ethanol content, followed by other eight variables related to tannin structure and acidity.
Significance of the study: These results increase the knowledge of chemical variables related to the perception of wine astringency and provide tools to control and optimise grape and wine production stages to modulate astringency and maximise quality and the consumer appeal of wines.
Idioma: Inglés
DOI: 10.20870/oeno-one.2019.53.3.2380
Año: 2019
Publicado en: Oeno One 53, 3 (2019), 499-509
ISSN: 2494-1271
Factor impacto JCR: 2.831 (2019)
Categ. JCR: FOOD SCIENCE & TECHNOLOGY rank: 45 / 138 = 0.326 (2019) - Q2 - T1
Factor impacto SCIMAGO: 0.619 - Horticulture (Q1) - Food Science (Q2)
Financiación: info:eu-repo/grantAgreement/ES/DGA-FSE/T53
Financiación: info:eu-repo/grantAgreement/ES/MINECO/AGL2014-59840
Financiación: info:eu-repo/grantAgreement/ES/MINECO/IJDC-2015-23444
Financiación: info:eu-repo/grantAgreement/ES/MINECO/RTC-2015-3379
Tipo y forma: Artículo (Versión definitiva)
Área (Departamento): Área Química Analítica (Dpto. Química Analítica)
Debe reconocer adecuadamente la autoría, proporcionar un enlace a la licencia e indicar si se han realizado cambios. Puede hacerlo de cualquier manera razonable, pero no de una manera que sugiera que tiene el apoyo del licenciador o lo recibe por el uso que hace.
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Material and results: Moristel grapes from different vineblocks and at different stages of ripening were collected. Eleven different wines were produced in 75 L tanks in triplicate, and further sensory factors were described by the rate-all-that-apply method with a trained panel of participants. The polyphenolic composition was characterised in wines by measuring the concentration and activity of tannins using UHPLC-UV/VIS, the mean degree of polymerisation (mDP. and the composition of tannins using thiolysis followed by UHPLC-MS. Conventional oenological parameters were analysed using FTIR and UV-Vis. Machine learning was applied to build models for predicting a wines astringency from its chemical composition. The best model was obtained using the support vector regressor (radial kernel) algorithm presenting a root-mean-square error (RMSE) value of 0.190.
Conclusions: The main variables of the astringency model were the % of procyanidins constituting tannins and ethanol content, followed by other eight variables related to tannin structure and acidity.
Significance of the study: These results increase the knowledge of chemical variables related to the perception of wine astringency and provide tools to control and optimise grape and wine production stages to modulate astringency and maximise quality and the consumer appeal of wines.
Idioma: Inglés
DOI: 10.20870/oeno-one.2019.53.3.2380
Año: 2019
Publicado en: Oeno One 53, 3 (2019), 499-509
ISSN: 2494-1271
Factor impacto JCR: 2.831 (2019)
Categ. JCR: FOOD SCIENCE & TECHNOLOGY rank: 45 / 138 = 0.326 (2019) - Q2 - T1
Factor impacto SCIMAGO: 0.619 - Horticulture (Q1) - Food Science (Q2)
Financiación: info:eu-repo/grantAgreement/ES/DGA-FSE/T53
Financiación: info:eu-repo/grantAgreement/ES/MINECO/AGL2014-59840
Financiación: info:eu-repo/grantAgreement/ES/MINECO/IJDC-2015-23444
Financiación: info:eu-repo/grantAgreement/ES/MINECO/RTC-2015-3379
Tipo y forma: Artículo (Versión definitiva)
Área (Departamento): Área Química Analítica (Dpto. Química Analítica)
Debe reconocer adecuadamente la autoría, proporcionar un enlace a la licencia e indicar si se han realizado cambios. Puede hacerlo de cualquier manera razonable, pero no de una manera que sugiera que tiene el apoyo del licenciador o lo recibe por el uso que hace. Exportado de SIDERAL (2023-09-13-11:02:00)
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Artículos > Artículos por área > Química Analítica
Registro creado el 2021-02-23, última modificación el 2023-09-14