000099313 001__ 99313
000099313 005__ 20230914083317.0
000099313 0247_ $$2doi$$a10.20870/oeno-one.2019.53.3.2380
000099313 0248_ $$2sideral$$a122830
000099313 037__ $$aART-2019-122830
000099313 041__ $$aeng
000099313 100__ $$0(orcid)0000-0001-7225-2272$$aSáenz-Navajas, M.P.$$uUniversidad de Zaragoza
000099313 245__ $$aModelling wine astringency from its chemical composition using machine learning algorithms
000099313 260__ $$c2019
000099313 5060_ $$aAccess copy available to the general public$$fUnrestricted
000099313 5203_ $$aAims: 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.
000099313 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T53$$9info:eu-repo/grantAgreement/ES/MINECO/AGL2014-59840$$9info:eu-repo/grantAgreement/ES/MINECO/IJDC-2015-23444$$9info:eu-repo/grantAgreement/ES/MINECO/RTC-2015-3379
000099313 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000099313 590__ $$a2.831$$b2019
000099313 591__ $$aFOOD SCIENCE & TECHNOLOGY$$b45 / 138 = 0.326$$c2019$$dQ2$$eT1
000099313 592__ $$a0.619$$b2019
000099313 593__ $$aHorticulture$$c2019$$dQ1
000099313 593__ $$aFood Science$$c2019$$dQ2
000099313 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000099313 700__ $$aFerrero-Del-Teso, S.
000099313 700__ $$aRomero, M.
000099313 700__ $$aPascual, D.
000099313 700__ $$aDiaz, D.
000099313 700__ $$0(orcid)0000-0002-4353-2483$$aFerreira, V.$$uUniversidad de Zaragoza
000099313 700__ $$aFernández-Zurbano, P.
000099313 7102_ $$12009$$2750$$aUniversidad de Zaragoza$$bDpto. Química Analítica$$cÁrea Química Analítica
000099313 773__ $$g53, 3 (2019), 499-509$$pOENO One$$tOeno One$$x2494-1271
000099313 8564_ $$s127240$$uhttps://zaguan.unizar.es/record/99313/files/texto_completo.pdf$$yVersión publicada
000099313 8564_ $$s1843127$$uhttps://zaguan.unizar.es/record/99313/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000099313 909CO $$ooai:zaguan.unizar.es:99313$$particulos$$pdriver
000099313 951__ $$a2023-09-13-11:02:00
000099313 980__ $$aARTICLE