000110820 001__ 110820 000110820 005__ 20240319080954.0 000110820 0247_ $$2doi$$a10.1016/j.foodchem.2021.131168 000110820 0248_ $$2sideral$$a127159 000110820 037__ $$aART-2022-127159 000110820 041__ $$aeng 000110820 100__ $$aFerrero-del-Teso, S. 000110820 245__ $$aModeling grape taste and mouthfeel from chemical composition 000110820 260__ $$c2022 000110820 5060_ $$aAccess copy available to the general public$$fUnrestricted 000110820 5203_ $$aThis 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. 000110820 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T53$$9info:eu-repo/grantAgreement/ES/MICINN/RYC2019-027995-I/AEI/10.13039/501100011033$$9info:eu-repo/grantAgreement/ES/MINECO/AGL2017-87373-C3-3-R 000110820 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000110820 590__ $$a8.8$$b2022 000110820 591__ $$aCHEMISTRY, APPLIED$$b5 / 72 = 0.069$$c2022$$dQ1$$eT1 000110820 591__ $$aNUTRITION & DIETETICS$$b5 / 87 = 0.057$$c2022$$dQ1$$eT1 000110820 591__ $$aFOOD SCIENCE & TECHNOLOGY$$b9 / 142 = 0.063$$c2022$$dQ1$$eT1 000110820 594__ $$a14.9$$b2022 000110820 592__ $$a1.624$$b2022 000110820 593__ $$aAnalytical Chemistry$$c2022$$dQ1 000110820 593__ $$aMedicine (miscellaneous)$$c2022$$dQ1 000110820 593__ $$aFood Science$$c2022$$dQ1 000110820 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000110820 700__ $$aSuárez, A. 000110820 700__ $$0(orcid)0000-0002-3698-6719$$aFerreira, C.$$uUniversidad de Zaragoza 000110820 700__ $$aPerenzoni, D. 000110820 700__ $$aArapitsas, P. 000110820 700__ $$aMattivi, F. 000110820 700__ $$0(orcid)0000-0002-4353-2483$$aFerreira, V.$$uUniversidad de Zaragoza 000110820 700__ $$aFernández-Zurbano, P. 000110820 700__ $$aSáenz-Navajas, M. P. 000110820 7102_ $$12005$$2595$$aUniversidad de Zaragoza$$bDpto. Matemática Aplicada$$cÁrea Matemática Aplicada 000110820 7102_ $$12009$$2750$$aUniversidad de Zaragoza$$bDpto. Química Analítica$$cÁrea Química Analítica 000110820 773__ $$g371 (2022), 131168[10 pp.]$$pFood chem.$$tFood Chemistry$$x0308-8146 000110820 8564_ $$s1917696$$uhttps://zaguan.unizar.es/record/110820/files/texto_completo.pdf$$yVersión publicada 000110820 8564_ $$s2547419$$uhttps://zaguan.unizar.es/record/110820/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000110820 909CO $$ooai:zaguan.unizar.es:110820$$particulos$$pdriver 000110820 951__ $$a2024-03-18-13:20:33 000110820 980__ $$aARTICLE