000110645 001__ 110645
000110645 005__ 20230519145449.0
000110645 0247_ $$2doi$$a10.1016/j.foodchem.2021.130405
000110645 0248_ $$2sideral$$a125996
000110645 037__ $$aART-2021-125996
000110645 041__ $$aeng
000110645 100__ $$0(orcid)0000-0002-3698-6719$$aFerreira, Chelo$$uUniversidad de Zaragoza
000110645 245__ $$aAn assessment of voltammetry on disposable screen printed electrodes to predict wine chemical composition and oxygen consumption rates
000110645 260__ $$c2021
000110645 5060_ $$aAccess copy available to the general public$$fUnrestricted
000110645 5203_ $$aThe present work aimed at determining the applicability of linear sweep voltammetry coupled to disposable carbon paste electrodes to predict chemical composition and wine oxygen consumption rates (OCR) by PLSmodeling of the voltammetric signal. Voltammetric signals were acquired in a set of 16 red commercial wines. Samples were extensively characterized including SO2, antioxidant indexes, metals and polyphenols measured by HPLC. Wine OCRs were calculated by measuring oxygen consumption under controlled oxidation conditions. PLS-Regression models were calculated to predict chemical variables and wine OCRs from first order difference voltammogram curves. A significant number of fully validated models predicting chemical variables from voltammetric signals were obtained. Interestingly, monomeric and polymerized anthocyanins can be differently predicted from the first and second wave of the first derivative of voltammograms, respectively. This fast, cheap and easy-to-use approach presents an important potential to be used in wineries for rapid wine chemical characterization.
000110645 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/RYC2019-027995-I/AEI/10.13039/501100011033$$9info:eu-repo/grantAgreement/ES/MINECO/AGL2017-87373-C3-1-R$$9info:eu-repo/grantAgreement/ES/MINECO/AGL2017-87373-C3-3-R$$9info:eu-repo/grantAgreement/ES/MINECO/RTC-2016-4935-2
000110645 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000110645 590__ $$a9.231$$b2021
000110645 592__ $$a1.489$$b2021
000110645 594__ $$a13.1$$b2021
000110645 591__ $$aCHEMISTRY, APPLIED$$b6 / 73 = 0.082$$c2021$$dQ1$$eT1
000110645 593__ $$aFood Science$$c2021$$dQ1
000110645 591__ $$aNUTRITION & DIETETICS$$b6 / 90 = 0.067$$c2021$$dQ1$$eT1
000110645 593__ $$aAnalytical Chemistry$$c2021$$dQ1
000110645 591__ $$aFOOD SCIENCE & TECHNOLOGY$$b8 / 144 = 0.056$$c2021$$dQ1$$eT1
000110645 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000110645 700__ $$aSaenz-Navajas, María Pilar
000110645 700__ $$0(orcid)0000-0001-7931-574X$$aCarrascon, Vanesa
000110645 700__ $$aNaes, Tormod
000110645 700__ $$aFernandez-Zurbano, Purificación
000110645 700__ $$0(orcid)0000-0002-4353-2483$$aFerreira, Vicente$$uUniversidad de Zaragoza
000110645 7102_ $$12005$$2595$$aUniversidad de Zaragoza$$bDpto. Matemática Aplicada$$cÁrea Matemática Aplicada
000110645 7102_ $$12009$$2750$$aUniversidad de Zaragoza$$bDpto. Química Analítica$$cÁrea Química Analítica
000110645 773__ $$g365 (2021), 130405 [9 pp.]$$pFood chem.$$tFood Chemistry$$x0308-8146
000110645 8564_ $$s1548709$$uhttps://zaguan.unizar.es/record/110645/files/texto_completo.pdf$$yPostprint
000110645 8564_ $$s785691$$uhttps://zaguan.unizar.es/record/110645/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000110645 909CO $$ooai:zaguan.unizar.es:110645$$particulos$$pdriver
000110645 951__ $$a2023-05-18-14:41:45
000110645 980__ $$aARTICLE