An assessment of voltammetry on disposable screen printed electrodes to predict wine chemical composition and oxygen consumption rates
•Voltammetric signals were measured with disposable carbon paste sensors (DCPS)•DCPS could be suitable for predicting oxygen consumption rates.•Validated PLS models predicted chemical variables from voltammetric signals.•DCPS coupled to PLS-modeling is a promising tool to be used in wineries. The pr...
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Published in | Food chemistry Vol. 365; p. 130405 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
15.12.2021
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Subjects | |
Online Access | Get full text |
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Summary: | •Voltammetric signals were measured with disposable carbon paste sensors (DCPS)•DCPS could be suitable for predicting oxygen consumption rates.•Validated PLS models predicted chemical variables from voltammetric signals.•DCPS coupled to PLS-modeling is a promising tool to be used in wineries.
The 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 PLS-modeling 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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0308-8146 1873-7072 |
DOI: | 10.1016/j.foodchem.2021.130405 |