: Astringency is an essential sensory attribute of red wine closely related to the saliva precipitation upon contact with the wine. In this study a data matrix of 52 physico-chemical parameters was used to predict the Saliva Precipitation Index (SPI) in 110 Italian mono-varietal red wines using partial least squares regression (PLSr) with variable selection by Variable Importance for Projection (VIP) and the significance of regression coefficients. The final PLSr model, evaluated using a test data set, had 3 components and yielded an R2test of 0.630 and an RMSEtest of 0.994, with 19 independent variables whose regression coefficients were all significant at p < 0.05. Variables selected in the final model according to the decreasing magnitude of their absolute regression coefficient include the following: Procyanidin B1, Epicatechin terminal unit, Total aldehydes, Protein content, Vanillin assay, 520 nm, Polysaccharide content, Epigallocatechin PHL, Tartaric acid, Volatile acidity, Titratable acidity, Catechin terminal unit, Proanthocyanidin assay, pH, Tannin-Fe/Anthocyanin, Buffer capacity, Epigallocatechin PHL gallate, Catechin + epicatechin PHL, and Tannin-Fe. These results can be used to better understand the physico-chemical relationship underlying astringency in red wine.

Galaz, C., Ricci, A., Parpinello, G.P., Gambuti, A., Rinaldi, A., Moio, L., et al. (2023). Multivariate prediction of Saliva Precipitation Index for relating selected chemical parameters of red wines to the sensory perception of astringency. CURRENT RESEARCH IN FOOD SCIENCE, 7, 1-12 [10.1016/j.crfs.2023.100626].

Multivariate prediction of Saliva Precipitation Index for relating selected chemical parameters of red wines to the sensory perception of astringency

Galaz, Cristian
Membro del Collaboration Group
;
Ricci, Arianna
Membro del Collaboration Group
;
Parpinello, Giuseppina Paola
Membro del Collaboration Group
;
Versari, Andrea
Membro del Collaboration Group
2023

Abstract

: Astringency is an essential sensory attribute of red wine closely related to the saliva precipitation upon contact with the wine. In this study a data matrix of 52 physico-chemical parameters was used to predict the Saliva Precipitation Index (SPI) in 110 Italian mono-varietal red wines using partial least squares regression (PLSr) with variable selection by Variable Importance for Projection (VIP) and the significance of regression coefficients. The final PLSr model, evaluated using a test data set, had 3 components and yielded an R2test of 0.630 and an RMSEtest of 0.994, with 19 independent variables whose regression coefficients were all significant at p < 0.05. Variables selected in the final model according to the decreasing magnitude of their absolute regression coefficient include the following: Procyanidin B1, Epicatechin terminal unit, Total aldehydes, Protein content, Vanillin assay, 520 nm, Polysaccharide content, Epigallocatechin PHL, Tartaric acid, Volatile acidity, Titratable acidity, Catechin terminal unit, Proanthocyanidin assay, pH, Tannin-Fe/Anthocyanin, Buffer capacity, Epigallocatechin PHL gallate, Catechin + epicatechin PHL, and Tannin-Fe. These results can be used to better understand the physico-chemical relationship underlying astringency in red wine.
2023
Galaz, C., Ricci, A., Parpinello, G.P., Gambuti, A., Rinaldi, A., Moio, L., et al. (2023). Multivariate prediction of Saliva Precipitation Index for relating selected chemical parameters of red wines to the sensory perception of astringency. CURRENT RESEARCH IN FOOD SCIENCE, 7, 1-12 [10.1016/j.crfs.2023.100626].
Galaz, Cristian; Ricci, Arianna; Parpinello, Giuseppina Paola; Gambuti, Angelita; Rinaldi, Alessandra; Moio, Luigi; Rolle, Luca; Paissoni, Maria Aless...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/950392
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