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.
Multivariate prediction of Saliva Precipitation Index for relating selected chemical parameters of red wines to the sensory perception of astringency
Marangon, Matteo;Marangon, Christine;
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.File | Dimensione | Formato | |
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2023 Curr Res Food Sci Astringeny prediction.pdf
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