Determination of the age of dry red wine by multivariate techniques using color parameters and pigments. (November 2021)

• Record Type: Journal Article
• Authors: Han, Guomin; Dai, Lingmin; Sun, Yuhang; Li, Chao; Ruan, ShiLi; Li, Jiming; Xu, Yan
• Is Part Of: Food control. Volume 129(2021)
• Publication Date: 2021-11
• Subjects: Wine -- Age -- Color -- Pigments -- Partial least squares -- Multiple linear regression; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2021.108253
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: Vintage is a decisive factor for market price of commercial bottled dry red wines. Aim of this study was to use color parameters and pigments for the prediction of the age of dry red wine by means of partial least squares (PLS) and multiple linear regression (MLR). Both models allow the prediction of the age accurately, with high coefficients of determination (R 2 ), and low enough root mean square error of cross-validation (RMSECV). Although less variables were required to obtain a successful MLR model, its prediction results were as accurate as the model obtained by PLS. The ratio of the first polymeric pigments (TFPP) and malvidin-3-glycoside was demonstrated to be a promising and practical chemical parameter for the rapid and simple age assessment of the dry red wines. Highlights: Significant pigmented polymers for wine age is analyzed by HPLC-DAD. Color parameters and pigments highly correlated with dry red wine age are obtained. PLS and MLR models are established to predict the vintage of dry red wine. The predictive ability of dry red wine vintage of best PLS and MLR models is compared.