Principal Investigator(s): Chris Beaver, Thomas S Collins, and James Harbertson
Organization: Viticulture and Enology Program, Washington State University Tri-Cities, 2710 Crimson Way, Richland, WA 99354, USA
Emails: (C.B.); (T.S.C.),
Telephone: 509-372-7506

Academic Editors: Teresa Escribano-Bailón and Ignacio García-Estévez
Received: 19 February 2020; Accepted: 26 March 2020; Published: 30 March 2020

The primary objective of this work was to optimize red wine phenolic prediction with models built from wine ultraviolet–visible absorbance spectra. Three major obstacles were addressed to achieve this, namely algorithm election, spectral multicollinearity, and phenolic evolution over time. For algorithm selection, support vector regression, kernel ridge regression, and kernel partial least squares regression were compared. For multicollinearity, the spectrum of malvidin chloride was used as an external standard for spectral adjustment. For phenolic evolution, spectral data were collected during fermentation as well as once a week for four weeks after fermentation had ended. Support vector regression gave the most accurate predictions among the three algorithms tested. Additionally, malvidin chloride proved a useful standard for phenolic spectral transformation and isolation. As for phenolic evolution, models needed to be calibrated and validated throughout the aging process to ensure predictive accuracy. In short, red wine phenolic prediction by the models built in this work can be realistically achieved, although periodic model re-calibration and expansion from data obtained using known phenolic assays is recommended to maintain model accuracy.

mathematical modeling; red wine phenolics; UV–vis spectroscopy

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Enology // Phenolics //