Next Article in Journal
Antagonism of Histamine H3 receptors Alleviates Pentylenetetrazole-Induced Kindling and Associated Memory Deficits by Mitigating Oxidative Stress, Central Neurotransmitters, and c-Fos Protein Expression in Rats
Next Article in Special Issue
Chemistry and Reactivity of Tannins in Vitis spp.: A Review
Previous Article in Journal
Antimalarial Agents as Therapeutic Tools Against Toxoplasmosis—A Short Bridge between Two Distant Illnesses
Previous Article in Special Issue
Production of Ellagitannin Hexahydroxydiphenoyl Ester by Spontaneous Reduction of Dehydrohexa-hydroxydiphenoyl Ester
Open AccessArticle

Model Optimization for the Prediction of Red Wine Phenolic Compounds Using Ultraviolet–Visible Spectra

Viticulture and Enology Program, Washington State University Tri-Cities, 2710 Crimson Way, Richland, WA 99354, USA
Author to whom correspondence should be addressed.
Academic Editors: Teresa Escribano-Bailón and Ignacio García-Estévez
Molecules 2020, 25(7), 1576;
Received: 19 February 2020 / Revised: 20 March 2020 / Accepted: 26 March 2020 / Published: 30 March 2020
(This article belongs to the Special Issue Tannin Analysis, Chemistry, and Functions)
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 selection, 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. View Full-Text
Keywords: mathematical modeling; red wine phenolics; UV–vis spectroscopy mathematical modeling; red wine phenolics; UV–vis spectroscopy
MDPI and ACS Style

Beaver, C.; Collins, T.S.; Harbertson, J. Model Optimization for the Prediction of Red Wine Phenolic Compounds Using Ultraviolet–Visible Spectra. Molecules 2020, 25, 1576.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop