Home
Resources
Publications
Model Optimization for the Prediction of Red Wine Phenolic Compounds Using Ultraviolet-Visible Spectra

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

Chris Beaver, Thomas S Collins, James Harbertson

Molecules. 2020 Mar 30;25(7):1576.

PMID: 32235496

Abstract:

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.

Chemicals Related in the Paper:

Catalog Number	Product Name	Structure	CAS Number	Price
AP643845	Malvidin chloride		643-84-5	Price

As a specialist in analytical chemical Products, Alfa Chemistry offers consumers a wealth of raw materials and a full range of technologies and services.

QUICK LINKS

HEADQUARTERS

Tel:

Fax:

Email: