Integrating cyclic voltammetry and stepwise regression to explore wine evolution during bottle ageing

Abstract

Cyclic Voltammetry (CV) is a powerful, rapid, and versatile tool in wine analysis, enabling the characterization of polyphenols and antioxidants, monitoring of oxidation and ageing, estimation of reactive components such as SO₂, and providing fingerprints for classification and ageing studies. In the present research, CV was applied to study the evolution of thirty-six white wines during bottle ageing. Wines were supplied by local producers after bottling, stored in controlled conditions (20 ± 5 °C and 70 ± 10 % RH) and analyzed after two, six, ten, and twelve months.

In addition to CV, analyses consisted of seventy-seven analytical parameters relevant for wine evolution and ageing, including free and total sulfur dioxide, flavanols, carbonyl and volatile compounds, a browning assay (POM-test), wine color and total phenolics. Data were processed by stepwise regression, an automated feature selection method in machine learning and statistics that iteratively adds or removes predictor variables to build the best-fit regression model.

Using this algorithm, a parsimonious subset of voltammetric data showing conditional associations with other analytical variables was identified, evaluating which potential/current coordinates in the voltammogram best predict how these variables changed over time during storage. Regression models were optimized for each analytical parameter; few points, up to thirteen, of the voltammograms collected after two months of ageing were generally able to predict the values of different analytical parameters after twelve months, with the best fitting obtained for flavanols.

Results demonstrate that CV can be used for predicting the ageing potential of a wine, allowing winemakers to decide more knowingly the strategies for stabilizing their products at bottling and modelling their shelf-life. This study shall be improved by increasing the sample size and the variability of the wines, as well as by testing more powerful machine learning predictive tools, such as neural networks.

References

1. Comuzzo, P. Integrating Cyclic Voltammetry and Stepwise Regression to Explore Wine Evolution during Bottle Ageing. Recording of the presentation delivered at Enoforum Spain 2026, Zaragoza, 20–21 May 2026. Available soon on Infowine Premium: https://vinidea.it/es/brand/infowine/