Predicting the Specific Gravity of Must During Fermentation Using Machine Learning Models

In this article, the process of fermentation in winemaking is investigated. The authors analyze fermentation data and propose several machine learning models to assess the effectiveness of ongoing fermentation processes based on measurements of specific gravity and temperature. To evaluate the performance of each model, various metrics such as R-squared, Mean Squared Error, Root Mean Squared Error, and Maximum Absolute Error are employed. The results suggest that the models have the potential to predict fermentation outcomes in winemaking and provide winemakers with a valuable tool to optimize the process and ensure high-quality wine production.