Modelling of Catechin Extraction from Red Grape Solids under Conditions That Simulate Red Wine Fermentation

Abstract

Digital control systems are well established in many industries and could find application in the wine sector. Of critical importance to red wine quality, the efficient and targeted extraction of polyphenols from red grape solids during alcoholic fermentation could be a focus for automation. Smart technologies such as model predictive control (MPC) or fuzzy logic appear ideal for application in a complex process such as wine polyphenol extraction, but require mathematical models that accurately describe the system. The aim of this study was to derive and validate a model describing the extraction of catechin (a representative polyphenol) from red grape solids under simulated fermentation conditions. The impact of ethanol, fermentable sugar, and temperature on extraction rate was determined, with factor conditions chosen to emulate those present in industry practice. A first-order approach was used to generate an extraction model based on mass conservation that incorporated temperature and sugar dependency. Coefficients of determination (R2) for all test scenarios exceeded 0.94, indicating a good fit to the experimental data. Sensitivity analysis for the extraction rate and internal cross-validation showed the model to be robust, with a small standard error in cross-validation (SECV) of 0.11 and a high residual predictive deviation (RPD) of 17.68. The model that was developed is well suited to digital technologies where low computational overheads are desirable, and industrial application scenarios are presented for future implementation of the work.