Mathematical modeling of water sorption isotherms in specialty coffee beans processed by wet and semidry postharvest methods.

This study investigates the experimental assessment and mathematical modeling of the water sorption isotherms in dried specialty coffee beans processed by wet and semidry postharvest methods. The wet and semidry sorption isotherms were experimentally obtained over a range of water activities between 0.1 and 0.85 at temperatures of 25, 35, and 45 °C using the dynamic dew point method (DDI). Mathematical modeling was conducted to describe the influence of water activity, temperature, and postharvest method on the equilibrium moisture content. Twelve conventional sorption equations and four machine learning techniques were employed for modeling, using 75% of the experimental data for training and 25% for validation. The selection of the best model was carried out via multifactor Analysis of Variance (ANOVA). Experimental results showed that wet and semidry coffee beans exhibited a type II S-shaped isotherm (Brunauer-Emmett-Teller classification) and a significant (p < 0.05) influence of temperature on sorption curves. Additionally, the mucilaginous coating found in semidry coffee beans provided a protective role against water sorption. The Support Vector Machine (SVM) model provided the best fit for describing the sorption isotherms (mean relative error, MRE < 1% and adjusted coefficient of determination, R > 99%), demonstrating its robustness in predicting the equilibrium moisture content as a function of water activity, temperature, and postharvest processing method. This mathematical model could serve as a virtual representation of the storage process, facilitating real-time decision-making to enhance coffee quality management during storage.