Collazos-Escobar Gentil A, Hurtado-Cortés Valeria, Bahamón-Monje Andrés Felipe, Gutiérrez-Guzmán Nelson
Centro Surcolombiano de Investigación en Café (CESURCAFÉ), Departamento de Ingeniería Agrícola, Universidad Surcolombiana, 410001, Neiva-Huila, Colombia.
Grupo de Análisis y Simulación de Procesos Agroalimentarios (ASPA), Instituto Universitario de Ingeniería de Alimentos-FoodUPV, Universitat Politècnica de València, Camí de Vera S/N, Edificio 3F, 46022, Valencia, Spain.
Sci Rep. 2025 Jan 31;15(1):3898. doi: 10.1038/s41598-024-83702-y.
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.
本研究调查了采用湿法和半干法收获后处理的特种干咖啡豆中水吸附等温线的实验评估和数学建模。使用动态露点法（DDI）在25、35和45°C的温度下，在0.1至0.85的一系列水分活度范围内通过实验获得了湿法和半干法吸附等温线。进行了数学建模，以描述水分活度、温度和收获后处理方法对平衡水分含量的影响。采用了十二个传统吸附方程和四种机器学习技术进行建模，使用75%的实验数据进行训练，25%进行验证。通过多因素方差分析（ANOVA）选择最佳模型。实验结果表明，湿法和半干法咖啡豆呈现出II型S形等温线（Brunauer-Emmett-Teller分类），温度对吸附曲线有显著（p < 0.05）影响。此外，在半干法咖啡豆中发现的黏液涂层对水吸附起到了保护作用。支持向量机（SVM）模型最适合描述吸附等温线（平均相对误差，MRE < 1%，调整后的决定系数，R > 99%），证明了其在预测作为水分活度、温度和收获后处理方法函数的平衡水分含量方面的稳健性。该数学模型可作为储存过程的虚拟表示，有助于实时决策，以加强储存期间的咖啡质量管理。
