Model-based investigation of water adsorption in Achira (Canna edulis K.) biscuits

The water adsorption properties of Achira biscuits were investigated using a mathematical model-based strategy to understand the relationship between water activity and moisture content due to the fact that it is highly relevant for its storage. For this purpose, the water adsorption isotherms of commercial Achira biscuits were firstly experimentally determined by the dynamic dew point (DDI) method at 25, 35, and 45 °C and ATR–FTIR spectroscopy was used to detect the functional groups. Secondly, the Guggenheim-Anderson-de Boer (GAB) equation, 4 empirical models (Smith, Oswin, Peleg and DLP), and 5 machine-learning techniques (Regression trees, Random Forest, k-Nearest Neighbors, Artificial Neural Networks and Support Vector Machine) were used to model the isotherms. The fundamentals of water adsorption were analyzed via GAB model parameters, adsorption surface area, spreading pressure, effective pore size, and thermodynamic properties, such as enthalpy-entropy and Gibbs free energy. The experimental results reflected a type III water isotherm and a moderate temperature effect. The GAB model quantified the effect of temperature on the adsorption properties, which reflected a lower degree of hygroscopicity at high temperatures. The Support Vector Machine Learning model provided the best mathematical description of the water adsorption isotherms considering the temperature effect (MRE99%).