Investigation of the drying kinetics of a residual nut mixture using mathematical models and artificialneural networks

The objective of this study was to evaluate the potential for the sustainable reuse and the value of the residual nut mixture (RNM) by-products (cashew nut, peanut, and soybean) after extraction. To investigate the drying kinetics, the RNM was dried at various temperatures (50 to 80 C). The Balbay and ahin model, which had a high coefficient of determination (R2) of 99.62–99.96%, a low root mean square error of 0.007–0.021, and χ2 of 0.001–0.005, was the one that best fit the experimental data out of the eight mathematical models that were used. Artificial neural networks showed higher and faster prediction capacity than the mathematical models. The effective moisture diffusion coefficient (Deff) increased gradually with the temperature, with an activation energy (Ea) of 13.67 kJ∙mol–1. The RNM powder produced by the optimal drying process (60 C for 3.75 h) has a bright colour, high polyphenol content (2.68 mg gallic acid equivalent (GAE)∙g–1) and antioxidant activity, low moisture content (4.9%) and relatively high nutritional value, especially protein (27.27%), lipid (40.19%), and fibre (4.2%). Under these conditions, not only is efficient drying and preservation achieved, but the quality of the by-product powder is also maintained.