Kinetic and thermodynamic characterization of thermosonication-assisted extraction of bioactives from lapsi fruit juice optimized by ANN-GA

Abstract

The present study focuses on an extensive investigation into thermosonication as a novel process for enhancing the extraction of bioactive functional compounds from lapsi fruit juice. Process optimization was achieved by using artificial neural networks (ANN) coupled with a genetic algorithm (GA), finding optimum conditions to be 75% amplitude, 40 °C, and 60 minutes. The release performance of principal functional compounds, such as total phenolic content, total flavonoid content, antioxidant activity, and ascorbic acid, was also investigated under these optimized conditions. Among all the kinetic models used, the pseudo-second-order model demonstrated the best correlation (R² = 0.99, χ² = 0.09), which indicated that compound release occurred by chemisorption processes. The process also revealed an initial rapid extraction phase, which was then followed by a steady plateau at 60 minutes. The activation energies were in the range of 29.45 to 35.23 kJ mol⁻¹, and this indicated the temperature dependence of the process. Thermodynamic studies revealed that the extraction was spontaneous under all tested temperatures (ΔG: −5.45 to −18.46 kJ mol⁻¹), endothermic in nature (ΔH: 5.44 to 15.64 kJ mol⁻¹), entropy-guided (ΔS: 18.03 to 57.23 J mol⁻¹ K⁻¹) and, therefore, capable of efficiently breaking down the cell walls and facilitating mass transfer. Optimal extraction efficiency was achieved at 40 °C, beyond which heat-induced degradation of labile compounds was observed. Kinetic and thermodynamic studies confirm that thermosonication enables efficient, spontaneous, and temperature-dependent extraction of bioactives from Lapsi juice. The findings support its feasibility for energy-efficient, scalable industrial applications.