Ultrasound-Assisted Deep Eutectic Solvent-Based Extraction of Polysaccharides from Okra: Optimization by Response...

Plant-derived polysaccharides are critical bioactive compounds; however, conventional extraction methods are often inefficient, energy-intensive, and may compromise their bioactivity. Ultrasound-assisted deep eutectic solvent (UA-DES) extraction offers a greener alternative by integrating acoustic cavitation with tunable solvent properties; however, optimization remains complex due to the interaction of multiple processing variables. This study reports a novel application of ultrasound-assisted deep eutectic solvent (UA-DES) extraction for okra polysaccharides (OPs), with process optimization using response surface methodology (RSM) and artificial neural network (ANN) modeling to identify optimal conditions and clarify nonlinear extraction behavior. Among the tested DES systems, choline chloride-citric acid (CCA) exhibited the highest extraction performance. Single-factor experiments and RSM identified sonication time and liquid-solid ratio as key variables. The ANN model achieved higher predictive accuracy than RSM and captured nonlinear and synergistic parameter interactions that were not evident in traditional response surfaces, providing deeper insight into process behavior. Under optimized conditions (2 h, 80 °C, 190 W, 60 mL/g), UA-DES extraction produced 23.56 % OPs and 80.75 % DPPH• scavenging activity, representing 94 % higher yield and 28 % greater antioxidant activity than hot-water ultrasonic (HWU) extraction. UA-DES-derived OPs contained higher contents of uronic acids, total sugars, and glucans, and uniquely included arabinose absent in HWU extracts. Structural analyses revealed pyranose configurations, amorphous crystallinity, and porous microstructures, which collectively contribute to improved solubility and bioactivity. Overall, UA-DES extraction using CCA provides an eco-efficient strategy for producing high-value okra polysaccharides. The integrated RSM-ANN framework enables precise optimization and enhanced mechanistic understanding, supporting UA-DES as a scalable, green technology for the production of functional polysaccharides.