Ultrasound assisted aqueous two-phase extraction of polysaccharides from Cornus officinalis fruit: Modeling, optimization, purification, and characterization

Abstract

Ultrasound assisted aqueous two-phase extraction of polysaccharides from Cornus officinalis fruit was modeled by response surface methodology (RSM) and artificial neural network (ANN), and optimized using genetic algorithm coupled with ANN (GA-ANN). Statistical analysis showed that the models obtained by RSM and ANN could accurately predict the Cornus officinalis polysaccharides (COPs) yield. However, ANN prediction was more accurate than RSM. The optimum extraction parameters to achieve the highest COPs yield (7.85 ± 0.09)% was obtained at the ultrasound power of 350 W, extraction temperature of 51 ℃, liquid-to-solid ratio of 17 mL/g, and extraction time of 38 min. Subsequently, the crude COPs were further purified via DEAE-52 and Sephadex G-100 chromatography to obtain a homogenous fraction (COPs-4-SG, 33.64 kDa) that contained galacturonic acid, arabinose, mannose, glucose, and galactose in a molar ratio of 34.82:14.19:6.75:13.48:12.26. The structure of COPs-4-SG was also characterized with UV–vis, fourier-transform infrared spectroscopy (FT–IR), atomic force microscopy (AFM), scanning electron microscopy (SEM), Congo-red test, and circular dichroism (CD). The findings provide a feasible way for the extraction, purification, and optimization of polysaccharides from plant resources