Modeling, optimization, purification, and characterization of polysaccharides from Lilium lancifolium Thunb

Abstract

Ultrasound assisted aqueous two-phase extraction of polysaccharides from Lilium lancifolium Thunb 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 Lilium lancifolium Thunb polysaccharides (LLPs) yield. However, ANN prediction was more accurate than RSM. The optimum extraction parameters to achieve the highest LLPs yield (15.17 ± 0.21)% was obtained at the ultrasound power of 250 W, extraction temperature of 63 °C, liquid-to-solid ratio of 21 mL/g, and extraction time of 32 min. Subsequently, the crude LLPs were further purified via DEAE-52 and Sephadex G-100 chromatography to obtain a homogenous fraction (LLPs-2-SG, 421.41 kDa) that contained mannose, glucose, and galactose in a molar ratio of 10.52:23.06:7.19. The structure of LLPs-2-SG was characterized with UV–vis, FT–IR, AFM, and SEM. The findings provide a critical method for the extraction, separation and purification of polysaccharides from natural resources.