Separation of principal component dihydromyricetin from Ampelopsis grossedentata by high-speed counter-current chromatography and its interaction with corn starch.

Ampelopsis grossedentata (AG) is an industrial crop in the grape family, which has been used as a dual-purpose plant for medicine and tea with high medicinal values. However, little is reported on the separation technology of active components from AG and processing technology of AG products. High-speed counter-current chromatography (HSCCC) was applied to separate the principal component dihydromyricetin (DMY) from AG. DMY is added to starch-based products to improve food quality. The interaction between corn starch (CS) and DMY was investigated to predict and control the structure and function of starch-based foods. Results show that DMY with 97.13% purity was successfully obtained by HSCCC using a solvent system composed of light petroleum-ethyl acetate-methanol-water-trichloroacetic acid (1:3:1:3:0.01, v/v/v/v/v). Fourier-transform infrared spectroscopy (FT-IR) exhibits that the interactions between CS and DMY included hydrogen bond and noncovalent bond. X-ray diffraction (XRD) shows that DMY could increase the relative crystallinity of CS. Low-field nuclear magnetic resonance results (LF-NMR) imply that DMY decreased the spin relaxation time (T2 ) and inhibited the mobility of free water. Atomic force microscopy (AFM) results suggest that DMY changed the surface morphology of CS through hydrogen bond interaction. Moreover, the results of confocal laser scanning microscopy (CLSM) and scanning electron microscopy (SEM) indicate that DMY could enlarge the pores and change the microstructure of CS-DMY complexes. The findings promote the development of industrial CS-based products and utilization of corn crop.

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

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.