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◆ Industrial Crops and Products2026-05-01· Yield (engineering)

Intelligent optimization of deep eutectic solvent extraction for Siraitia grosvenorii polysaccharides: A hybrid RSM and neural network approach for enhanced yield and bioactivity

Fengling Yang, Yihong Tan, Wenlin Zhou, Cui Li, Cuilan Huang, Haiyi Zhong, Lini Huo, Peiyuan Li

原始摘要(英文原文)· Original abstract
Plant-derived polysaccharides represent an important class of bioactive natural products with wide applications in functional foods and pharmaceuticals. This study developed an efficient and eco-friendly deep eutectic solvent (DES)-based method for the extraction of Siraitia grosvenorii polysaccharides (SGP). The DES extraction process was optimized using a combined approach of Response Surface Methodology (RSM) and a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN), with key parameters including molar ratio, moisture content, and liquid-to-solid ratio. Density functional theory (DFT) calculations provided molecular-level insight into the hydrogen-bonding interactions governing the DES system. The optimized DES extraction with ethanol precipitation (SGP-DE) resulted in a higher polysaccharide yield (88.47 ± 0.84%) compared to conventional hot water extraction (SGP-W, 65.39 ± 0.38%), along with a smaller molecular weight and more homogeneous structure. Structural analyses indicated that the DES-extracted SGPs were amorphous and exhibited improved thermal stability. In vitro bioactivity evaluations revealed that SGP-DE and SGP-DM (DES with methanol precipitation) possessed significantly enhanced inhibitory activity against α-amylase and α-glucosidase, as well as superior antioxidant capacity, attributable to their optimized structural properties. These findings establish DES as a highly effective and sustainable alternative for extracting high-value polysaccharides, providing a strong foundation for their application in health-promoting products.
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Intelligent optimization of deep eutectic solvent extraction for Siraitia grosvenorii polysaccharides: A hybrid RSM and neural network approach for enhanced yield and bioactivity — 科研速览 Science Skim