[关键词]
[摘要]
该研究联合响应面试验与人工神经网络耦合遗传算法优化超声波辅助纤维素酶法提取益智仁多糖的工艺条件。经回归模型和人工神经网络模型分析并结合验证试验,益智仁多糖提取最佳条件为液料比20 mL/g,提取时间30 min,酶添加质量分数4%,超声波功率300 W,该条件下,多糖得率为4.20%。红外光谱显示该多糖具有多糖特征吸收峰,含有糖醛酸,结构中存在吡喃糖环。体外抗氧化测定结果显示,多糖质量浓度为300 μg/mL时,总抗氧化能力为80.76 μmol TE/mL,铁原子还原能力为133.6 μmol FeE/mL。为进一步评估其生物活性,采用2,4,6-三硝基苯磺酸诱导斑马鱼建立肠炎模型,发现超声波辅助酶法提取的益智仁多糖可以显著提高肠炎斑马鱼体内的总抗氧化活性,降低炎症因子INF-γ的表达水平,提高抗炎因子IL-10的表达水平,减少一氧化氮的产生。这些结果表明,益智仁多糖具有潜在的抗炎和抗氧化活性,为益智仁功能产品创制提供了理论依据。
[Key word]
[Abstract]
The optimal conditions for ultrasonic-assisted cellulase extraction of bioactive polysaccharides from alpiniae oxyphylla fructus (AOF) were clarified using response surface methodology and artificial neural network coupled with genetic algorithm. The optimized conditions were a solvent-to-solid ratio of 20 mL/g, cellulase mass fraction of 4%, and ultrasonic power of 300 W for 20 min, which yielded a total polysaccharide extraction of 4.20%. Fourier transform infrared spectra revealed that the extracted sample had polysaccharide characteristic absorption peaks containing uronic acid and pyranose rings. The results of in vitro antioxidant assays showed that the total antioxidant capacity of AOF polysaccharides was 80.76 μmol TE/mL at 300 μg/mL and the ferric reducing antioxidant power was 133.6 μmol FeE/mL. To further evaluate the biological activity of AOF polysaccharides, a zebrafish colitis model was established using 2,4,6-trinitrobenzene sulfonic acid. The results showed that AOF polysaccharides prepared via ultrasonic-assisted cellulase extraction significantly promoted the total antioxidant activity, decreased the expression of the proinflammatory factor interferon-γ, increased the expression of the anti-inflammatory factor interleukin-10, and decreased the production of nitric oxide in zebrafish with colitis. These results indicate that the AOF polysaccharides have anti-inflammatory and antioxidant effects, providing a theoretical basis for the development of AOF products.
[中图分类号]
[基金项目]
国家重点研发计划项目(2022YFD1600303;2021YFD1600100-404);三亚中国农业科学院国家南繁研究院“南繁专项”项目(YYLH2307);湖南省重点研发计划项目(2022SK2018)