Zeng Yufeng, Zhong Yu, Li Shanyu, Deng Yiheng, Ma Yidan, Huang Jiayi, Li Hai, He Qiuxing
School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Zhongshan 528458, China.
Xinjiang Uygur Autonomous Region Engineering Research Center for Cistanche, Xinjiang 830000, China.
Food Chem. 2025 Nov 15;492(Pt 2):145440. doi: 10.1016/j.foodchem.2025.145440. Epub 2025 Jul 4.
A novel method for green extraction of echinacoside and acteoside from Cistanche deserticola (C. deserticola) using natural deep eutectic solvents (NADES) has been developed, integrating conductor-like screening model for realistic solvents (COSMO-RS) and artificial neural network-genetic algorithm (ANN-GA) optimization to maximize efficiency. Among 48 NADES evaluated, choline chloride-propylene glycol (1:3 M ratio, 30 wt% water content) was identified as the optimal solvent through COSMO-RS and single-factor optimization. The ANN model (3-9-1 topology, R > 0.99) efficiently captured nonlinear relationships between extraction parameters, with GA identifying the optimal conditions. The maximum yields of echinacoside and acteoside under optimized conditions were 7.84 ± 0.28 mg/g and 1.12 ± 0.02 mg/g, respectively, which were 1.97-3.98 times higher than those obtained with conventional solvents. This work demonstrates the synergistic potential of COSMO-RS and ANN-GA for solvent screening and extraction process optimization, offering a scalable framework for extracting other bioactive substances.
开发了一种使用天然深共熔溶剂（NADES）从肉苁蓉中绿色提取松果菊苷和毛蕊花糖苷的新方法，该方法集成了真实溶剂类导体筛选模型（COSMO-RS）和人工神经网络-遗传算法（ANN-GA）优化以实现效率最大化。在评估的48种NADES中，通过COSMO-RS和单因素优化确定氯化胆碱-丙二醇（摩尔比1:3，水含量30 wt%）为最佳溶剂。ANN模型（3-9-1拓扑结构，R>0.99）有效捕捉了提取参数之间的非线性关系，GA确定了最佳条件。优化条件下松果菊苷和毛蕊花糖苷的最大产率分别为7.84±0.28 mg/g和1.12±0.02 mg/g，比使用传统溶剂获得的产率高1.97-3.98倍。这项工作证明了COSMO-RS和ANN-GA在溶剂筛选和提取工艺优化方面的协同潜力，为提取其他生物活性物质提供了一个可扩展的框架。
