香菇多糖
COSMO-RS公司
萃取(化学)
化学
深共晶溶剂
溶解度
共晶体系
溶剂化
溶剂
色谱法
计算机科学
有机化学
离子液体
多糖
合金
催化作用
作者
Dayuan Wang,Min Zhang,Chung Lim Law,Lujun Zhang
出处
期刊:Food Chemistry
[Elsevier]
日期:2023-07-29
卷期号:430: 136990-136990
被引量:18
标识
DOI:10.1016/j.foodchem.2023.136990
摘要
Using natural deep eutectic solvents (NDES) for green extraction of lentinan from shiitake mushroom is a high-efficiency method. However, empirical and trial-and-error methods commonly used to select suitable NDES are unconvincing and time-consuming. Conductor-like screening model for realistic solvation (COSMO-RS) is helpful for the priori design of NDES by predicting the solubility of biomolecules. In this study, 372 NDES were used to evaluate lentinan dissolution capability via COSMO-RS. The results showed that the solvent formed by carnitine (15 wt%), urea (40.8 wt%), and water (44.2 wt%) exhibited the best performance for the extraction of lentinan. In the extraction stage, an artificial neural network coupled with genetic algorithm (ANN-GA) was developed to optimize the extraction conditions and to analyze their interaction effects on lentinan content. Therefore, COSMO-RS and ANN-GA can be used as powerful tools for solvent screening and extraction process optimization, which can be extended to various bioactive substance extraction.
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