School of Life Science, Shanxi University, Taiyuan 030006, China; Xinghuacun College of Shanxi University, Taiyuan 030006, China.
School of Life Science, Shanxi University, Taiyuan 030006, China.
Food Chem. 2025 Jan 15;463(Pt 3):141353. doi: 10.1016/j.foodchem.2024.141353. Epub 2024 Sep 19.
In this study, vortex-assisted liquid-liquid microextraction (VA-LLME) based on hydrophobic deep eutectic solvents (HDES) was used to efficiently and sustainably extract five phenolic acids and tetramethylpyrazine (TMP) from Shanxi aged vinegar (SAV). The VA-LLME technique was employed to investigate the extraction mechanism of HDES with the best extraction performance for the target compounds using a conductor-like screening model for real solvents (COSMO-RS). An artificial neural network combined with a genetic algorithm (ANN-GA) was developed to optimize the extraction conditions based on single-factor and response surface methodology, while also analyzing the interactive effects on the phenolic acids and TMP in the extracted solution during the extraction phase. The optimized conditions were determined, and the greenness of the procedure was evaluated using an analytical greenness metric, indicating that this technique can serve as a green alternative for the determination of phenolic acids and TMP in SAV.
在这项研究中，基于疏水深共晶溶剂（HDES）的涡旋辅助液 - 液微萃取（VA-LLME）被用于从山西老陈醋（SAV）中高效且可持续地提取五种酚酸和四甲基吡嗪（TMP）。VA-LLME 技术被用于研究 HDES 的萃取机制，使用导体相似性筛选模型预测真实溶剂（COSMO-RS），以获得对目标化合物具有最佳萃取性能的 HDES。采用人工神经网络结合遗传算法（ANN-GA），在单因素和响应面法的基础上对萃取条件进行优化，同时分析萃取阶段中萃取液中酚酸和 TMP 的相互作用效应。确定了最佳条件，并使用分析绿色度指标评估了该程序的绿色性，表明该技术可以作为测定 SAV 中酚酸和 TMP 的绿色替代方法。
