Extraction of chlorogenic acid from Lonicera japonica Thunb. using deep eutectic solvents: Process optimization and mechanism exploration by the combination of experiment and molecular simulation

The green extraction of chlorogenic acid (CGA) from Lonicera japonica Thunb. using deep eutectic solvents (DESs) was systematically investigated in the study. Quantum chemical calculations (QC) were adopted to evaluate the σ -profiles of CGA and DESs. Subsequent extraction experiments revealed that amongst the 15 DESs studied, those showing better complementarity in polar regions of σ -profile with that of CGA exhibited higher CGA yield, accompanied by lower infinite dilution activity coefficient ( γ ∞ ) of CGA in these solvents. Betaine/glycerol (BET/GLY) demonstrated superior extraction performance and was selected for process optimization via Box-Behnken Design. The established model, validated by ANOVA analysis, indicated that the optimized conditions resulted in a CGA yield 1.9 times higher than that with ethanol extraction. To elucidate the mechanistic basis for performance differences among betaine-based DESs, molecular simulations were conducted, revealing that higher CGA yields correlated with increased interaction energies and greater numbers of hydrogen bonds within the system. Notably, among the six active hydrogen atoms in CGA, three participated in intramolecular H-bonding, while the phenolic hydroxyl group of CGA primarily interacted with the carboxyl group of BET, serving as the dominant contributor during extraction. Furthermore, the highest CGA yield from BET/GLY was likely due to synergistic cooperation between BET, GLY, and H 2 O to interact with CGA. These findings highlight the importance of identifying key interaction sites in both target molecules and extractants to rationally design tailored solvents for enhanced extraction performance. This work provides a strategy integrating computational insights and experimental optimization to advance sustainable extraction of bioproducts.