Development of a colorlessCentella asiatica(L.) Urb. extract using a natural deep eutectic solvent (NADES) and microwave-assisted extraction (MAE) optimized by response surface methodology

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Abstract

This study outlines a green process forCentella asiatica(L.) Urb. (CA) extraction. Natural deep eutectic solvents (NADESs) and microwave-assisted extraction (MAE) were combined to provide a high bioactive compound yield and high antioxidant activity. Among the NADESs evaluated, the combination of acetylcholine chloride : malic acid : water (1 : 2 : 2): water (40 : 60) was the best for extraction. These conditions provide high madecassoside (MS) (21.7 mg g−1dry weight) and asiaticoside (AS) (12.7 mg g−1dry weight) yields, with greater than 80% (v/v) EtOH (13.3 mg g−1MS and 7.80 mg g−1AS). In addition, the extracts from this process showed higher antioxidant activity (IC50= 0.26 mg mL−1) than the CA aqueous EtOH and water extracts. Moreover, the color of the extract products was less green than that of the extracts prepared using EtOH and aqueous EtOH as solvents, which are suitable for cosmeceutical products. Response surface methodology (RSM) was used for MAE optimization. The ANOVA data from the central composition design (CCD) of RSM were fitted with quadratic models yielding acceptableR2(>0.93), adjustedR2(>0.87), predictedR2(>0.81), and nonsignificant lack of fit (p  > 0.05) values. The quadratic model was validated using optimal conditions (30 s, power 300 W, and a liquid to solid ratio 20 mL g−1), and the model validation showed more than 80% accuracy in both MS and AS yields. This research presented an effective green process for CA extraction, which resulted in an environmentally friendly CA extract requiring little energy consumption and no organic solvents.

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Phaisan, S., Makkliang, F., Putalun, W., Sakamoto, S., & Yusakul, G. (2021). Development of a colorlessCentella asiatica(L.) Urb. extract using a natural deep eutectic solvent (NADES) and microwave-assisted extraction (MAE) optimized by response surface methodology. RSC Advances, 11(15), 8741–8750. https://doi.org/10.1039/d0ra09934a

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