Dispersive liquid–liquid microextraction with a deep eutectic solvent coupled with GC-MS for the determination of chiral carvone in herbaceous plants

Abstract

Carvone is an important flavoring substance and widely used in the food and chemical industries. Carvone has two optical isomers (L-carvone and D-carvone), which have different aroma characteristics. However, the green extraction of natural carvone and isolation of chiral carvone present challenges, leading to its limited application. In this study, a deep eutectic solvent (DES) was prepared and used as a green extraction solvent for the extraction of carvone from herbaceous plants, and then a chiral chromatography column combined with GC-MS was used for the separation and detection of L-carvone and D-carvone. Response surface methodology was used to optimize the extraction conditions including the volume of DES, extraction time and extraction temperature. The results showed that the extraction recoveries and intra-day precision (n = 6) were 83.5–101.3% and 3.2–6.1% when the volume of DES was 5 mL, and the extraction time and temperature were 25 min and 51 °C, respectively. The MLOD and MLOQ of L-carvone and D-carvone were 8.0 mg kg−1 and 25.0 mg kg−1, respectively. The real sample detection results revealed that about 235.8–1600.0 mg kg−1 of L-carvone was detected in Mentha spicata L., and 6658.5–9788.6 mg kg−1 of D-carvone in Anethum graveolens L. seeds. The established method can be an effective method for the detection of chiral carvone in herbaceous plants.

Graphical abstract: Dispersive liquid–liquid microextraction with a deep eutectic solvent coupled with GC-MS for the determination of chiral carvone in herbaceous plants

Supplementary files

Article information

Article type
Paper
Submitted
05 Nov 2024
Accepted
08 Feb 2025
First published
11 Feb 2025

Anal. Methods, 2025, Advance Article

Dispersive liquid–liquid microextraction with a deep eutectic solvent coupled with GC-MS for the determination of chiral carvone in herbaceous plants

Y. Wu, M. Liang, Y. Xian, R. Chen, J. Hu, H. Dai, C. Liu and X. Hou, Anal. Methods, 2025, Advance Article , DOI: 10.1039/D4AY02002B

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