Metabolomics-Based Analysis Reveals Differences in Non-Volatile Components of Artemisiae argyi Folium with Different Aging Durations
7 affiliations
- 1School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, China
- 2Guangdong Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
- 3South China National Botanical Garden, Guangzhou 510650, China
- 4Tea-Mugwort Industry Development Center of Tongbai County, Nanyang 474750, China
- 5Artemisia argyi Branch Center, National Engineering Research Center for Modernization of Traditional Chinese Medicine, Guangzhou 510610, China
- 6College of Life Science and Technology, Jinan University, Guangzhou 510610, China
- 7University of Chinese Academy of Sciences, Beijing 100049, China
Abstract
Artemisiae argyi Folium (AAF) is traditionally recognized to exhibit improved quality with prolonged aging duration. However, the differences in its non-volatile metabolite profile among AAF samples with different aging durations are still poorly characterized. In this study, ultra-performance liquid chromatography coupled to Orbitrap tandem mass spectrometry (UPLC-Orbitrap-MS/MS) in positive ion mode, combined with metabolomics approaches, was used to analyze AAF samples representing different aging durations from 0 to 7 years. A total of 138 compounds were putatively annotated across the eight AAF groups. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) showed distinct metabolic separation among the eight sample groups. Statistical analysis further identified 48 differential metabolites that satisfied the screening criteria. Mfuzz clustering classified the 48 differential metabolites into three major temporal patterns: an overall decline, higher relative abundances in the middle-to-late and early aging-duration groups. Furthermore, nine exploratory candidate markers associated with aging duration were selected, including kaempferol, apigenin, luteolin, rutin, esculetin, isofraxidin, ferulic acid, argyinolide H, and matricarin. Collectively, these results show new insights into the metabolomic differences associated with different aging durations and lay the foundation for screening aging-duration-associated candidate markers and future quality evaluation of AAF.
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BibTeX
@article{cai2026metabolomicsbased,
title = {{Metabolomics-Based Analysis Reveals Differences in Non-Volatile Components of Artemisiae argyi Folium with Different Aging Durations}},
author = {Jiaojiao Cai and Hanxiang Li and Chuang Liu and Yanbin Liu and Qiaoli Wang and Xiaofeng Liao and Sichen Mao and Yaguan Zeng and Wei Zhang},
journal = {Molecules},
year = {2026},
month = nov,
doi = {10.3390/molecules31213995},
url = {https://www.mdpi.com/1420-3049/31/21/3995},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Metabolomics-Based Analysis Reveals Differences in Non-Volatile Components of Artemisiae argyi Folium with Different Aging Durations
AU - Jiaojiao Cai
AU - Hanxiang Li
AU - Chuang Liu
AU - Yanbin Liu
AU - Qiaoli Wang
AU - Xiaofeng Liao
AU - Sichen Mao
AU - Yaguan Zeng
AU - Wei Zhang
PY - 2026
DA - 2026/11/05
JO - Molecules
DO - 10.3390/molecules31213995
UR - https://www.mdpi.com/1420-3049/31/21/3995
AB - Artemisiae argyi Folium (AAF) is traditionally recognized to exhibit improved quality with prolonged aging duration. However, the differences in its non-volatile metabolite profile among AAF samples with different aging durations are still poorly characterized. In this study, ultra-performance liquid chromatography coupled to Orbitrap tandem mass spectrometry (UPLC-Orbitrap-MS/MS) in positive ion mode, combined with metabolomics approaches, was used to analyze AAF samples representing different aging durations from 0 to 7 years. A total of 138 compounds were putatively annotated across the eight AAF groups. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) showed distinct metabolic separation among the eight sample groups. Statistical analysis further identified 48 differential metabolites that satisfied the screening criteria. Mfuzz clustering classified the 48 differential metabolites into three major temporal patterns: an overall decline, higher relative abundances in the middle-to-late and early aging-duration groups. Furthermore, nine exploratory candidate markers associated with aging duration were selected, including kaempferol, apigenin, luteolin, rutin, esculetin, isofraxidin, ferulic acid, argyinolide H, and matricarin. Collectively, these results show new insights into the metabolomic differences associated with different aging durations and lay the foundation for screening aging-duration-associated candidate markers and future quality evaluation of AAF.
SN - 1420-3049
ER -