Metabolomics-Based Analysis Reveals Differences in Non-Volatile Components of Artemisiae argyi Folium with Different Aging Durations

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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Publication Details

Journal
Molecules
Published
2026-11-05
DOI
https://doi.org/10.3390/molecules31213995
Primary Topic
Traditional Chinese Medicine Analysis
Type
article
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article

Metabolomics-Based Analysis Reveals Differences in Non-Volatile Components of Artemisiae argyi Folium with Different Aging Durations

Sichen Mao, Hanxiang Li, 廖晓峰, Yaguan Zeng et al.
Molecules
Traditional Chinese Medicine Analysis
article

Metabolomics-Based Analysis Reveals Differences in Non-Volatile Components of Artemisiae argyi Folium with Different Aging Durations

Sichen Mao, Hanxiang Li, 廖晓峰, Yaguan Zeng, Chuang Liu, Wei Zhang, Qiaoli Wang, Yanbin Liu, Jiaojiao Cai
article en

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.

MoleculesVol. 31(21)
Jinan University (CN), Guangdong Pharmaceutical University (CN), Chinese Academy of Sciences (CN), South China Botanical Garden (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 7%
Traditional Chinese Medicine Analysis
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