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<p>Guangchenpi (GCP, <i>Citrus reticulata</i>), a well-known traditional Chinese medicine and homology food condiment, is predominantly consumed in Asia. Extending its aging process enhances its medicinal properties, flavor, and profitability. However, pinpointing the aging year of GCP remains a bottleneck of the industry. This study represents the first comprehensive characterization of 32 glycosidically bound volatiles in GCP, employing a combination of Amberlite XAD-2 resin extraction and gas chromatography-mass spectrometry (GC–MS) analysis. The identified compounds include bioactive substances such as 5-hydroxymethylfurfural and acorenone B. Fourteen glycosidically bound volatiles exhibited a significant correlation with the aging duration of GCP. Additionally, the aging process was associated with dynamic shifts in the microbial community, notably an enrichment of <i>Lactobacillus</i> and <i>Oceanobacillus</i>, which potentially influenced the volatile compound profile. Machine learning integration revealed a model based on the content and relative weight of the 14 glycosidically bound volatiles, demonstrating high accuracy in determining aging years of GCP. This model could aid in detecting adulteration and standardizing the GCP market.</p>

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Glycosidically bound volatiles profiling reveals a new approach to effectively gauge aging years of Guangchenpi (Citrus reticulata ‘Chachiensis’)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月30日

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  • Yuan Liu,
  • Xueqin Wang,
  • Zhaoyang Yan,
  • Huan Wen,
  • Qiuhong Chen,
  • Yang Hu,
  • Hongjian Huang,
  • Chunling Liu,
  • Jiwu Zeng,
  • Jiajing Chen,
  • Juan Xu

摘要

Guangchenpi (GCP, Citrus reticulata), a well-known traditional Chinese medicine and homology food condiment, is predominantly consumed in Asia. Extending its aging process enhances its medicinal properties, flavor, and profitability. However, pinpointing the aging year of GCP remains a bottleneck of the industry. This study represents the first comprehensive characterization of 32 glycosidically bound volatiles in GCP, employing a combination of Amberlite XAD-2 resin extraction and gas chromatography-mass spectrometry (GC–MS) analysis. The identified compounds include bioactive substances such as 5-hydroxymethylfurfural and acorenone B. Fourteen glycosidically bound volatiles exhibited a significant correlation with the aging duration of GCP. Additionally, the aging process was associated with dynamic shifts in the microbial community, notably an enrichment of Lactobacillus and Oceanobacillus, which potentially influenced the volatile compound profile. Machine learning integration revealed a model based on the content and relative weight of the 14 glycosidically bound volatiles, demonstrating high accuracy in determining aging years of GCP. This model could aid in detecting adulteration and standardizing the GCP market.