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<p>To characterize sensory differences among commercially available Chenxiang Tieguanyin with different labeled vintages and establish an objective discrimination method, Tieguanyin samples labeled from 5 to 40 years were analyzed using chemometrics and molecular sensory approaches. Amino acids and ester-type catechins decreased significantly with increasing labeled vintage, whereas theabrownin content increased steadily. Volatile profiles remained relatively stable between 10 and 25 years, while aroma characteristics gradually shifted from floral/fruity to aged, woody, and medicinal attributes, accompanied by enhanced mellowness and smoothness. A total of 22 key differential volatiles associated with labeled vintage were identified. Molecular docking and aroma recombination experiments were combined to systematically validate their contribution to the typical aged aroma of Tieguanyin. Among the machine-learning models, the convolutional neural network (CNN) exhibited the best classification performance, achieving 92.86% accuracy. This study provides an effective strategy for quality evaluation and labeled vintage discrimination of Chenxiang Tieguanyin.</p>

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Aroma differences and labeled vintage discrimination of commercial Chenxiang Tieguanyin from different production years

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月28日

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  • Xiaowen Hu,
  • Jiaxin Fang,
  • Shuaibo Shao,
  • Zhenhan Rui,
  • Zhendong Zhang,
  • Anru Zheng,
  • Caiyun Tian,
  • Chengzhe Zhou,
  • Zhelin Wan,
  • Zhong Wang,
  • Yuqiong Guo

摘要

To characterize sensory differences among commercially available Chenxiang Tieguanyin with different labeled vintages and establish an objective discrimination method, Tieguanyin samples labeled from 5 to 40 years were analyzed using chemometrics and molecular sensory approaches. Amino acids and ester-type catechins decreased significantly with increasing labeled vintage, whereas theabrownin content increased steadily. Volatile profiles remained relatively stable between 10 and 25 years, while aroma characteristics gradually shifted from floral/fruity to aged, woody, and medicinal attributes, accompanied by enhanced mellowness and smoothness. A total of 22 key differential volatiles associated with labeled vintage were identified. Molecular docking and aroma recombination experiments were combined to systematically validate their contribution to the typical aged aroma of Tieguanyin. Among the machine-learning models, the convolutional neural network (CNN) exhibited the best classification performance, achieving 92.86% accuracy. This study provides an effective strategy for quality evaluation and labeled vintage discrimination of Chenxiang Tieguanyin.