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<p><i>Ga</i><i>rde</i><i>nia jasminoides</i> Ellis (GJE), a primary medicinal and edible herb, exhibits quality variations influenced by production region. To identify the key factors affecting the inconsistent quality of GJE, accurately trace its authentic origin, and ensure stable intrinsic quality, this study develops a systematic analytical approach for GJE geographic origin tracing and quality evaluation based on chemical sensory multimodal data. Fingerprint profiles of GJE were established using water extracts from 33 batches from six Chinese provinces. Six key quality markers were screened and characterized. In addition, sensory indicators for nitrogen oxides, sulfides, bitterness, and sourness were identified. By integrating quantitative data with intelligent sensory technology data and applying six machine learning models, 90% accuracy was achieved in identifying the geographical origin of GJE. The results above demonstrate that machine learning methods based on “chemical sensory multimodality” offer significant advantages for distinguishing GJE production regions, establishing a research paradigm for the precise identification of high-quality production areas for medicinal and edible herbs.</p> Graphical Abstract <p></p>

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Unraveling Key Factors Underlying the Geographical Variation of Gardenia jasminoides Ellis: A Novel Strategy for Coordinating Multimodal Data to Trace the Origin and Control the Quality of Medicinal and Edible Homologous Substances

  • 期刊论文

  • 发表日期：

    2026年5月12日

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  • Beibei Yang,
  • Shihan Wang,
  • Ya Chen,
  • Tao Ding,
  • Yulong Zhu,
  • Hong Wu

摘要

Gardenia jasminoides Ellis (GJE), a primary medicinal and edible herb, exhibits quality variations influenced by production region. To identify the key factors affecting the inconsistent quality of GJE, accurately trace its authentic origin, and ensure stable intrinsic quality, this study develops a systematic analytical approach for GJE geographic origin tracing and quality evaluation based on chemical sensory multimodal data. Fingerprint profiles of GJE were established using water extracts from 33 batches from six Chinese provinces. Six key quality markers were screened and characterized. In addition, sensory indicators for nitrogen oxides, sulfides, bitterness, and sourness were identified. By integrating quantitative data with intelligent sensory technology data and applying six machine learning models, 90% accuracy was achieved in identifying the geographical origin of GJE. The results above demonstrate that machine learning methods based on “chemical sensory multimodality” offer significant advantages for distinguishing GJE production regions, establishing a research paradigm for the precise identification of high-quality production areas for medicinal and edible herbs.

Graphical Abstract