Deep learning-based fusion of color and spectral features from hyperspectral imaging for the origin identification of Salvia miltiorrhiza Article Swipe
Ruibin Bai
,
Feng Xiong
,
Hui Wang
,
Meiqi Luan
,
Zhou Jun-hui
,
Xiufu Wan
,
Zihan Zhao
,
Xiaobo Zhang
,
Chu Zhang
,
Jian Yang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1097/st9.0000000000000079
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1097/st9.0000000000000079
Background: Salvia miltiorrhiza Bunge, commonly known as “Danshen” in China due to the distinctive red color of its roots, is one of the most widely used traditional Chinese medicines. It is cultivated in various regions across China, and environmental differences among these regions can affect the secondary metabolites of plants, thereby influencing the quality of S. miltiorrhiza . In recent years, increasing demand for S. miltiorrhiza has exacerbated the problem of origin fraud. Therefore, ensuring the authenticity of its geographical origin is crucial for the sustainable development of the industry. Objective: The red coloration of S. miltiorrhiza is closely associated with the content of its primary active compounds, particularly tanshinones. Therefore, both its internal chemical composition and external color characteristics serve as key indicators for quality assessment. This study utilized hyperspectral imaging technology to evaluate its potential in classifying the geographical origin of S. miltiorrhiza . Methods: Spectral data reflecting the internal chemical properties of S. miltiorrhiza were integrated with color information representing its external features through 3 levels of data fusion. These fused datasets were then combined with deep learning algorithms to achieve accurate origin classification. Results: The results demonstrated that the Transformer model combined with soft-voting decision-level fusion achieved the highest classification accuracy of 98.72% by integrating image color and short-wave infrared spectral data. Conclusion: This study demonstrates that integrating hyperspectral imaging spectral data with color information provides a reliable and innovative approach for verifying the authenticity and traceability of S. miltiorrhiza .
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Deep learning-based fusion of color and spectral features from hyperspectral imaging for the origin identification of Salvia miltiorrhizaWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-09-01Full publication date if available
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Ruibin Bai, Feng Xiong, Hui Wang, Meiqi Luan, Zhou Jun-hui, Xiufu Wan, Zihan Zhao, Xiaobo Zhang, Chu Zhang, Jian YangList of authors in order
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https://doi.org/10.1097/st9.0000000000000079Publisher landing page
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diamondOpen access status per OpenAlex
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https://doi.org/10.1097/st9.0000000000000079Direct OA link when available
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| primary_location.source.display_name | Science of Traditional Chinese Medicine |
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| publication_date | 2025-09-01 |
| publication_year | 2025 |
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