Deep learning-based fusion of color and spectral features from hyperspectral imaging for the origin identification of Salvia miltiorrhiza
10.1097/st9.0000000000000079
- Author:
Bai RUIBIN
1
;
Xiong FENG
;
Wang HUI
;
Luan MEIQI
;
Zhou JUNHUI
;
Wan XIUFU
;
Zhao ZIHAN
;
Zhang XIAOBO
;
Zhang CHU
;
Yang JIAN
Author Information
1. State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs,National Resource Center for Chinese Materia Medica,China Academy of Chinese Medical Sciences,Beijing,China;Evaluation and Research Center of Daodi Herbs of Jiangxi Province,Nanchang,China
- Publication Type:Journal Article
- Keywords:
Salvia miltiorrhiza Bunge;
Origin traceability;
Data-level fusion;
Feature-level fusion;
Decision-level fusion;
Transformer
- From:
Science of Traditional Chinese Medicine
2025;3(3):250-258
- CountryChina
- Language:English
-
Abstract:
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 geo-graphical 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 represent-ing 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.
