文献类型： 外文期刊
第一作者： Li, Guangyao
作者： Li, Guangyao;Li, Jieqing;Li, Guangyao;Wang, Yuanzhong;Liu, Honggao
作者机构：
关键词： G. elata Bl.; Identification of origin; Spectra; Machine learning; 3DCOS-ResNet
期刊名称：FOOD CHEMISTRY （ 影响因子：9.8； 五年影响因子：9.7 ）
ISSN： 0308-8146
年卷期： 2025 年 464 卷
页码：
收录情况： SCI
摘要： The content of the active ingredient in G. elata Bl. is affected by the soil and climate of different regions, so geographical traceability is essential to ensure its quality, commercial value. This study used a combination of NIRS and various chemometric methods to establish an effective geotraceability method for G. elata Bl.. Firstly, a traditional machine learning model was built based on the SF dataset NIRS, and a ResNet model was built based on NIRS generated 2DCOS images and 3DCOS images. Secondly, the model performance was validated using the ZT dataset. The results show that the 3DCOS-ResNet model performs the best with 100.00 % and 95.45 % test set and EV accuracy, respectively. This study provides a theoretical basis for regulators to quickly ensure the authenticity of G. elata Bl. sources. However, more data and in-depth studies are needed in the future to validate and improve the applicability of the model.
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