Intelligent geographical origin traceability of Pu-erh tea based on multispectral feature fusion
文献类型： 外文期刊
作者： Chen, Xin 1 ; Zhang, Ting 2 ; Wu, Runqiu 2 ; Zhang, Xianzhe 2 ; Xie, Hongyu 2 ; Wang, Shaojie 2 ; Zhang, Hui 2 ; Ni, Dejiang 2 ; Yu, Zhi 2 ; Yang, Yijian 3 ; Zhang, De 2 ; Liang, Pei 1 ;
作者机构： 1.China Jiliang Univ, Coll Opt & Elect Technol, Hangzhou 310018, Peoples R China
2.Huazhong Agr Univ, Coll Hort & Forestry Sci, Natl Key Lab Germplasm Innovat & Utilizat Fruit &, Wuhan 430070, Peoples R China
3.Yunnan Acad Agr Sci, Tea Res Inst, Kunming 650000, Peoples R China
关键词： Pu-erh tea traceability; Deep learning; Multispectral fusion; Channel attention
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 492 卷
页码：
收录情况： SCI
摘要： To achieve accurate origin traceability of Pu-erh tea, this study proposes a deep learning method based on multispectral fusion. By collecting Raman and near-infrared spectral data from five major origins, an improved ECA-ResNet network structure was designed, incorporating an optimized channel attention mechanism for adaptive feature extraction and fusion. While maintaining computational efficiency, the network effectively utilizes Raman spectroscopy's sensitivity to molecular skeleton vibrations and near-infrared spectroscopy's responsiveness to functional group characteristics. Experimental results demonstrate that the feature-fused model achieves a classification accuracy of 95.05 %, surpassing single-spectral and traditional deep learning methods. Mechanism analysis reveals that the model developed functionally differentiated feature channels, achieving organic fusion of multispectral information and exhibiting robust performance in identifying latitude differences among tea origins. This study provides a reliable technical solution to traceability of Pu-erh tea origin, contributing to the standardized development of the tea industry. Future advances in correlating spectral features with specific chemical markers will further enhance the interpretability and mechanistic understanding of multispectral analytical approaches in food authentication
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