Chemometrics and deep learning assisted infrared spectroscopic identification of Dendrobium species
文献类型： 外文期刊
第一作者： Li, Guangyao
作者： Li, Guangyao;Li, Guangyao;Hu, Qiang;Wang, Yuanzhong
作者机构：
关键词： FTIR; NIR; Chemometrics; 3DCOS
期刊名称：JOURNAL OF FOOD COMPOSITION AND ANALYSIS （ 影响因子：4.6； 五年影响因子：4.6 ）
ISSN： 0889-1575
年卷期： 2025 年 140 卷
页码：
收录情况： SCI
摘要： The bioactive constituents of Dendrobium genus plants differ among species, and this variety directly influences their culinary and therapeutic properties as well as their safety. Consequently, the development of a swift and efficient technique for identifying Dendrobium species is crucial to guaranteeing the scientific integrity and safety of their application. This study employed Fourier transform infrared (FTIR) and near-infrared (NIR) spectroscopic techniques, along with chemometrics, to identify various Dendrobium species. The results showed that the chemical compositions of different species of Dendrobium were relatively similar while the contents varied. NIR spectroscopy coupled with OPLS-DA, FTIR coupled with the SVM model, and NIR spectroscopy coupled with the ResNet model are fast, non-destructive, and effective methods for the identification of Dendrobium species. Among them, the ResNet model based on NIR three-dimensional correlation spectroscopy (3DCOS) has the best performance. The accuracy of both training and test sets based on different datasets is 100.00 %, and the accuracy of the external validation set is greater than 95.00 %. This study provides a scientific and effective method for the rapid identification of Dendrobium species and provides a useful reference for future research and application of other plant species.
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