Rapid and accurate identification of Gastrodia elata Blume species based on FTIR and NIR spectroscopy combined with chemometric methods
文献类型： 外文期刊
作者： Li, Guangyao 1 ; Li, Jieqing 1 ; Liu, Honggao 3 ; Wang, Yuanzhong 2 ;
作者机构： 1.Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Peoples R China
2.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Peoples R China
3.Zhaotong Univ, Yunnan Key Lab Gastrodia & Fungi Symbiot Biol, Zhaotong 657000, Yunnan, Peoples R China
关键词： Gastrodia elata Blume; Species; FTIR; NIR; Machine learning; Deep learning
期刊名称：TALANTA （ 影响因子：6.1； 五年影响因子：5.5 ）
ISSN： 0039-9140
年卷期： 2025 年 281 卷
页码：
收录情况： SCI
摘要： Different varieties of Gastrodia elata Blume (G. elata Bl.) have different qualities and different contents of active ingredients, such as polysaccharide and gastrodin, and it is generally believed that the higher the active ingredients, the better the quality of G. elata Bl. and the stronger the medicinal effects. Therefore, effective identification of G. elata Bl. species is crucial and has important theoretical and practical significance. In this study, first unsupervised PCA and t-SNE are established for data visualisation, follow by traditional machine learning (PLS-DA, OPLS-DA and SVM) models and deep learning (ResNet) models were established based on the fourier transform infrared (FTIR) and near infrared (NIR) spectra data of three G. elata Bl. species. The results show that PLS-DA, OPLS-DA and SVM models require complex preprocessing of spectral data to build stable and reliable models. Compared with traditional machine learning models, ResNet models do not require complex spectral preprocessing, and the training and test sets of ResNet models built based on raw NIR and low-level data fusion (FTIR + NIR) spectra reach 100 % accuracy, the external validation set based on low-level data fusion reaches 100 % accuracy, and the external validation set based on NIR has only one sample classification error and no overfitting.
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