Zhang Tingsong, Liu Ziyuan, Ma Qing, Hu Dong, Dai Yujia, Zhang Xinfeng, Zhou Zhu
College of Opto-Electro-Mechanical Engineering, Zhejiang A&F University, Hangzhou 311300, China.
State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China.
Foods. 2024 May 27;13(11):1676. doi: 10.3390/foods13111676.
Dendrobium, a highly effective traditional Chinese medicinal herb, exhibits significant variations in efficacy and price among different varieties. Therefore, achieving an efficient classification of Dendrobium is crucial. However, most of the existing identification methods for Dendrobium make it difficult to simultaneously achieve both non-destructiveness and high efficiency, making it challenging to truly meet the needs of industrial production. In this study, we combined Laser-Induced Breakdown Spectroscopy (LIBS) with multivariate models to classify 10 varieties of Dendrobium. LIBS spectral data for each Dendrobium variety were collected from three circular medicinal blocks. During the data analysis phase, multivariate models to classify different Dendrobium varieties first preprocess the LIBS spectral data using Gaussian filtering and stacked correlation coefficient feature selection. Subsequently, the constructed fusion model is utilized for classification. The results demonstrate that the classification accuracy of 10 Dendrobium varieties reached 100%. Compared to Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), our method improved classification accuracy by 14%, 20%, and 20%, respectively. Additionally, it outperforms three models (SVM, RF, and KNN) with added Principal Component Analysis (PCA) by 10%, 10%, and 17%. This fully validates the excellent performance of our classification method. Finally, visualization analysis of the entire research process based on t-distributed Stochastic Neighbor Embedding (t-SNE) technology further enhances the interpretability of the model. This study, by combining LIBS and machine learning technologies, achieves efficient classification of Dendrobium, providing a feasible solution for the identification of Dendrobium and even traditional Chinese medicinal herbs.
石斛是一种高效的传统中药材，不同品种的石斛在功效和价格上存在显著差异。因此，实现石斛的高效分类至关重要。然而，现有的大多数石斛鉴定方法难以同时实现无损检测和高效率，难以真正满足工业生产的需求。在本研究中，我们将激光诱导击穿光谱（LIBS）与多变量模型相结合，对10个品种的石斛进行分类。从三个圆形药块中收集每个石斛品种的LIBS光谱数据。在数据分析阶段，用于分类不同石斛品种的多变量模型首先使用高斯滤波和堆叠相关系数特征选择对LIBS光谱数据进行预处理。随后，利用构建的融合模型进行分类。结果表明，10个石斛品种的分类准确率达到了100%。与支持向量机（SVM）、随机森林（RF）和K近邻（KNN）相比，我们的方法分类准确率分别提高了14%、20%和20%。此外，它比添加主成分分析（PCA）的三个模型（SVM、RF和KNN）分别高出10%、10%和17%。这充分验证了我们分类方法的优异性能。最后，基于t分布随机邻域嵌入（t-SNE）技术对整个研究过程进行可视化分析，进一步提高了模型的可解释性。本研究通过结合LIBS和机器学习技术，实现了石斛的高效分类，为石斛乃至传统中药材的鉴定提供了可行的解决方案。
