石斛,一种高效的中草药,不同品种之间的功效和价格差异显著。因此,实现石斛的有效分类至关重要。然而,现有的大多数石斛识别方法难以同时实现无损性和高效率,使其具有挑战性,以真正满足工业生产的需求。在这项研究中,我们将激光诱导击穿光谱(LIBS)与多变量模型相结合,对石斛的10个品种进行分类。每个石斛品种的LIBS光谱数据从三个圆形药块收集。在数据分析阶段,多变量模型对不同石斛品种进行分类首先利用高斯滤波和叠加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100%。与支持向量机(SVM)相比,随机森林(RF),和K-最近邻居(KNN),我们的方法将分类精度提高了14%,20%,20%,分别。此外,它优于三个模型(SVM,射频,和KNN)增加了10%的主成分分析(PCA),10%,和17%。这充分验证了我们的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。这项研究,通过结合LIBS和机器学习技术,实现石斛的高效分类,为石斛乃至中草药的鉴别提供了可行的解决方案。
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.