Liu Yuge, Wang Qianqian, Luo Tianzhong, Zhao Zhifang, Wang Leifu, Xu Shuai, Zhou Hao, Zhao Jiquan, Zhou Zixiao, Teng Geer
School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China.
Bioengineering (Basel). 2025 Sep 8;12(9):964. doi: 10.3390/bioengineering12090964.
New technologies and equipment for medicine analysis and diagnostics have always been critical in clinical medication and pharmaceutical production. Especially in the field of traditional Chinese medicine (TCM) where the chemical composition is not fully clear, cross-device analysis and identification using the same technology can sometimes even lead to misjudgments. Akebia species, capable of inducing heat clearing, diuresis, and anti-inflammatory effects, show great potential in clinical applications. However, the three commonly used species differ in pharmacological effects and therefore should not be used interchangeably. We proposed a method combining LIBS with random forest for species identification and established a modeling and verification scheme across device platforms. Spectra of three Akebia species were collected using two LIBS systems equipped with spectrometers of different resolutions. The data acquired from the low-resolution spectrometer were used for model training, while the data from the high-resolution spectrometers were used for testing. A spectral correction and feature selection (SCFS) method was proposed, in which spectral data were first corrected using a standard lamp, followed by feature selection via analysis of variance (ANOVA) to determine the optimal number of discriminative features. The highest classification accuracy of 80.61% was achieved when 28 features were used. Finally, a post-processing (PP) strategy was applied, where abnormal spectra in the test set were removed using density-based spatial clustering of applications with noise (DBSCAN), resulting in a final classification accuracy of 85.50%. These results demonstrate that the proposed "SCFS-PP" framework effectively enhances the reliability of cross-instrument data utilization and expands the applicability of LIBS in the field of TCM.
医学分析和诊断的新技术与设备在临床用药和制药生产中一直至关重要。特别是在化学成分尚未完全明确的传统中药领域，使用相同技术进行跨设备分析和鉴定有时甚至会导致误判。具有清热、利尿和抗炎作用的木通属植物在临床应用中显示出巨大潜力。然而，三种常用的木通属植物在药理作用上存在差异，因此不应相互替代使用。我们提出了一种将激光诱导击穿光谱（LIBS）与随机森林相结合的物种鉴定方法，并建立了跨设备平台的建模和验证方案。使用配备不同分辨率光谱仪的两个LIBS系统收集了三种木通属植物的光谱。从低分辨率光谱仪获取的数据用于模型训练，而高分辨率光谱仪的数据用于测试。提出了一种光谱校正和特征选择（SCFS）方法，其中首先使用标准灯对光谱数据进行校正，然后通过方差分析（ANOVA）进行特征选择，以确定最佳判别特征数量。使用28个特征时，实现了最高80.61%的分类准确率。最后，应用了一种后处理（PP）策略，即使用基于密度的带有噪声应用空间聚类（DBSCAN）去除测试集中的异常光谱，最终分类准确率达到85.50%。这些结果表明，所提出的“SCFS-PP”框架有效地提高了跨仪器数据利用的可靠性，并扩展了LIBS在中药领域的适用性。
