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基于激光诱导分解光谱 (LIBS) 的跨仪器数据利用,用于识别Akebia物种

Yuge Liu^1,2,3, Qianqian Wang^1,2,3,4, Tianzhong Luo^1,2,3,4

¹School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.

Bioengineering (Basel, Switzerland)

|September 27, 2025

概括

这项研究引入了一种新方法,将激光诱导分解光谱 (LIBS) 与随机森林相结合,用于在传统中医中准确识别阿基比亚物种. 开发的"SCFS-PP"框架提高了TCM分析的跨设备可靠性.

科学领域:

分析化学分析化学
药理学是指药理学,即药理学是指药理学.
生物技术是生物技术.

背景情况:

由于药理效应不同,准确识别传统中医 (TCM) 品种至关重要.
传统中医分析面临着不清晰的化学成分和跨设备可变性的挑战.
阿克比亚物种具有治疗潜力,但需要精确的分化才能安全临床使用.

研究的目的:

开发一种可靠的方法来区分三种常用的阿克比亚物种.
建立基于LIBS的物种识别跨设备建模和验证方案.
为了提高LIBS在TCM分析中的准确性和适用性.

主要方法:

采集了三种Akebia物种的光谱数据,使用了两种LIBS系统,具有不同的光谱仪分辨率.
开发了一种使用标准灯和差异分析 (ANOVA) 的光谱校正和特征选择 (SCFS) 方法.
实施了后处理 (PP) 策略,涉及DBSCAN用于异常光谱的去除.

主要成果:

在28个选定的特征中,SCFS方法实现了80.61%的初始分类准确度.
综合的SCFS-PP框架提高了最终分类准确度,达到85.50%.
在TCM中LIBS应用中证明有效地提高了跨仪器数据可靠性.

结论:

拟议的"SCFS-PP"框架显著提高了在LIBS分析中跨仪器数据利用的可靠性.
这种方法扩大了LIBS在传统中医药中准确识别物种的适用性.
准确识别Akebia物种对于其有效和安全的临床应用至关重要.

关键词:

分析差异的分析.基于密度的应用程序的空间聚类与噪声.激光诱导的分解光谱学进行光谱校正.传统的中国医药传统的中国医药.

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