激光诱导分解光谱和拉曼光谱的融合用于基于机器学习的矿物识别
Yujia Dai1, Ziyuan Liu1, Shangyong Zhao1
1College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China.
Molecules (Basel, Switzerland)
|July 27, 2024
概括
本研究介绍了一种集成的激光诱导分解光谱 (LIBS) 和拉曼光谱 (RS) 系统,用于快速矿物识别. 通过将LIBS和RS数据与机器学习相结合,在分类六种矿物类型时获得了98.4%的准确性.
科学领域:
- 地质化学和材料科学 材料科学
- 分析化学 分析化学
- 计算科学 计算科学
背景情况:
- 准确的矿物鉴定对于地质学,金学和矿物学至关重要.
- 像LIBS和RS这样的光谱方法为in situ矿物分析提供了元素和结构洞察力.
研究的目的:
- 开发和评估一个集成的LIBS-RS系统,以有效地对矿物进行分类.
- 将机器学习算法应用于融合的光谱数据,以提高识别准确度.
主要方法:
- 使用了一种综合系统,结合了激光诱导分解光谱 (LIBS) 和拉曼光谱 (RS).
- 使用了光谱数据融合,缩小维度 (t-SNE),特征选择 (费舍尔分数) 和机器学习模型 (PLS-DA,K-ELM).
- 在建模之前进行了光谱数据的预处理.
主要成果:
- 集成的LIBS-RS系统有效地捕获了矿物特定的光谱特征.
- 机器学习模型,特别是K-ELM和PLS-DA,使用合并的LIBS-RS数据实现了98.4%的高分类精度.
- 变量重要性分析确定了与特定化学元素和分子键相应的关键光谱特征.
结论:
- 集成的LIBS-RS系统与机器学习相结合,为快速可靠的矿物分类提供了一种可行的方法.
- 这种方法在各种科学和工业应用中具有实时,现场矿物分析的巨大潜力.
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