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使用机器学习增强光谱技术分析衰老.

James T Stofel1, Ashwin P Rao2, Anil K Patnaik1

  • 1Department of Engineering Physics, Air Force Institute of Technology, Wright-Patterson AFB, Ohio, USA.

Applied spectroscopy
|August 21, 2024
PubMed
概括
此摘要是机器生成的。

这项研究使用激光诱导分解光谱 (LIBS) 和Raman光谱与机器学习来识别化合物. 这些方法准确地分类化,氧化和碳酸盐,并量化化合物内生长.

关键词:
图书馆 图书馆 图书馆 图书馆激光诱导的分解光谱学拉曼光谱法 拉曼光谱法数据融合数据融合和是的组成部分.老化的老化机器学习是机器学习.

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科学领域:

  • 分析化学 分析化学
  • 材料科学 材料科学 材料科学

背景情况:

  • 像LiH和LiOH这样的化合物在工业上很重要,但具有反应性.
  • 与H2O和CO2的反应导致二次化合物入,影响材料均性和应用.

研究的目的:

  • 开发和验证用于分析化合物混合物的光谱方法.
  • 量化评估化 (LiOH) 在化 (LiH) 中的氧化 (LiOH) 的生长.

主要方法:

  • 利用激光诱导分解光谱 (LIBS) 和拉曼光谱来获取光谱数据.
  • 采用机器学习,包括支持矢量机 (SVM) 分类器,用于对LiH,LiOH和Li2CO3的高保真分类.
  • 应用多变量回归技术,特别是部分最小平方回归 (PLSR),用于定量分析.

主要成果:

  • 在使用SVM分类器对LiH,LiOH和Li2CO3进行分类时实现了完美的预测准确度.
  • 使用PLSR开发了一个数据融合模型,将LIBS和Raman功能结合起来.
  • 优化的模型显示,LiOH在LiH中的生长的根平均平方误差为2.5 wt%,检测极限为6.3 wt%.

结论:

  • 与机器学习相结合的LIBS和拉曼光谱,为分析化合物混合物提供了强大的方法.
  • 开发的方法可以准确地对二次化合物形成进行分类和量化.
  • 这种技术对于确保工业应用中化学品的质量和性能至关重要.