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通过激光诱导分解光谱和机器学习算法来检测和分类碳化合物污染的土壤的方法的开发

Lucila Juliana Martino¹, Fernando Sebastián García Einschlag², Cristian Adrián D'Angelo³

¹UNCPBA, FCEx, Centro de Investigaciones en Física e Ingeniería del Centro de La Provincia de Buenos Aires (CIFICEN, CONICET-CICPBA-UNCPBA), Pinto 399, Tandil, Argentina. lumartino91@gmail.com.

Environmental science and pollution research international

|February 5, 2025

概括

这项研究引入了一种使用激光诱导分解光谱 (LIBS) 和机器学习来检测和分类碳化合物污染的土壤的新方法,实现了超过90%的环境监测准确度.

科学领域:

环境科学环境科学
分析化学分析化学
频谱学是一种光谱学.

背景情况:

石油勘探的增加导致泄漏,造成环境和公共卫生风险.
有效地分析受污染的地点需要准确地检测和分类碳化合物污染的土壤.

研究的目的:

开发一种可靠和可解释的方法来检测和分类碳化合物污染的土壤.
将激光诱导分解光谱 (LIBS) 与机器学习相结合,用于环境现场分析.

主要方法:

优化了11个碳化合物污染土壤样本和一个对照样本的LIBS实验参数.
应用主要组件分析 (PCA) 以减少尺寸性和减轻过拟合.
利用线性差异分析 (LDA) 来进行可靠和可解释的样本分类.

主要成果:

碳化合物污染土壤的分类准确度超过90%.
在类似的碳化合物类别 (如汽油,油) 之间成功区分,准确度为100%.
证明模型能够识别相关污染物类别之间的微妙差异.

结论:

结合LIBS和机器学习的方法在环境监测方面取得了重大进展.
这种方法提高了在土壤中识别和区分各种碳化合物的能力.
开发的技术对于分析受污染的地点和告知修复策略非常有价值.

关键词:

分类分类分类分类.碳化合物碳化合物是一种碳化合物.激光诱导的分解光谱学线性差异分析线性差异分析主要组件分析的主要组件分析.土壤污染土壤污染

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