来自手持式激光诱导分解光谱 (LIBS) 和X射线光 (XRF) 分析仪的光谱数据融合,用于在模拟的分散事故中改进的检测
Janos I Braun1, Paige E Anderson2, Justin I Borrero Negrón2
1Department of Chemical and Materials Engineering, New Mexico State University, Las Cruces, New Mexico, USA.
Applied spectroscopy
|December 27, 2024
概括
这项研究融合了X射线光和激光诱导分解光谱的光谱数据,以准确测量替代 (CeO2) 土壤污染. 数据融合显著提高了预测准确度,实现了环境监测的高精度和灵敏度.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 地质化学 地质化学
背景情况:
- 准确量化土壤中的放射性污染对环境安全至关重要.
- 传统方法可能缺乏复杂矩阵所需的灵敏度或特异性.
- 检测替代物对于验证补救策略至关重要.
研究的目的:
- 开发和验证一种新的中级数据融合方法,用于量化土壤中的氧化 (CeO2) 污染.
- 为了比较在独立与融合光谱数据上训练的机器学习模型的性能.
- 建立一种高度敏感和精确的方法来检测环境样本中的替代物.
主要方法:
- 使用手持式X射线光 (XRF) 和激光诱导分解光谱 (LIBS) 分析仪.
- 实施监督机器学习回归模型 (人工神经网络,增强组合).
- 通过将XRF和LIBS光谱数据的主要组件分数结合起来,进行中级数据融合.
主要成果:
- 结合XRF和LIBS的主要组件得分,显著提高了模型的精度和灵敏度.
- 在融合数据上训练的人工神经网络,与使用独立传感器数据的模型相比,显示出更高的性能.
- 在融合特征上训练的增强组合模型实现了~10^-6的根-平均-平方误差和~10^-5重量%的检测极限.
结论:
- XRF和LIBS光谱数据的中级数据融合为准确的土壤污染量化提供了强大的方法.
- 开发的方法提供了一个数量级的 plutonium 替代品检测能力的改进.
- 这种技术在环境监测和核设施整治方面具有显著的前景.
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