机器学习增强评估手持激光诱导分解光谱 (LIBS) 分析性能用于岩石样本的多元件分析
Giorgio S Senesi1, Olga De Pascale1, Ignazio Allegretta2
1CNR-Istituto per la Scienza e Tecnologia dei Plasmi (ISTP) Sede di Bari, Via Amendola, 122/D, 70126 Bari, Italy.
手持式激光诱导分解光谱 (hLIBS) 提供了岩石现场特征. 更高的光谱分辨率是准确地化学分析的关键,使用多变量方法,如部分最小平方 (PLS).
科学领域:
- 地质化学 地质化学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 手持激光诱导分解光谱学 (hLIBS) 是一种新兴的在现场岩石分析技术.
- hLIBS仪器提供定性和定量地质化学数据,但性能在制造商之间有所不同.
- 有效的校准方法对于可靠的hLIBS数据至关重要.
研究的目的:
- 为了比较两个商业 hLIBS 工具的分析性能.
- 评估多变量校准方法 (PLS,RF,ANN) 与地质化学表征的单变量分析的有效性.
- 为了确定光谱分辨率对hLIBS定量分析的影响.
主要方法:
- 使用了两个商业的hLIBS仪器,具有降噪和部分最小平方 (PLS) 校准.
- 应用休假一次退休交叉验证 (LOOCV) 用于模型验证.
- 探索随机森林 (RF) 和人工神经网络 (ANN) 算法来建模非线性关系,并将它们与单变量分析进行比较.
主要成果:
- 多变量方法显著优于地质化学量化的单变量分析.
- 用皮尔森系数 (R2) 和平方根平均误差 (RMSE) 来评估21种经过认证的参考材料的预测性能.
- 谱分辨率被确定为影响多变量校准性能的主要因素.
结论:
- 对于高光谱分辨率的hLIBS仪器,部分最小平方 (PLS) 校准是有效的.
- 补充算法 (RF,ANN) 是必要的,以达到最佳的结果与较低光谱分辨率的仪器.
- hLIBS,特别是具有高光谱分辨率和适当的多变量校准,是实地地化学分析的强大工具.
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