Temperature based segmentation for spectral data of laser-induced plasmas for quantitative compositional analysis of brass alloys submerged in water
Temperature based segmentation for spectral data of laser-induced plasmas for quantitative compositional analysis of brass alloys submerged in water
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DOI：
10.1016/j.sab.2016.08.025
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Tomoko Takahashi;B. Thornton;Takumi Sato;T. Ohki;K. Ohki;T. Sakka
中科院分区：
文献类型：
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作者：
Tomoko Takahashi;B. Thornton;Takumi Sato;T. Ohki;K. Ohki;T. Sakka
本研究描述了一种利用激光诱导等离子体定量测定水中黄铜合金成分的方法。主成分回归（PCR）分析和偏最小二乘（PLS）回归分析应用于长ns脉冲产生的等离子体的光谱测量。在分析中，将激励温度波动对信号的非线性影响作为系统误差处理。这些误差对分析性能的影响是通过应用PCR和PLS与温度分段数据库进行评估。将分析结果与不考虑激发温度的传统方法进行比较，结果表明，所提出的数据库分割方法提高了准确性，PCR模型中Cu和Zn的预测均方根误差（RMSEP）分别为2.7%和2.8%，PLS模型中Cu和Zn的预测均方根误差（RMSEP）分别为2.9%和1.8%。结果表明，系统效应导致了水下等离子体的波动，适当的数据库分割可以提高PCR和PLS方法的性能。
This study describes a method to quantify the composition of brass alloys submerged in water using laser-induced plasmas. Principal component regression (PCR) analysis and partial least squares (PLS) regression analysis are applied to spectral measurements of plasmas generated using a long-ns duration pulse. The non-linear effects of excitation temperature fluctuations on the signals are treated as systematic errors in the analysis. The effect of these errors on the analytical performance is evaluated by applying PCR and PLS with a temperature segmented database. The results of the analysis are compared to conventional methods that do not consider the excitation temperature and it is demonstrated that the proposed database segmentation improves accuracy, with root-mean square errors of prediction (RMSEP) of 2.7% and 2.8% for Cu and Zn in the PCR model and 2.9% and 1.8% for Cu and Zn in the PLS model, respectively. The results indicate that systematic effects contribute to fluctuation of underwater plasmas, where appropriate database segmentation can improve the performance of the PCR and PLS methods.