Use of chemometrics and laser-induced breakdown spectroscopy for quantitative analysis of major and minor elements in aluminum alloys
Use of chemometrics and laser-induced breakdown spectroscopy for quantitative analysis of major and minor elements in aluminum alloys
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DOI：
10.1366/000370207780220813
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发表时间：
2007-03-01
影响因子：
3.5
通讯作者：
Hubert, Joseph
中科院分区：
文献类型：
--
作者：
Doucet, Francois R.;Belliveau, Thomas F.;Hubert, Joseph
本文利用化学计量学和激光诱导等离子体光谱分析技术，利用商用激光诱导击穿(LIBS)光谱仪，对铝合金中的主次元素进行了定量分析。多变量校正使用单个多变量回归模型中所有元素的整个信号矩阵。这使得能够解释变量之间的相关性，在传统的单变量建模中通常被称为矩阵效应。与传统的单变量校正相比，对整个信号矩阵建模提高了稳健性，因为它可以补偿矩阵效应。在进行多变量校正之前，使用了几种非线性数据预处理方法来校正分析信号的非线性行为。采用多元校正和三次隐式非线性数据预处理相结合的方法得到了最准确的结果。所建立的多元校正方法对主要合金元素的校正准确度优于5%。在此基础上，成功地将化学计量学和激光诱导等离子体光谱分析技术应用于铝中主次合金化元素的定量分析。
In the present work, quantitative analysis of major and minor elements in aluminum alloys is investigated using chemometrics and laser-induced plasma spectroscopy with a commercially available laser-induced breakdown (LIBS) spectrometer. Multivariate calibrations use the entire signal matrix for all elements in a single multivariate regression model. This enables accounting for the correlation between variables often referred to as matrix effects in conventional univariate modeling. Modeling the entire signal matrix improves robustness over traditional univariate calibration since it can compensate for matrix effects. Several nonlinear data pretreatment methods have been used to correct for nonlinear behaviors of the analytical signals prior to performing the multivariate calibration. The use of multivariate calibration in combination with cubic implicit nonlinear data pretreatment showed the most accurate results. The accuracy reported with the developed multivariate calibration is better than 5% for the major alloying elements. Based on the results obtained, the use of chemometrics and laser-induced plasma spectroscopy have been successfully applied to the quantitative analysis of major and minor alloying elements in aluminum.