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Use of chemometrics and laser-induced breakdown spectroscopy for quantitative analysis of major and minor elements in aluminium alloys

机译:使用化学计量学和激光诱导击穿光谱法定量分析铝合金中的主要和次要元素

摘要

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.
机译:在当前的工作中,使用化学计量学和激光诱导等离子体光谱以及可商购的激光诱导击穿(LIBS)光谱仪研究了铝合金中主要和次要元素的定量分析。多元校准将整个信号矩阵用于单个多元回归模型中的所有元素。这使得能够考虑变量之间的相关性,在常规单变量建模中这些变量之间通常被称为矩阵效应。建模整个信号矩阵比传统的单变量校准具有更高的鲁棒性,因为它可以补偿矩阵效应。在执行多元校准之前,已使用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。多元校准与三次隐式非线性数据预处理的组合使用显示了最准确的结果。对于主要合金元素,开发的多元校准报告的准确性优于5%。根据获得的结果,化学计量学和激光诱导等离子体光谱的使用已成功地应用于铝中主要和次要合金元素的定量分析。

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