Matrix Effects in Quantitative Analysis of Laser-Induced Breakdown Spectroscopy (LIBS) of Rock Powders Doped with Cr, Mn, Ni, Zn, and Co
Matrix Effects in Quantitative Analysis of Laser-Induced Breakdown Spectroscopy (LIBS) of Rock Powders Doped with Cr, Mn, Ni, Zn, and Co
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DOI：
10.1177/0003702816685095
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发表时间：
2017-02
影响因子：
3.5
通讯作者：
K. Lepore;C. Fassett;E. Breves;S. Byrne;S. Giguere;T. Boucher;J. M. Rhodes;M. Vollinger;C. Anderson;R. Murray;M. Dyar
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文献类型：
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作者：
K. Lepore;C. Fassett;E. Breves;S. Byrne;S. Giguere;T. Boucher;J. M. Rhodes;M. Vollinger;C. Anderson;R. Murray;M. Dyar
利用激光诱导击穿光谱法获得定量的化学信息是具有挑战性的，因为地质材料的整体组成具有可变性。由这种可变性引起的化学基质效应产生的峰面积变化与微量元素浓度的变化不成比例。因此，使用单变量校准来预测地质样品中的微量元素浓度受到高度不确定性的困扰。这项工作评估了单变量小元素预测的准确性作为样品主元素矩阵组成的函数，并检查了限制单变量校准预测准确性的因素。将5种不同的样品基质分别掺杂10-85 000 ppm的Cr、Mn、Ni、Zn和Co，然后分别在175种混合物中使用x射线荧光、电感耦合等离子体原子发射光谱和激光诱导击穿光谱进行测量，激光诱导击穿光谱在三种不同的激光能量（1.9、2.8和3.7 mJ）下进行测量。利用掺杂剂、基质、归一化/非归一化和能量密度的不同组合，建立了微量元素浓度的单变量预测模型；采用均方根预测误差和留一交叉验证对模型精度进行评价。结果表明，当预测样本与训练集样本的SiO2含量相近时，采用归一化方法进行微量元素预测具有优势。归一化还减轻了由于激光/样品耦合效应和不同能量密度的使用而产生的光谱差异。对与训练集不同的矩阵中的小元素进行预测，可以将预测的不确定性提高一个数量级。总的来说，单变量校准的质量主要取决于一个持续的、可测量的峰的可用性，这个峰具有良好的跃迁概率，几乎没有来自未知和用于训练它的光谱中的邻近峰的干扰。
Obtaining quantitative chemical information using laser-induced breakdown spectroscopy is challenging due to the variability in the bulk composition of geological materials. Chemical matrix effects caused by this variability produce changes in the peak area that are not proportional to the changes in minor element concentration. Therefore the use of univariate calibrations to predict trace element concentrations in geological samples is plagued by a high degree of uncertainty. This work evaluated the accuracy of univariate minor element predictions as a function of the composition of the major element matrices of the samples and examined the factors that limit the prediction accuracy of univariate calibrations. Five different sample matrices were doped with 10–85 000 ppm Cr, Mn, Ni, Zn, and Co and then independently measured in 175 mixtures by X-ray fluorescence, inductively coupled plasma atomic emission spectrometry, and laser-induced breakdown spectroscopy, the latter at three different laser energies (1.9, 2.8, and 3.7 mJ). Univariate prediction models for minor element concentrations were created using varying combinations of dopants, matrices, normalization/no normalization, and energy density; the model accuracies were evaluated using root mean square prediction errors and leave-one-out cross-validation. The results showed the superiority of using normalization for predictions of minor elements when the predicted sample and those in the training set had matrices with similar SiO2 contents. Normalization also mitigates differences in spectra arising from laser/sample coupling effects and the use of different energy densities. Prediction of minor elements in matrices that are dissimilar to those in the training set can increase the uncertainty of prediction by an order of magnitude. Overall, the quality of a univariate calibration is primarily determined by the availability of a persistent, measurable peak with a favorable transition probability that has little to no interference from neighboring peaks in the spectra of both the unknown and those used to train it.