变量重要性-反向传播人工神经网络辅助激光诱导击穿光谱测定铁矿石中硅、铝、钙和镁含量

doi:10.3964/j.issn.1000-0593(2023)10-3132-11

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摘要：快速准确测定铁矿石中的硅、铝、钙、镁含量对铁矿石质量评价具有重要作用。受制于多变量分析方法过拟合现象以及不同种类样品基体效应，使用激光诱导击穿光谱（LIBS）准确测定铁矿石中硅、铝、钙、镁含量仍然是当前存在的挑战。采用变量重要性-反向传播人工神经网络（VI-BP-ANN）辅助LIBS定量分析铁矿石中硅（以SiO₂计）、铝（以Al₂O₃计）、钙（以CaO计）和镁（以MgO计）的含量。在这项研究中，收集了12种244批铁矿石代表性样品的LIBS光谱，优化了光谱预处理方法，使用随机森林（RF）对LIBS光谱特征的重要性进行了测量，使用袋外(OOB)误差优化RF模型参数，变量重要性阈值用于优化BP-ANN校准模型的输入变量。变量重要性阈值和神经元数量通过五折交叉验证（5-CV）的测定系数（R²）和均方根误差（RMSE）进行优化。结果显示测试样本SiO₂、Al₂O₃、CaO和MgO含量预测均方根误差（RMSEP）分别为0.377 2 wt%、0.133 9 wt%、0.059 2 wt%和0.141 1 wt%，R²分别为0.970 1、0.955 4、0.987 1、0.997 5。相比于使用相同的预处理方法作为PLS、SVM、RF和BP-ANN四种模型的输入，VI-BP-ANN在校准集和预测集都显示出出色的预测能力。结果表明LIBS与VI-BP-ANN的结合有潜力在实际应用中实现铁矿石硅、铝、钙、镁含量的快速准确预测。

关键词：铁矿石；反向传播人工神经网络；变量重要性；定量分析；激光诱导击穿光谱

Abstract：The rapid and accurate determination of calcium, magnesium, aluminium and silicon content in iron ore plays an important role in iron ore quality assessment. The accurate determination of calcium (CaO), magnesium (MgO), aluminium (Al₂O₃) and silicon (SiO₂) in iron ore using laser-induced breakdown spectroscopy (LIBS) remains a challenge due to the overfitting of multivariate analysis methods and matrix effects between different types of samples. In this paper, variable importance-back propagation artificial neural network (VI-BP-ANN) assisted LIBS was used for the first time to quantify the content of SiO₂, Al₂O₃, CaO and MgO in iron ore. In this study, LIBS spectra of 12 representative samples of 244 batches of iron ore were collected, spectral pre-processing methods were optimised, the importance of LIBS spectral features was measured using random forest (RF), RF model parameters were optimised using out-of-bag (OOB) errors, and variable importance thresholds were used to optimise the input variables for the BP-ANN calibration model. The variable importance thresholds and the number of neurons were optimised by five-fold cross-validation (5-CV) of the coefficient of determination (R²) and root mean square error (RMSE). The results showed root mean square error of prediction (RMSEP) for the SiO₂, Al₂O₃, CaO, MgO content of the test samples were 0.372 3 wt%, 0.129 8 wt%, 0.052 4 wt% and 0.149 0 wt% respectively, with R² of 0.977 1, 0.950 4, 0.987 8 and 0.997 7, respectively. Compared to using the same preprocessing method as input to the three PLS, SVM and RF models, the VI-BP- ANN model showed excellent performance in both the calibration dataset and prediction dataset. The results indicate that the combination of LIBS and VI-BP-ANN has the potential to achieve fast and accurate prediction of calcium, magnesium, aluminium and silicon content of iron ore in practical application.

Key words：Iron ore; Back propagation artificial neural network; Variable importance; Quantitative analysis; Laser-induced breakdown spectroscopy

收稿日期: 2022-06-25 修订日期: 2023-04-23

中图分类号:

O657.319

基金资助: 海关总署科研项目(2020HK253)资助

通讯作者: 吴晓红 E-mail: wuxiaohong_2196@163.com

作者简介: 刘曙，1982年生，上海海关工业品与原材料检测技术中心研究员 e-mail: liu_shu@customs.gov.cn

引用本文:

刘曙，金悦，苏飘，闵红，安雅睿，吴晓红. 变量重要性-反向传播人工神经网络辅助激光诱导击穿光谱测定铁矿石中硅、铝、钙和镁含量[J]. 光谱学与光谱分析, 2023, 43(10): 3132-3142.
LIU Shu, JIN Yue, SU Piao, MIN Hong, AN Ya-rui, WU Xiao-hong. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142.

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