Su Piao, Wu Xiaohong, Li Chen, Yan Chenglin, An Yarui, Liu Shu
Technical Center for Industrial Product and Raw Material Inspection and Testing of Shanghai Customs, Shanghai, China.
College of Materials & Chemistry, 47863University of Shanghai for Science and Technology, Shanghai, China.
Appl Spectrosc. 2023 Feb;77(2):140-150. doi: 10.1177/00037028221141102. Epub 2022 Nov 18.
Focus in quality assessment of iron ore is the content of total iron (TFe). Laser-induced breakdown spectroscopy (LIBS) technology possesses the merits of rapid, in situ, real-time multielement analysis for iron ore, but its application to quantitative TFe content is subject to interference of the iron matrix effect and the lack of suitable data mining tools. Here, a new method of LIBS-based variable importance back propagation artificial neural network (VI-BP-ANN) for quantitative TFe content in iron ore was first proposed. After the LIBS spectra of 80 representative iron samples were obtained, random forest (RF) was optimized by out-of-bag (OOB) error and then used to measure and rank variable importance. The variable importance thresholds and the number of neurons were optimized with five-fold cross-validation (CV) with correlation coefficient (R) and root mean square error (RMSE). With using only 1.40% of full spectral variables to construct BP-ANN model, the resulted R, the root mean squared error of prediction (RMSEP) and the modeling time of the final VI-BP-ANN model was 0.9450, 0.3174 wt%, and 24 s, respectively. Compared with full spectrum-based model, for example, BP-ANN, RF, support vector machine (SVM), and PLS and VI-RF model, the VI-BP-ANN model reduced overfitting and obtained the highest R and the lowest RMSE both for calibration and prediction. Meanwhile, the characteristics of variables selected by VI were analyzed. In addition to the elemental emission lines of Ca, Al, Na, K, Mn, Si, Mg, Ti, Zr, and Li, partial spectral baselines of 540-610 nm and 820-970 nm were also selected as characteristic variables, which indicated that VI can take into full consideration the elemental interactions and the spectral baselines. Our approach shows that LIBS combined with VI-BP-ANN is able to quantify TFe content rapidly and accurately in iron ore.
铁矿石质量评估的重点是全铁（TFe）含量。激光诱导击穿光谱（LIBS）技术具有对铁矿石进行快速、原位、实时多元素分析的优点，但其在定量TFe含量方面的应用受到铁基体效应的干扰以及缺乏合适的数据挖掘工具的限制。在此，首次提出了一种基于LIBS的可变重要性反向传播人工神经网络（VI-BP-ANN）方法用于定量铁矿石中的TFe含量。在获得80个代表性铁样品的LIBS光谱后，通过袋外（OOB）误差对随机森林（RF）进行优化，然后用于测量和排序变量重要性。利用相关系数（R）和均方根误差（RMSE），通过五折交叉验证（CV）对变量重要性阈值和神经元数量进行优化。仅使用全光谱变量的1.40%来构建BP-ANN模型，最终VI-BP-ANN模型的R、预测均方根误差（RMSEP）和建模时间分别为0.9450、0.3174 wt%和24 s。与基于全光谱的模型（如BP-ANN、RF、支持向量机（SVM）、偏最小二乘法（PLS）和VI-RF模型）相比，VI-BP-ANN模型减少了过拟合，在校准和预测方面均获得了最高的R和最低的RMSE。同时，分析了由VI选择的变量的特征。除了Ca、Al、Na、K、Mn、Si、Mg、Ti、Zr和Li的元素发射线外，540 - 610 nm和820 - 970 nm的部分光谱基线也被选为特征变量，这表明VI能够充分考虑元素间相互作用和光谱基线。我们的方法表明，LIBS与VI-BP-ANN相结合能够快速、准确地定量铁矿石中的TFe含量。
