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
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*
1. Technical Center for Industrial Product and Raw Material Inspection and Testing of Shanghai Customs District, Shanghai 200135, China
2. College of Materials & Chemistry, University of Shanghai for Science and Technology, Shanghai 200093, China
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.
LIU Shu,JIN Yue,SU Piao, et al. 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[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142.
[1] Wang P, Li N, Yan C, et al. Analytical Methods, 2019, 11(27): 3419.
[2] General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China and Standardization Administration of China(中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会). GB/T 6730.13—2007 Iron Ores-Determination of Calcium and Magnesium Content-EGTA-CyDTA Titrimetric Method(GB/T 6730.13—2007 铁矿石 钙和镁含量的测定 EGTA-CyDTA滴定法), 2007.
[3] General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China and Standardization Administration of China(中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会). GB/T 6730.11—2007 Iron Ores-Determination of Aluminium Content-EDTA Titrimetric Method (GB/T 6730.11—2007 铁矿石 铝含量的测定 EDTA滴定法), 2007.
[4] General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China and Standardization Administration of China(中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会). GB/T 6730.9—2016 Iron Ores—Determination of Silicon Content—The Silicomolybdic Blue Spectrophotometric Method Reduced by Ammonium Ferrous Sulfate (GB/T 6730.9—2016 铁矿石 硅含量的测定 硫酸亚铁铵还原-硅钼蓝分光光度法), 2016.
[5] General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China and Standardization Administration of China(中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会). GB/T 6730.14—2017 Iron Ores—Determination of Calcium Content—Flame atomic Absorption Spectrometric Method (GB/T 6730.14—2017 铁矿石 钙含量的测定 火焰原子吸收光谱法), 2017.
[6] General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China and Standardization Administration of China(中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会). GB/T 6730.74—2017 Iron Ores—Determination of Magnesium Content—Flame Atomic Absorption Spectrometric Method (GB/T 6730.74—2017 铁矿石 镁含量的测定 火焰原子吸收光谱法), 2017.
[7] State Administration for Market Regulation and Standardization Administration of China(国家市场监督管理总局,中国国家标准化管理委员会). GB/T 6730.56—2019 Iron Ores—Determination of Aluminum Content—Flame Atomic Absorption Spectrometric Method (GB/T 6730.56—2019 铁矿石 铝含量的测定 火焰原子吸收光谱法), 2019.
[8] General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China and Standardization Administration of China(中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会). GB/T 6730.63—2006 Iron Ores—Determination of Aluminum, Calcium, Magnesium, Manganese, Phosphorus, Silicon and Titanium Content—Inductively Coupled Plasma Atomic Emission Spectrometric Method (GB/T 6730.63—2006 铁矿石 铝、钙、镁、锰、磷、硅和钛含量的测定 电感耦合等离子体发射光谱法), 2006.
[9] General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China and Standardization Administration of China(中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会). GB/T 6730.62—2005 Iron Ores—Determination of Calcium, Silicon, Manganese, Titanium, Phosphorus Magnesium, Aluminium and Barium Content Wavelength Dispersive X-ray Fluorescence Spectrometric Method (GB/T 6730.62—2005 铁矿石 钙、硅、镁、钛、磷、锰、铝和钡含量的测定 波长色散X射线荧光光谱法), 2005.
[10] Chen T, Zhang T, Li H. TrAC Trends in Analytical Chemistry, 2020, 133: 116113.
[11] YANG Ya-wen, YAN Cheng-lin, XU Ding, et al(杨雅雯, 严承琳, 徐 鼎, 等). Metallurgical Analysis(冶金分析), 2020, 40(12): 14.
[12] Wang Z, Afgan M S, Gu W, et al. TrAC Trends in Analytical Chemistry, 2021, 143: 116385.
[13] Grant K J, Paul G L, O'Neill J A. Applied Spectroscopy, 1991, 45(4): 701.
[14] Death D L, Cunningham A P, Pollard L J. Spectrochimica Acta Part B: Atomic Spectroscopy, 2008, 63(7): 763.
[15] Hao Z Q, Li C M, Shen M, et al. Optics Express, 2015, 23(6): 7795.
[16] Guo Y M, Guo L B, Hao Z Q, et al. Journal of Analytical Atomic Spectrometry, 2018, 33(8): 1330.
[17] ZHAO Wen-ya, MIN Hong, LIU Shu, et al(赵文雅, 闵 红, 刘 曙, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(7): 1998.
[18] Wang J, Shi M, Zheng P, et al. Journal of Applied Spectroscopy, 2018, 85(1): 190.
[19] Sun C, Tian Y, Gao L, et al. Scientific Reports, 2019, 9: 11363.
[20] Yang Y, Li C, Liu S, et al. Analytical Methods, 2020, 12(10): 1316.
[21] Liu K, Tian D, Xu H, et al. Analytical Methods, 2019, 11(37): 4769.
[22] Tang H, Zhang T, Yang X, et al. Analytical Methods, 2015, 7(21): 9171.
[23] Liu K, Tian D, Wang H, et al. Analytical Methods, 2019, 11(9): 1174.