Establishment of Hyperspectral Prediction Model of Water Content in Anshan-Type Magnetite

XIE Xiaoxiao; BAI Yang; ZHANG Jiuling; JIA Yuna

doi:10.15898/j.ykcs.202409070183

Rock and Mineral Analysis > 2024 > 43(6): 901-913. > DOI: 10.15898/j.ykcs.202409070183

XIE Xiaoxiao，BAI Yang，ZHANG Jiuling，et al. Establishment of Hyperspectral Prediction Model of Water Content in Anshan-Type Magnetite[J]. Rock and Mineral Analysis，2024，43（6）：901−913. DOI: 10.15898/j.ykcs.202409070183

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Establishment of Hyperspectral Prediction Model of Water Content in Anshan-Type Magnetite

1.
College of Emergency Management and Safety Engineering, North China University of Science and Technology, Tangshan 063210, China
2.
College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China

More Information

Received Date: September 06, 2024
Revised Date: October 15, 2024
Accepted Date: October 17, 2024
Available Online: October 30, 2024
Published Date: October 30, 2024

Abstract

HIGHLIGHTS
(1) The spectrum of magnetite has absorption characteristics around 990nm, 1440nm and 1920nm, which can be used to distinguish different mineral components.

(2) Using spectral transformation to highlight spectral characteristics and CARS to reduce redundant information, the prediction performance and stability of the model are improved.

(3) Establishing the identification model of magnetite water content, analyzing the precision and error of the model, and optimizing the best model, solves the shortcomings of traditional methods in detection sensitivity and accuracy, and improves the accuracy and reliability of the model in complex ore monitoring.

ABSTRACT

The high water content of iron ore will reduce its machinability, which is not conducive to the smooth progress of mineral processing, sintering, smelting and tailings treatment. Therefore, it is very important to control the water content of iron ore reasonably for improving mining production efficiency, reducing energy consumption and reducing waste of raw materials. However, due to the complexity of iron ore composition and properties, traditional detection techniques (such as loss on drying method and resistance method) have shortcomings in sensitivity and accuracy. Three kinds of Anshan-type magnetite from a certain area in Tangshan, Hebei Province, were selected to test hyperspectral data under different water contents (0−40.0%). Using S-G smoothing filtering (S-G), multivariate scattering correction (MSC), standard normal transformation (SNV), second derivative (SD), reciprocal logarithm (LR) and continuum removal (CR) to preprocess the data, the spectral characteristics and their correlation with water content were analyzed. In order to further improve the prediction ability of the model, the competitive adaptive reweighting method (CARS) was used to optimize the characteristic band, and a prediction model was established by combining random forest regression (RFR), least squares support vector regression (LSSVR) and particle swarm optimization least squares support vector regression (PSO-LSSVR). The prediction effects of different magnetite water content models were compared, and finally the best model was selected to improve the accuracy of water content detection in mineral processing and smelting. The results show that: (1) when the water content of Anshan-type magnetite samples with different particle sizes changes, the change trend of their spectral curves is generally consistent, and the reflectivity is negatively correlated with the water content, it shows obvious absorption characteristics around 990nm, 1440nm and 1920nm; the Pearson correlation coefficient (r) of spectral data pretreated by MSC and SNV can reach −0.950(412nm) and −0.964 (421nm), respectively. (2) Among the three models, the PSO-LSSVR model is the most stable, and the SNV-CARS-LSSVR model with granularity of 0.3−0.5mm and the MSC-CARS-PSO-LSSVR model with granularity of 0.5−2mm are preferred. The prediction set determination coefficients (R²) of the models are 0.778 and 0.789, and the root mean square error (RMSE) were 5.45% and 5.41%, respectively. Compared with previous studies, a more stable water content prediction model of Anshan magnetite was constructed by combining data preprocessing, CARS feature screening and nonlinear regression algorithm, which provides higher precision support for water content detection in mining production.
- hyperspectrum,
- magnetite,
- water content,
- regression model,
- precision of prediction

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References (43)

References

[1]	鲁银鹏, 孟郁苗, 黄小文, 等. 宁芜盆地玢岩型铁矿尾矿元素与矿物组成特征[J]. 岩矿测试, 2024, 43(2): 259−269. doi: 10.15898/j.ykcs.202210120194 Lu Y P, Meng Y M, Huang X W, et al. Element and mineral characteristics of tailings in the porphyry-type iron deposit from Ningwu Basin[J]. Rock and Mineral Analysis, 2024, 43(2): 259−269. doi: 10.15898/j.ykcs.202210120194
[2]	王伟, 李勇, 樊金虎, 等. 辽河群富铁表壳岩系磁铁矿微量元素组成对古元古代铁矿成因的制约——以周家地区为例[J]. 岩石学报, 2024, 40(10): 3103−3113. doi: 10.18654/1000-0569/2024.10.09 Wang W, Li Y, Fan J H, et al. Trace element geochemistry of the magnetite from the iron-rich supracrustal rocks of the Liaohe Group: Constraints on the genesis of oaleoproterozoic iron ores, a case study from the Zhoujia area[J]. Acta Petrologica Sinica, 2024, 40(10): 3103−3113. doi: 10.18654/1000-0569/2024.10.09
[3]	刘善军, 王东, 毛亚纯, 等. 智能矿山中的岩矿光谱智能感知技术与研究进展[J]. 金属矿山, 2021(7): 1−15. doi: 10.19614/j.cnki.jsks.202107001 Liu S J, Wang D, Mao Y C, et al. Intelligent spectrum sensing technology and research progress of rock and ore in intelligent mine[J]. Metal Mine, 2021(7): 1−15. doi: 10.19614/j.cnki.jsks.202107001
[4]	黄华雨, 丁启东, 张俊华, 等. 基于地面高光谱的宁夏银北地区农田不同土层盐碱化信息反演[J/OL]. 应用生态学报(2024-09-30). https://doi.org/10.13287/j.1001-9332.202411.017. Huang H Y, Ding Q D, Zhang J H, et al. Ground-based hyperspectral inversion of salinization and alkalinization information of different soil layers in farmland in Yinbei area, Ningxia, China[J/OL]. Chinese Journal of Applied Ecology (2024-09-30). https://doi.org/10.13287/j.1001-9332.202411.017.
[5]	王延仓, 朱玉晨, 齐焱鑫, 等. 离散小波去噪后冬小麦叶片含水量高光谱估算[J]. 光谱学与光谱分析, 2024, 44(9): 2559−2567. doi: 10.3964/j.issn.1000-0593(2024)09-2559-09 Wang Y C, Zhu Y C, Qi Y X, et al. Study on quantitative inversion of leaf water content of winter wheat based on discrete wavelet technique[J]. Spectroscopy and Spectral Analysis, 2024, 44(9): 2559−2567. doi: 10.3964/j.issn.1000-0593(2024)09-2559-09
[6]	LaCour R A, Heindel J P, Head-Gordon T. Predicting the Raman spectra of liquid water with a monomer-field model[J]. The Journal of Physical Chemistry Letters, 2023, 14(51): 11742−11749. doi: 10.1021/acs.jpclett.3c02873
[7]	蒋航, 郭娜, 张柯凡, 等. 花岗伟晶岩型稀有金属矿床蚀变系统与矿物光谱-地球化学特征耦合性研究——以川西打枪沟矿区为例[J]. 岩石学报, 2024, 40(1): 197−214. doi: 10.18654/1000-0569/2024.01.11 Jiang H, Guo N, Zhang K F, et al. Coupled study of alteration system and spectral-geochemical characteristics of granite-pegmatitic type rare metal deposits, associated with Daqianggou mining area in Western Sichuan Province[J]. Acta Petrologica Sinica, 2024, 40(1): 197−214. doi: 10.18654/1000-0569/2024.01.11
[8]	虞茉莉, 刘善军, 宋亮, 等. 不同含水量尾砂的光谱特征与遥感模型[J]. 光谱学与光谱分析, 2019, 39(10): 3096−3101. doi: 10.3964/j.issn.1000-0593(2019)10-3096-06 Yu M L, Liu S J, Song L, et al. Spectral characteristics and remote sensing model of tailings with different water contents[J]. Spectroscopy and Spectral Analysis, 2019, 39(10): 3096−3101. doi: 10.3964/j.issn.1000-0593(2019)10-3096-06
[9]	王东升, 王海龙, 张芳, 等. 砂岩的近红外光谱特征及其含水量反演[J]. 光谱学与光谱分析, 2022, 42(11): 3368−3372. doi: 10.3964//j.issn.1000-0593(2022)11-3368-05 Wang D S, Wang H L, Zhang F, et al. Near-infrared spectral characteristics of sandstone and inversion of water content[J]. Spectroscopy and Spectral Analysis, 2022, 42(11): 3368−3372. doi: 10.3964//j.issn.1000-0593(2022)11-3368-05
[10]	唐振豪, 张智勇, 沙鑫, 等. 近红外水光谱组学技术及其应用近十年研究进展[J]. 分析测试学报, 2024, 43(7): 1086−1096. doi: 10.12452/j.fxcsxb.24032806 Tang Z H, Zhang Z Y, Sha X, et al. Research progress in near infrared aquaphotomics technology and its applications in the past decade[J]. Journal of Instrumental Analysis, 2024, 43(7): 1086−1096. doi: 10.12452/j.fxcsxb.24032806
[11]	陈定芳, 吴月峰, 桂卉, 等. 水光谱组学研究现状及对中药归经理论的特殊影响[J]. 湖南中医药大学学报, 2021, 41(12): 1986−1992. doi: 10.3969/j.issn1674-070X.2021.12.030 Chen D F, Wu Y F, Gui H, et al. Current status of hydrospectromics research and its special impact on meridian tropism theory for the Chinese materia medica[J]. Journal of Hunan University of Chinese Medicine, 2021, 41(12): 1986−1992. doi: 10.3969/j.issn1674-070X.2021.12.030
[12]	赵汇珍, 陈勇, 涂聪, 等. 柳江盆地髫髻山组凝灰岩地球化学与熔体包裹体水含量特征[J/OL]. 岩矿测试(2024-09-28). https://doi.org/10.15898/j.ykcs.202404030074. Zhao H Z, Chen Y, Tu C, et al. Geochemical characteristics and water content of melt inclusions in the tuff of the Tiaojishan Formation, Liujiang Basin[J/OL]. Rock and Mineral Analysis (2024-09-28). https://doi.org/10.15898/j.ykcs.202404030074.
[13]	孟俊贞, 杨小权, 李志萍. 基于遥感技术的地下水埋深和储变量监测评估研究进展[J/OL]. 华北水利水电大学学报(自然科学版) (2024-09-30). http://kns.cnki.net/kcms/detail/41.1432.TV.20240929.1327.002.html Meng J Z, Yang X Q, Li Z P. Overview of the research progress of groundwater depth and storage volume assessment based on remote sensing[J/OL]. Journal of North China University of Water Resources and Electric Power (Natural Science Edition) (2024-09-30). http://kns.cnki.net/kcms/detail/41.1432.TV.20240929.1327.002.html
[14]	高源, 孙兰香, 李翔宇, 等. 基于LIBS在线分析烧结矿混合料成分及校正水分影响[J]. 中国激光, 2023, 50(19): 198−206. doi: 10.3788/CJL221270 Gao Y, Sun L X, Li X Y, et al. On-line analysis of sinter mixture composition and correction of moisture influence based on laser-induced breakdown spectroscopy[J]. Chinese Journal of Lasers, 2023, 50(19): 198−206. doi: 10.3788/CJL221270
[15]	Maurais J, Orban F, Dauphinais E, et al. Monitoring moisture content and evaporation kinetics from mine slurries through albedo measurements to help predict and prevent dust emissions[J]. Royal Society Open Science, 2021, 8(7): 210414. doi: 10.1098/rsos.210414
[16]	梁业恒, 邓孺孺, 梁钰婕, 等. 重金属污染水体背景下的底质反射率光谱特征及其对离水反射率贡献影响分析[J]. 光谱学与光谱分析, 2024, 44(1): 111−117. doi: 10.3964/j.issn.1000-0593(2024)01-0111-07 Liang Y H, Deng R R, Liang Y J, et al. Spectral characteristics of sediment reflectance under the background of heavy metal polluted water and analysis of its contribution to water-leaving reflectance[J]. Spectroscopy and Spectral Analysis, 2024, 44(1): 111−117. doi: 10.3964/j.issn.1000-0593(2024)01-0111-07
[17]	梁业恒, 邓孺孺, 黄靖岚, 等. 典型重金属污染水体光谱特征分析——以广东省大宝山尾矿水为例[J]. 光谱学与光谱分析, 2019, 39(10): 3237−3244. doi: 10.3964/j.issn.1000-0593(2019)10-3237-08 Liang Y H, Deng R R, Huang J L, et al. The spectral characteristic analysis of typical heavy metal polluted water—A case study of mine drainage in Dabao Mountain, Guangdong Province, China[J]. Spectroscopy and Spectral Analysis, 2019, 39(10): 3237−3244. doi: 10.3964/j.issn.1000-0593(2019)10-3237-08
[18]	曹粤, 包妮沙, 周斌, 等. 基于实测光谱和国产高分五号高光谱卫星的铁尾矿表层含水率遥感反演方法研究[J]. 光谱学与光谱分析, 2023, 43(4): 1225−1233. doi: 10.3964/j.issn.1000-0593(2023)04-1225-09 Cao Y, Bao N S, Zhou B, et al. Research on remote sensing inversion method of surface moisture content of iron tailings based on measured spectra and domestic Gaofen-5 hyperspectral high-resolution satellites[J]. Spectroscopy and Spectral Analysis, 2023, 43(4): 1225−1233. doi: 10.3964/j.issn.1000-0593(2023)04-1225-09
[19]	刘海琪, 刘善军, 丁瑞波. 颗粒度对高品位赤铁矿可见光-近红外光谱的影响研究[J]. 金属矿山, 2022(4): 158−162. doi: 10.19614/j.cnki.jsks.202204022 Liu H Q, Liu S J, Ding R B. Effect of particle size on visible-near infrared spectral of high grade hematite[J]. Metal Mine, 2022(4): 158−162. doi: 10.19614/j.cnki.jsks.202204022
[20]	李想, 张永彬, 刘明月, 等. 滨海湿地土壤质地高光谱估测模型对比分析[J]. 光谱学与光谱分析, 2024, 44(9): 2568−2576. doi: 10.3964/j.issn.1000-0593(2024)09-2568-09 Li X, Zhang Y B, Liu M Y, et al. Comparative analysis of hyperspectral estimation models for soil texture in coastal wetlands[J]. Spectroscopy and Spectral Analysis, 2024, 44(9): 2568−2576. doi: 10.3964/j.issn.1000-0593(2024)09-2568-09
[21]	杨新艳, 李东东, 叶文清, 等. 基于激光诱导击穿光谱的标准加入法研究进展[J/OL]. 激光技术(2024-09-30). http://kns.cnki.net/kcms/detail/51.1125.tn.20240830.0959.002.html. Yang X Y, Li D D, Ye W Q, et al. Research progress of standard addition method based on laser-induced breakdown spectroscopy[J/OL]. Laser Technology (2024-09-30). http://kns.cnki.net/kcms/detail/51.1125.tn.20240830.0959.002.html.
[22]	Engel J, Gerretzen J, Szymańska E, et al. Breaking with trends in pre-processing[J]. TrAC Trends in Analytical Chemistry, 2013, 50: 96−106. doi: 10.1016/j.trac.2013.04.015
[23]	Upadhyay R, Gupta A, Mishra H N, et al. At-line quality assurance of deep-fried instant noodles using pilot scale visible-NIR spectroscopy combined with deep-learning algorithms[J]. Food Control, 2022, 133: 108580. doi: 10.1016/j.foodcont.2021.108580
[24]	陈蓓, 郑恩让, 郭拓. 多种光谱变量筛选算法在红参提取近红外建模中的应用[J]. 光谱学与光谱分析, 2021, 41(8): 2443−2449. doi: 10.3964/j.issn.1000-0593(2021)08-2443-07 Chen B, Zheng E R, Guo T. Application of various algorithms for spectral variable selection in NIRS modeling of Red Ginseng extraction[J]. Spectroscopy and Spectral Analysis, 2021, 41(8): 2443−2449. doi: 10.3964/j.issn.1000-0593(2021)08-2443-07
[25]	Li H D, Liang Y Z, Xu Q S, et al. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration[J]. Analytica Chimica Acta, 2009, 648(1): 77−84. doi: 10.1016/j.aca.2009.06.046
[26]	Chapman J, Tomasello B, Carr S. Bifurcation in correlation length of the Ising model on a Toblerone lattice[J]. Journal of Statistical Mechanics: Theory and Experiment, 2024(9): 93214−93214. doi: 10.1088/1742-5468/ad784f
[27]	庞江, 张烨毓, 黄毅, 等. 铁含量对白云石拉曼光谱特征的影响[J]. 岩矿测试, 2023, 42(4): 852−862. doi: 10.15898/j.ykcs.202211030210 Pang J, Zhang Y Y, Huang Y, et al. Effect of Fe content on Raman spectral characteristics of dolomite[J]. Rock and Mineral Analysis, 2023, 42(4): 852−862. doi: 10.15898/j.ykcs.202211030210
[28]	Yang D, Hu J. A detection method of oil content for maize kernels based on CARS feature selection and deep sparse autoencoder feature extraction[J]. Industrial Crops and Products, 2024, 222: 119464. doi: 10.1016/j.indcrop.2024.119464
[29]	Bocklitz T W, Salah F S, Vogler N, et al. Pseudo-HE images derived from CARS/TPEF/SHG multimodal imaging in combination with Raman-spectroscopy as a pathological screening tool[J]. BMC Cancer, 2016, 16: 1−11. doi: 10.1186/s12885-016-2520-x
[30]	黄华丹, 邓健豪, 曹庸, 等. 基于近红外光谱技术快速检测广式酱油发酵过程中主要理化指标的含量[J/OL]. 现代食品科技(2024-09-30). https://doi.org/10.13982/j.mfst.1673-9078.2024.12.0716. Huang H D, Deng J H, Cao Y, et al. Rapid determination of main physicochemical indexes in the fermentation process of Cantonese soy sauce based on near infrared spectroscopy[J/OL]. Modern Food Science and Technology (2024-09-30). https://doi.org/10.13982/j.mfst.1673-9078.2024.12.0716.
[31]	冼丽铧, 朱薪蓉, 卢德浩, 等. 联合运用多光谱和激光雷达技术构建的林分生物量估算模型[J]. 东北林业大学学报, 2024, 52(8): 85−94. doi: 10.13759/j.cnki.dlxb.2024.08.010 Xian L H, Zhu X R, Lu D H, et al. Estimation models of forest stand biomass using combined multi-spectral and LiDAR technologies[J]. Journal of Northeast Forestry University, 2024, 52(8): 85−94. doi: 10.13759/j.cnki.dlxb.2024.08.010
[32]	郭军, 曲亮, 邵丹, 等. 基于机器学习的地方鸡产蛋曲线拟合探索[J]. 中国畜牧兽医, 2024, 51(8): 3428−3437. doi: 10.16431/j.cnki.1671-7236.2024.08.021 Guo J, Qu L, Shao D, et al. Exploration of egg production curve fitting of local chickens based on machine learning[J]. China Animal Husbandry & Veterinary Medicine, 2024, 51(8): 3428−3437. doi: 10.16431/j.cnki.1671-7236.2024.08.021
[33]	李璇, 甘淑, 袁希平, 等. 洱海东岸海滨三种典型湿地植被光谱特征分析与识别建模[J]. 光谱学与光谱分析, 2024, 44(9): 2439−2444. doi: 103964/j.issn.1000-0593(2024)09-2439-06 Li X, Gan S, Yuan X P, et al. Spectral characteristic and identification modelling of three typical wetland vegetation along the seashore of the of the east coast of the Erhai Lake[J]. Spectroscopy and Spectral Analysis, 2024, 44(9): 2439−2444. doi: 103964/j.issn.1000-0593(2024)09-2439-06
[34]	王巧玲, 李双成. 云南省碳排放时空演变特征及影响因素分析[J/OL]. 中国环境科学(2024-09-30). https://doi.org/10.19674/j.cnki.issn1000-6923.20240928.001. Wang Q L, Li S C. Dynamics of carbon emissions in Yunnan Province: Spatiotemporal characteristics and influencing factors[J/OL]. China Environmental Science (2024-09-30). https://doi.org/10.19674/j.cnki.issn1000-6923.20240928.001.
[35]	王蕾, 李斌, 吴飞, 等. 基于改进极限学习机的电力市场实时电价预测方法[J]. 电子设计工程, 2024, 32(20): 21−25, 30. doi: 10.14022/j.issn1674-6236.2024.20.005 Wang L, Li B, Wu F, et al. Real time electricity price prediction method in the electricity market based on improved extreme learning machine[J]. Electronic Design Engineering, 2024, 32(20): 21−25, 30. doi: 10.14022/j.issn1674-6236.2024.20.005
[36]	崔峰, 何仕凤, 来兴平, 等. 基于LSSVR与灰色理论的急倾斜巨厚煤层群开采冒落高度与时滞特征研究[J]. 岩石力学与工程学报, 2024, 43(4): 822−837. doi: 10.13722/j.cnki.jrme.2023.0586 Cui F, He S F, Lai X P, et al. Study on collapse height and time delayed characteristics in the mining of steeply inclined extra-thick coal seam group based on LSSVR and grey theory[J]. Chinese Journal of Rock Mechanics and Engineering, 2024, 43(4): 822−837. doi: 10.13722/j.cnki.jrme.2023.0586
[37]	王小辉, 李圣普. 基于GBO和LSSVR的丝网印刷产品墨量转移预测模型研究[J]. 数字印刷, 2022(6): 79−84. doi: 10.19370/j.cnki.cn10-1304/ts.2022.06.010 Wang X H, Li S P. Prediction model of ink transfer rate for screen prints based on garden balsam optimization and least squares support vector regression[J]. Printing and Digital Media Technology Study, 2022(6): 79−84. doi: 10.19370/j.cnki.cn10-1304/ts.2022.06.010
[38]	蔡鸣, 朱光, 李论, 等. 基于PSO-LSSVR的机器人磨抛材料去除模型[J]. 组合机床与自动化加工技术, 2024(1): 174−177, 182. doi: 10.13462/j.cnki.mmtamt.2024.01.038 Cai M, Zhu G, Li L, et al. Robot polishing material removal model based on PSO-LSSVR[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2024(1): 174−177, 182. doi: 10.13462/j.cnki.mmtamt.2024.01.038
[39]	Chen J, Hu Y, Zhu Q, et al. A novel battery health indicator and PSO-LSSVR for LiFePO₄ battery SOH estimation during constant current charging[J]. Energy, 2023, 282: 128782. doi: 10.1016/j.energy.2023.128782
[40]	李志尚, 赵龙, 宗洪祥, 等. 机器学习型分子力场在金属材料相变与变形领域的研究进展[J]. 金属学报, 2024, 60(10): 1388−1404. doi: 10.11900/0412.1961.2024.00139 Li Z S, Zhao L, Zong H X, et al. Machine-learning force fields for metallic materials: Phase transformations and deformations[J]. Acta Metallurgica Sinica, 2024, 60(10): 1388−1404. doi: 10.11900/0412.1961.2024.00139
[41]	常爱莲, 黄乐, 张郡铄, 等. 页岩气储层中甲烷运移的变分数阶导数模型研究[J]. 力学季刊, 2024, 45(3): 825−833. doi: 10.15959/j.cnki.0254-0053.2024.03.020 Chang A L, Huang L, Zhang J S, et al. Study of variable-order fractional derivative model for CH₄ transport in shale gas reservoirs[J]. Chinese Quarterly of Mechanics, 2024, 45(3): 825−833. doi: 10.15959/j.cnki.0254-0053.2024.03.020
[42]	李孟倩, 李鸣铎, 汪金花, 等. 铁矿粉铁品位高光谱精确估测方法研究[J]. 金属矿山, 2023(3): 206−213. doi: 10.19614/j.cnki.jsks.202303028 Li M Q, Li M D, Wang J H, et al. Study on hyperspectral accurate estimation method of iron grade for iron ore powder[J]. Metal Mine, 2023(3): 206−213. doi: 10.19614/j.cnki.jsks.202303028
[43]	毛亚纯, 文杰, 曹旺, 等. 基于鞍山式铁矿成像光谱的融合算法研究[J]. 光谱学与光谱分析, 2024, 44(9): 2620−2625. doi: 10.3964/j.issn.1000-0593(2024)09-2620-06 Mao Y C, Wen J, Cao W, et al. Fusion algorithm research based on imaging spectrum of Anshan iron ore[J]. Spectroscopy and Spectral Analysis, 2024, 44(9): 2620−2625. doi: 10.3964/j.issn.1000-0593(2024)09-2620-06