[1] Khajehzadeh N, Haavisto O, Koresaar L. On-stream and quantitative mineral identification of tailing slurries using LIBS technique[J]. Minerals Engineering, 98, 101-109(2016).
[2] Guo L B, Zhang D, Sun L X et al. Development in the application of laser-induced breakdown spectroscopy in recent years: A review[J]. Frontiers of Physics, 16, 22500(2021).
[3] Harmon R S, Senesi G S. Laser-induced breakdown spectroscopy-A geochemical tool for the 21st century[J]. Applied Geochemistry, 128, 104929(2021).
[4] Jolivet L, Leprince M, Moncayo S et al. Review of the recent advances and applications of LIBS-based imaging[J]. Spectrochimica Acta Part B: Atomic Spectroscopy, 151, 41-53(2019).
[5] Khajehzadeh N, Haavisto O, Koresaar L. On-stream mineral identification of tailing slurries of an iron ore concentrator using data fusion of LIBS, reflectance spectroscopy and XRF measurement techniques[J]. Minerals Engineering, 113, 83-94(2017).
[6] Shang D, Sun L X, Qi L F et al. Quantitative analysis of laser-induced breakdown spectroscopy iron ore slurry based on cyclic variable filtering and nonlinear partial least squares[J]. Chinese Journal of Lasers, 48, 2111001(2021).
[7] Xie Y M, Sun L X, Yuan D C et al. Quantitative analysis of iron slurry based on laser induced breakdown spectroscopy combined with mutual information feature selection partial least squares method[J]. Metallurgical Analysis, 42, 18-24(2022).
[8] Chen T, Sun L X, Yu H B et al. Efficient weakly supervised LIBS feature selection method in quantitative analysis of iron ore slurry[J]. Applied Optics, 61, D22(2022).
[9] Myakalwar A K, Spegazzini N, Zhang C et al. Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection[J]. Scientific Reports, 5, 13169(2015).
[10] Shin S, Moon Y, Lee J et al. Signal processing for real-time identification of similar metals by laser-induced breakdown spectroscopy[J]. Plasma Science and Technology, 21, 034011(2019).
[11] Kong H Y, Sun L X, Hu J T et al. Automatic method for selecting characteristic lines based on genetic algorithm to quantify laser-induced breakdown spectroscopy[J]. Spectroscopy and Spectral Analysis, 36, 1451-1457(2016).
[12] Wang G D, Sun X, Wang W et al. A feature selection method combined with ridge regression and recursive feature elimination in quantitative analysis of laser induced breakdown spectroscopy[J]. Plasma Science and Technology, 22, 074002(2020).
[13] Deng F, Ding Y, Chen Y J et al. Quantitative analysis of the content of nitrogen and sulfur in coal based on laser-induced breakdown spectroscopy: Effects of variable selection[J]. Plasma Science and Technology, 22, 074005(2020).
[14] 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, 648, 77-84(2009).
[15] Roy A, Chakraborty S. Support vector machine in structural reliability analysis: A review[J]. Reliability Engineering & System Safety, 233, 109126(2023).
[16] Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L et al. A comprehensive survey on support vector machine classification: Applications, challenges and trends[J]. Neurocomputing, 408, 189-215(2020).
[17] Pasquini C. Near infrared spectroscopy: A mature analytical technique with new perspectives‐A review[J]. Analytica Chimica Acta, 1026, 8-36(2018).
[18] Araújo M C U, Saldanha T C B, Galvão R K H et al. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis[J]. Chemometrics and Intelligent Laboratory Systems, 57, 65-73(2001).
[19] Niu F P, Li X G, Bai Y G et al. Hyperspectral estimation model of soil organic carbon content based on genetic algorithm fused with continuous projection algorithm[J]. Spectroscopy and Spectral Analysis, 43, 2232-2237(2023).