[1] [1] YAN C,ZHANG T,SUN Y,et al.A hybrid variable selection method based on wavelet transform and mean impact value for calorific value determination of coal using laser-induced breakdown spectroscopy and kernel extreme learning machine[J].Spectrochimica Acta Part B Atomic Spectroscopy,2019,154:75-81.

[2] [2] CAMPANELLA B,GRIFONI E,LEGNAIOLI S,et al.Classification of wrought aluminum alloys by ANN evaluation of LIBS spectra from aluminum scrap samples[J].Spectrochimica Acta Part B Atomic Spectroscopy,2017,134:52-57.

[3] [3] BOUCHER T F,OZANNE M V,CARMOSINO M L,et al.A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy[J].Spectrochimica Acta Part B Atomic Spectroscopy,2015,107:1-10.

[4] [4] QI J,ZHANG T,TANG H,et al.Rapid classification of archaeological ceramics via laser-induced breakdown spectroscopy coupled with random forest[J].Spectrochimica Acta Part B Atomic Spectroscopy,2018,149:288-293.

[6] [6] KUMAR M A, SPEGAZZINI N, ZHANG C, et al.Less is more:avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection[J].Scientific Reports,2015,5:1-10.

[7] [7] MEHMOOD T,LILAND K H,SNIPEN L,et al.A review of variable selection methods in partial least squares regression[J].Chemometrics & Intelligent Laboratory Systems,2012,118:62-69.

[8] [8] GUEZENOC J,BASSEL L,GALLET-BUDYNEK A,et al.Variables selection: a critical issue for quantitative laser-induced breakdown spectroscopy[J].Spectrochimica Acta Part B Atomic Spectroscopy,2017,134:6-10.

[9] [9] REMUS J J,GOTTFRIED J L,HARMON R S,et al.Archaeological applications of laser-induced breakdown spectroscopy:an example from the Coso Volcanic Field,California,using advanced statistical signal processing analysis[J].Applied Optics,2010,49(13):120-131.

[10] [10] YAN C H,LIANG J,ZHAO M J,et al.A novel hybrid feature selection strategy in quantitative analysis of laser-induced breakdown spectroscopy[J].Analytica Chimica Acta,2019,1080:35-42.

[12] [12] ALAA T.Principal component analysis-a tutorial[J].Int.J.of Applied Pattern Recognition,2016,3(3):197-240.