Signal processing for real-time identification of similar metals by laser-induced breakdown spectroscopy
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摘要
Laser-induced breakdown spectroscopy(LIBS) is regarded as a promising technique for realtime sorting of scrap metals due to its capability of fast multi-elemental and in-air analysis. This work reports a method for signal processing which ensures high accuracy and high speed during similar metal sorting by LIBS. Similar metals such as aluminum alloys or stainless steel are characterized by nearly the same constituent elements with slight variations in elemental concentration depending on metal type. In the proposed method, the original data matrix is substantially reduced for fast processing by selecting new input variables(spectral lines) using the information for the constituent elements of similar metals. Specifically, principal component analysis(PCA) of full-spectra LIBS data was performed and then, based on the loading plots, the input variables of greater significance were selected in the order of higher weights for each constituent element. The results for the classification test with aluminum alloy, copper alloy,stainless steel and cast steel showed that the classification accuracy of the proposed method was nearly the same as that of full-spectra PCA, but the computation time was reduced by a factor of 20 or more. The results demonstrated that incorporating the information for constituent elements can significantly accelerate classification speed without loss of accuracy.
Laser-induced breakdown spectroscopy(LIBS) is regarded as a promising technique for realtime sorting of scrap metals due to its capability of fast multi-elemental and in-air analysis. This work reports a method for signal processing which ensures high accuracy and high speed during similar metal sorting by LIBS. Similar metals such as aluminum alloys or stainless steel are characterized by nearly the same constituent elements with slight variations in elemental concentration depending on metal type. In the proposed method, the original data matrix is substantially reduced for fast processing by selecting new input variables(spectral lines) using the information for the constituent elements of similar metals. Specifically, principal component analysis(PCA) of full-spectra LIBS data was performed and then, based on the loading plots, the input variables of greater significance were selected in the order of higher weights for each constituent element. The results for the classification test with aluminum alloy, copper alloy,stainless steel and cast steel showed that the classification accuracy of the proposed method was nearly the same as that of full-spectra PCA, but the computation time was reduced by a factor of 20 or more. The results demonstrated that incorporating the information for constituent elements can significantly accelerate classification speed without loss of accuracy.
Laser-induced breakdown spectroscopy(LIBS) is regarded as a promising technique for realtime sorting of scrap metals due to its capability of fast multi-elemental and in-air analysis. This work reports a method for signal processing which ensures high accuracy and high speed during similar metal sorting by LIBS. Similar metals such as aluminum alloys or stainless steel are characterized by nearly the same constituent elements with slight variations in elemental concentration depending on metal type. In the proposed method, the original data matrix is substantially reduced for fast processing by selecting new input variables(spectral lines) using the information for the constituent elements of similar metals. Specifically, principal component analysis(PCA) of full-spectra LIBS data was performed and then, based on the loading plots, the input variables of greater significance were selected in the order of higher weights for each constituent element. The results for the classification test with aluminum alloy, copper alloy,stainless steel and cast steel showed that the classification accuracy of the proposed method was nearly the same as that of full-spectra PCA, but the computation time was reduced by a factor of 20 or more. The results demonstrated that incorporating the information for constituent elements can significantly accelerate classification speed without loss of accuracy.
引文
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[8]Mehari F et al 2016 Plasma Sci.Technol.18 654
[9]Kashiwakura S and Wagatsuma K 2015 ISIJ Int.55 2391
[10]Noll R et al 2014 Spectrochim.Acta B 93 41
[11]Noll R et al 2018 J.Anal.At.Spectrom.33 945
[12]Vors E,Tchepidjian K and Sirven J B 2016 Spectrochim.Acta B 117 16
[13]Campanella B et al 2017 Spectrochim.Acta B 134 52
[14]Zhang C et al 2018 Sensors 18 95
[15]Schr?der S et al 2013 Icarus 223 61
[16]Myakalwar K A et al 2015 Sci.Rep.5 13169
[17]Tan M M et al 2012 Appl.Spectrosc.66 262
[18]Yan C H et al 2016 Anal.Methods 8 6216
[19]Chen X et al 2018 Biomed.Opt.Express 9 1057
[20]Li W T et al 2018 J.Anal.At.Spectrom.33 461
[21]Moon Y et al 2016 Biomed.Opt.Express 7 1626
[22]Standard Reference Materials in NIST(https://doi.org/10.6028/NIST.SP.260-176-2018)
[23]Brammer Standard Company,Inc.,Houston,USA(http://brammerstandard.com)
[24]Varmuza K and Filzmoser P 2009 Introduction to Multivariate Statistical Analysis in Chemometrics(Boca Raton,FL:CRCPress)
[2]Martchek K J 2000 The importance of recycling to the environmental profile of metal products ed D L Stewart Jr,J C Daley and R L Stephens Recycling of Metals and Engineered Materials(Pittsburgh:The Minerals,Metals and Materials Society)
[3]Gurell J et al 2012 Spectrochim.Acta B 74-75 46
[4]Werheit P et al 2011 J.Anal.At.Spectrom.26 2166
[5]Aberkane S M et al 2017 Anal.Methods 9 3696
[6]Liang L et al 2014 Appl.Opt.53 544
[7]Cremers D A and Radziemski L J 2006 Handbook of LaserInduced Breakdown Spectroscopy(New York:Wiley)
[8]Mehari F et al 2016 Plasma Sci.Technol.18 654
[9]Kashiwakura S and Wagatsuma K 2015 ISIJ Int.55 2391
[10]Noll R et al 2014 Spectrochim.Acta B 93 41
[11]Noll R et al 2018 J.Anal.At.Spectrom.33 945
[12]Vors E,Tchepidjian K and Sirven J B 2016 Spectrochim.Acta B 117 16
[13]Campanella B et al 2017 Spectrochim.Acta B 134 52
[14]Zhang C et al 2018 Sensors 18 95
[15]Schr?der S et al 2013 Icarus 223 61
[16]Myakalwar K A et al 2015 Sci.Rep.5 13169
[17]Tan M M et al 2012 Appl.Spectrosc.66 262
[18]Yan C H et al 2016 Anal.Methods 8 6216
[19]Chen X et al 2018 Biomed.Opt.Express 9 1057
[20]Li W T et al 2018 J.Anal.At.Spectrom.33 461
[21]Moon Y et al 2016 Biomed.Opt.Express 7 1626
[22]Standard Reference Materials in NIST(https://doi.org/10.6028/NIST.SP.260-176-2018)
[23]Brammer Standard Company,Inc.,Houston,USA(http://brammerstandard.com)
[24]Varmuza K and Filzmoser P 2009 Introduction to Multivariate Statistical Analysis in Chemometrics(Boca Raton,FL:CRCPress)