Accordingtothemultipleresearchesinthelastcoupleofyears,laser-inducedbreakdownspectroscopy(LIBS)hasshownagreatpotentialforrapidanalysisinsteelindustry.Nevertheless,theaccuracyandprecisionmaybelimitedbycomplexmatrixeffectandself-absorptioneffectofLIBSseriously.Anovelmultivariatecalibrationmethodbasedongeneticalgorithm-kernelextremelearningmachine(GA-KELM)isproposedforquantitativeanalysisofmultipleelements(Si,Mn,Cr,Ni,V,Ti,Cu,Mo)inforty-sevencertifiedsteelandironsamples.First,thestandardizedpeakintensitiesofselectedspectralinesareusedastheinputofmodel.Then,thegeneticalgorithmisadoptedtooptimizethemodelparametersduetoitsobviouscapabilityinfindingtheglobaloptimumsolution.Basedonthesetwostepsabove,thekernelmethodisintroducedtocreatekernelmatrixwhichisusedtoreplacethehiddenlayer’soutputmatrix.Finally,theleastsquareisappliedtocalculatethemodel’soutputweight.InordertoverifythepredictivecapabilityoftheGA-KELMmodel,theR-squarefactor(R2),Root-mean-squareErrorsofCalibration(RMSEC),Root-mean-squareErrorsofPrediction(RMSEP)ofGA-KELMmodelarecomparedwiththetraditionalPLSalgorithm,respectively.TheresultsconfirmthatGA-KELMcanreducetheinterferencefrommatrixeffectandself-absorptioneffectandissuitableformulti-elementscalibrationofLIBS.
