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Laser induced breakdown spectroscopy technique is employed for quantitativeanalysis of aluminum samples by different classical machine learningapproaches. A Q-switch Nd:YAG laser at fundamental harmonic of 1064 nm isutilized for creation of LIBS plasma for prediction of constituentconcentrations of the aluminum standard alloys. In current research,concentration prediction is performed by linear approaches of support vectorregression, multiple linear regression, principal component analysis integratedwith MLR and SVR, and as well as nonlinear algorithms of artificial neuralnetwork, kernelized support vector regression , and the integration oftraditional principal component analysis with KSVR, and ANN. Furthermore,dimension reduction is applied on various methodologies by PCA algorithm forimproving the quantitative analysis. The results presented that the combinationof PCA with KSVR algorithm model had the best efficiency in predictions of themost of elements among other classical machine learning algorithms.

Using various machine learning algorithms for quantitative analysis in Laser induced breakdown spectroscopy