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