Quantitative Analysis Of Aluminum Alloys By Using Laser-induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy(LIBS)is an analytical method with optical emission spectroscopy that uses a laser pulse to vaporize,atomize,and excite a hot plasma as the spectroscopic emission source.Although LIBS has demonstrated its versatility and attractive features in many fields,the quantitative analysis ability of LIBS is considered as its Achilles’ heel.From a fundamental point of view,this can be due to the complex na-ture of laser-induced plasma as the spectroscopic emission source for LIBS application.The temporal and spatial characterization of laser-induced plasma is considered as one of the key points for the LIBS technique.On the other hand,from the analytical point of view,LIBS is usually characterized by direct laser ablation.This can be however quite limiting,especially for some types of materials such as powders or liquids.Proper sample preparation or treat-ment allowing the deposition of a thin homogeneous film on a metallic surface could greatly improve the analytical performance of LIBS for these types of materials.This thesis presents three basic contributions to LIBS analysis.The first contribution is analyzing quantitatively for the LIBS standard aluminum alloy samples in different dis-tances and different delay times by using two methods which are the calibration curve(CC)method and the partial least squares(PLS)method.The numerical results for the quan-titative analysis of the major and the trace elements of aluminum alloy samples show the(PLS)method outperforms the(CC)method in terms of the regression coefficient.More-over,(PLS)method used in laser-induced breakdown spectroscopy was ideal for simultane-ous quantitative analysis of various aluminum alloy elements.The second contribution is a quantitative analysis of the Chromium(Cr)element in liquid by using three methods,which are the calibration curve(CC)method,the partial least squares(PLS)method,and the neu-ral network method.The numerical results of the experiment show that the three methods present a good outperforms in terms of the regression coefficient,where the regression co-efficient values of the linear fit are more than 0.97.Furthermore,we observed that the(PLS)method shown the best results in the regression coefficient compared to other methods.The third contribution is laser-induced breakdown spectroscopy(LIBS)combined with an arti-ficial neural network(ANN)that were investigated to classify three leaves of plant samples(Ligustrum lucidum Ait,Viburnum odoratissinum,and Bamboo).The relationship between the correct classification rate(CCR)and the settings of ANN was discussed.The CCR of the ANN model for test set data achieved 99.99%with a multilayer perceptron network.