Quantitative analysis of steel and iron by laser-induced breakdown spectroscopy using GA-KELM

According to the multiple researches in the last couple of years, laser-induced breakdown spectroscopy (LIBS) has shown a great potential for rapid analysis in steel industry. Nevertheless, the accuracy and precision may be limited by complex matrix effect and self-absorption effect of LIBS seriously. A novel multivariate calibration method based on genetic algorithm-kernel extreme learning machine (GA-KELM) is proposed for quantitative analysis of multiple elements (Si, Mn, Cr, Ni, V, Ti, Cu, Mo) in forty-seven certified steel and iron samples. First, the standardized peak intensities of selected spectra lines are used as the input of model. Then, the genetic algorithm is adopted to optimize the model parameters due to its obvious capability in finding the global optimum solution. Based on these two steps above, the kernel method is introduced to create kernel matrix which is used to replace the hidden layer's output matrix. Finally, the least square is applied to calculate the model's output weight. In order to verify the predictive capability of the GA-KELM model, the R-square factor (R2), Root-mean-square Errors of Calibration (RMSEC), Root-mean-square Errors of Prediction (RMSEP) of GA-KELM model are compared with the traditional PLS algorithm, respectively. The results confirm that GA-KELM can reduce the interference from matrix effect and self-absorption effect and is suitable for multi-elements calibration of LIBS.