On-line Detection and Analysis of Alloy Steel Elements Based on the LIBS Technology and Random Forest Regression

The Laser Induced Breakdown Spectroscopy (LIBS) technology can be used to detect the elements in the alloy steel in real time. Quantitative analysis method of the traditional LIBS technology mainly has the calibration method and calibration free method, but there are two shortcomings: low prediction accuracy and over fitting. Random Forest Regression (RFR) algorithm can be used for classification and regression, can effectively avoid “overfitting” phenomenon. Therefore, in this paper, we combine the random forest regression algorithm with laser induced breakdown spectroscopy applied to the detection of the concentration of alloy steel elements in the metallurgy industry. At the same time, compared with partial least squares method based on the LIBS, the results show that the random forest algorithm combined with the LIBS technology has the higher prediction accuracy, lower root mean square error and better robustness.