首页|A random-forest-assisted artificial-neural-network method for analysis of steel using laser-induced breakdown spectroscopy
A random-forest-assisted artificial-neural-network method for analysis of steel using laser-induced breakdown spectroscopy
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NSTL
Elsevier
A random-forest (RF)-assisted artificial-neural-network (ANN) method (RF-ANN method) was developed to improve the analytical performance of laser-induced breakdown spectroscopy (LIBS) in evaluating the composition of low-alloy steel. The RF algorithm was employed to identify the weight values of spectral variables in the quantitative analysis, and the weight values were used as the criteria to select the spectral variables. The selected variables were then used as inputs to train the ANN, which was then used as the analysis model. Compared to the calibration curve method, the RF-ANN method performs well in nonlinear fitting and utilizes an abundance of spectral information; moreover, it can accommodate large amounts of plasma data via filter spectral variables, thus overcoming the problem of the ANN training speed being too slow for practical use due to the redundancy in spectral variables. The results revealed an improvement in the analytical performance of LIBS using the RF-ANN method to predict the concentrations of chromium (Cr) and vanadium (V) in low-alloy steel samples. Specifically, the root-mean-square errors of cross-validation (RMSECV) for Cr and V were reduced from 0.060 and 0.042 wt% using the calibration curve method to 0.021 and 0.007 wt%, respectively, using the RF-ANN method.
NSTL
Elsevier
