Determination of minor metal elements in steel using laser-induced breakdown spectroscopy combined with machine learning algorithms

Abstract The properties of a steel are crucially influenced by the contained minor elements, including metals, such as Mn, Cr and Ni. The determination of their concentrations using laser-induced breakdown spectroscopy (LIBS) represents a great help in many application scenarios, especially with in situ and online measurement requirements. Such determination can be significantly perturbed by spectral interferences with Fe I and Fe II lines which is particularly dense in the VIS and near UV ranges. Univariate regression can sometimes, lead to calibration models with modest analytical performances. In this work, multivariate calibration models are developed using a machine learning approach. We first show the regression results with univariate models. The development of multivariate models is then briefly presented, in successive steps of data pretreatment, feature selection with SelectKBest algorithm and regression model training with back-propagation neural network (BPNN). The analytical performances obtained with the developed multivariate models are compared with those obtained with the univariate models. We demonstrate in such way, the efficiency of the machine learning approach in the development of multivariate models for calibration and prediction with LIBS spectra acquired from steel samples. In particular, the prediction trueness (relative error of prediction) and precision (relative standard deviation) for the determination of the above mentioned metal elements in steel reach the respective values of 1.13%, 2.85%, 7.20% (for Mn, Cr, Ni) and 6.68%, 3.96%, 6.52% (for Mn, Cr, Ni) with the used experimental condition and measurement protocol.