Multielement simultaneous quantitative analysis of trace elements in stainless steel via full spectrum laser-induced breakdown spectroscopy

Abstract

Laser-Induced Breakdown Spectroscopy (LIBS) instruments are increasingly recognized as valuable tools for detecting trace metal elements due to their simplicity, rapid detection, and ability to perform simultaneous multi-element analysis. Traditional LIBS modeling often relies on empirical or machine learning-based feature band selection to establish quantitative models. In this study, we introduce a novel approach—simultaneous multi-element quantitative analysis based on the entire spectrum, which enhances model establishment efficiency and leverages the advantages of LIBS. By logarithmically processing the spectra and quantifying the cognitive uncertainty of the model, we achieved remarkable predictive performance (R2) for trace elements Mn, Mo, Cr, and Cu (0.9876, 0.9879, 0.9891, and 0.9841, respectively) in stainless steel. Our multi-element model shares features and parameters during the learning process, effectively mitigating the impact of matrix effects and self-absorption. Additionally, we introduce a cognitive error term to quantify the cognitive uncertainty of the model. The results suggest that our approach has significant potential in the quantitative analysis of trace elements, providing a reliable data processing method for efficient and accurate multi-task analysis in LIBS. This methodology holds promising applications in the field of LIBS quantitative analysis.