Progress Toward Machine Learning Methodologies for Laser-Induced Breakdown Spectroscopy With an Emphasis on Soil Analysis

Optical emission spectroscopy of laser-produced plasmas, commonly known as laser-induced breakdown spectroscopy (LIBS), is an emerging analytical tool for rapid soil analysis. However, specific challenges with LIBS exist, such as matrix effects and quantification issues, which require further study in the application of LIBS, particularly for the analysis of heterogeneous samples, such as soils. Advancements in the applications of machine learning (ML) methods can address some of these issues, advancing the potential for LIBS in soil analysis. This article aims to review the progress of LIBS application combined with ML methods, focusing on methodological approaches used in reducing matrix effect, feature selection, quantification analysis, soil classification, and self-absorption. The performance of various adopted ML approaches is discussed, including their shortcomings and advantages, to provide researchers with a clear picture of the current status of ML applications in LIBS for improving its analytical capability. The challenges and prospects of LIBS development in soil analysis are proposed, offering a path toward future research. This review article emphasizes ML tools for LIBS soil analysis, which are broadly relevant for other LIBS applications.

Automated summary

Optical emission spectroscopy, known as LIBS, is a rapid soil analysis technique. However, challenges like matrix effects and quantification issues need further study, especially for heterogeneous samples like soils. Advancements in machine learning can overcome these challenges and enhance the potential of LIBS in soil analysis.