recent advances in machine learning methodologies for LIBS quantitative analysis

Abstract

The mapping between LIBS spectral data to the quantitative results can become highly complicated and nonlinear due to experimental conditions, sample surface state, matrix effect, self-absorption, etc. Therefore, the accurate quantitative analysis is the longstanding dream of the LIBS community. The advantages of machine learning in dealing with high-dimensional and nonlinear problems have made it a cutting-edge hot topic in quantitative LIBS in recent years. This chapter introduces the current bottlenecks in quantitative LIBS, sorts out the data processing methods, and reviews the research status and progress of conventional machine learning methods such as PLS, SVM, LSSVM, Lasso, and artificial neural network-based methods. By comparing the results of different methods, the perspective of future developments on learning-based methods is discussed. This chapter aims to review the applications of the combination of quantitative LIBS and machine learning methods and demonstrate the performance of different machine learning methods based on experimental results.