Rapid and reliable identification of mineral species is a challenging but crucial task with promising application prospects in mineralogy, metallurgy, and geology. Spectroscopic techniques such as laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy (RS) efficiently capture the elemental composition and structural information of minerals, making them a potential tool for in situ and real-time analysis of minerals. This study introduces an integrated LIBS-RS system and the fusion of LIBS and RS spectra coupled with machine learning to classify six different types of natural mineral. In order to visualize the separability of different mineral species clearly, the spectral data were projected into low-dimensional space through t-distributed stochastic neighbor embedding (t-SNE). Additionally, the Fisher score (FS) was used to identify important variables that contribute to the data classification, and the corresponding chemical elements and molecular bonds were then interpreted. The between-minerals difference in the feature spectral intensity of LIBS and RS variables could also be observed. After the minerals spectra were pre-processed, the relationship between spectral intensity and the mineral category was modeled using machine learning methods, including partial least squares–discriminant analysis (PLS-DA) and kernel extreme learning machine (K-ELM). The results show that K-ELM and PLS-DA based on the fusion LIBS-RS data achieved the highest accuracy of 98.4%. These findings demonstrate the feasibility of the integrated LIBS-RS system combined with machine learning for the fast and reliable classification of minerals.

This review provides a comparatively comprehensive and systematic introduction to recent developments in chemical composition imaging based on LIBS, with a focus on the principle, operation, and recent development.

The simplicity and low-cost way to improve qualitative and quantitative analytical performance has always been a crucial concern for laser-induced breakdown spectroscopy (LIBS), and many scientists have been engaged in this evolving research direction. In this review, we investigated an update on recent developments in light-field modulated operation in LIBS. It covered a brief description of LIBS, optical polarization, and beam shaping. Here, the optical polarization is divided into laser beam polarization and plasma polarization. In addition, the methodology and development of light-field modulated LIBS were summarized and discussed. Finally, the existing problems with light-field modulated LIBS were presented, along with some of their own insights and the future direction of their development. This review will provide a guideline for LIBS researchers with basic knowledge, which is very useful in the signal optimization of LIBS research and applications.

An experimental solution that combines hyperspectral imaging (HSI) and laser-induced breakdown spectroscopy (LIBS) applied to large-scale material surface chemical analysis is investigated..

Ultrafast laser filament-induced breakdown spectroscopy (FIBS) is a potential technique for quantitative analysis of trace elements. In this work, we investigate the effect of the distance between focusing lens and target surface on the FIBS quantitative analysis of Mn element in aluminum alloys, and several major parameters are calculated such as the linear correlation coefficient (R ²), limits of detection (LOD), relative standard deviation (RSD), and root-mean-square error of cross-validations (RMSECV). The results show that the quantitative analysis parameter values before and after filament position are different. The optimal value can be obtained at the filament region, the average values of total 23 positions of R ², LOD, RSD, and RMAECV were 99.45%, 1.41 mg/kg, 7.12%, and 0.56%, respectively. Besides, the spatial distributions of quantitative analysis parameter values in filament region are noticeable, and this is essentially due to intensity clamping effect in a filament.