Laser-Induced Breakdown Spectroscopy for Material Identification

Abstract

Laser-Induced Breakdown Spectroscopy (LIBS) is an advanced technique for rapid, in-situ, and non-destructive material identification across scientific and industrial fields. It operates by focusing a high-energy laser pulse on a material surface to generate micro-plasma, which emits characteristic radiation unique to the elemental composition. This paper reviews recent developments in LIBS, including system operation, spectral analysis, and classification methods. It proposes an optimized experimental approach covering laser parameters, sample preparation, plasma diagnostics, and spectral calibration. Advanced data analysis techniques such as Principal Component Analysis (PCA) and Support Vector Machines (SVM) are integrated to enhance classification accuracy. Experiments on metallic, polymer, and geological samples demonstrate both qualitative and quantitative performance, achieving classification accuracy above 95%. Compared to conventional methods like XRF and AAS, LIBS offers faster analysis and better portability. Key challenges such as matrix effects, self-absorption, and spectral interference are discussed, along with mitigation strategies like calibration-free LIBS and multi-pulse excitation. The study concludes that LIBS is a highly effective tool for real-time material identification in applications such as industrial quality control, environmental monitoring, and defense. Future work focuses on integrating deep learning and developing miniaturized systems.