Recent Advances in LIBS for Forensic Gunshot Residue Analysis: Applications, Limitations, and Future Prospects

Gunshot residue (GSR) analysis is a fundamental component of forensic investigations, providing crucial evidence for reconstructing shooting incidents, identifying suspects, estimating firing distance, and linking firearms to crime scenes. Conventional analytical techniques, particularly scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM–EDS), have long served as the benchmark for inorganic GSR detection due to their high sensitivity and ability to characterize particle morphology and elemental composition. However, these methods often require extensive sample preparation, sophisticated instrumentation, prolonged analysis times, and laboratory-based operation, limiting their applicability for rapid forensic screening. Laser-Induced Breakdown Spectroscopy (LIBS) can effectively be used for GSR analysis owing to its rapid, minimally destructive, and multi-element detection capabilities. LIBS enables direct elemental analysis with little or no sample preparation and can be applied to diverse substrates, including skin, clothing, firearms, spent cartridge cases, and environmental surfaces. Recent advances in laser technology, spectrometer design, gated detectors, and portable instrumentation have significantly enhanced the analytical performance of LIBS. Furthermore, the integration of LIBS with chemometric and machine learning algorithms has substantially improved the discrimination of GSR from environmental contaminants and facilitated ammunition source classification. This review provides a comprehensive overview of the principles of LIBS, the formation and composition of gunshot residue, sampling strategies, instrumentation, experimental methodologies, spectral interpretation, and recent forensic applications. Comparative discussions with established analytical techniques highlight the strengths and limitations of LIBS in terms of sensitivity, specificity, portability, analytical speed, and operational cost.