Laser-induced breakdown spectroscopy (LIBS): calibration challenges, combination with other techniques, and spectral analysis using data science

Abstract

Laser-Induced Breakdown Spectroscopy (LIBS) is a versatile and powerful analytical technique widely used for rapid, in situ elemental analysis across various fields, from industrial quality control to planetary exploration. This review addresses the critical aspects and emerging trends in LIBS, focusing on calibration challenges, integrating complementary techniques (data fusion), and applying data science for spectral analysis. Calibration is a fundamental challenge in LIBS due to matrix effects, signal drift, and variations in experimental conditions. Recent advancements aim to develop matrix-independent calibration models and employ machine learning algorithms to improve calibration accuracy and robustness. LIBS has also proven invaluable in space exploration, particularly on Mars. Instruments like ChemCam and SuperCam have successfully utilized LIBS to perform real-time chemical analysis of the Martian surface, providing critical insights into its composition and history. The review further explores the advancements in multivariate calibration techniques for handling complex and multi-component systems. Techniques such as Partial Least Squares (PLS) regression and Principal Component Analysis (PCA) are increasingly employed to address the high dimensionality of LIBS data, enhancing the precision and reliability of the analysis. In addition, combining LIBS with other instrumental analytical techniques expands its analytical capabilities. Data fusion strategies integrating LIBS with techniques like Raman spectroscopy, X-ray fluorescence (XRF), and hyperspectral imaging provide a more comprehensive understanding of material composition. These integrated systems, supported by sophisticated data fusion algorithms, offer unprecedented insights and accuracy. Finally, applying data science in LIBS transforms spectral inspection and analysis. Machine learning and deep learning methods are being adopted to automate and enhance the processing and interpretation of LIBS spectra, uncovering complex patterns and improving analysis accuracy. The future lies in leveraging big data analytics and real-time processing to address more complex analytical challenges. In conclusion, LIBS is evolving rapidly, driven by advancements in calibration methods, techniques integration, and data science. This review highlights the potential of LIBS to continue pushing the boundaries of material analysis and its significant contributions to diverse scientific and industrial fields.