Abstract
Laser-Induced Breakdown Spectroscopy (LIBS) has become a powerful imaging technique for elemental characterization in analytical chemistry due to its advantages over other techniques. Major, minor, and trace elements are detected with high measurement dynamic, a low limit of detection and a high acquisition rate, allowing for the quick analysis of large sample surfaces. Today, chemometric tools are commonly used to ensure the most comprehensive and unbiased exploration of such spectroscopic data. However, the integration of the signal from a wavelength assumed to be specific to the element of interest remains the basic tool for generating a chemical distribution map from a hyperspectral dataset. This classical approach is based on a strong assumption, the specificity of the chemical information on the spectral domain being considered. Any spectral interference inevitably result in the generation of a biased distribution image. In this publication, we demonstrate how Principal Component Analysis (PCA) can diagnose the potential presence of a spectral interference and how Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) can ultimately correct it if necessary using a LIBS imaging dataset obtained from the analysis of a complex rock sample. The proposed approach combines the simplicity and effectiveness of the integration method with the diagnostic and correction capabilities of chemometric tools, providing a comprehensive solution for spectral interference in LIBS imaging.