chemometrics; ion recombination; laser-induced breakdown spectroscopy; LIBS; MCR-ALS; mean field-independent components analysis; MFICA; molecular formation; multivariate curve resolution-alternating least squares; temporal evolution;ARTIFICIAL NEURAL-NETWORKS; QUANTITATIVE-ANALYSIS; TEMPORAL EVOLUTION; WAVELET-TRANSFORM; ABSORPTION-SPECTROSCOPY; ANALYTICAL-CHEMISTRY; CHEMOMETRIC METHODS; LIBS; IDENTIFICATION; OPTIMIZATION

Laser-induced breakdown spectroscopy (LIBS) is an analytical technique allowing the determination of elemental concentrations in a variety of matrices in the solid, liquid, and gaseous phases. Because of the inherent complexity of the signal and to the high dimensionality of experimental data, chemometrics has been more and more applied in LIBS to perform samples identification or quantitative measurements. But multivariate methods can also be used for the description and physical interpretation of the plasma, particularly to exploit the temporal dimension of the LIBS signal, which is usually neglected in spectrochemical measurements. In this work, time-resolved spectra of a pure aluminum sample were treated with 2 methods, mean field-independent components analysis and multivariate curve resolution-alternating least squares, applying non-negativity constraints for scores and components in both cases. Results obtained were compared with reference univariate measurements of the emission of the species of interest (ions, neutral atoms, and molecules). The interpretation of scores and components provided a physical description of phenomena that take place between species in the plasma, like ionic recombination and molecules formation. Overall, mean field-independent components analysis and multivariate curve resolution-alternating least squares yield equivalent solutions with our dataset. This new approach is very promising for the treatment of time-resolved data obtained by LIBS.