Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX plutonium oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO2 samples, little is known about the impact that both the chemical structure and oxidation states of PuOXA [i.e. Pu(III) and Pu(IV)] have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed iron oxalate (FeOXA) samples. We report the enhancement of spectral feature selection using principal component analysis (PCA), which enables the analytical superiority of machine learning (ML) algorithms such as linear discriminant analysis, quadratic discriminant analysis, partial least squares regression (PLSR), support vector regression (SVR), and random forest regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e. PLSR and SVR) can achieve acceptable performance, with ML models outperforming conventional calibration regressions. Furthermore, the application of nonlinearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. This is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.