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

Journal Article · Thu Jan 08 00:00:00 EST 2026 · Nuclear Science and Engineering

DOI:https://doi.org/10.1080/00295639.2025.2586186· OSTI ID:3012375

Borrero-Negrón, Justin I. ^[1]; Realmuto, Eric W. ^[1]; Compitiello, Ava M. ^[1]; Rao, Ashwin P. ^[2]; Villa-Aleman, Eliel ^[3]; Darvin, Jason R. ^[3]; Hartig, Kyle C. ^[4]

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1. University of Florida, Gainesville, FL (United States)
2. Air Force Research Laboratory Kirtland AFB, NM (United States)
3. Savannah River National Laboratory, Aiken, SC (United States)
4. University of Florida, Gainesville, FL (United States); Savannah River National Laboratory, Aiken, SC (United States)

Enhanced characterization and phase identification of post-PUREX Pu 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 of 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 FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), 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 machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly 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. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

• Research Organization: University of Florida, Gainesville, FL (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: NA0004142; 89303321CEM000080
• OSTI ID: 3012375
• Journal Information: Nuclear Science and Engineering, Journal Name: Nuclear Science and Engineering; ISSN 0029-5639; ISSN 1943-748X
• Publisher: Informa UK Limited
• Country of Publication: United States
• Language: English

Related Subjects

Oxalates
laser-induced breakdown spectroscopy
nuclear forensics
nuclear nonproliferation
phase discrimination