A Piecewise Local Partial Least Squares (PLS) Method for the Quantitative Analysis of Plutonium Nitrate Solutions

Abstract

We have developed a piecewise local (PL) partial least squares (PLS) analysis method for total plutonium measurements by absorption spectroscopy in nitric acid-based nuclear material processing streams. Instead of using a single PLS model that covers all expected solution conditions, the method selects one of several local models based on an assessment of solution absorbance, acidity, and Pu oxidation state distribution. The local models match the global model for accuracy against the calibration set, but were observed in several instances to be more robust to variations associated with measurements in the process. The improvements are attributed to the relative parsimony of the local models. Not all of the sources of spectral variation are uniformly present at each part of the calibration range. Thus, the global model is locally overfitting and susceptible to increased variance when presented with new samples. A second set of models quantifies the relative concentrations of Pu(III), (IV), and (VI). Standards containing a mixture of these species were not at equilibrium due to a disproportionation reaction. Therefore, a separate principal component analysis is used to estimate of the concentrations of the individual oxidation states in these standards in the absence of independent confirmatory analysis. The PL analysis approach is generalizable to other systems where the analysis of chemically complicated systems can be aided by rational division of the overall range of solution conditions into simpler sub-regions.

Chemometrics and Experimental Design for the Quantification of Nitrate Salts in Nitric Acid: Near-Infrared Spectroscopy Absorption Analysis

“…18 Partial least squares regression (PLSR) is a statistical, multivariate approach relating spectral features to concentration in multicomponent systems. 19,20 PLSR relates the independent (X matrix) and dependent (Y matrix) variables with linear combinations of latent variables. X represents an n  m matrix of n samples across m wavelengths (i.e., spectra), and Y represents the response matrix (i.e., concentration) for n samples.…”

“…PLS reduces the dimensionality of the predictive variables and finds correlations with established success. 6,14,16,19,20 To predict the concentration of an analyte from an unknown spectrum (i.e., a spectrum that is not in the training set), PLS models must be built from a representative set of calibration samples that include all spectrally active components over the range of expected concentrations (i.e., factor space). [21][22][23][24][25] PLSR models are subject to uncertainty, which causes variance in the predictions of the model.…”

Monitoring the Caustic Dissolution of Aluminum Alloy in a Radiochemical Hot Cell Using Raman Spectroscopy

“…However, the quantification of multiple and/or confounding species in solution has been made possible by advancements in mathematical approaches such as multivariate regression analysis. 5–12…”

“…Oak Ridge National Laboratory, Oak Ridge, USA Process monitoring of aqueous solutions using optical spectroscopy (e.g., Raman spectroscopy) has been used in a variety of processing environments and across a range of experimental conditions from microscale to industrial-scale online operations (e.g., radiochemical reprocessing of used nuclear fuel). [4][5][6][7][8][9][10] However, spectroscopic analysis can be difficult to apply to complex systems when multiple species are present. Complex chemical systems result in spectra with overlapping bands, matrix effects, baseline shifts, and simultaneously changing spectroscopic responses.…”

“…However, the quantification of multiple and/or confounding species in solution has been made possible by advancements in mathematical approaches such as multivariate regression analysis. [5][6][7][8][9][10][11][12] Partial least squares regression (PLSR) is one of the most commonly used techniques used across multiple disciplines to generate predictive models. 13,14 Partial least squares (PLS) methods are ideal for spectral analyses since the data is inherently over-determined, noisy, and correlated.…”