Measuring Nd(III) Solution Concentration in the Presence of Interfering Er(III) and Cu(II) Ions: A Partial Least Squares Analysis of Ultraviolet–Visible Spectra

Abstract

Optical spectroscopy is a powerful characterization tool with applications ranging from fundamental studies to real-time process monitoring. However, it can be difficult to apply to complex samples that contain interfering analytes which are common in processing streams. Multivariate (chemometric) analysis has been examined for providing selectivity and accuracy to the analysis of optical spectra and expanding its potential applications. Here we will discuss chemometric modeling with an in-depth comparison to more simplistic analysis approaches and outlining how chemometric modeling works while exploring the limits on modeling accuracy. Understanding the limitations of the chemometric model can provide better analytical assessment regarding the accuracy and precision of the analytical result. This will be explored in the context of UV-vis absorbance of neodymium (Nd3+) in the presence of interferents, erbium (Er3+) and copper (Cu2+) under conditions simulating the liquid-liquid extraction approach used to recycle plutonium (Pu) and uranium (U) in used nuclear fuel worldwide. The selected chemometric model, partial least squares regression (PLS), accurately quantifies Nd3+ with a low percentage error in the presence of interfering analytes and even under conditions that the training set does not describe.

Simultaneous quantification of uranium(vi), samarium, nitric acid, and temperature with combined ensemble learning, laser fluorescence, and Raman scattering for real-time monitoring

“…PLSR (PLS-2) was attempted first, as it is one of the most widely applied techniques to correlate convoluted and covarying spectral features to analyte concentration. 2,3,27,28 The global PLSR model was built using most of the spectrum (410-750 nm) after applying a first derivative with a first-order polynomial and 31 smoothing points (1,1,31). Eight latent variables (i.e., factors) were included in the model based on the RMSECV versus latent variable plot (Fig.…”

“…One of the most traditional supervised techniques is called partial least squares regression (PLSR). [25][26][27][28] This factor analysis method iteratively relates two data matrices, the independent X (i.e., spectra) and dependent Y (i.e., concentrations), using combinations of latent variables (LV). PLSR models are built using a training set that covers the expected conditions and a validation set that tests the model's ability to predict samples not included in the training set.…”

“…These samples can be selected using optimal experimental designs that are the most flexible and effective option when a small number of experimental runs is desired. [29][30][31][32][33] Although PLSR has been used with great success to model systems with overlapping spectral features in numerous systems, 27 it does not always account for systems with a high degree of multicollinearity. 34,35 Multicollinearity must be accounted for in a LIFS system for monitoring U(VI) concentration because two independent variables (i.e., U(VI) concentration and temperature) are highly correlated in the regression model.…”

“…One of the most traditional supervised techniques is called partial least squares regression (PLSR). 25–28 This factor analysis method iteratively relates two data matrices, the independent X ( i.e. , spectra) and dependent Y ( i.e.…”

“…Although PLSR has been used with great success to model systems with overlapping spectral features in numerous systems, 27 it does not always account for systems with a high degree of multicollinearity. 34,35 Multicollinearity must be accounted for in a LIFS system for monitoring U( vi ) concentration because two independent variables ( i.e.…”