Restoring important process information from complex optical spectra with MCR-ALS: Case study of actinide reduction in spent nuclear fuel reprocessing

Debus, B.; Kirsanov, Dmitry; Ruckebusch, Cyril; Agafonova-Moroz, Marina; Бабаин, В. А.; Лумпов, А. А.; Legin, Andrey

doi:10.1016/j.chemolab.2015.05.023

Cited by 13 publications

(11 citation statements)

References 26 publications

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“…Preprocessing of UV-Vis spectra was done to minimize the instrumental noise and allow the model to focus on data that contained the chemical information. 11,37 Raw and post-processed training set spectra are presented in Fig. S1 (Supplemental Material).…”

Section: Chemometric Modelingmentioning

confidence: 99%

“…[1][2][3][4][5][6][7][8][9] A variety of optical techniques are available and can provide a level of chemical characterization that cannot be supplied via many other techniques. Examples include enabling identification and quantification of chemical speciation, 10 oxidation state, 11 and coordination environment. 12,13 In addition, spectroscopy generally has a simple experimental setup 14 and can be easily converted to process-friendly configurations to allow remote measurements.…”

Section: Introductionmentioning

confidence: 99%

“…While chemometric modeling has been used to complete the spectral analysis of many systems, 10,11,[13][14][15][16][17][18][19][20] open questions remain regarding what their limits are in relation to accurate analysis of complex samples. Herein these limits are explored in the context of impacts that could be expected in off-normal processing conditions or unanticipated research system changes.…”

Section: Introductionmentioning

confidence: 99%

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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

et al. 2021

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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.

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Section: Chemometric Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2021

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“…Most multicomponent systems measured using spectroscopic multivariate responses and sensors can produce multiple data matrices which can be analyzed by chemometric methods based on a bilinear model factor decomposition (matrix factorization) of the measured data. These multicomponent systems include chemical reactions [ 2 ], industrial processes [ 3 ], chromatography [ 4 ], spectroscopic images [ 5 ], food [ 6 ], biological [ 7 ] agricultural [ 8 ], medical [ 9 ], and environmental samples [ 10 ]. This bilinear model factor decomposition or matrix factorization has been widely used for the analysis of different types of data due to the great variety of analytical instruments [ 1 ] producing bilinear data such as the multivariate extension of the linear Beer’s law in UV-Vis absorption spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Flexible Implementation of the Trilinearity Constraint in Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) of Chromatographic and Other Type of Data

Zhang

Tauler

2022

Molecules

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Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) can analyze three-way data under the assumption of a trilinear model using the trilinearity constraint. However, the rigid application of this constraint can produce unrealistic solutions in practice due to the inadequacy of the analyzed data to the characteristics and requirements of the trilinear model. Different methods for the relaxation of the trilinear model data requirements have been proposed, like in the PARAFAC2 and in the direct non-trilinear decomposition (DNTD) methods. In this work, the trilinearity constraint of MCR-ALS is adapted to different data scenarios where the profiles of all or some of the components of the system are shifted (not equally synchronized) or even change their shape among different slices in one of their data modes. This adaptation is especially useful in gas and liquid chromatography (GC and LC) and in Flow Injection Analysis (FIA) with multivariate spectroscopic detection. In a first data example, a synthetic LC-DAD dataset is built to investigate the possibilities of the proposed method to handle systematic changes (shifts) in the retention times of the elution profiles and the results are compared with those obtained using alternative methods like ATLD, PARAFAC, PARAFAC2 and DNTD. In a second data example, multiple wine samples were simultaneously analyzed by GC-MS where elution profiles presented large deviations (shifts) in their peak retention times, although they still preserve the same peak shape. Different modelling scenarios are tested and the results are also compared. Finally, in the third example, sample mixtures of acid compounds were analyzed by FIA under a pH gradient and monitored by UV spectroscopy and also examined by different chemometric methods using a different number of components. In this case, however, the departure of the trilinear model comes from the acid base speciation of the system depending on the pH more than from the shifting of the FIA diffusion profiles.

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“…In complex systems with multiple chemical species, the measured optical signals will be proportionally complex. The resulting spectral overlap, matrix effects, ionic strength effects, or signal interferences can inhibit accurate or timely response. , …”

Section: Introductionmentioning

confidence: 99%

Online, Real-Time Analysis of Highly Complex Processing Streams: Quantification of Analytes in Hanford Tank Sample

Lines

Tse

Felmy

et al. 2019

Ind. Eng. Chem. Res.

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Processing of hazardous materials is a crucial example where online monitoring can significantly reduce operation risk, cost, and time. This is particularly true in the case of the Hanford site, where nuclear materials from the Cold War era are being processed for environmental cleanup efforts. In exceedingly complex streams such as those at Hanford, online and real-time monitoring can be challenging due to the complexity of instrument signals. Further obstacles are imposed by the caustic nature of processing streams, as well as the radiation damage inflicted on instruments and probes. Online monitoring based on Raman spectroscopy enables the detection of many Hanford tank species of interest. Nine chemical species that comprise the majority of tank waste by volume, including Al(OH)4 –, C2O4 2–, CO3 2–, CrO4 2–, NO3 –, NO3 –, OH–, PO4 3–, and SO4 2–, were detected and quantified. Real-time analysis of Raman signal allows for immediate quantification of target analytes and was successfully accomplished through the use of chemometric models. Furthermore, irradiation tests revealed that Raman monitoring systems can effectively continue to operate even after receiving 1 × 107 rad of γ dose. The online, real-time monitoring system developed here was successfully used to simultaneously quantify nine target analytes in a real sample collected from Hanford tank AP-105.

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Restoring important process information from complex optical spectra with MCR-ALS: Case study of actinide reduction in spent nuclear fuel reprocessing

Cited by 13 publications

References 26 publications

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

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

Flexible Implementation of the Trilinearity Constraint in Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) of Chromatographic and Other Type of Data

Online, Real-Time Analysis of Highly Complex Processing Streams: Quantification of Analytes in Hanford Tank Sample

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