Multivariate chemometric methods and Vis-NIR spectrophotometry for monitoring plutonium-238 anion exchange column effluent in a radiochemical hot cell

Sadergaski, Luke R.; Myhre, Kristian; Delmau, Lætitia H.

doi:10.1016/j.talo.2022.100120

Cited by 19 publications

(27 citation statements)

References 30 publications

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“…To demonstrate greater applicability in real-world processing solutions, future work will include additional factors in the training set to account for self-absorption, quenching, and additional peak overlap effects. 11,43 These species may include fission products ( e.g. , Zr, Mo, Ru); lanthanides ( e.g.…”

Section: Discussionmentioning

confidence: 99%

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

Sadergaski

Andrews

2022

Analyst

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Section: Discussionmentioning

confidence: 99%

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

Sadergaski

Andrews

2022

Analyst

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“…A certain number of representative samples are selected by Kennard-Stone (Zhang et al 2017;Sadergaski et al 2022) algorithm, and then, the SDDSIj and SDPDSj at wavelength j are calculated according to Eqs. 1 and 2 above.…”

Section: Screening and Optimization Of Stable And Consistent Wavelengthsmentioning

confidence: 99%

Transfer strategy for near infrared analysis model of holocellulose and lignin based on improved slope/bias algorithm

Wang

Xiong

et al. 2022

BioRes

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Model transfer techniques in near infrared spectroscopy are important for avoiding duplicate modeling, sharing samples and data resources, and reducing the human and material consumption required for modeling. Use of the slope/bias correction algorithm (S/B) based on screening wavelengths with consistent and stable signals (SWCSS) for model transfer is a new strategy. To enable sharing of near infrared analysis models of pulp holocellulose and lignin content in two different types of spectroscopic instruments, a combined SWCSS-S/B algorithm was proposed. The stable and consistent wavelengths between the spectroscopic instruments screened by the SWCSS method reduced the differences between the instruments, thereby improving the universality and transmission accuracy of the S/B method. The SWCSS-S/B based model transfer method reduced the predicted standard deviation RMSEP of holocellulose and lignin contents of the samples measured on the target spectrometer of the from 5.4686 and 7.6823 to 1.2133 and 1.3494, respectively. This result showed a significant improvement in the transfer effect compared to the SWCSS and S/B correction results alone, and the prediction of holocellulose was better than that of the prediction effect of lignin. The method has fewer wavelength variables involved in model transfer, fast transfer speed, and high prediction accuracy, which provides a new solution for the wide application of NIR analytical models.

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“…Optical techniques are also flexible and can be integrated into a variety of process batch or stream types. This approach could benefit numerous fields in nuclear science and technology (e.g., radioisotope production), but several challenges must be addressed prior to adoption at the industrial scale. , …”

Section: Introductionmentioning

confidence: 99%

“…This approach could benefit numerous fields in nuclear science and technology (e.g., radioisotope production), but several challenges must be addressed prior to adoption at the industrial scale. 5,6 The use of time-resolved laser-induced fluorescence spectroscopy (TRLFS) is well-established for the detection of numerous lanthanides (e.g., Ce 3+ , Pr 3+ , Eu 3+ , Tb 3+ , Gd 3+ , Dy 3+ , Sm 3+ , and Tm 3+ ) and actinides (e.g., UO 2 2+ , Am 3+ , Cm 3+ , Cf 3+ , Bk 3+ , and Es 3+ ), species highly relevant to the nuclear field. 7−18 Most research on lanthanide luminescence in aqueous environments has been concerned with Eu(III) and Tb(III).…”

Section: Introductionmentioning

confidence: 99%

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Pursuit of the Ultimate Regression Model for Samarium(III), Europium(III), and LiCl Using Laser-Induced Fluorescence, Design of Experiments, and a Genetic Algorithm for Feature Selection

2023

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Laser-induced fluorescence spectroscopy, Raman scattering, and partial least squares regression models were optimized for the quantification of samarium (0−150 μg mL −1 ), europium (0−75 μg mL −1 ), and lithium chloride (0.1−12 M) with a transformational preprocessing strategy. Selecting combinations of preprocessing methods to optimize the prediction performance of regression models is frequently a major bottleneck for chemometric analysis. Here, we propose an optimization tool using an innovative combination of optimal experimental designs for selecting preprocessing transformation and a genetic algorithm (GA) for feature selection. A D-optimal design containing 26 samples (i.e., combinations of preprocessing strategies) and a userdefined design (576 samples) did not statistically lower the root mean square error of the prediction (RMSEP). The greatest improvement in prediction performance was achieved when a GA was used for feature selection. This feature selection greatly lowered RMSEP statistics by an average of 53%, resulting in the top models with percent RMSEP values of 0.91, 3.5, and 2.1% for Sm(III), Eu(III), and LiCl, respectively. These results indicate that preprocessing corrections (e.g., scatter, scaling, noise, and baseline) alone cannot realize the optimal regression model; feature selection is a more crucial aspect to consider. This unique approach provides a powerful tool for approaching the true optimum prediction performance and can be applied to numerous fields of spectroscopy and chemometrics to rapidly construct models.

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Multivariate chemometric methods and Vis-NIR spectrophotometry for monitoring plutonium-238 anion exchange column effluent in a radiochemical hot cell

Cited by 19 publications

References 30 publications

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

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

Transfer strategy for near infrared analysis model of holocellulose and lignin based on improved slope/bias algorithm

Pursuit of the Ultimate Regression Model for Samarium(III), Europium(III), and LiCl Using Laser-Induced Fluorescence, Design of Experiments, and a Genetic Algorithm for Feature Selection

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