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

Autor:	Sadergaski LR; Radioisotope Science and Technology Division, 6146Oak Ridge National Laboratory, Oak Ridge, TN, USA., Toney GK; Radioisotope Science and Technology Division, 6146Oak Ridge National Laboratory, Oak Ridge, TN, USA., Delmau LH; Radioisotope Science and Technology Division, 6146Oak Ridge National Laboratory, Oak Ridge, TN, USA., Myhre KG; Radioisotope Science and Technology Division, 6146Oak Ridge National Laboratory, Oak Ridge, TN, USA.
Jazyk:	angličtina
Zdroj:	Applied spectroscopy [Appl Spectrosc] 2021 Sep; Vol. 75 (9), pp. 1155-1167. Date of Electronic Publication: 2021 Jan 22.
DOI:	10.1177/0003702820987281
Abstrakt:	Implementing remote, real-time spectroscopic monitoring of radiochemical processing streams in hot cell environments requires efficiency and simplicity. The success of optical spectroscopy for the quantification of species in chemical systems highly depends on representative training sets and suitable validation sets. Selecting a training set (i.e., calibration standards) to build multivariate regression models is both time- and resource-consuming using standard one-factor-at-a-time approaches. This study describes the use of experimental design to generate spectral training sets and a validation set for the quantification of sodium nitrate (0-1 M) and nitric acid (0.1-10 M) using the near-infrared water band centered at 1440 nm. Partial least squares regression models were built from training sets generated by both D- and I-optimal experimental designs and a one-factor-at-a-time approach. The prediction performance of each model was evaluated by comparing the bias and standard error of prediction for statistical significance. D- and I-optimal designs reduced the number of samples required to build regression models compared with one-factor-at-a-time while also improving performance. Models must be confirmed against a validation sample set when minimizing the number of samples in the training set. The D-optimal design performed the best when considering both performance and efficiency by improving predictive capability and reducing number of samples in the training set by 64% compared with the one-factor-at-a-time approach. The experimental design approach objectively selects calibration and validation spectral data sets based on statistical criterion to optimize performance and minimize resources.
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu