Remote Sensing\nof Nitric Acid and Temperature via\nDesign of Experiments, Chemometrics, and Raman Spectroscopy

This study presents an effective method for the quantification\nof nitric acid (0.1–9 M) and the temperature (20–60\n°C) through optimal experimental design, chemometrics, and Raman\nspectroscopy. Raman spectroscopy can be deployed using fiber-optic\ncables in hot cell environments to support processing operations in\nthe nuclear field and industry. Chemical operations frequently use\nnitric acid and operate at nonambient temperatures either by design\nor by circumstance. Examples of Raman spectroscopy for the quantification\nof nitric acid with applications in the industrial field are profuse.\nHowever, the effect of temperature on quantification is often ignored\nand should be considered in real-world scenarios. Statistical design\nof experiments was used to build training sets for partial least-squares\nregression and support vector regression (SVR) models. The SVR model\nwith a nonlinear kernel outperformed the top partial least-squares\nmodels with respect to temperature and resulted in percent root-mean-square\nerror of prediction of 1.8% and 2.3% for nitric acid and temperature,\nrespectively. The D-optimal design strategy decreased the sampling\ntime by 75% compared to a more traditional seven-level full factorial\noption. The new method advances chemometric applications within and\nbeyond the nuclear field and industry.