Design of Experiments, Chemometrics, and Raman Spectroscopy for the Quantification of Hydroxylammonium, Nitrate, and Nitric Acid

Abstract

Selecting optimal combinations of preprocessing methods is a major holdup for chemometric analysis. The analyst decides which method(s) to apply to the data, frequently by highly subjective or inefficient means, such as user experience or trial and error. Here, we present a user-friendly method using optimal experimental designs for selecting preprocessing transformations. We applied this strategy to optimize partial least square regression (PLSR) analysis of Stokes Raman spectra to quantify hydroxylammonium (0–0.5 M), nitric acid (0–1 M), and total nitrate (0–1.5 M) concentrations. The best PLSR model chosen by a determinant (D)-optimal design comprising 26 samples (i.e., combinations of preprocessing methods) was compared with PLSR models built with no preprocessing, a user-selected preprocessing method (i.e., trial and error), and a user-defined design strategy (576 samples). The D-optimal selection strategy improved PLSR prediction performance by more than 50% compared with the raw data and reduced the number of combinations by more than 95.5%.

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

“…S8 ‡). 29,30 The RMSEP% for Sm 3+ , HNO 3 , and temperature all fall below the goal level of 5%. While the U(VI) RMSEP% misses this objective, the ensemble model vastly improves the prediction accuracy compared to the initial PLSR models.…”

“…Numerous derivative orders, polynomial orders, and smoothing points were tested. 30 Trimming the spectra reduces the dimensionality of large spectra files and can help reduce the error of prediction in multivariate modeling. The trimmed region included in the chemometric model was varied based on the behavior being modeled.…”

“…A Tukey-Kramer significance test was used to statistically compare the RMSEP values for multiple regression models following a method outlined previously. 29,30 Additional details can be found in the ESI. ‡…”

“…In addition to U(VI) and Sm 3+ emission peaks, Stokes Raman scattering features corresponding to free acid (H + ), nitrate anions (NO 3 − ), and the O-H stretching region were identified simultaneously. 26,30 Unperturbed nitrate ions have three Raman active bands including ν 1 (∼1048 cm −1 ), ν 3 (∼1415 cm −1 ), and ν 4 (∼717 cm −1 ). The O-H vibrational stretching region consists of several overlapping bands attributed to various H 2 O and O-H (free and bound) vibrations (455-475 nm).…”

“…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.…”