Optical
sensors and chemometric models were leveraged for the quantification
of uranium(VI) (0–100 μg mL–1), europium
(0–150 μg mL–1), samarium (0–250
μg mL–1), praseodymium (0–350 μg
mL–1), neodymium (0–1000 μg mL–1), and HNO3 (2–4 M) with varying
corrosion product (iron, nickel, and chromium) levels using laser
fluorescence, Raman scattering, and ultraviolet–visible–near-infrared
absorption spectra. In this paper, an efficient approach to developing
and evaluating tens of thousands of partial least-squares regression
(PLSR) models, built from fused optical spectra or multimodal acquisitions,
is discussed. Each PLSR model was optimized with unique preprocessing
combinations, and features were selected using genetic algorithm filters.
The 7-factor D-optimal design training set contained just 55 samples
to minimize the number of samples. The performance of PLSR models
was evaluated by using an automated latent variable selection script.
PLS1 regression models tailored to each species outperformed a global
PLS2 model. PLS1 models built using fused spectra data and a multimodal
(i.e., analyzed separately) approach yielded similar information,
resulting in percent root-mean-square error of prediction values of
0.9–5.7% for the seven factors. The optical techniques and
data processing strategies established in this study allow for the
direct analysis of numerous species without measuring luminescence
lifetimes or relying on a standard addition approach, making it optimal
for near-real-time, in situ measurements. Nuclear reactor modeling
helped bound training set conditions and identified elemental ratios
of lanthanide fission products to characterize the burnup of irradiated
nuclear fuel. Leveraging fluorescence, spectrophotometry, experimental
design, and chemometrics can enable the remote quantification and
characterization of complex systems with numerous species, monitor
system performance, help identify the source of materials, and enable
rapid high-throughput experiments in a variety of industrial processes
and fundamental studies.