Analytical spectroscopy methods have shown many possible uses for
nuclear material diagnostics and measurements in recent studies. In
particular, the application potential for various atomic spectroscopy
techniques is uniquely diverse and generates interest across a wide
range of nuclear science areas. Over the last decade, techniques such
as laser-induced breakdown spectroscopy, Raman spectroscopy, and x-ray
fluorescence spectroscopy have yielded considerable improvements in
the diagnostic analysis of nuclear materials, especially with machine
learning implementations. These techniques have been applied for
analytical solutions to problems concerning nuclear forensics, nuclear
fuel manufacturing, nuclear fuel quality control, and general
diagnostic analysis of nuclear materials. The data yielded from atomic
spectroscopy methods provide innovative solutions to problems
surrounding the characterization of nuclear materials, particularly
for compounds with complex chemistry. Implementing these optical
spectroscopy techniques can provide comprehensive new insights into
the chemical analysis of nuclear materials. In particular, recent
advances coupling machine learning methods to the processing of atomic
emission spectra have yielded novel, robust solutions for nuclear
material characterization. This review paper will provide a summation
of several of these recent advances and will discuss key experimental
studies that have advanced the use of analytical atomic spectroscopy
techniques as active tools for nuclear diagnostic measurements.
