  Classification and identification of amino acids in aqueous solutions is
important in the study of biomacromolecules. Laser Induced Breakdown
Spectroscopy (LIBS) uses high energy laser-pulses for ablation of chemical
compounds whose radiated spectra are captured and recorded to reveal molecular
structure. Spectral peaks and noise from LIBS are impacted by experimental
protocols. Current methods for LIBS spectral analysis achieves promising
results using PCA, a linear method. It is well-known that the underlying
physical processes behind LIBS are highly nonlinear. Our work set out to
understand the impact of LIBS spectra on suitable neighborhood size over which
to consider pattern phenomena, if nonlinear methods capture pattern phenomena
with increased efficacy, and how they improve classification and identification
of compounds. We analyzed four amino acids, polysaccharide, and a control
group, water. We developed an information theoretic method for measurement of
LIBS energy spectra, implemented manifold methods for nonlinear dimensionality
reduction, and found while clustering results were not statistically
significantly different, nonlinear methods lead to increased classification
accuracy. Moreover, our approach uncovered the contribution of micro-wells
(experimental protocol) in LIBS spectra. To the best of our knowledge, ours is
the first application of Manifold methods to LIBS amino-acid analysis in the
research literature.
