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Classification and identification of amino acids in aqueous solutions isimportant in the study of biomacromolecules. Laser Induced BreakdownSpectroscopy (LIBS) uses high energy laser-pulses for ablation of chemicalcompounds whose radiated spectra are captured and recorded to reveal molecularstructure. Spectral peaks and noise from LIBS are impacted by experimentalprotocols. Current methods for LIBS spectral analysis achieves promisingresults using PCA, a linear method. It is well-known that the underlyingphysical processes behind LIBS are highly nonlinear. Our work set out tounderstand the impact of LIBS spectra on suitable neighborhood size over whichto consider pattern phenomena, if nonlinear methods capture pattern phenomenawith increased efficacy, and how they improve classification and identificationof compounds. We analyzed four amino acids, polysaccharide, and a controlgroup, water. We developed an information theoretic method for measurement ofLIBS energy spectra, implemented manifold methods for nonlinear dimensionalityreduction, and found while clustering results were not statisticallysignificantly different, nonlinear methods lead to increased classificationaccuracy. Moreover, our approach uncovered the contribution of micro-wells(experimental protocol) in LIBS spectra. To the best of our knowledge, ours isthe first application of Manifold methods to LIBS amino-acid analysis in theresearch literature.

Investigating Manifold Neighborhood size for Nonlinear Analysis of LIBS Amino Acid Spectra