Investigating Manifold Neighborhood size for Nonlinear Analysis of LIBS Amino Acid Spectra
Piyush K. Sharma, Gary Holness, and Poopalasingam Sivakumar, Yuri, Markushin, Noureddine Melikechi

TL;DR
This study explores how nonlinear manifold methods can improve the analysis and classification of amino acid spectra obtained from LIBS, revealing the significance of neighborhood size and experimental protocols.
Contribution
It introduces the first application of manifold methods to LIBS amino-acid analysis, demonstrating improved classification accuracy and insights into experimental factors.
Findings
Nonlinear methods increase classification accuracy.
Neighborhood size impacts pattern recognition.
Experimental protocols influence LIBS spectra.
Abstract
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…
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Taxonomy
TopicsLaser-induced spectroscopy and plasma · Analytical chemistry methods development · Mass Spectrometry Techniques and Applications
MethodsPrincipal Components Analysis
