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

2021-05-25 · Piyush K. Sharma, Gary Holness, Poopalasingam Sivakumar, Yuri Markushin, Noureddine Melikechi

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.

📄 PDF Abstract BibTeX arXiv:2105.12089

Code (0)

등록된 구현이 없습니다.

Tasks

ClusteringDimensionality Reduction

Methods 이 논문이 사용한 방법론

PCA Principle Components Analysis (PCA) is an unsupervised method primary used for dimensionality reduction within machine learning. PCA is calculated via a singular value…

Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment

2002-12-07 · Zhenyue Zhang, Hongyuan Zha

Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications. In this paper we present a new algorithm for manif…

Data Visualization Dimensionality Reduction

Investigating Image Manifolds of 3D Objects: Learning, Shape Analysis, and Comparisons

2025-03-09 · Benjamin Beaudett, Shenyuan Liang, Anuj Srivastava

Despite high-dimensionality of images, the sets of images of 3D objects have long been hypothesized to form low-dimensional manifolds. What is the nature of such manifolds? How do they differ across objects and object cl…

Sparse Manifold Clustering and Embedding

2011-12-01 · NeurIPS 2011 12 · Ehsan Elhamifar, René Vidal

We propose an algorithm called Sparse Manifold Clustering and Embedding (SMCE) for simultaneous clustering and dimensionality reduction of data lying in multiple nonlinear manifolds. Similar to most dimensionality reduct…

Clustering Dimensionality Reduction

Local and Global Structure Preservation Based Spectral Clustering

2022-10-23 · Kajal Eybpoosh, Mansoor Rezghi, Abbas Heydari

Spectral Clustering (SC) is widely used for clustering data on a nonlinear manifold. SC aims to cluster data by considering the preservation of the local neighborhood structure on the manifold data. This paper extends Sp…

Clustering

Manifold Matching using Shortest-Path Distance and Joint Neighborhood Selection

2014-12-12 · Cencheng Shen, Joshua T. Vogelstein, Carey E. Priebe

Matching datasets of multiple modalities has become an important task in data analysis. Existing methods often rely on the embedding and transformation of each single modality without utilizing any correspondence informa…