Selecting and visualizing the spectral variability relevant for sample classification using principal component analysis-Reference-Cited by-同舟云学术

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Selecting and visualizing the spectral variability relevant for sample classification using principal component analysis

Published:2020 Issue:7 Volume:35 Page:1435-1440
ISSN:0267-9477
Container-title:Journal of Analytical Atomic Spectrometry
language:en
Short-container-title:J. Anal. At. Spectrom.

Author:

Robledo José I.¹²³⁴^ORCID,Cuestas Eloisa⁵⁶⁷⁴^ORCID

Affiliation:

1. Bariloche Atomic Center (CAB)

2. National Scientific and Technical Research Council (CONICET)

3. Bariloche

4. Argentina

5. IFEG

6. National Scientific and Technical Research Council (CONICET) and FAMAF National University of Córdoba

7. X5000HUA - Córdoba

Abstract

Reconstructing a measured spectrum by selecting the relevant variability by means of principal component analysis.

Funder

Consejo Nacional de Investigaciones Científicas y Técnicas

Publisher

Royal Society of Chemistry (RSC)

Subject

Spectroscopy,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2020/JA/D0JA00148A

Reference17 articles.

1. R. A. Johnson and D. W.Wichern , Applied Multivariate Statistical Analysis , Prentic Hall, Inc. , New Jersey , 1992

2. Impact of Laser-Induced Breakdown Spectroscopy data normalization on multivariate classification accuracy

3. Principal component analysis-assisted energy dispersive X-ray fluorescence spectroscopy for non-invasive quality assurance characterization of complex matrix materials

4. Rapid Screening of DNA–Ligand Complexes via 2D-IR Spectroscopy and ANOVA–PCA

5. Quantitative speciation of manganese oxide mixtures by RIXS/RRS spectroscopy

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