⌘+K

Pulse Explore Communities Researchers Journals

Home Communities Explore

January 1, 2020Journal of Analytical Atomic SpectrometryOpen Access

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

José Ignacio Robledo Eloisa Cuestas

View Full Paper

Ask AI

Bookmark

Share

Abstract

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

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Robledo et al. (2020) studied this question.

synapsesocial.com/papers/6aaa27a2f8c08cbefbf6c52c https://doi.org/10.1039/d0ja00148a

View Full Paper

Ask AI

Bookmark

Share

Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Impact of Laser-Induced Breakdown Spectroscopy data normalization on multivariate classification accuracyOriginal Research · 2016 · 89 citations
2Principal component analysis‐assisted energy dispersive X‐ray fluorescence spectroscopy for non‐invasive quality assurance characterization of complex matrix materialsOriginal Research · 2012 · 16 citations
3Rapid Screening of DNA–Ligand Complexes via 2D-IR Spectroscopy and ANOVA–PCAOriginal Research · 2018 · 41 citations
4Quantitative speciation of manganese oxide mixtures by RIXS/RRS spectroscopyOriginal Research · 2017 · 11 citations
5Exploratory Methodology for Retrieving Oxidation State Information from X-ray Resonant Raman Scattering SpectrometryOriginal Research · 2015 · 24 citations

Visualizing Cell State Transition Using Raman Spectroscopy2014
Energy-Dispersive Total-Reflection Resonant Inelastic X-ray Scattering as a Tool for Elemental Speciation in Contaminated Water2018
Smoothing and Differentiation of Data by Simplified Least Squares Procedures.1964
Process-accompanying analysis of solid intermediate and end products in the manufacturing process of high-purity tungsten by ETV-ICP-OESJournal of Analytical Atomic Spectrometry · 2026
IRSAC-corrected CF-LIBS validated by ICP-OES for soil classification using machine learningJournal of Analytical Atomic Spectrometry · 2026

More from Journal of Analytical Atomic Spectrometry

More from Consejo Nacional de Investigaciones Científicas y Técnicas

More from Universidad Nacional de Córdoba

More from Bariloche Atomic Centre

More from Institute of Empirical Health Economics