Addressing the sparsity of laser-induced breakdown spectroscopy data with randomized sparse principal component analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F21%3APU140691" target="_blank" >RIV/00216305:26620/21:PU140691 - isvavai.cz</a>
Result on the web
<a href="https://pubs.rsc.org/en/content/articlepdf/2021/JA/D1JA00067E" target="_blank" >https://pubs.rsc.org/en/content/articlepdf/2021/JA/D1JA00067E</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1039/d1ja00067e" target="_blank" >10.1039/d1ja00067e</a>
Alternative languages
Result language
angličtina
Original language name
Addressing the sparsity of laser-induced breakdown spectroscopy data with randomized sparse principal component analysis
Original language description
Emission spectra yielded by laser-induced breakdown spectroscopy (LIBS) exhibit high dimensionality, redundancy, and sparsity. The high dimensionality is often addressed by principal component analysis (PCA) which creates a low dimensional embedding of the spectra by projecting them into the score space. However, PCA does not effectively deal with the sparsity of the analysed data, including LIBS spectra. Consequently, sparse PCA (SPCA) was proposed for the analysis of high-dimensional sparse data. Nevertheless, SPCA remains underutilized for LIBS applications. Thus, in this work, we show that SPCA combined with genetic algorithms offers marginal improvements in clustering and quantification using multivariate calibration. More importantly, we show that SPCA significantly improves the interpretability of loading spectra. In addition, we show that the loading spectra yielded by SPCA differ from those yielded by sparse partial least squares regression. Finally, by using the randomized SPCA (RSPCA) algorithm for carrying out SPCA, we indirectly demonstrate that the analysis of LIBS data can greatly benefit from the tools developed by randomized linear algebra: RSPCA offers a 20-fold increase in computation speed compared to PCA based on singular value decomposition.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10406 - Analytical chemistry
Result continuities
Project
<a href="/en/project/LM2018110" target="_blank" >LM2018110: CzechNanoLab research infrastructure</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Analytical Atomic Spectrometry
ISSN
0267-9477
e-ISSN
1364-5544
Volume of the periodical
36
Issue of the periodical within the volume
6
Country of publishing house
GB - UNITED KINGDOM
Number of pages
12
Pages from-to
1410-1421
UT code for WoS article
000649277600001
EID of the result in the Scopus database
2-s2.0-85109586197