On the utilization of principal component analysis in laser-induced breakdown spectroscopy data analysis, a review

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F18%3APU128293" target="_blank" >RIV/00216305:26620/18:PU128293 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1016/j.sab.2018.05.030" target="_blank" >http://dx.doi.org/10.1016/j.sab.2018.05.030</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.sab.2018.05.030" target="_blank" >10.1016/j.sab.2018.05.030</a>

Result language

angličtina

Original language name

On the utilization of principal component analysis in laser-induced breakdown spectroscopy data analysis, a review

Original language description

An implementation of a fast, robust, and effective algorithm is inevitable in modern multivariate data analysis (MVDA). The principal component analysis (PCA) algorithm is becoming popular not only in the spectroscopic community because it complies with the qualities mentioned above. PCA is, therefore, often used for the rocessing of detected multivariate signal (characteristic spectra). Over the past decade, PCA has been adopted by the Laser-Induced Breakdown Spectroscopy (LIBS) community and the number of scientific articles referring to PCA steadily increases. The interest in PCA is not caused only by the basic need to obtain a fast data visualization on a lower dimensional scale and to inspect the most prominent variables. Most recently, PCA has also been applied to yield unconventional data analyses, i.e. processing of large scale LIBS maps. However, a rapid development of LIBS-related instrumentation and applications has led to some non-uniform methodologies in the implementation and utilization of MVDA, including PCA. Thus, in this work, we critically assess and elaborate on the approaches to utilize PCA in LIBS data processing. The aim of this article is also to derive some implications and to suggest advice in data preprocessing, visualization, dimensionality reduction, model building, classification, quantification and non-conventional multivariate mapping. This review reflects also other MVDA algorithms than PCA and consequently, presented conclusions and recommendations can be generalized.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10301 - Atomic, molecular and chemical physics (physics of atoms and molecules including collision, interaction with radiation, magnetic resonances, Mössbauer effect)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Spectrochimica Acta Part B

ISSN

0584-8547

e-ISSN

—

Volume of the periodical

148

Issue of the periodical within the volume

-

Country of publishing house

GB - UNITED KINGDOM

Number of pages

19

Pages from-to

65-82

UT code for WoS article

000445311800009

EID of the result in the Scopus database

2-s2.0-85048439338