Optimization of libs measurement parameters via multivariate chemometrics for the classification purpose

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F16%3A00093818" target="_blank" >RIV/00216224:14740/16:00093818 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.esas2016.mke.org.hu/" target="_blank" >http://www.esas2016.mke.org.hu/</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Optimization of libs measurement parameters via multivariate chemometrics for the classification purpose

Popis výsledku v původním jazyce

The outputs of LIBS analysis are multivariate data sets with several thousand to tens of thousands variables in one spectrum. Such a comprehensive set of information contained in a single spectrum offers a challenge for processing all at once, quickly and efficiently. Multivariate analysis makes it possible by reducing large files of the complex, multivariate data to a smaller number of factors describing the differences between the samples. Chemometrics algorithms have already been applied on LIBS data for classification or quantification purposes. When focusing on classification, papers published in the past few years confirm the interest in multivariate classification approach. The most used multivariate classification method is principal component analysis (PCA).

Název v anglickém jazyce

Optimization of libs measurement parameters via multivariate chemometrics for the classification purpose

Popis výsledku anglicky

The outputs of LIBS analysis are multivariate data sets with several thousand to tens of thousands variables in one spectrum. Such a comprehensive set of information contained in a single spectrum offers a challenge for processing all at once, quickly and efficiently. Multivariate analysis makes it possible by reducing large files of the complex, multivariate data to a smaller number of factors describing the differences between the samples. Chemometrics algorithms have already been applied on LIBS data for classification or quantification purposes. When focusing on classification, papers published in the past few years confirm the interest in multivariate classification approach. The most used multivariate classification method is principal component analysis (PCA).

Druh

O - Ostatní výsledky

CEP obor

CA - Anorganická chemie

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0068" target="_blank" >ED1.1.00/02.0068: CEITEC - central european institute of technology</a><br>

Návaznosti

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

Rok uplatnění

2016

Kód důvěrnosti údajů

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