DIMENSIONALITY REDUCTION OF MULTI-VARIATE LASER SPECTROSCOPY DATA

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F18%3APU130588" target="_blank" >RIV/00216305:26620/18:PU130588 - isvavai.cz</a>

Výsledek na webu

<a href="http://16cssc2018.spektroskopie.cz/files/CSSC_2018_BOOK_OF_ABSTRACTS_FINAL.pdf" target="_blank" >http://16cssc2018.spektroskopie.cz/files/CSSC_2018_BOOK_OF_ABSTRACTS_FINAL.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

DIMENSIONALITY REDUCTION OF MULTI-VARIATE LASER SPECTROSCOPY DATA

Popis výsledku v původním jazyce

State-of-the-art Laser-Induced Breakdown Spectroscopy (LIBS) instruments enable high repetition rate analysis. With this experimental settings, the mapping of sample surfaces provides large data sets. The richness of information is in spectra (objects) as well as wavelengths (variables). Processing such multivariate data is, thus, a challenging task demanding more sophisticated approaches. Utilization of advanced statistical algorithms, referred to as multivariate data analysis algorithms or chemometrics, are of great interest in contemporary data processing [1-3]. Moreover, elemental composition (relation of individual elements) and structural complexity (relation of individual matrices) provides additional valuable information in understanding of the heterogeneity of, e.g., biological and geological samples. In our work, we bring an introduction to the utilization of Principal Component Analysis to processing of LIBS data. Our efforts tackled mainly the dimensionality reduction in both, objects and

Název v anglickém jazyce

DIMENSIONALITY REDUCTION OF MULTI-VARIATE LASER SPECTROSCOPY DATA

Popis výsledku anglicky

State-of-the-art Laser-Induced Breakdown Spectroscopy (LIBS) instruments enable high repetition rate analysis. With this experimental settings, the mapping of sample surfaces provides large data sets. The richness of information is in spectra (objects) as well as wavelengths (variables). Processing such multivariate data is, thus, a challenging task demanding more sophisticated approaches. Utilization of advanced statistical algorithms, referred to as multivariate data analysis algorithms or chemometrics, are of great interest in contemporary data processing [1-3]. Moreover, elemental composition (relation of individual elements) and structural complexity (relation of individual matrices) provides additional valuable information in understanding of the heterogeneity of, e.g., biological and geological samples. In our work, we bring an introduction to the utilization of Principal Component Analysis to processing of LIBS data. Our efforts tackled mainly the dimensionality reduction in both, objects and

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/LQ1601" target="_blank" >LQ1601: CEITEC 2020</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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