Processing of large-scale laser spectroscopy data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F18%3APU130618" target="_blank" >RIV/00216305:26620/18:PU130618 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Processing of large-scale laser spectroscopy data

Popis výsledku v původním jazyce

State-of-the-art laser-ablation based spectroscopic instruments provide data sets with increasing size, number of objects (spectra) and variables (wavelengths). In this work we concentrate namely on the Optical Emission Spectroscopic techniques, such as Laser-Induced Breakdown Spectroscopy and Laser Ablation Inductively Plasma Optical Emission Spectroscopy. However, presented algorithms are applicable also to the Mass Spec techniques. Typically, the data set is overloaded with information, analytically relevant as well as redundant, e.g. originating from noise and background. Processing such multivariate data is, thus, a challenging task demanding sophisticated approaches. A use of advanced statistical algorithms, referred to as multivariate data analysis algorithms or chemometrics, is of great interest in the contemporary data processing. Most often Principal Component Analysis and Self-Organizing Maps are implemented. Our efforts tackled mainly the dimensionality reduction in both, objects and varia

Název v anglickém jazyce

Processing of large-scale laser spectroscopy data

Popis výsledku anglicky

State-of-the-art laser-ablation based spectroscopic instruments provide data sets with increasing size, number of objects (spectra) and variables (wavelengths). In this work we concentrate namely on the Optical Emission Spectroscopic techniques, such as Laser-Induced Breakdown Spectroscopy and Laser Ablation Inductively Plasma Optical Emission Spectroscopy. However, presented algorithms are applicable also to the Mass Spec techniques. Typically, the data set is overloaded with information, analytically relevant as well as redundant, e.g. originating from noise and background. Processing such multivariate data is, thus, a challenging task demanding sophisticated approaches. A use of advanced statistical algorithms, referred to as multivariate data analysis algorithms or chemometrics, is of great interest in the contemporary data processing. Most often Principal Component Analysis and Self-Organizing Maps are implemented. Our efforts tackled mainly the dimensionality reduction in both, objects and varia

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10406 - Analytical chemistry

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