Multivariate models for data library transfer in laser spectroscopy
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F19%3APU135025" target="_blank" >RIV/00216305:26620/19:PU135025 - isvavai.cz</a>
Výsledek na webu
<a href="http://libs.ceitec.cz/files/281/213.pdf" target="_blank" >http://libs.ceitec.cz/files/281/213.pdf</a>
DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Multivariate models for data library transfer in laser spectroscopy
Popis výsledku v původním jazyce
czLaser-Induced Breakdown Spectroscopy (LIBS) serves as an exceptional platform for a fastsample analysis. Detected laser-induced plasma (LIP) spectrum is composed of uniqueinformation (i.e. chemical fingerprint) characterizing the sample from which it originates. Atypical LIBS analysis, obtained with applying repetition rate, results in a great number ofmeasurements (spectra). The size of the data matrix is intensified by the number of variablescarried by each LIP spectrum. Therefore, the processing of given data matrix calls forsophisticated statistical algorithms. Chemometric (or Multivariate Data Analysis; MVDA)algorithms are becoming an inevitable part of the spectroscopic analysis [1]. A successfulMVDA implementation is based on the well-balanced data pre-processing (outlier filtering,signal standardization, variable down-selection, etc.). First and the most common step is thedimensionality reduction and data visualization. This is consecutively followed by the modelcreation, further classificat
Název v anglickém jazyce
Multivariate models for data library transfer in laser spectroscopy
Popis výsledku anglicky
czLaser-Induced Breakdown Spectroscopy (LIBS) serves as an exceptional platform for a fastsample analysis. Detected laser-induced plasma (LIP) spectrum is composed of uniqueinformation (i.e. chemical fingerprint) characterizing the sample from which it originates. Atypical LIBS analysis, obtained with applying repetition rate, results in a great number ofmeasurements (spectra). The size of the data matrix is intensified by the number of variablescarried by each LIP spectrum. Therefore, the processing of given data matrix calls forsophisticated statistical algorithms. Chemometric (or Multivariate Data Analysis; MVDA)algorithms are becoming an inevitable part of the spectroscopic analysis [1]. A successfulMVDA implementation is based on the well-balanced data pre-processing (outlier filtering,signal standardization, variable down-selection, etc.). First and the most common step is thedimensionality reduction and data visualization. This is consecutively followed by the modelcreation, further classificat
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
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OECD FORD obor
10406 - Analytical chemistry
Návaznosti výsledku
Projekt
Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2019
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů