Regulační diagramy pro chemometrické vyhodnocování Ramanových spekter

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F05%3A00014726" target="_blank" >RIV/60461373:22340/05:00014726 - 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

Control charts for chemometric evaluation of Raman spectra

Popis výsledku v původním jazyce

Principal component analysis (PCA) is a method used for evaluation of vibration spectra that allows separating important information from the minor effects. From the point of view of data structure the most valuable information is depicted in score graphs of several Principal Components (PCs), the contribution of various spectral regions can be viewed in the x-loadings charts. Questionable problems of PCA are the identification of outliers for various spectral variables and various PCs and mainly how tocompare mutually multiple large spectral data sets. To solve these issues we attempt to introduce control charts in multivariate spectral analysis integrating them with PCA. A utility was developed to provide comparison of new samples (spectral data sets) according to previous set (calibration) for data in individual PCs. Similar spectral data sets are then inside the control limits while the different spectral data exceed the limits. The correlated data in individual PCs can be easily

Název v anglickém jazyce

Control charts for chemometric evaluation of Raman spectra

Popis výsledku anglicky

Principal component analysis (PCA) is a method used for evaluation of vibration spectra that allows separating important information from the minor effects. From the point of view of data structure the most valuable information is depicted in score graphs of several Principal Components (PCs), the contribution of various spectral regions can be viewed in the x-loadings charts. Questionable problems of PCA are the identification of outliers for various spectral variables and various PCs and mainly how tocompare mutually multiple large spectral data sets. To solve these issues we attempt to introduce control charts in multivariate spectral analysis integrating them with PCA. A utility was developed to provide comparison of new samples (spectral data sets) according to previous set (calibration) for data in individual PCs. Similar spectral data sets are then inside the control limits while the different spectral data exceed the limits. The correlated data in individual PCs can be easily

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

CB - Analytická chemie, separace

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2005

Kód důvěrnosti údajů

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

Název periodika

Journal Mol.Struct.

ISSN

0022-2860

e-ISSN

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Svazek periodika

744

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

6

Strana od-do

259-264

Kód UT WoS článku

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EID výsledku v databázi Scopus

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