Fast Classification of Brick Samples by Combination of Principal Component Analysis and Linear Discriminant Analysis Using Standoff Laser-Induced Breakdown Spectroscopy

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F13%3A00070570" target="_blank" >RIV/00216224:14740/13:00070570 - 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

Fast Classification of Brick Samples by Combination of Principal Component Analysis and Linear Discriminant Analysis Using Standoff Laser-Induced Breakdown Spectroscopy

Popis výsledku v původním jazyce

LDA is a multivariate statistical method for discrimination of objects up to a finite number of categories, based on a certain subset of all objects (training set). The principle of this method is maximizing the ratio of the between-class variance to thewithin-class variance. The decision rules obtained by classification of training set are later applied to the testing set. PCA was applied to reduce a dimensionality and the scores were computed from whole spectra (200 ? 1000 nm) of 29 brick samples obtained by stand-off LIBS. After dividing the sample set into training and test samples first few principal components were used as inputs to LDA and the predictions of localities were computed. The results suggest that stand-off LIBS in combination with advanced statistical methods has a big potential for archaeological in-field measurements. By examining of PCA scores there was also discovered a dependence of firing temperature of bricks.

Název v anglickém jazyce

Fast Classification of Brick Samples by Combination of Principal Component Analysis and Linear Discriminant Analysis Using Standoff Laser-Induced Breakdown Spectroscopy

Popis výsledku anglicky

LDA is a multivariate statistical method for discrimination of objects up to a finite number of categories, based on a certain subset of all objects (training set). The principle of this method is maximizing the ratio of the between-class variance to thewithin-class variance. The decision rules obtained by classification of training set are later applied to the testing set. PCA was applied to reduce a dimensionality and the scores were computed from whole spectra (200 ? 1000 nm) of 29 brick samples obtained by stand-off LIBS. After dividing the sample set into training and test samples first few principal components were used as inputs to LDA and the predictions of localities were computed. The results suggest that stand-off LIBS in combination with advanced statistical methods has a big potential for archaeological in-field measurements. By examining of PCA scores there was also discovered a dependence of firing temperature of bricks.

Druh

O - Ostatní výsledky

CEP obor

CB - Analytická chemie, separace

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)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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