Classification of biominerals by means of Laser Induced Breakdown Spectroscopy (LIBS) and Artificial Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F10%3APU89378" target="_blank" >RIV/00216305:26210/10:PU89378 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Classification of biominerals by means of Laser Induced Breakdown Spectroscopy (LIBS) and Artificial Neural Networks
Original language description
The aim of this work is to analyze the feasibility of Artificial Neural Network (ANN) for the identification and classification of mineralized tissues and bio-mineral structures based on spectra obtained by LIBS. In this study, a set of different archeological samples are used to train and evaluate the ANN. For the identification of unknown samples the spectral emission of the plasma was first measured for known set of materials. The obtained spectra was used to train ANN and the output was defined as type of material (i.e. shell, mortar, soil, ceramic, tooth or bone).
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
CB - Analytical chemistry, separation
OECD FORD branch
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Result continuities
Project
<a href="/en/project/ME10061" target="_blank" >ME10061: Development of calibration-free quantitative laser-induced breakdown spectroscopy (LIBS)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
ICP Information Newsletter
ISSN
0161-6951
e-ISSN
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Volume of the periodical
2010
Issue of the periodical within the volume
1-2
Country of publishing house
US - UNITED STATES
Number of pages
2
Pages from-to
137-138
UT code for WoS article
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EID of the result in the Scopus database
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