Optimization of libs measurement parameters via multivariate chemometrics for the classification purpose
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F16%3A00093818" target="_blank" >RIV/00216224:14740/16:00093818 - isvavai.cz</a>
Result on the web
<a href="http://www.esas2016.mke.org.hu/" target="_blank" >http://www.esas2016.mke.org.hu/</a>
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Optimization of libs measurement parameters via multivariate chemometrics for the classification purpose
Original language description
The outputs of LIBS analysis are multivariate data sets with several thousand to tens of thousands variables in one spectrum. Such a comprehensive set of information contained in a single spectrum offers a challenge for processing all at once, quickly and efficiently. Multivariate analysis makes it possible by reducing large files of the complex, multivariate data to a smaller number of factors describing the differences between the samples. Chemometrics algorithms have already been applied on LIBS data for classification or quantification purposes. When focusing on classification, papers published in the past few years confirm the interest in multivariate classification approach. The most used multivariate classification method is principal component analysis (PCA).
Czech name
—
Czech description
—
Classification
Type
O - Miscellaneous
CEP classification
CA - Inorganic chemistry
OECD FORD branch
—
Result continuities
Project
<a href="/en/project/ED1.1.00%2F02.0068" target="_blank" >ED1.1.00/02.0068: Central european institute of technology</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů