Benchmark classification dataset for laser-induced breakdown spectroscopy

No Thumbnail Available
Date
2020-02-13
Authors
Képeš, Erik
Vrábel, Jakub
Střítežská, Sára
Pořízka, Pavel
Kaiser, Jozef
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Nature
Altmetrics
Abstract
In this work, we present an extensive dataset of laser-induced breakdown spectroscopy (LIBS) spectra for the pre-training and evaluation of LIBS classification models. LIBS is a well-established spectroscopic method for in-situ and industrial applications, where LIBS is primarily applied for clustering and classification tasks. As such, our dataset is aimed at helping with the development and testing of classification and clustering methodologies. Moreover, the dataset could be used to pre-train classification models for applications where the amount of available data is limited. The dataset consists of LIBS spectra of 138 soil samples belonging to 12 distinct classes. The spectra were acquired with a state-of-the-art LIBS system. Lastly, the composition of each sample is also provided, including estimated uncertainties.
Description
Citation
Scientific data. 2020, vol. 7, issue 1, p. 1-4.
https://www.nature.com/articles/s41597-020-0396-8
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en
Study field
Comittee
Date of acceptance
Defence
Result of defence
Document licence
Creative Commons Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
Collections
Citace PRO