Benchmarking in Laser-Induced Breakdown Spectroscopy

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F20%3APU137844" target="_blank" >RIV/00216305:26620/20:PU137844 - isvavai.cz</a>
Result on the web
<a href="http://icpinformation.org/Winter_Conference.html" target="_blank" >http://icpinformation.org/Winter_Conference.html</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Benchmarking in Laser-Induced Breakdown Spectroscopy
Original language description
The recent technological boom in LIBS resulted in the production of very large spectroscopic data.1 Various processing techniques and methods have been developed over time with ranging applicability and performance. Well-established algorithms based on classical statistics are not anymore usable for more advanced processing of large high-dimensional data. On the other side, modern Machine Learning techniques (Neural Networks, Support Vector Machines, etc.) are very often overused or applied in an incorrect way. Establishing a robust benchmark for a specific task (classification or quantification,...) is necessary to distinguish between approaches and select a “correct” solution/s to each problem. We are presenting a challenging benchmark for material classification through LIBS spectra. It consists of 138 physical samples, separated into 12 categories according to their elemental composition. For each sample 500 spectra of dimension 40,002 wavelength values are available (in training part of the datas
Czech name
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Czech description
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Project
<a href="/en/project/LQ1601" target="_blank" >LQ1601: CEITEC 2020</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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