Quantification of alloying elements in steel targets: The LIBS 2022 regression contest

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F23%3APU149581" target="_blank" >RIV/00216305:26620/23:PU149581 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0584854723000976?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0584854723000976?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.sab.2023.106710" target="_blank" >10.1016/j.sab.2023.106710</a>

Result language

angličtina

Original language name

Quantification of alloying elements in steel targets: The LIBS 2022 regression contest

Original language description

We present the results of the regression contest organized for the LIBS 2022 conference. While the motivation and design of the contest are briefly presented, the work focuses on the methodologies of the three bestperforming teams. The employed spectral preprocessing strategies, choice of regression models and its optimization are detailed for each team separately. The aim of the contest reflects the long-term challenges faced by quantitative laser-induced breakdown spectroscopy (LIBS) analysis. Thus, the contest was designed with the purpose of providing a transparent platform for comparing and evaluating the large range of data processing tools available in the LIBS literature. Namely, the contest consisted of the quantification of two major (Cr, Ni) and two minor (Mn, Mo) elements in 15 steel targets. For constructing an appropriate regression model, spectra of 42 targets were provided. The spectra were collected using a commercially available laboratory-based LIBS system and made publicly available. The contest lasted 53 days during which the teams did not receive feedback. In total, 21 teams participated out of which the three best-performing methodologies are presented here. A single linear partial least squares model and two artificial neural network regression models are presented. The corresponding feature selection strategies included emission line selection, spectral range selection, and automatized wavelength selection. Various spectral normalization strategies and data augmentation strategies are also presented.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

21100 - Other engineering and technologies

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

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

Name of the periodical

Spectrochimica Acta Part B

ISSN

0584-8547

e-ISSN

—

Volume of the periodical

206

Issue of the periodical within the volume

106710

Country of publishing house

GB - UNITED KINGDOM

Number of pages

20

Pages from-to

„“-„“

UT code for WoS article

001055346600001

EID of the result in the Scopus database

2-s2.0-85161017704