Issue 4, 2024

Interpreting neural networks trained to predict plasma temperature from optical emission spectra

Abstract

We explore the application of artificial neural networks (ANNs) for predicting plasma temperatures in Laser-Induced Breakdown Spectroscopy (LIBS) analysis. Estimating plasma temperature from emission spectra is often challenging due to spectral interference and matrix effects. Traditional methods like the Boltzmann plot technique have limitations, both in applicability due to various matrix effects and in accuracy owing to the uncertainty of the underlying spectroscopic constants. Consequently, ANNs have already been successfully demonstrated as a viable alternative for plasma temperature prediction. We leverage synthetic data to isolate temperature effects from other factors and study the relationship between the LIBS spectra and temperature learnt by the ANN. We employ various post-hoc model interpretation techniques, including gradient-based methods, to verify that ANNs learn meaningful spectroscopic features for temperature prediction. Our findings demonstrate the potential of ANNs to learn complex relationships in LIBS spectra, offering a promising avenue for improved plasma temperature estimation and enhancing the overall accuracy of LIBS analysis.

Graphical abstract: Interpreting neural networks trained to predict plasma temperature from optical emission spectra

Supplementary files

Article information

Article type
Paper
Submitted
23 Oct 2023
Accepted
12 Mar 2024
First published
12 Mar 2024
This article is Open Access
Creative Commons BY-NC license

J. Anal. At. Spectrom., 2024,39, 1160-1174

Interpreting neural networks trained to predict plasma temperature from optical emission spectra

E. Képeš, H. Saeidfirozeh, V. Laitl, J. Vrábel, P. Kubelík, P. Pořízka, M. Ferus and J. Kaiser, J. Anal. At. Spectrom., 2024, 39, 1160 DOI: 10.1039/D3JA00363A

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