Interpreting convolutional neural network classifiers applied to laser-induced breakdown optical emission spectra

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F24%3APU155235" target="_blank" >RIV/00216305:26620/24:PU155235 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14330/24:00137458

Result on the web

<a href="https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0039914023006975?via%3Dihub" target="_blank" >https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0039914023006975?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.talanta.2023.124946" target="_blank" >10.1016/j.talanta.2023.124946</a>

Result language

angličtina

Original language name

Interpreting convolutional neural network classifiers applied to laser-induced breakdown optical emission spectra

Original language description

Laser-induced breakdown spectroscopy (LIBS) is a well-established industrial tool with emerging relevance in high-stakes applications. To achieve its required analytical performance, LIBS is often coupled with advanced pattern-recognition algorithms, including machine learning models. Namely, artificial neural networks (ANNs) have recently become a frequently applied part of LIBS practitioners' toolkit. Nevertheless, ANNs are generally applied in spectroscopy as black-box models, without a real insight into their predictions. Here, we apply various post-hoc interpretation techniques with the aim of understanding the decision-making of convolutional neural networks. Namely, we find synthetic spectra that yield perfect expected classification predictions and denote these spectra class-specific prototype spectra. We investigate the simplest possible convolutional neural network (consisting of a single convolutional and fully connected layers) trained to classify the extended calibration dataset collected for the ChemCam laser-induced breakdown spectroscopy instrument of the Curiosity Mars rover. The trained convolutional neural network predominantly learned meaningful spectroscopic features which correspond to the elements comprising the major oxides found in the calibration targets. In addition, the discrete convolution operation with the learnt filters results in a crude baseline correction.

Czech name

—

Czech description

—

Type

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

CEP classification

—

OECD FORD branch

10406 - Analytical chemistry

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

2024

Confidentiality

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

Name of the periodical

TALANTA

ISSN

0039-9140

e-ISSN

1873-3573

Volume of the periodical

266

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

11

Pages from-to

„“-„“

UT code for WoS article

001044992600001

EID of the result in the Scopus database

2-s2.0-85165081844