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Machine learning in laser-induced breakdown spectroscopy as a novel approach towards experimental parameter optimization

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F22%3A00128810" target="_blank" >RIV/00216224:14310/22:00128810 - isvavai.cz</a>

  • Alternative codes found

    RIV/00216305:26620/22:PU143915

  • Result on the web

    <a href="https://doi.org/10.1039/D1JA00389E" target="_blank" >https://doi.org/10.1039/D1JA00389E</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1039/d1ja00389e" target="_blank" >10.1039/d1ja00389e</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Machine learning in laser-induced breakdown spectroscopy as a novel approach towards experimental parameter optimization

  • Original language description

    Similar to other analytical techniques, the performance of laser-induced breakdown spectroscopy (LIBS) is significantly influenced by the selection of optimal experimental parameters. The optimization of LIBS is challenging because the laser–matter interaction and subsequent plasma formation are influenced not only by selected experimental parameters but also by the physical and mechanical properties of the sample. The goal of this work is to develop an artificial neural network (ANN) that is able to predict the signal-to-noise ratio (SNR) of selected spectral lines based on specific experimental parameters (laser pulse energy and gate delay) and on the sample's physical and mechanical properties. The ANN training was based on input data obtained from a high number of measurements of three certified materials with highly different mechanical and physical properties (low alloyed steel, glass, and aluminium alloy) with 2079 combinations of experimental parameters – gate delay (GD) and laser pulse energy (E). The ANN was optimized in terms of the number of neurons and hidden layers. The minimal number of input data points was studied with emphasis on the ANN prediction accuracy expressed as the determination coefficient R2 (predicted vs. measured values). The number of input data points was studied from three points of view – a minimal number of experimental parameters for one matrix, a minimal amount of data from different matrices, and a minimal number of different spectral lines. It has been shown that at least 20 different combinations of experimental parameters are necessary for one matrix to obtain reasonable performance of the ANN. However, only ten combinations are needed when a new matrix is added to the working model. It has also been shown that the prediction accuracy is poor for spectral lines which were not part of the training data. Finally, the ANN was utilized to predict the SNR of selected spectral lines in a specific range of experimental parameters. The parameters with the maximal SNR were studied, and the values were discussed with an emphasis on sample properties. It has been concluded that the optimization process can be substituted or significantly shortened by means of the ANN.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10400 - Chemical sciences

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2022

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Journal of Analytical Atomic Spectrometry

  • ISSN

    0267-9477

  • e-ISSN

    1364-5544

  • Volume of the periodical

    37

  • Issue of the periodical within the volume

    3

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    10

  • Pages from-to

    603-612

  • UT code for WoS article

    000758343300001

  • EID of the result in the Scopus database

    2-s2.0-85127153013