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Different Hybrid Prediction's Machine Learning Algorithms for Quantitative Analysis in Laser-Induced Breakdown Spectroscopy

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Journal of Applied Spectroscopy Aims and scope

Laser-induced breakdown spectroscopy (LIBS) technique is employed for quantitative analysis of aluminum samples by different classical machine learning approaches. A Q-switch Nd:YAG laser at a fundamental harmonic of 1064 nm is utilized for the creation of LIBS plasma in order to predict constituent concentrations of the aluminum standard alloys. In the current research, concentration prediction is performed by linear approaches of support vector regression (SVR), multiple linear regression (MLR), principal component analysis (PCA) integrated with MLR (PCA–MLR), and SVR (PCA–SVR), as well as nonlinear algorithms of artificial neural network (ANN), kernelized support vector regression (KSVR), and the integration of traditional principal component analysis with KSVR (PCA–KSVR), and ANN (PCA–ANN). Furthermore, dimension reduction is applied to various methodologies by the PCA algorithm in order to improve the quantitative analysis. The results indicated that the combination of PCA with the KSVR algorithm model had the best efficiency in predicting most of the elements among other classical machine learning algorithms.

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Authors and Affiliations

  1. Department of Industrial Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran

    Mohsen Rezaei

  2. Department of Physics, K. N Toosi University of Technology, Tehran, Iran

    Fatemeh Rezaei

  3. Department of Physics, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Parvin Karimi

Authors
  1. Mohsen Rezaei
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  2. Fatemeh Rezaei
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  3. Parvin Karimi
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Correspondence to Fatemeh Rezaei.

Additional information

Abstract of article is published in Zhurnal Prikladnoi Spektroskopii, Vol. 90, No. 3, p. 528, May–June, 2023.

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Rezaei, M., Rezaei, F. & Karimi, P. Different Hybrid Prediction's Machine Learning Algorithms for Quantitative Analysis in Laser-Induced Breakdown Spectroscopy. J Appl Spectrosc 90, 705–716 (2023). https://doi.org/10.1007/s10812-023-01585-9

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  • DOI: https://doi.org/10.1007/s10812-023-01585-9

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