Open Access
Issue |
EPJ Web Conf.
Volume 288, 2023
ANIMMA 2023 – Advancements in Nuclear Instrumentation Measurement Methods and their Applications
|
|
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Article Number | 01005 | |
Number of page(s) | 8 | |
Section | Fundamental Physics | |
DOI | https://doi.org/10.1051/epjconf/202328801005 | |
Published online | 21 November 2023 |
- S. Moncayo, L. Duponchel, N. Mousavipak, et al., “Exploration of megapixel hyperspectral LIBS images using principal component analysis, ” J. Anal. At. Spectrom., vol. 33, 210–220, 2 2018. doi: 10.1039/c7ja00398f. [CrossRef] [Google Scholar]
- R. Finotello, M. Tamaazousti, and J.-B. Sirven, “HyperPCA: A powerful tool to extract elemental maps from noisy data obtained in LIBS mapping of materials, ” Spectrochim. Acta Part B, vol. 192, 106418, 2022. doi: 10.1016/j.sab.2022.106418. [CrossRef] [Google Scholar]
- R. Sattmann, I. Monch, H. Krause, et al., “Laser-induced breakdown spectroscopy for polymer identification, ” Appl. Spectrosc., no. 3, 456–461, 1998. doi: 10.1366/0003702981943680. [CrossRef] [Google Scholar]
- L.-N. Li, X.-F. Liu, F. Yang, et al., “A review of artificial neural network based chemometrics applied in laser-induced breakdown spectroscopy analysis, ” Spectrochim. Acta Part B, vol. 180, 106183, 2021. doi: https://doi.org/10.1016/j.sab.2021.106183. [CrossRef] [Google Scholar]
- V. C. Costa, D. V. Babos, J. P. Castro, et al., “Calibration strategies applied to laser-induced breakdown spectroscopy: A critical review of advances and challenges, ” J. Braz. Chem. Soc., vol. 31, no. 12, 2439–2451, 2020. doi: 10.21577/0103-5053.20200175. [Google Scholar]
- V. Motto-Ros, S. Moncayo, F. Trichard, et al., “Investigation of signal extraction in the frame of laser induced breakdown spectroscopy imaging, ” Spectrochim. Acta Part B, vol. 155, 127–133, 2019. doi: 10.1016/j.sab.2019.04.004. [CrossRef] [Google Scholar]
- N. C. Dingari, I. Barman, A. K. Myakalwar, et al., “Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability, ” Anal. Chem., vol. 84, no. 6, 2686–2694, 2012. doi: 10.1021/ac202755e. [CrossRef] [PubMed] [Google Scholar]
- P. Yaroshchyk, D. L. Death, and S. J. Spencer, “Comparison of principal components regression, partial least squares regression, multi-block partial least squares regression, and serial partial least squares regression algorithms for the analysis of Fe in iron ore using LIBS, ” J. Anal. At. Spectrom., vol. 27, no. 1, 92–98, 2012. doi: 10.1039/c1ja10164a. [CrossRef] [Google Scholar]
- T. Takahashi and B. Thornton, “Quantitative methods for compensation of matrix effects and self-absorption in laser induced breakdown spectroscopy signals of solids, ” Spectrochim. Acta Part B, vol. 138, 31–42, 2017. doi: 10.1016/j.sab.2017.09.010. [CrossRef] [Google Scholar]
- E. D’Andrea, S. Pagnotta, E. Grifoni, et al., “A hybrid calibrationfree/artificial neural networks approach to the quantitative analysis of LIBS spectra, ” Appl. Phys. B, vol. 118, no. 3, 353–360, 2015. doi: 10.1007/s00340-014-5990-z. [CrossRef] [Google Scholar]
- L. Narlagiri and V. R. Soma, “Simultaneous quantification of Au and Ag composition from Au-Ag bi-metallic LIBS spectra combined with shallow neural network model for multi-output regression, ” Appl. Phys. B: Lasers Opt., vol. 127, no. 9, 135, 2021. doi: 10.1007/s00340-02107681-y. [CrossRef] [Google Scholar]
- T. Chen, L. Sun, H. Yu, et al., “Deep learning with laserinduced breakdown spectroscopy (LIBS) for the classification of rocks based on elemental imaging, ” Appl. Geochem., vol. 136, 105135, 2022. doi: 10.1016/j.apgeochem.2021.105135. [CrossRef] [Google Scholar]
- R. Caruana, “Multitask learning: A knowledge-based source of inductive bias, ” in Proceedings of the Tenth International Conference on International Conference on Machine Learning, ser. Icml’93, Amherst, MA, USA: Morgan Kaufmann Publishers Inc., 1993, 41–48, isbn: 1558603077. doi: 10.1016/b978-1-55860-307-3.50012-5. [Google Scholar]
- R. B. Anderson, J. F. BellIII, R. C. Wiens, et al., “Clustering and training set selection methods for improving the accuracy of quantitative laser induced breakdown spectroscopy, ” Spectrochim. Acta Part B, vol. 70, 24–32, 2012. doi: 10.1016/j.sab.2012.04.004. [CrossRef] [Google Scholar]
- F. O. Borges, G. H. Cavalcanti, G. C. Gomes, et al., “A fast method for the calculation of electron number density and temperature in laserinduced breakdown spectroscopy plasmas using artificial neural networks, ” Appl. Phys. B, vol. 117, no. 1, 437–444, 2014. doi: 10.1007/s00340-014-5852-8. [CrossRef] [Google Scholar]
- S. Chen, H. Pei, J. Pisonero, et al., “Simultaneous determination of lithology and major elements in rocks using laserinduced breakdown spectroscopy (LIBS) coupled with a deep convolutional neural network, ” J. Anal. At. Spectrom., vol. 37, no. 3, 508–516, 2022. doi: 10.1039/d1ja00406a. [CrossRef] [Google Scholar]
- J.-M. Mermet, “Limit of quantitation in atomic spectrometry: An unambiguous concept?” Spectrochim. Acta Part B, vol. 63, no. 2, 166–182, 2008, Honoring Issue A Collection of Papers on Atomic, Molecular and Laser Spectroscopy Dedicated to James D. Winefordner. doi: 10.1016/j.sab.2007.11.029. [CrossRef] [Google Scholar]
- W. Zhao, C. Li, C. Yan, et al., “Interpretable deep learning assisted laserinduced breakdown spectroscopy for brand classification of iron ores, ” Anal. Chim. Acta, vol. 1166, 338574, 2021. doi: 10.1016/j.aca.2021.338574. [CrossRef] [Google Scholar]
- X. Zhang, J. Xu, J. Yang, et al., “Understanding the learning mechanism of convolutional neural networks in spectral analysis, ” Anal. Chim. Acta, vol. 1119, 41–51, 2020. doi: 10.1016/j.aca.2020.03.055. [CrossRef] [Google Scholar]
- T. Völker, G. Wilsch, I. B. Gornushkin, et al., “Interlaboratory comparison for quantitative chlorine analysis in cement pastes with laserinduced breakdown spectroscopy, ” Spectrochim. Acta Part B, vol. 202, 106632, 2022. doi: 10.1016/j.sab.2023.106632. [CrossRef] [Google Scholar]
- J. Sansonetti, Handbook of Basic Atomic Spectroscopic Data, NIST Standard Reference Database 108, 2003. doi: 10.18434/T4FW23. [Google Scholar]
- J. Bergstra, R. Bardenet, Y. Bengio, et al., “Algorithms for hyperparameter optimization, ” in Advances in Neural Information Processing Systems, J. Shawe-Taylor, R. Zemel, P. Bartlett, et al., Eds., vol. 24, Curran Associates, Inc., 2011, doi:10.5555/2986459.2986743. [Google Scholar]
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