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AMT | Articles | Volume 13, issue 10
Atmos. Meas. Tech., 13, 5369–5377, 2020
https://doi.org/10.5194/amt-13-5369-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 13, 5369–5377, 2020
https://doi.org/10.5194/amt-13-5369-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 09 Oct 2020

Research article | 09 Oct 2020

Quantification of toxic metals using machine learning techniques and spark emission spectroscopy

Seyyed Ali Davari and Anthony S. Wexler

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Cited articles

Abbasi, H., Rauter, G., Guzman, R., Cattin, P. C., and Zam, A.: Laser-induced breakdown spectroscopy as a potential tool for autocarbonization detection in laserosteotomy, J. Biomed. Opt., 23, 071206, https://doi.org/10.1117/1.JBO.23.7.071206, 2018. a
Axente, E., Hermann, J., Socol, G., Mercadier, L., Beldjilali, S. A., Cirisan, M., Luculescu, C. R., Ristoscu, C., Mihailescu, I. N., and Craciun, V.: Accurate analysis of indium–zinc oxide thin films via laser-induced breakdown spectroscopy based on plasma modeling, J. Anal. Atom. Spectrom., 29, 553–564, 2014. a
Baudelet, M., Guyon, L., Yu, J., Wolf, J.-P., Amodeo, T., Fréjafon, E., and Laloi, P.: Femtosecond time-resolved laser-induced breakdown spectroscopy for detection and identification of bacteria: A comparison to the nanosecond regime, J. Appl. Phys., 99, 084701, https://doi.org/10.1063/1.2187107, 2006. a
Boucher, T. F., Ozanne, M. V., Carmosino, M. L., Dyar, M. D., Mahadevan, S., Breves, E. A., Lepore, K. H., and Clegg, S. M.: A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy, Spectrochim. Acta B, 107, 1–10, 2015. a
Braga, J. W. B., Trevizan, L. C., Nunes, L. C., Rufini, I. A., Santos Jr, D., and Krug, F. J.: Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser induced breakdown spectrometry, Spectrochim. Acta B, 65, 66–74, 2010. a, b
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Traditional instruments for detection and quantification of toxic metals in the atmosphere are expensive. In this study, we have designed, fabricated, and tested a low-cost instrument, which employs cheap components to detect and quantify toxic metals. Advanced machine learning (ML) techniques have been used to improve the instrument's performance. This study demonstrates how the combination of low-cost sensors with ML can address problems that traditionally have been too expensive to be solved.
Traditional instruments for detection and quantification of toxic metals in the atmosphere are...
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