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Rapid Identification and Classification of Metal Waste by Laser-Induced Breakdown Spectroscopy

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Journal of Applied Spectroscopy Aims and scope

The rapid identification and classification of metal garbage has been experimentally investigated. By combining laser-induced breakdown spectroscopy (LIBS) and machine learning, metal garbage can be effectively identified through spectral analysis. In this work, a novel method for metal garbage classification was developed, and a LIBS system was self-developed. As an example of metal recycling, five types of metal were adopted. Several characteristic lines of Al, W, Fe, Cu, Sn, Pb, and C were identified. For a more effective classification, principal component analysis was conducted to reduce the dimension of the spectra. Samples after the dimension reduction were classified by using K-nearest neighbors, and five types were obtained, exhibiting a final classification accuracy of 97.18%. Moreover, a mathematical model of the linear formulas between spectrum and concentration was established to achieve quantitative analysis with Fe taken as an example, laying the foundation for more refined classification.

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Authors and Affiliations

  1. Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, China

    Zhuoyan Zhou, Wenhan Gao, Cong Yu & Yuzhu Liu

  2. Institute of Physics, University of Sindh, Jamshoro, Pakistan

    Saifullah Jamali

  3. Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection, Nanjing University of Information Science & Technology, Nanjing, China

    Yuzhu Liu

Authors
  1. Zhuoyan Zhou
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  2. Wenhan Gao
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  3. Saifullah Jamali
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  4. Cong Yu
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  5. Yuzhu Liu
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Corresponding author

Correspondence to Yuzhu Liu.

Additional information

Abstract of article is published in Zhurnal Prikladnoi Spektroskopii, Vol. 91, No. 2, p. 310, March–April, 2024.

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Zhou, Z., Gao, W., Jamali, S. et al. Rapid Identification and Classification of Metal Waste by Laser-Induced Breakdown Spectroscopy. J Appl Spectrosc 91, 397–404 (2024). https://doi.org/10.1007/s10812-024-01733-9

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  • DOI: https://doi.org/10.1007/s10812-024-01733-9

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