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Rapid Identification and Classification of Metal Waste by Laser-Induced Breakdown Spectroscopy
Abstract
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.
About the Authors
Z. Zhou
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
China
China
Nanjing
W. Gao
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
China
China
Nanjing
S. Jamali
Institute of Physics, University of Sindh
Pakistan
Pakistan
Jamshoro
C. Yu
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
China
China
Nanjing
Y. Liu
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; Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection,
Nanjing University of Information Science & Technology
China
China
Nanjing
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Review
For citations:
Zhou Z., Gao W., Jamali S., Yu C., Liu Y. Rapid Identification and Classification of Metal Waste by Laser-Induced Breakdown Spectroscopy. Zhurnal Prikladnoii Spektroskopii. 2024;91(2):310. (In Russ.)
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