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- Title
Rapid Identification and Classification of Metal Waste by Laser-Induced Breakdown Spectroscopy.
- Authors
Zhou, Zhuoyan; Gao, Wenhan; Jamali, Saifullah; Yu, Cong; Liu, Yuzhu
- 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.
- Subjects
METAL wastes; LASER-induced breakdown spectroscopy; K-nearest neighbor classification; METAL recycling; PRINCIPAL components analysis; COPPER
- Publication
Journal of Applied Spectroscopy, 2024, Vol 91, Issue 2, p397
- ISSN
0021-9037
- Publication type
Article
- DOI
10.1007/s10812-024-01733-9