A hybrid approach for metal element identification by using laser-induced breakdown spectroscopy data

31 January 2023 A hybrid approach for metal element identification by using laser-induced breakdown spectroscopy data

Haofeng Zeng, Zhuoxian Zhang, Sicong Liu

Proceedings Volume 12505, Earth and Space: From Infrared to Terahertz (ESIT 2022); 125050I (2023) https://doi.org/10.1117/12.2664527
Event: Earth and Space: From Infrared to Terahertz (ESIT 2022), 2022, Nantong, China

Abstract

Recycling scrap metal is an important way to protect the ecological environment. Design effective yet efficient techniques to automatically identify recyclable scrap metals is an important task within this topic. Due to the advantages of fast response and high accuracy, laser-induced breakdown spectroscopy (LIBS) recently played an important role in the mineral identification. However, the identification accuracy of peak-seeking is greatly affected by the data quality of the LIBS spectrum, whereas machine learning methods may be greatly affected by the number of training data. By considering the above open issues, this paper proposes a hybrid algorithm based on support vector machine (SVM) and element peak-seeking. By investing the identified difference of the major element (with the largest composition in the alloy) and the general element (with composition more than 1% in the alloy) between peak-seeking and SVM, three integration types (i.e., rejection, partial acceptance, complete acceptance) are defined. The final recognition result is generated according to different integration types and the corresponding integration methods. To verify the feasibility of the proposed approach, a simulated alloy LIBS database was established based on 31 metal elements and the simulated alloy LIBS data according to their compositions. Comparing with the result obtained by only using SVM, the proposed method greatly improved the recognition accuracy. The accuracy of identifying all general elements increased from 8% to 74.5%. Experimental results confirmed the effectiveness of the proposed method in identification of general metal elements in terms of higher detection accuracy.

Citation Download Citation

Haofeng Zeng, Zhuoxian Zhang, and Sicong Liu "A hybrid approach for metal element identification by using laser-induced breakdown spectroscopy data", Proc. SPIE 12505, Earth and Space: From Infrared to Terahertz (ESIT 2022), 125050I (31 January 2023); https://doi.org/10.1117/12.2664527

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KEYWORDS

Laser induced breakdown spectroscopy

Metals

Chemical elements

Databases

Principal component analysis

Machine learning

Titanium

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