Identification accuracy improvement for steel species using a least squares support vector machine and laser-induced breakdown spectroscopy
| Autor: | Yangmin Guo, Shisong Tang, Changjin Che, Yanwu Chu, Lianbo Guo, Xiaomei Lin, Yun Tang, Jingjun Lin |
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| Rok vydání: | 2018 |
| Předmět: |
business.industry
010401 analytical chemistry Alloy steel Pattern recognition engineering.material Linear discriminant analysis 01 natural sciences 0104 chemical sciences Analytical Chemistry 010309 optics Support vector machine Identification (information) Statistical classification 0103 physical sciences Partial least squares regression Least squares support vector machine engineering Laser-induced breakdown spectroscopy Artificial intelligence business Spectroscopy Mathematics |
| Zdroj: | Journal of Analytical Atomic Spectrometry. 33:1545-1551 |
| ISSN: | 1364-5544 0267-9477 |
| DOI: | 10.1039/c8ja00216a |
| Popis: | The classification of discarded alloy steel for recycling purposes and the identification of the alloy steels used in spare parts are very important in modern society. In this work, two typical classification methods, partial least squares discriminant analysis (PLS-DA) and a support vector machine (SVM), were used to study the classification of steels with similar constituents. Forty (40) steel species were detected using laser-induced breakdown spectroscopy (LIBS), and the identification by the PLS-DA and SVM models was 96.25% and 95% accurate, respectively. Based on these two classification algorithms, the least squares support vector machine (LSSVM) algorithm was used to further improve the identification accuracy. The kernel function parameter and error penalty factor of the LSSVM model were 0.4353 and 6.9644, respectively. The results showed that the identification accuracy reached 100%; therefore, combining LIBS with the LSSVM algorithm proved to be an effective approach for accurately identifying steel species with similar constituents. |
| Databáze: | OpenAIRE |
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