24 October 2017 A method derived from genetic algorithm, principal component analysis and artificial neural networks to enhance classification capability of laser-Induced breakdown spectroscopy
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Proceedings Volume 10461, AOPC 2017: Optical Spectroscopy and Imaging; 1046107 (2017) https://doi.org/10.1117/12.2281493
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China
Abstract
Selection of characteristic lines is a critical work for both qualitative and quantitative analysis of laser-induced breakdown spectroscopy; it usually needs a lot of time and effort. A novel method combining genetic algorithm, principal component analysis and artificial neural networks (GA-PCA-ANN) is proposed to automatically extract the characteristic spectral segments from the original spectra, with ample feature information and less interference. On the basis of this method, three selection manners: selecting the whole spectral range, optimizing a fixed-length segment and optimizing several non-fixed-length sub-segments were analyzed; and their classification results of steel samples were compared. It is proved that selecting a fixed-length segment with an appropriate segment length achieves better results than selecting the whole spectral range; and selecting several non-fixed-length sub-segments obtains the best result with smallest amount of data. The proposed GA-PCA-ANN method can reduce the workload of analysis, the usage of bandwidth and cost of spectrometers. As a result, it can enhance the classification capability of laser-induced breakdown spectroscopy.
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P. Zhang, P. Zhang, L. X. Sun, L. X. Sun, H. Y. Kong, H. Y. Kong, H. B. Yu, H. B. Yu, M. T. Guo, M. T. Guo, P. Zeng, P. Zeng, } "A method derived from genetic algorithm, principal component analysis and artificial neural networks to enhance classification capability of laser-Induced breakdown spectroscopy", Proc. SPIE 10461, AOPC 2017: Optical Spectroscopy and Imaging, 1046107 (24 October 2017); doi: 10.1117/12.2281493; https://doi.org/10.1117/12.2281493
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