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- Title
Study on the Screening Method of Aerosol Spectral Data by LIBS Combined with Dictionary Learning.
- Authors
LI Yuting; WEI Zhong; CHEN Jing; CHEN Wenjie; YUAN Tongshan; WANG Qixuan; JIANG Yan; DING Yu
- Abstract
Aerosol is an important component of the atmosphere, which has an important impact on climate and ecological environment. When laser-induced breakdown spectroscopy (LIBS) is used for aerosol detection, a large number of invalid spectra are collected due to the discrete distribution of aerosols. In this study, a method to filter effective spectral data by combining dictionary learning, a K-SVD-SVM method, was proposed. By preparing seven different concentrations of NaCl aerosol samples, 5 000 spectral data of 10% NaCl solution were selected for classification, of which 70% were used as training sets and 30% as test sets. When the number of dictionary basis vectors was set to 3, the model classification performance was optimal, and the harmonic mean(F1) of accuracy, precision, recall, precision, and recall went to 96%, 95%, 95% and 0.95, respectively. In addition, the K-SVD-SVM method was used to screen seven aerosol samples with different concentrations, and the GA-ELM model was input for quantitative analysis. At the same time, the unscreened original spectral data was input into the quantitative model for comparison. The RMSE and R² of the unscreened original data test set were 0.030 3 and 0.872 6, and were increased to 0.018 7 and 0.980 9 after screening the spectrum. The results showed that the K-SVD-SVM method had good classification performance, and the effective data selected by this method could provide data support for quantitative analysis of elements in aerosols.
- Publication
Chinese Journal of Inorganic Analytical Chemistry / Zhongguo Wuji Fenxi Huaxue, 2024, Vol 14, Issue 2, p176
- ISSN
2095-1035
- Publication type
Article
- DOI
10.3969/j.issn.2095-1035.2024.02.006