1. Industrial polymers classification using laser-induced breakdown spectroscopy combined with self-organizing maps and K-means algorithm
- Author
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Yangmin Guo, Qianqian Sun, Jiaming Li, Lianbo Guo, Shisong Tang, Duan Jun, and Yun Tang
- Subjects
Self-organizing map ,chemistry.chemical_classification ,Materials science ,010401 analytical chemistry ,k-means clustering ,Atomic emission spectroscopy ,02 engineering and technology ,Polymer ,021001 nanoscience & nanotechnology ,01 natural sciences ,Atomic and Molecular Physics, and Optics ,0104 chemical sciences ,Electronic, Optical and Magnetic Materials ,chemistry ,visual_art ,visual_art.visual_art_medium ,Laser-induced breakdown spectroscopy ,Electrical and Electronic Engineering ,Polycarbonate ,0210 nano-technology ,Cluster analysis ,Spectroscopy ,Biological system - Abstract
To extend the industrial polymer species classification and improve its efficiency. Laser-induced breakdown spectroscopy (LIBS) combined with unsupervised learning algorithms of self-organizing maps (SOM) and K-means was employed to differentiate industrial polymers in the open air. Only the intensities of non-metallic lines, including two molecular band lines (C-N(0,0) 388.3 nm and C2(0,0) 516.5 nm) and four atomic emission lines (C I 247.9 nm, H I 656.3 nm, N I 746.9 nm and O I 777.3 nm) were used. Firstly, the SOM neural network with adjusting spectral weightings (ASW) was applied to separate 20 kinds of polymers preliminarily. The results were obtained in the output space which indicated that 18 kinds of polymers have been separated except for polycarbonate (PC) and polystyrene (PS). Afterwards, the K-means clustering algorithm was utilized to separate PC and PS. The accuracy of the industrial polymers classification for 20 kinds of polymers was 99.2%. It demonstrated that the feasibility of clustering of industrial polymers using LIBS.
- Published
- 2018