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Particle classification based on polarized light scattering and machine learning
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Paper Abstract

This paper focuses the data processing and then the multi-class classification of suspended particles using a new polarized light measurement scheme. Detection of multidimensional polarization parameters keeps the advantages of fast detection speed and non-invasive local analysis of light scattering method, and increases the information dimension of the analyzed particles. However, the polarization indices are numerous and interrelated. It is difficult to complete classification prediction by a few specific indices. More advanced algorithms are needed. In our research, we selected six kinds of representative particles and three typical machine learning algorithms. k-NN, Neural network and SVM methods were used to construct the classification models and solve different classification tasks. By comparison, we evaluated these models in terms of their performance for classification tasks in different aspects. Furthermore, we discuss how to improve the models by feature selection, and a rough prediction of the capability of each polarization index to reflect the particulate features was made.

Paper Details

Date Published: 25 October 2018
PDF: 9 pages
Proc. SPIE 10822, Real-time Photonic Measurements, Data Management, and Processing III, 108220J (25 October 2018); doi: 10.1117/12.2501071
Show Author Affiliations
Dongjian Zhan, Tsinghua Univ. (China)
Nan Zeng, Tsinghua Univ. (China)
Sirui Chen, Tsinghua Univ. (China)
Yonghong He, Tsinghua Univ. (China)
Hui Ma, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 10822:
Real-time Photonic Measurements, Data Management, and Processing III
Ming Li; Bahram Jalali; Keisuke Goda; Kevin K. Tsia, Editor(s)

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