Machine Learning Powered Microalgae Classification by Use of Polarized Light Scattering Data

被引:15
|
作者
Zhuo, Zepeng [1 ,2 ]
Wang, Hongjian [2 ]
Liao, Ran [2 ]
Ma, Hui [1 ,2 ]
机构
[1] Tsinghua Univ, Dept Phys, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Shenzhen Int Grad Sch, Shenzhen 518055, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 07期
基金
中国国家自然科学基金;
关键词
polarized light scattering; suspended particles; machine learning; REAL-TIME; IDENTIFICATION; PHYTOPLANKTON; ALGAE;
D O I
10.3390/app12073422
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Microalgae are widely distributed in the ocean, which greatly affects the ocean environment. In this work, a dataset is presented, including the polarized light scattering data of 35 categories of marine microalgae. To analyze the dataset, several machine learning algorithms are applied and compared, such as linear discrimination analysis (LDA) and two types of support vector machine (SVM). Results show that non-linear SVM performs the best among these algorithms. Then, two data preparation approaches for non-linear SVM are compared. Subsequently, more than 10 categories of microalgae out of the dataset can be identified with an accuracy greater than 0.80. The basis of the dataset is shown by finding the categories independent to each other. The discussions about the performance of different incident polarization of light gives some clues to design the optimal incident polarization of light for future instrumentation. With this proposed technique and the dataset, these microalgae can be well differentiated by polarized light scattering data.
引用
收藏
页数:11
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