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Chen Y, Wang H, Liao R, Li H, Wang Y, Zhou H, Li J, Huang T, Zhang X, Ma H. Rapidly Measuring Scattered Polarization Parameters of the Individual Suspended Particle with Continuously Large Angular Range. BIOSENSORS 2022; 12:bios12050321. [PMID: 35624622 PMCID: PMC9138884 DOI: 10.3390/bios12050321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 05/05/2022] [Accepted: 05/09/2022] [Indexed: 11/16/2022]
Abstract
Suspended particles play a vital role in aquatic environments. We propose a method to rapidly measure the scattered polarization parameters of individual suspended particles with continuously large angular range (PCLAR), from 60° to 120° in one shot. A conceptual setup is built to measure PCLAR with 20 kHz; to verify the setup, 10 μm-diameter silica microspheres suspended in water, whose PCLAR are consistent with those simulated by Mie theory, are measured. PCLAR of 6 categories of particles are measured, which enables high-accuracy classification with the help of a convolutional neural network algorithm. PCLAR of different mixtures of Cyclotella stelligera and silica microspheres are measured to successfully identify particulate components. Furthermore, classification ability comparisons of different angular-selection strategies show that PCLAR enables the best classification beyond the single angle, discrete angles and small-ranged angles. Simulated PCLAR of particles with different size, refractive index, and structure show explicit discriminations between them. Inversely, the measured PCLAR are able to estimate the effective size and refractive index of individual Cyclotella cells. Results demonstrate the method’s power, which intrinsically takes the advantage of the optical polarization and the angular coverage. Future prototypes based on this concept would be a promising biosensor for particles in environmental monitoring.
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Affiliation(s)
- Yan Chen
- School of Physics and Optoelectronic Engineering, Yangtze University, Jingzhou 434023, China; (Y.C.); (H.Z.)
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (H.W.); (H.L.); (Y.W.); (J.L.); (T.H.); (H.M.)
| | - Hongjian Wang
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (H.W.); (H.L.); (Y.W.); (J.L.); (T.H.); (H.M.)
| | - Ran Liao
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (H.W.); (H.L.); (Y.W.); (J.L.); (T.H.); (H.M.)
- Correspondence:
| | - Hening Li
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (H.W.); (H.L.); (Y.W.); (J.L.); (T.H.); (H.M.)
| | - Yihao Wang
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (H.W.); (H.L.); (Y.W.); (J.L.); (T.H.); (H.M.)
| | - Hu Zhou
- School of Physics and Optoelectronic Engineering, Yangtze University, Jingzhou 434023, China; (Y.C.); (H.Z.)
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (H.W.); (H.L.); (Y.W.); (J.L.); (T.H.); (H.M.)
| | - Jiajin Li
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (H.W.); (H.L.); (Y.W.); (J.L.); (T.H.); (H.M.)
| | - Tongyu Huang
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (H.W.); (H.L.); (Y.W.); (J.L.); (T.H.); (H.M.)
| | - Xu Zhang
- College of Chemistry and Environmental Engineering, Yangtze University, Jingzhou 434020, China;
| | - Hui Ma
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (H.W.); (H.L.); (Y.W.); (J.L.); (T.H.); (H.M.)
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Machine Learning Powered Microalgae Classification by Use of Polarized Light Scattering Data. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073422] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
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.
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