Citation: Mubashir Hussain, Xiaolong Liu, Jun Zou, Jian Yang, Zeeshan Ali, Hamood Ur Rehman, Nongyue He, Jianguo Dai, Yongjun Tang. On-chip classification of micro-particles using laser light scattering and machine learning[J]. Chinese Chemical Letters, ;2022, 33(4): 1885-1888. doi: 10.1016/j.cclet.2021.09.044 shu

On-chip classification of micro-particles using laser light scattering and machine learning

Mubashir Hussain ^a ,
Xiaolong Liu ^a ,
Jun Zou ^b ,
Jian Yang ^a ,
Zeeshan Ali ^a ,
Hamood Ur Rehman ^c ,
Nongyue He ^{d, *,} ,
Jianguo Dai ^{a, *,} ,
Yongjun Tang ^{a, *,}

a.
Innovation Practice, Shenzhen Polytechnic, Shenzhen 518055, China
b.
School of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan 411104, China
c.
Department of Electrical Engineering, College of EME, National University of Sciences and Technology, H-12 Islamabad, Pakistan
d.
State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China

nyhe1958@163.com

nyhe@seu.edu.cn

jgdai@szpt.edu.cn

tangyongjun@szpt.edu.cn

Received Date: 17 July 2021
Revised Date: 9 September 2021
Accepted Date: 13 September 2021
Available Online: 20 September 2021

Figures(3)

The rapid detection of microparticles exhibits a broad range of applications in the field of science and technology. The proposed method differentiates and identifies the 2 µm and 5 µm sized particles using a laser light scattering. The detection method is based on measuring forward light scattering from the particles and then classifying the acquired data using support vector machines. The device is composed of a microfluidic chip linked with photosensors and a laser device using optical fiber. Connecting the photosensors and laser device using optical fibers makes the device more diminutive in size and portable. The prepared sample containing microspheres was passed through the channel, and the surrounding photosensors measured the scattered light. The time-domain features were evaluated from the acquired scattered light, and then the SVM classifier was trained to distinguish the particle's data. The real-time detection of the particles was performed with an overall classification accuracy of 96.06%. The optimum conditions were evaluated to detect the particles with a minimum concentration of 0.2 µg/mL. The developed system is anticipated to be helpful in developing rapid testing devices for detecting pathogens ranging between 2 µm to 10 µm.
- Particle's detection,
- Laser light scattering,
- Waveform features,
- Support vector machines,
- Lab-on-chip
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沈阳化工大学材料科学与工程学院沈阳 110142