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

Chinese Chemical Letters 2022年第4期33卷 1885-1888页

作者： Mubashir Hussain Xiaolong Liu Jun Zou Jian Yang Zeeshan Ali Hamood Ur Rehman Nongyue He Jianguo Dai Yongjun Tang Innovation Practice Shenzhen PolytechnicShenzhen 518055China School of Materials and Chemical Engineering Hunan Institute of EngineeringXiangtan 411104China Department of Electrical Engineering College of EMENational University of Sciences and TechnologyH-12 IslamabadPakistan State Key Laboratory of Bioelectronics School of Biological Science and Medical EngineeringSoutheast UniversityNanjing 210096China

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/m L. The developed system is anticipated to be helpful in developing rapid testing devices for detecting pathogens ranging between 2 μm to 10 μm.

关键词： Particle’s detection Laser light scattering waveform features Support vector machines Lab-on-chip

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