Online Particle Detection Based on Polarization Ratio Measurement and Support Vector Machine

A real-time particle detection and recognition method based on polarization ratio measurement and support vector machine is proposed. A dual-wavelength semiconductor laser was used as the light source. Additionally, a highly sensitive avalanche photodiode was employed to measure the two polarization components of scattered light, following which the polarization ratio of the scattered light was measured for particle classification. Furthermore, we combined a support vector machine and a neural network model to further increase the accuracy of particle classification and recognition. For the binary and ternary classifications in our study, the classification accuracy increases from 64% and 83% to 100% and 98%, respectively. This method has excellent application prospects in the fields of pharmacy, cosmetics, industrial production control, and detection.