提出一种基于偏振比检测和支持向量机的颗粒物实时检测与分类方法。采用双波长的半导体激光器作为光源，使用高灵敏度的雪崩光电二极管分别测量散射光的两个偏振分量，计算出单个颗粒散射光的偏振比，从而实现颗粒分类与识别。结合支持向量机算法与神经网络模型可进一步提升颗粒物的分类精度。针对所研究的二元及三元分类场景，分类精度分别由64%和83%提升至100%和98%。该方法在制药、化妆品以及工业生产控制与检测等领域具有很好的应用前景。

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.