High-Throughput Microplastic Differentiation Using Pixel-Based Polarization Classification

Microplastics (MPs), as global emerging contaminants, pose a persistent threat to ecosystems and human health. However, current MP differentiation techniques are typically time-consuming and labor-intensive, limiting their applicability for environmental monitoring. This paper proposes a high-throughput MP differentiation method called pixel-based polarization classification (PBPC). The setup can acquire backscattered Mueller matrix images of multiple MPs. For each pixel of a single MP, 59 polarization parameters are derived from its Mueller matrix to represent a pixel polarization vector (PPV). A total of 20 types of MPs are measured in the data set, with at least 1 million PPVs for each type. Three different machine learning classifiers are trained respectively, and the optimal one achieves an accuracy of 90.24% in PPV classification. The results are visualized as the region classification image, and the pixel classification proportions of each MP are further evaluated. In this work, the high-throughput capability of PBPC to differentiate MPs with diverse morphologies is demonstrated by standard samples. For environmental MP samples, the detection results remain consistent with mu-FTIR, validating the robustness and generalization of PBPC. Moreover, the characterization of PPVs is analyzed, and the impact of abnormal pixels caused by imaging overexposure is quantitatively assessed. A detailed differentiation of two MPs with varying densities, HDPE and LDPE, highlights PBPC's sensitivity to subtle structural differences. This work demonstrates PBPC's potential as a promising tool for high-throughput MP differentiation, which would facilitate environmental monitoring and MP pollution assessment.