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Statistical Mueller matrix driven discrimination of suspended particles
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Researchers developed a statistical method using polarized light scattering to distinguish between different types of suspended particles. This technique has potential applications for identifying and characterizing microplastic particles in water samples.
An effective method to calculate the statistical Mueller matrix (SMM) of suspended particles based on polarized light scattering is presented that takes advantage of the Stokes vectors measurement of individual particles. The calculation method of the SMM is derived based on statistics. Experimental results of Microcystis samples confirm that the SMM can characterize cells of different states. Then, pairwise contrast experiments indicate the great prospect of the SMM applied on the discrimination of suspended particles. It helps to find the optimal incident polarization state to discriminate suspended particles, and it has optimal discrimination ability. The parameter derived from the SMM can simultaneously discriminate particles including microalgae, microplastics, and sand-like particles.
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