The pervasive contamination of aquatic environments by microplastic particles necessitates the development of rapid, cost-effective and field-deployable detection methodologies to complement established but laboratory-bound spectroscopic techniques such as Fourier-transform infrared and Raman microscopy. The demand for field-suitable methods with a broad accessibility comes from researchers themselves. In this review we systematically examine recent advances in optical methods for microplastics identification with a particular emphasis on birefringence as a key diagnostic feature of partially crystalline synthetic polymers. In particular, we analyze three complementary technological directions: liquid crystal-based sensors that exploit orientational order disruptions at interfaces for label-free microplastics detection; polarization holographic imaging combined with machine learning for high-throughput particle classification; and on-chip polarization light microscopy enabling compact and portable analyzing systems. Liquid crystal platforms demonstrate exceptional sensitivity to submicron particles and enable real-time visualization of microplastics aggregation at aqueous interfaces, though they currently lack polymer-specific chemical identification. Conversely, smart polarization holography integrated with Stokes polarimetry and deep learning algorithms achieves over 90% accuracy in distinguishing microplastics from natural particles while processing up to 10,000 particles per minute. Emerging on-chip polarized light microscopy offers a pathway toward miniaturized, low-cost devices suitable for field applications. By synthesizing insights from foundational studies, this review identifies convergent interdisciplinary trends—particularly the integration of artificial intelligence with multimodal optical imaging—and outlines persistent challenges including standardization, interference from natural organic matter, and the transition from laboratory prototypes to robust field-deployable instruments. The systematization of birefringence-based approaches aims to guide future research towards integrated monitoring systems capable of addressing water quality concerns.
Kudreyko et al. (Mon,) studied this question.