Identification of Microplastics Based on the Fractal Properties of Their Holographic Fingerprint

Water plastic pollution is a serious problem affecting sealife, marine habitats, and the food chain. Artificial intelligence-enabled coherent imaging has recently shown exciting advances in the field of environmental monitoring, and portable holographic microscopes are good candidates to map the microparticles content of marine waters. The "holographic fingerprint" due to coherent light diffraction is rich in information, fully encoded into the complex wavefront scattered by the sample. Hence, proper analysis of the wavefronts reconstructed from digital holograms can unlock new possibilities in the fields of diagnostics and environmental monitoring. Fractal geometry well describes natural objects and allows inferring added-value information on the way these fill 2D spaces and 3D volumes. The most abundant micron-scale class of objects that populate marine waters consists of microalgae named diatoms, which are of interest as bioindicators of water quality. Here we investigate the fractal properties of holographic patterns of diatoms and microplastics, considering a heterogeneous mixture of five types of plastic materials and 55 different species of microalgae. We show that, different from the case of weak scattering objects, a small set of fractal parameters is able to characterize these two large ensembles. As an applicative example, we carry out classification tests to show the possibility to identify the two classes with high accuracy. This new holographic fractal description of scattering micro-objects could be used in the near future for in situ automatic mapping of microplastic pollutants and for taxonomy of diatoms as water quality bioindicators, screened onboard holographic systems.