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

Abstract

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.

Liquid Crystals as Multifunctional Interfaces for Trapping and Characterizing Colloidal Microplastics

“…[104] In addition, MPs in environmental samples are accompanied by organic and inorganic debris, and it will be necessary to either remove the debris from the MPs (e.g., by using filtration) or develop methods that distinguish the LC optical response to MPs from natural debris. [51][52][53][54][55][56][105][106][107] For systems where the assembly patterns and LC responses are complex, AI-based techniques appear well-suited to identify individual plastic components. In particular, in systems containing mixtures of MPs that differ in shape, size, and chemical composition, AI approaches are likely to be needed.…”

Morphological profiling by high-throughput single-cell biophysical fractometry

“…(2) Enabling large-scale single-cell fractometry by the ultrafast QPI operation in multi-ATOM, at the speed at least 100 times faster than the existing QPI modalities that rely on camera technology for image recording 29 . We note that another form of QPI, digital holographic imaging, has also recently been employed to perform fractal analysis of non-biological microparticles and microalgae at a moderate throughput 30 , 31 . Combined with the high-throughput microfluidics platform 8 , 25 , 26 , 32 , this approach enables single-cell fractometry at a throughput of at least 10,000 cells/sec without sacrificing the subcellular imaging resolution.…”

Toward an All-Optical Fingerprint of Synthetic and Natural Microplastic Fibers by Polarization-Sensitive Holographic Microscopy

“…Furthermore, numerical focus extension has been demonstrated to be particularly useful for samples with complex and\or elongated structures. , For all these reasons, DH can be seen as an effective alternative investigation modality on MP and micron-sized fiber fragments (MFFs), especially to overcome the issue related to dry measurements and filtration steps. The DH adverse aspect is the lack of specificity so that, in recent years, many efforts have been made to overcome this gap, for example, combining DH with artificial intelligence (AI) to monitor, identify, and count MPs in heterogeneous water samples; ,, particularly, as some authors have described, a non-contact and non-invasive method that combines 3D phase-contrast imaging with machine learning (ML) based on a proper analysis of holographic phase-contrast patterns relying on fractal geometry. The method is viable to discern between MPs and the microplankton within a wide scale range.…”