Intelligent polarization-sensitive holographic flow-cytometer: Towards specificity in classifying natural and microplastic fibers

Abstract

Micron size fiber fragments (MFFs), both natural and synthetic, are ubiquitous in our life, especially in textile clothes, being necessary in modern society. In the Earth's aquatic ecosystem, microplastic fibers account for ~91% of microplastic pollution, thus deserving notable attention as one of the most alarming ecological problems. Accurate automatic identification of MFFs discharges in specific upstream locations is highly demanded. Computational microscopy based on Digital Holography (DH) and machine learning has been demonstrated to identify microplastics in respect to microalgae. However, DH is a non-specific optical tool, meaning it cannot distinguish different types of plastic materials. On the other hand, materials-specific assessments are pivotal to establish the environmental impact of different textile products and production processes. Spectroscopic assays can be employed to identify microplastics for their intrinsic specificity, although they are generally low-throughput and require large concentrations to enable effective measurements. Conversely, MFFs are usually finely dispersed within a water sample. Here we rely on a polarization-resolved holographic flow cytometer in a Lab-on-Chip (LoC) platform for analysing MFFs. We demonstrate that two important objectives can be achieved, i.e. adding material specificity through polarization analysis while operating in a microfluidic stream modality. Through a machine learning numerical pipeline, natural fibers (i.e. cotton and wool) can be clearly separated from synthetic microfilaments, namely PA6, PA6.6, PET, PP. Moreover, the proposed system can accurately distinguish between different polymers under investigation, thus fulfilling the specificity goal. We extract and select different features from amplitude, phase and birefringence maps retrieved from the digital holograms. These are shown to typify MFFs without the need for sample pre-treatment or large concentrations. The simplicity of the DH method for identifying MFFs in LoC-based flow cytometers could promote the use of polarization resolved field-portable analysis systems suitable for studying pollution caused by washing processes of synthetic textiles.

Keywords: Birefringence; Machine learning; Microfibers; Microplastics; Polarization digital holography.