Microplastic Identification via Holographic Imaging and Machine Learning

Abstract

Microplastics (MPs) are a major environmental concern due to their possible impact on water pollution, wildlife, and the food chain. Reliable, rapid, and high‐throughput screening of MPs from other components of a water sample after sieving and/or digestion is still a highly desirable goal to avoid cumbersome visual analysis by expert users under the optical microscope. Here, a new approach is presented that combines 3D coherent imaging with machine learning (ML) to achieve accurate and automatic detection of MPs in filtered water samples in a wide range at microscale. The water pretreatment process eliminates sediments and aggregates that fall out of the analyzed range. However, it is still necessary to clearly distinguish MPs from marine microalgae. Here, it is shown that, by defining a novel set of distinctive “holographic features,” it is possible to accurately identify MPs within the defined analysis range. The process is specifically tailored for characterizing the MPs' “holographic signatures,” thus boosting the classification performance and reaching accuracy higher than 99% in classifying thousands of items. The ML approach in conjunction with holographic coherent imaging is able to identify MPs independently from their morphology, size, and different types of plastic materials.

A Review of Holography in the Aquatic Sciences: In situ Characterization of Particles, Plankton, and Small Scale Biophysical Interactions

“…For this to work, is it key to develop not only fast and efficient automated classification algorithms, but also those feasible to be developed in a light-weight architecture (Guo et al, 2021). Previous and ongoing efforts toward automated classification of detected particles using various machine learning techniques, including convolutional neural networks, still need holograms to be reconstructed and processed (Davies et al, 2015;Bianco et al, 2020). Recent work has focused on the application of deep learning techniques to extract features from the interference patterns recorded on the raw holograms (Shao et al, 2020;Guo et al, 2021).…”

“…Consequently, improvements in monitoring and detection techniques of microplastics have become crucial to understanding this issue (Garaba and Dierssen, 2018;Mai et al, 2018). Recent laboratory studies have shown that holography can be a valuable tool in the detection of microplastics in the ocean (Merola et al, 2018;Bianco et al, 2020). Future efforts geared toward testing these in field environments are needed.…”

Neuroblastoma Cells Classification Through Learning Approaches by Direct Analysis of Digital Holograms

“…Quantitative phase imaging provides highly informative content if compared to other imaging modalities, e.g. bright-field or fluorescence microscopy, allowing the calculation of unique cells characteristics [3]. A well-established analysis method to classify cells phenotyping relies on the joint use of features engineering and suitable classifiers, thus implementing conventional Machine Learning (ML) paradigms.…”

“…A well-established analysis method to classify cells phenotyping relies on the joint use of features engineering and suitable classifiers, thus implementing conventional Machine Learning (ML) paradigms. This has been exploited for blood cells characterization [4], sick cells identification [5], marine micro-organisms identification [3], just to name a few. Recently, a sub-class of ML, namely Deep Learning, has gained credits as the elective approach for advanced image analysis in microscopy [6], [7], [8], [9], [10], [11], [12].…”

Silicon Nanomembrane Filtration and Imaging for the Evaluation of Microplastic Entrainment along a Municipal Water Delivery Route

“…An ideal way to prepare samples for these techniques would be to utilize a direct filtration method to concentrate the particulate mass for imaging and characterization. Effective machine learning techniques can help categorize particles given clear imaging criteria [15]. Simply drying a drop of liquid and immobilizing particles on a surface can create samples for characterization on a smaller scale, but this method cannot interrogate larger liquid volumes for sparse numbers of particles efficiently.…”