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On the use of machine learning for microplastic identification from holographic phase-contrast signatures
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This study applied machine learning to identify microplastic types from holographic phase-contrast imaging signatures, achieving rapid automated classification. Automated identification tools are important for scaling up microplastic monitoring in marine waters where manual identification is too slow and labor-intensive.
Microplastics (MPs) are not degradable pollutant, yet curbing their consumption to safeguard marine waters is not an easy task. Plastic fragmentation makes detection and discrimination of micron sized elements a difficult task, decelerating plastic recovery policies, especially in marine waters. MPs of all types and shapes are subject to continuous research for defining standard identification protocols. Among the most recent methodologies, digital holography (DH) demonstrated to be an efficient and reliable imaging tool for MPs discrimination. DH furnishes quantitative information of samples in a label-free mode and with flexible refocusing capabilities. DH refocuses the probed object in post-processing by using numerical solutions of the diffraction integral, and can be designed to be compacted for high-throughput field portable microfluidic systems. Here we show the fruitful combination of DH and artificial intelligence to identify and characterize micro-plastics. We show how fractal geometry applied to DH phase-contrast maps plays an important role in providing a set of features that well describe MPs and micro-plankton. Classification results prove the reliability of DH fractal features. Besides, we show that adding multiple information channels to a DH system allows mimicking material specificity at a certain extent. In particular, we discriminate between different types of synthetic and natural microfibers by combining AI and a polarization-resolved DH system. We distinguish microfibers by training several classifiers using features calculated from the Jones matrix and birefringence features. Results corroborate the use of conventional machine learning applied to holographic maps to solve a variety of classification issues in water pollution assays.
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