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Use of a convolutional neural network for the classification of microbeads in urban wastewater
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Researchers developed a convolutional neural network model to classify and identify microbeads from cosmetic products in urban wastewater, demonstrating that deep learning approaches can provide a practical and scalable standard for automated microplastic characterization in water treatment contexts.
Scientists are on the lookout for a practical model that can serve as a standard for sorting out, identifying, and characterizing microplastics which are common occurrences in water sources and wastewaters. The microbeads (MBs) used in cosmetics and discharged into the sewer systems after use cause substantial microplastics pollution in the receiving waters. Today, the use of plastic microbeads in cosmetics is banned. The existing use cases are to be discontinued within a few years. Yet, there are no restrictions regarding the use of microbeads in a number of industries, cleaning products, pharmaceuticals and medical practices. In this context, the determination and classification of MBs which had so far been discharged to water sources and which continue to be discharged, represent crucial problems. In this work, we examined a new approach for the classification of MBs based on microscopic images. For classification purposes, Convolutional Neural Network (CNN) -a Deep Learning algorithm- was employed, whereas GoogLeNet architecture served as the model. The network is built from scratch, and trained then after tested on a total of 42928 images containing MBs in 5 distinct cleansers. The study performed with the CNN which achieved a classification performance of 89% for MBs in wastewater.
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