Application of laser speckles and deep learning in discriminating between the size and concentrations of supermicroplastics

Abstract

In the study, we have combined speckle metrology and deep learning tools in discriminating supermicroplastics (SMPs) sizes and concentrations. Polystyrene spheres used as SMPs were introduced in the container filled with salt water. The particles were illuminated with the 635 nm laser, and the scattered light was recorded with the CMOS camera. For the simulation studies, different sized particles (2 µm, 20 µm, and 200 µm) and concentrations were used. Speckles were analyzed using a deep learning algorithm to distinguish particles sizes and concentrations. It was demonstrated that the convolutional neural networks (CNNs) trained with speckles could distinguish feeble differences in speckle patterns depending on particle sizes and concentrations. Deep learning was found to be capable of distinguishing different particle sizes and concentrations from the speckle patterns. We suggest our combined technique could be effectively used in investigating MPs in the ocean where it remains challenging to conduct in situ surveys and obtain the SMP distribution in deeper regions of the ocean.