Rapid detection and quantification of Nile Red-stained microplastic particles in sediment samples.

The distribution and migration processes of microplastics (MPs) in the marine sediments have yet to be fully elucidated. To estimate the contamination levels and distribution patterns, and develop countermeasures, the amount of MPs must be understood. Rapid and efficient processing of numerous samples is also needed to detect and determine MP contamination. However, whatever the sample of interest, MP analysis is time consuming. This is especially the case for deep-sea sediments, where the particle sizes are small and pretreatment processes are complex and time-consuming. To address the need for rapid and efficient detection of MPs, we propose a novel method for automatically identifying and counting Nile Red (NR)-stained sedimentary MP particles captured under a stereoscopic fluorescence microscope. In this study, we demonstrated the utility of the developed system by comparing its recovery rate and analysis time with those of the conventional methods used for manual processing. The developed method can efficiently detect MPs of sizes between 18 and 500 µm and classify them as fibers or grains (or fragments). This means that our method can efficiently detect MPs as small as 100 µm found in deep-sea sediments. The semi-automated MP detection system gave a counting time of 4.2-8.8 s per particle-as the number of particles increases, the analysis time per particle decreases. Similarly, when the number of particles counted using a stereomicroscope and image analysis software was set at 100, the automatic measurement method using a flow cell could measure 50-80% of the total number of particles, depending on the type of MPs. By using artificial particulate and fibrous MPs as training data and combining them with a machine learning system, we were able to build a system that can classify both types with 98% accuracy (100% for fibers and 96% for grains). In natural samples, approximately 150 µm (20-350 µm in range) MPs were detected, and the number was consistent with previous studies. This demonstrates the effectiveness of the method we developed. We established a rapid detection method for the number and form of MPs using a continuous semi-automated method, combining NR staining and artificial intelligence. Although this method does not allow the identification of polymer types, it enables that rapid and reliable quantification of MPs numbers. The new method established in this study is expected to improve the accuracy of information on the distribution, destination, and quantity of MPs. It is also relatively easy to use and can transfer technology in various fields, from citizen science to rapid diagnosis on research vessels in the open ocean.