Lensless shadow microscopy-based shortcut analysis strategy for fast quantification of microplastic fibers released to water

• Published in: Water research (Oxford) Vol. 258; p. 121758
• Main Authors: Su, Yu, Yang, Chenqi, Peng, Yao, Yang, Cheng, Wang, Yanhua, Wang, Yong, Yan, Feng, Xing, Baoshan, Ji, Rong
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.07.2024
• Subjects: Algorithms; automatic detection; Deep learning algorithms; detection limit; Environmental Monitoring - methods; filtration; Lensless shadow microscopy; melamine; Microplastic fibers; Microplastics; Microscopy - methods; On-chip imaging; water; Water - chemistry; Water Pollutants, Chemical - analysis; water pollution; Water pollution monitoring; Lensless shadow microscopy; Microplastic fibers; On-chip imaging; Water pollution monitoring; Deep learning algorithms
• ISSN: 0043-1354; 1879-2448; 1879-2448
• DOI: 10.1016/j.watres.2024.121758

•A high-resolution field-of-view LSM was developed for on-chip imaging of MPFs.•The LSM imaging of MPFs was faster than routinely on filter membranes (90 s vs. minutes).•Deep learning algorithms were trained to determine number and size of MPFs in images.•The algorithms analyzed MPFs much quicker than manual measurement (60 s vs. hours).•On-chip imaging coupled with algorithms is a new strategy for rapid MPF quantification. Fast quantification is the primary challenge in monitoring microplastic fiber (MPF) pollution in water. The process of quantifying the number of MPFs in water typically involves filtration, imaging on a filter membrane, and manual counting. However, this routine workflow has limitations in terms of speed and accuracy. Here, we present an alternative analysis strategy based on our high-resolution lensless shadow microscope (LSM) for rapid imaging of MPFs on a chip and modified deep learning algorithms for automatic counting. Our LSM system was equipped with wide field-of-view submicron-pixel imaging sensors (>1 cm2; ∼500 nm/pixel) and could simultaneously capture the projection image of >3-μm microplastic spheres within 90 s. The algorithms enabled accurate classification and detection of the number and length of >10-μm linear and branched MPFs derived from melamine cleaning sponges in each image (∼0.4 gigapixels) within 60 s. Importantly, neither MPF morphology (dispersed or aggregated) nor environmental matrix had a notable impact on the automatic recognition of the MPFs by the algorithms. This new strategy had a detection limit of 10 particles/mL and significantly reduced the time of MPF imaging and counting from several hours with membrane-based methods to just a few minutes per sample. The strategy could be employed to monitor water pollution caused by microplastics if an efficient sample separation and a comprehensive sample image database were available. [Display omitted]