Digital holographic microplastics detection and characterization in heterogeneous samples via deep learning

Abstract

Detecting and quantifying microplastic particles have become important problems in environmental monitoring in recent years. In the natural environment, microplastic and nanoplastic particles are often mixed with large pieces of plastic, microalgae, microorganisms, and leaf fragments, etc., making them difficult to be distinguished. In addition, the microplastics themselves are made of different materials and have various shapes. As a result, the conventional classification methods based mostly on morphological characteristics cannot accurately classify microplastics in a complex environment, which brings great challenges to their detection and analysis. We have developed a classification and detection method based on digital holographic imaging and deep learning, which effectively classifies the types of microplastic particles by using the holographic interference fringe features of microplastic particles. With heterogeneous samples containing microplastic particles, microalgae and other substances, we are able to demonstrate the strength of our technique in the detection and characterization of the microplastics. Indeed, the results show that the deep learning network can automatically extract the features of holographic images of different particles in such samples, and delineate with good sensitivity the feature differences in the digital holograms that are caused by optical path differences introduced by various kinds of particles. Furthermore, this holographic feature-based classification is not affected by material morphological characteristics and has good robustness.