Automated method for routine microplastic detection and quantification

The “Microplastome” – A Holistic Perspective to Capture the Real-World Ecology of Microplastics

“…Of the three quantitative parameters (absolute abundance, mass, and volume), absolute abundance quantified by number of individuals should be the most labor-efficient one as well as the one with the lowest detection limit for particle size, which can be more accurately and rapidly determined with the development of high-throughput microplastic identification technology. − Volume, which incorporates the three-dimensional (3D) properties of microplastics, is perhaps the most comprehensive metric . The overall volume of a microplastome can be estimated using the newly established Barchiesi model .…”

“…With the rapid development of high-throughput and automated identification, quantification, and classification technologies for microplastics, large-scale microplastic data can be more readily available to researchers. − In this context, this work would serve as a guide for investigating the distribution, transport, effect, and risk of microplastics from massive and multidimensional microplastic data. We anticipate that this new paradigm will serve as a catalyst for the generation of novel insights and concepts within the scientific community and among policymakers.…”

“…The detection technology for microplastics is currently advancing at a rapid pace. The successful application and refinement of advanced detection techniques, such as mass spectrometry (MS), micro-Fourier-transform infrared (μ-FTIR) spectroscopy, micro-Raman (μ-Raman) spectroscopy, laser direct infrared (LD-IR) spectroscopy, and Nile Red (NR) fluorescent staining, as well as their combinations, in microplastic research have significantly enhanced the efficiency of identifying and quantifying microplastics in complex biological and environmental samples. − Furthermore, with the evolution of artificial intelligence (AI), the automatic identification and classification of microplastics based on machine learning are also becoming increasingly sophisticated. − The swift progress in microplastic detection and analysis technology facilitates the acquisition of extensive microplastic data. In this context, it becomes particularly vital to approach microplastic research with a holistic perspective to unveil the distribution, fate, and transport patterns of microplastics, as well as their impacts on the environment and organisms.…”

“… 29 − 31 Furthermore, with the evolution of artificial intelligence (AI), the automatic identification and classification of microplastics based on machine learning are also becoming increasingly sophisticated. 32 − 34 The swift progress in microplastic detection and analysis technology facilitates the acquisition of extensive microplastic data. In this context, it becomes particularly vital to approach microplastic research with a holistic perspective to unveil the distribution, fate, and transport patterns of microplastics, as well as their impacts on the environment and organisms.…”

Image processing techniques for measuring primary microplastic abundance in various of dispersant

“…These include long detection times, imprecise detection rates, high equipment costs, and the need for complex tools. According to previously published material, the traditional manual approach that makes use of a microscope is where the developed methods for measuring MP quantification begin [14,15,16], partially automatic using methods for coloring [17,18,19,20], and technology that is being developed [21,22] with image processing, and can quantify microplastics automatically, in real time, and objectively. Therefore, this study proposes a new method for measuring primary microplastics using image processing techniques approach.…”