Unveiling Microplastics with Hyperspectral Raman Imaging: From Macroscale Observations to Real-world Applications

The widespread use of plastic materials, owing to their several advantageous properties, has resulted in a considerable increase in plastic consumption. Consequently, the production of primary and secondary microplastics has also increased. To identify, categorize, and quantify microplastics, several analytical methods, such as thermal analysis and spectroscopic methods, have been developed. They generally offer little insight into the size and shape of microplastics, require time-consuming sample preparation and classification, and are susceptible to background interference. Herein, we created a macroscale hyperspectral Raman method to quickly quantify and characterize large volumes of plastics. Using this approach, we successfully obtained Raman spectra of five different types of microplastics scattered over an area of 12.4mm × 12.4mm within just 500s and perfectly classified these microplastics using a machine learning method. Additionally, we demonstrated that our system is effective for obtaining Raman spectra, even when the microplastics are suspended in aquatic environments or bound to metal-mesh nets. These results highlight the considerable potential of our proposed method for real-world applications. Microplastics are recognized as ecological pollutants due to their sorption capability toward hazardous chemicals and their potential for ingestion by organisms. The rapid localization of microplastic hotspots is possible with the use of real-time and in situ monitoring. The macroscale hyperspectral Raman method allows quick analyzing of large sample volumes or areas, enabling researchers to process a greater number of samples in a shorter amount of time, and lays the foundation for more extensive monitoring campaigns. Additionally, our quick analytic method can offer more extensive information about the distribution and abundance of microplastics, considerably improving our comprehension of microplastic pollution. [Display omitted] •We successfully obtained a hyperspectral Raman image of a multi-component plastic sample covering an area of 12.4mm × 12.4mm in just 500s.•Machine learning model and image processing technique were adapted for imaging and classifying microplastics, sea sand, and airborne dust particles, effectively.•We demonstrated our system's effectiveness in acquiring Raman spectra of microplastics in aquatic environments and on metal mesh nets.