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 micro -plastics 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 suscep-tible 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 12.4 mm x 12.4 mm within just 550 s 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 envi-ronments or bound to metal-mesh nets. These results highlight the considerable potential of our proposed method for real-world applications.

Automated summary

The widespread use of plastic materials has led to an increase in plastic consumption, resulting in the production of primary and secondary microplastics. Current analytical methods lack insight into the size and shape of microplastics, and are time-consuming and susceptible to interference. In this study, a hyperspectral Raman method was developed to quickly quantify and characterize large volumes of plastics. The system successfully obtained Raman spectra of microplastics and effectively classified them, demonstrating its potential for real-world applications.