Customizable Machine-Learning Models for Rapid Microplastic Identification Using Raman Microscopy

Raman spectroscopy is commonly used in microplastics structures that disrupt the development of communal analytical tools. We report a strategy to overcome the issue using a database of highizable analytical tools to be easily created-a feature we demonstrate by creating machine-learning classification models using open-source random-forest, K-nearest neighbors, and multi-layer perceptron algorithms. These models yield >95% classification accuracy when trained on spectroscopic data with spectroscopic data downgraded to 1, 2, 4, or 8 cm(-1) spacings in Raman shift. The accuracy can be maintained even in non-ideal conditions, such as with spectroscopic sampling rates of 1 kHz and when microplastic particles are outside the focal plane of the laser. This approach enables the creation of classification models that are robust and adaptable to varied spectrometer setups and experimental needs.

Automated summary

In this study, Raman spectroscopy was used to address the disruptive effects of microplastic structures on communal analytical tools. By creating a database of highizable analytical tools and developing machine-learning classification models, the issue was overcome with >95% accuracy, even under non-ideal conditions.