Spectral Classification of Large-Scale Blended (Micro)Plastics Using FT-IR Raw Spectra and Image-Based Machine Learning

Microplastics (MPs) are currently recognized as emerging pollutants; their identification and classification are therefore essential during their monitoring and management. In contrast to most studies based on small datasets and library searches, this study developed and compared four machine learning-based classifiers and two large-scale blended plastic datasets, where a 1D convolutional neural network (CNN), decision tree, and random forest (RF) were fed with raw spectral data from Fourier transform infrared spectroscopy, while a 2D CNN used the corresponding spectral images as the input. With an overall accuracy of 96.43% on a small dataset and 97.44% on a large dataset, the 1D CNN outperformed other models. The 1D CNN was the best at predicting environment samples, while the RF was the most robust with less spectral data. Overall, RF and 2D CNNs might be evaluated for plastic identification with fewer spectral data; however, 1D CNNs were thought to be the most effective with sufficient spectral data. Accordingly, an open-source MP spectroscopic analysis tool was developed to facilitate a quick and accurate analysis of existing MP samples.

Automated summary

This study developed and compared four machine learning-based classifiers and used two large-scale blended plastic datasets to identify and classify microplastics (MPs). The 1D CNN model achieved the highest overall accuracy of 96.43% on a small dataset and outperformed other models. It was found that the RF model was the most robust with less spectral data, while the 2D CNN and RF models could be evaluated for plastic identification with fewer spectral data. An open-source MP spectroscopic analysis tool was developed for quick and accurate analysis of existing MP samples.