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.