Adaptable microplastic classification using similarity learning on µFTIR spectra collected from µFTIR focal plane array imaging

Deep learning on micro-Fourier transform infrared (µFTIR) spectra has the potential to provide a reliable, automated approach to classify and identify microplastics. However, deep learning models often come with certain limitations, including exhaustive dataset requirements, overfitting, and the need to retrain when new classes are introduced or new data are substantially different from the training set. This work explores a similarity learning approach to training deep learning models to address these issues for microplastic classification. A one-dimensional convolutional neural network (CNN) was trained by similarity learning on a dataset of µFTIR spectra acquired from 45 manufactured microplastic samples of 11 plastic compositions and compared with cross-entropy training of the same CNN architecture as well as classical machine learning algorithms. The CNN trained by similarity learning consistently yielded the highest accuracies (up to a 0.973 F1-score) across the multiple classes of microplastics. Notably, despite only training on microplastic spectra collected under pristine conditions, the CNN trained via similarity learning maintained the highest accuracy (up to a 0.905 F1-score) on a "noisy" dataset consisting of microplastics spiked onto filters with high amounts of exogenous background material. Furthermore, similarity learning combined with support-vector classifiers also allowed for the detection and separation of microplastic polymer-composition classes not contained in the training set. Overall, this approach is able to achieve high accuracy in microplastic classification despite challenges posed by the diversity of microplastic polymer compositions, limited time and resources for dataset preparation, and high amounts of background noise that are common in FTIR spectra collected from real-world microplastic samples.