Abstract

The development of an automatic microplastic (MPs) classification system using spectra is crucial due to the time-consuming and error-prone nature of analyzing individual spectra, especially with a large quantity of MPs. This study presents a classification system using a dual-modality dataset from micro-Fourier Transform Infrared Spectroscopy (μFTIR) for five common polymer types: polypropylene, polystyrene, polyethylene terephthalate, polyethylene, and polyamide. A comparison of machine learning models, including Decision Tree (DT), Extremely Randomized Trees (ET), Support Vector Classifier (SVC), and Multiclass Logistic Regression (LR), is conducted using features extracted by AlexNet, ResNet18, and Vision Transformer (ViT). Notably, the AlexNet with Logistic Regression (AlexNet-LR) model demonstrated exceptional performance, achieving a validation accuracy of 99.03 % and nearly perfect test scores of 99.99 %. However, ResNet18-LR was selected for web deployment due to its shorter training and inference times compared to AlexNet-LR, while still achieving 99 % validation and test accuracy. This highlights the effectiveness of using a dual-modality dataset for precise microplastic classification. MPsSpecClassify, a web-based application, was developed to enable users to efficiently identify MPs and improve microplastic pollution management.