Leveraging deep learning for automatic recognition of microplastics (MPs) via focal plane array (FPA) micro-FT-IR imaging

Abstract

The fast and accurate identification of MPs in environmental samples is essential for the understanding of the fate and transport of MPs in ecosystems. The recognition of MPs in environmental samples by spectral classification using conventional library search routines can be challenging due to the presence of additives, surface modification, and adsorbed contaminants. Further, the thickness of MPs also impacts the shape of spectra when FTIR spectra are collected in transmission mode. To overcome these challenges, PlasticNet, a deep learning convolutional neural network architecture, was developed for enhanced MP recognition. Once trained with 8000 + spectra of virgin plastic, PlasticNet successfully classified 11 types of common plastic with accuracy higher than 95%. The errors in identification as indicated by a confusion matrix were found to be caused by edge effects, molecular similarity of plastics, and the contamination of standards. When PlasticNet was trained with spectra of virgin plastic it showed good performance (92%+) in recognizing spectra that had increased complexity due to the presence of additives and weathering. The re-training of PlasticNet with more complex spectra further enhanced the model's capability to recognize complex spectra. PlasticNet was also able to successfully identify MPs despite variations in spectra caused by variations in MP thickness. When compared with the performance of the library search in identifying MPs in the same complex dataset collected from an environmental sample, PlasticNet achieved comparable performance in identifying PP MPs, but a 17.3% improvement. PlasticNet has the potential to become a standard approach for rapid and accurate automatic recognition of MPs in environmental samples analyzed by FPA FT-IR imaging.

Keywords: CNN; FPA micro-FT-IR imaging; MP identification; Spectra classification; Spectra variation.