Open-set convolutional neural network for infrared spectral classification of environmentally sourced microplastics

Abstract Microplastics (MPs) are considered a global emerging threat. Environmental MP monitoring is essential for understanding and managing MP pollution. Here, a convolutional neural network model was developed for the infrared spectral classification of MPs from the environment. During model development, data were collected from multiple sources (e.g. OpenSpecy) to ensure sufficient intra-class diversity, for improving generalization. Also, the uncertainty threshold and OpenMax (an open-set recognition technique) were applied to enable the model to handle unknown classes (classes not included in model training) that may be present in real-world environmental samples. Furthermore, a targeted data augmentation strategy was proposed to improve model performance across varying infrared spectral ranges. Results show that the targeted data augmentation improved classification accuracy by up to 4.6%. In the optimal uncertainty threshold range of 0.87 ± 0.01, the model achieved 93.1% accuracy on both the known-class test spectra (15,741 spectra) and the unknown-class test spectra (6,279 spectra). OpenMax also enabled the model to effectively handle the unknown-class test spectra. Notably, its ability to recognize unknown classes is more prominent at lower uncertainty thresholds. The strategies proposed here could potentially be extended to other fields, e.g., food safety, pharmaceutical quality control, where spectral classification is critical.