A Fuzzy Convolutional Neural Network for the Classification of Aerosol Particle Mass Spectral Patterns Generated by Single-Particle Mass Spectrometry

Air quality control is essential for assessing the impact on human health, environment and climate. Single-particle mass spectrometry (SPMS) is a powerful measurement tool for providing the chemical composition of air-transported particle matter (PM) in real-time. Common methods to classify PM according to characteristic ion patterns in their mass spectra are based on clustering methods which generally require manual postprocessing and are not suitable for real-time automated air quality monitoring. A number of automated classification models trained on labeled SPMS data were proposed recently by the authors. As it appeared, the most advanced methods of them, based on deep-learning convolutional neural networks (CNN), still have difficulties in distinguishing between classes of similar but distinctive mass spectra. In this work, we propose a novel fuzzy convolutional neural network (FCNN) combining fuzzy network and CNN to accurately classify particle mass spectral patterns. FCNN models integrate the respective advantages of fuzzy and neural networks to effectively separate non-isolated and overlapping features of similar patterns through fuzzy information, and also inherently optimize the fuzzy rule parameters through NN back propagation. To validate the performance of FCNN, a benchmark dataset with 37,406 samples in 13 particle classes was created. Compared to CNN, with FCNN 10 out of 13 classes could be classified with higher accuracy, especially those distinguishing subtle differences in the mass spectra. Applied to automated SPMS analysis, the proposed FCNN tackles the classification challenges posed by the chemical complexity of aerosol particles and opens up ways to foster the development of specific, real-time air quality monitoring and pollution source identification systems.