Attention improvement for data-driven analyzing fluorescence excitation-emission matrix spectra via interpretable attention mechanism

Abstract

Analyzing three-dimensional excitation-emission matrix (3D-EEM) spectra through machine learning models has drawn increasing attention, whereas the reliability of these machine learning models remains unclear due to their “black box” nature. In this study, the convolutional neural network (CNN) for classifying numbers of fluorescent components in 3D-EEM spectra was interpreted by gradient-weighted class activation mapping (Grad-CAM), guided Grad-CAM, and structured attention graphs (SAGs). Results showed that the original CNN classifier with high classification accuracy may make a classification based on misleading attention to the non-fluorescence area in 3D-EEM spectra. By removing Rayleigh scatterings in 3D-EEM spectra and integrating convolutional block attention module (CBAM) in CNN classifiers, the correct attention of the trained CNN classifier with CBAM greatly increased from 17.6% to 57.2%. This work formulated strategies for improving CNN classifiers associated with environmental fields and would provide great help for water determination in both natural and artificial environments.