SuperEEM: A Super-Resolution Reconstruction Framework for Rapid and Reliable Excitation–Emission Matrix Fluorescence Analysis

Abstract Excitation–emission matrix (EEM) fluorescence spectroscopy is widely used for characterizing complex samples in food analysis and environmental monitoring, but its practical deployment is limited by the trade-off between spectral resolution and acquisition speed. High-resolution EEM acquisition is time-consuming, whereas rapid low-resolution measurements lose analytically important spectral details. Herein, we introduce SuperEEM, a chemically constrained super-resolution framework for converting rapidly acquired sparse EEMs into analytically reliable high-resolution EEMs. Unlike generic image super-resolution approaches that mainly optimize visual similarity, SuperEEM couples an EEM-tailored U-Net–GAN architecture with a quantitative-fidelity-oriented loss function to preserve fluorescence intensity, peak topology, and excitation–emission relationships. The analytical reliability of SuperEEM was validated using three representative tasks: polycyclic aromatic hydrocarbon (PAH) quantification, edible oil classification, and beer classification. Compared with LR EEMs, SuperEEM-reconstructed EEMs substantially improved downstream performance, achieving PAH quantification comparable to original HR EEMs and improving classification accuracy from 77.4% to 90.3% for edible oils and from 73.3% to 86.7% for beers. Under the tested instrumental settings, SuperEEM enabled a 14-fold improvement in EEM acquisition efficiency while maintaining analytical reliability. A graphical user interface (GUI) was further developed to support model application, visualization, and result export. These results demonstrate the potential of SuperEEM for rapid, high-throughput fluorescence analysis.