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

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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.
Funding Information
  • Graduate Program in Physics at UNIFAL-MG (YLS-2026-ZY01002)

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