AI-supported smart monitoring of pollutants in aquatic ecosystems: Real-Time Automatic Detection through TRF Spectroscopic Analysis

This dataset was generated as part of the project “VAI-TRF: Validation of Artificial Intelligence Time Resolved Fluorescence method for the real-time monitoring and automatic identification of critical water pollutants”, conducted at the Institut Lumière Matière (iLM), Université Claude Bernard Lyon 1.The main objective is to develop an automatic pollutant identification method based on Time-Resolved Fluorescence (TRF) measurements combined with artificial intelligence models. Spectroscopic data (TRF method) The data were acquired using an Agilent Cary Eclipse fluorescence spectrophotometer in Time-Resolved Fluorescence (TRF) mode.Each measurement reports the fluorescence intensity for a pair of excitation and emission wavelengths. The excitation wavelengths range from 230 to 520 nm,and the emission wavelengths range from 520 to 610 nm,with an intensity value recorded for each (λ_exc, λ_em) pair. The files are provided as:• Raw CSV files exported directly from the instrument (wide format: repeated Wavelength/Intensity pairs for each λ_em)• 2D/3D images (PNG/JPG) generated from intensity matrices, used as input for AI models Dataset organization The dataset is organized into six pollutant / ligand classes:D – DTPMPO – DOTAN – NTAP – DTPAM – SPO (without pollutant)G – Glyphosate Each folder contains:• TRF spectrum CSV files• Image files derived from CSV data (intensity maps) Usage This dataset can be used for:• Automatic pollutant identification using AI• Training and comparison of classification models (CNN, ResNet, SVM, etc.)• Analysis of spectral signatures of terbium–pollutant complexes Python scripts can be used to:• Convert CSV files into long format (λ_exc, λ_em, intensity)• Generate images from intensity matrices• Train and evaluate AI models