aceMachine learning-enabled fluorescence spectrum source tracing of typical pollutants in industrial wastewater
Complex and difficult-to-trace pollutants in industrial wastewater are posing major challenges for regulation and treatment. The known source identification methods rely on rough water quality data and models, which limits the accuracy of source tracing. In this study, a convolutional neural network-principal component analysis-random forest (CNN-PCA-RF) fusion framework that directly utilizes the three-dimensional fluorescence excitation-emission matrix (EEM) data was established. An EEM database with five sect…
A machine learning framework working directly on 3D fluorescence spectra identified the source sector and outlet of industrial wastewater with roughly 90 to 97 percent accuracy in the study's tests.
Evidence
- Peer-reviewedSpectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy2026-10-07
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Truvace Impact Record TRV-2026-1744, v1: “Machine learning-enabled fluorescence spectrum source tracing of typical pollutants in industrial wastewater.” Truvace, 2026-10-17. /record/TRV-2026-1744 (accessed at citation time). sha256 1aa2336944adcf18…
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