Neural Networks vs. Whittaker Smoothing: Advanced Techniques for Scattering Signal Removal in 3D Fluorescence spectra

Fluorescence excitation emission matrices (EEMs) have a trilinear structure, aligning perfectly with the tensor rank decomposition, PARAFAC. Consequently, PARAFAC has become essential for extracting information from freshwater EEMs, pinpointing individual fluorophore groups, and tracking their behavior across diverse environment. However, EEMs of seawaters, with typically low organic matter, are often dominated by Rayleigh and Raman scattering, which deviates from the trilinear model. Traditional one-dimensional interpolation to eliminate these interferences varies in outcome based on its matrix application direction and struggles with noisy data. Our proposed techniques, employing Whittaker smoothing and CNN, effectively eliminate scattering signals, even in noise-rich scenarios. Notably, CNN adeptly preserves the overall EEM shape across various sizes and dimensions, establishing itself as an optimal choice for interpolating scattering zones in EEMs of organic matter-deficient freshwaters.