Predicting brown tide microalgae concentrations using reconstructed fluorescence spectroscopy combined with CNN

The non-destructive and sensitive three-dimensional excitation-emission matrix (3D-EEM) fluorescence spectroscopy shows great potential for real-time monitoring of brown tide, while current methods of 3D-EEM spectra combined with convolutional neural network (CNN) do not take full advantage of its spatial characteristics. To address this issue, we propose a data reconstruction method based on 3D-EEM spectra. By stratifying the 3D-EEM spectra according to fluorescence intensity, we can reconstruct the original two-dimensional data of the spectra into more informative three-dimensional data, then the reconstructed data are enhanced according to the characteristics of the reconstructed data by using the attention mechanism, so as to better utilize the spatial characteristics of the 3D-EEM spectra and to improve the performance of the prediction of microalgae concentration. Experimental result shows a significant improvement in the prediction of microalgae concentration using reconstructed data, the mean squared error (MSE) of the brown tide causal algae Aureococcus anophagefferens is reduced by 85.76%, and the mean squared errors of Chlorella and Synechococcus elongatus is also reduced by 96.52% and 63.76% respectively. This work demonstrates the role of convolutional neural network in 3D-EEM spectral analysis and highlights the feasibility of using 3D-EEM spectral reconstruction data to improve prediction accuracy.