Analysis of small three-dimensional fluorescence spectroscopy dataset using migration learning an example of phenol...

Three-dimensional fluorescence spectroscopy has been widely used to detect organic pollutants in water. However, the amount of data required for three-dimensional fluorescence spectroscopy analysis is relatively large, and the time cost of sample collection is high. The amount of data has become an unavoidable limitation of spectral analysis. This study takes the detection of phenol in industrial discharge wastewater as an example and proposes a transfer learning method for small fluorescence spectroscopy datasets. First, fluorescence spectra are generated by splitting them into linear combinations of positively and negatively distributed spectra. Then, based on the idea of transfer learning, the generated fluorescence spectra are used to train a task-specific pre-trained model, which is then transferred to the collected spectral dataset. Experimental results show that the prediction performance of the transfer learning method is improved by 50.08 % compared with that obtained by directly training the model using a small amount of spectral data. In addition, when the spectral data remains unchanged, the accuracy of the model can be improved to a certain extent by increasing the amount of spectral data used for pre-training. The transfer learning method proposed in this study further improves the prediction accuracy when data is limited, and the results of verification in real environments are also satisfactory. It provides a feasible solution to the problem of data limitations in three-dimensional fluorescence spectroscopy.