Application of 3D fluorescence spectroscopy and a convolutional neural network for oil emulsion species identification

Abstract

Increased pollution from oil emulsions may cause serious ecological damage. The development of a fast and effective emulsified oil species identification method plays a crucial role in dealing with oil spills on the sea surface. Because of this, this paper proposes an emulsified oil species category identification method based on 3D fluorescence spectroscopy and CNN. First, the 3D fluorescence spectra of emulsified oil were measured by an FLS1000 steady-state fluorescence spectrometer, and their fluorescence characteristics were analyzed in depth. Second, the 3D fluorescence spectral data of emulsified oil was extended using a conditional variational autoencoder (CVAE), and the spectral features were extracted using a pre-trained ResNet50. Finally, the spectral features extracted by ResNet50 were used as inputs to the hunter-prey optimized support vector machine (HPO-SVM), and the results show that the recognition accuracy of this method is 97 %, which is better than other classification models.