Classification of oil pollutants based on excitation-emission matrix fluorescence spectroscopy and two-dimensional discriminant analysis

Excitation-Emission Matrix (EEM) Fluorescence spectroscopy and two-dimensional (2D) discriminant analysis were used to classify oil pollutants. Firstly, the EEM fluorescence spectroscopy of the oil samples was collected using the FS920 steady-state fluorescence spectrometer, and EEM was preprocessed by removing scattering and normalization. Secondly, EEM was analyzed and characterized by parallel factor analysis (PARAFAC). Finally, all the collected samples were divided into training and test sets by the Kennard-Stone algorithm. The classification models of the training set samples were established by using 2D-PCA, 2D-LDA, PARAFAC-2DPCA, PARAFAC-2DLDA, PARAFAC-LDA and NPLS-DA algorithms, respectively. These models were then used to classify test set samples. The classification performance of the used models was assessed by accuracy, sensitivity and specificity. The best classification results in the used models were obtained by using 2D-PCA and 2D-LDA with 95% and 95% accuracy, respectively. These results provide an important reference for classification of oil pollutants.