Discrimination of Oil Contaminants Using Supervised Kohonen Network and Unconventional Steady State Fluorescence Spectroscopy: A Comparative Evaluation

Abstract

The rapid and reliable detection of oil contaminants is of crucial importance for environmental protection. This paper focused on the classification and identification of six types of oil using unconventional steady-state fluorescence spectroscopy coupled with XY-fused networks (XY-Fs). The excitation emission matrix fluorescence (EEMF) spectroscopy, synchronous fluorescence spectroscopy (SFS) and total synchronous fluorescence spectroscopy (TSFS) of the oil samples were measured by a FS920 steady-state fluorescence spectrometer. Further, the obtained results were compared with those obtained by using partial least square discriminant analysis (PLS-DA), LDA based on score matrix of multivariate curve resolution-alternating least squares (MCR-ALS-LDA) and parallel factor (PARAFAC-LDA) analysis. The experimental results showed that the best accuracy (100%) for the test samples was obtained by TSFS combined with XY-Fs. These results provide important references for discrimination of oil contaminants.

Keywords

• Unconventional steady-state fluorescence spectroscopy
• XY-fused networks
• total synchronous fluorescence spectroscopy
• oil contaminants