Determination of oil pollutants by three-dimensional fluorescence spectroscopy combined with improved pattern recognition algorithm

Abstract

Petroleum refineries are one of the main sources of hazardous air pollutants, so the accurate determination of petroleum pollutants is of great significance to maintain ecological balance. In this study, three-dimensional (3D) fluorescence spectroscopy combined with pattern recognition algorithm is adopted to distinguish the composition and content of oil pollutants efficiently and accurately. Three hundred samples of kerosene, diesel, and gasoline mixed solutions with different concentrations are prepared. The principal component analysis is used to extract the optimal feature variables, and the correlation coefficient method is used to obtain eight groups of principal component features in the spectra. The dimension is selected as 8, and the principal component score is calculated, which is used as the input data of the extension neural network. Next, the pattern recognition method is improved, and the designed neural network has functions of both resolution and measurement. The results of neural network pattern recognition are used as the input of the concentration network. The first 270 samples are used as the training samples to train the network model, and the remaining 30 samples are used as test samples, which are applied to the input layer of the trained neural network. The relative fluorescence intensity, relative slope, and comprehensive background parameters are used as the input parameters, and the extension neural network is used for pattern recognition and evaluation of oil pollutants. The experimental results show that the average recognition rate of the improved pattern recognition algorithm for oil pollutants is 98.43%, and the average recovery rate of concentration is 98.67%. Further, the average time for pattern recognition is 1.53 s, while the parallel factor analysis algorithm takes 2.89 s. This suggests that the improved extension neural network is an effective and reliable pattern recognition method for the identification of mixed oil pollutants.

Characterization of the interactions between Fulvic acid and Trypsin with Spectroscopic and Molecular Docking technology

“…The three‐dimensional fluorescence spectrum provides a wealth of data information and offers high precision in analysis. The introduction of drugs can significantly impact the three‐dimensional fluorescence spectrum of trypsin [36,37] …”

“…The introduction of drugs can significantly impact the three-dimensional fluorescence spectrum of trypsin. [36,37] Figures 7(a) and (b) depict the three-dimensional fluorescence spectra and contour maps of FA and FA-TRP systems. The "ridge" shaped PEAK a and PEAK b correspond to Rayleigh scattering peaks (l ex = l em ).…”

The Detection of Plastic and Petroleum Hydrocarbon Pollution at Sea with Laser-Induced Fluorescence

“…In recent years, the increasing abundance of plastic and oil pollutants at sea has become a growing concern worldwide. Oil pollution is mainly caused by wastewater discharged from factories and offshore oil leakage [1], while polymers like polyethylene (PE), polypropylene (PP) and polystyrene (PS) have been identified as a major source of microplastics in the western Mediterranean Sea [2]. Microplastics can accumulate in the food chain and cause negative effects on living organisms, due to their carcinogenic and toxic properties [3].…”