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.
Three-dimensional fluorescence spectroscopy is a fast, nondestructive analysis method with good selectivity and high precision, which provides a foundation for the development of the current smart agriculture system. In modern agriculture, where agricultural information is fully perceived, it is still very difficult to quickly and destructively detect the internal chemical composition of soil, crops and agricultural products. Accurate determination of oil pollutants in water by using three-dimensional fluorescence spectroscopy technology can provide a basis for crop irrigation and is of great significance for improving agricultural benefits. The fluorescence spectrum analysis method is adopted to distinguish three kinds of mineral oil-gasoline, kerosene and diesel. In order to make the distinguishment more intuitive and convenient, a new identification method for mineral oil is proposed. The three-dimensional fluorescence spectra of the experimental dimension are reduced into two-dimensional fluorescence spectra. The concrete operations are as follows: adopting the method of end-to-end data matrix to constitute a large Ex image, and then figuring out the envelope curve, processing and analyzing the envelope image. Four factors, such as the ranges of excitation wavelength when the relative fluorescence intensity is greater than 0.5, the optimal excitation wavelengths, their kurtosis coefficients and skewness coefficients, are to be selected as the distinguishing feature parameters of mineral oil, and thus different kinds of mineral oil can be distinguished directly according to the feature parameters. The experimental results show that the proposed method has a high resolution for different kinds of mineral oil. Accurate and fast spectral data analysis methods can make up for the deficiencies of other agricultural information perception methods, provide a basis for the application of smart agriculture in many aspects and have a positive significance for promoting the comprehensive intelligent development of agriculture.
Laser-induced fluorescence technology is an effective method for detecting oil pollutants. The laser induced fluorescence detection system was developed, and four parallel microchannel sample pools were designed to improve the detection accuracy of fluorescence signals. The grating scanning system is improved by introducing grating feedback link to improve the wavelength scanning accuracy of the system.The experimental results show that the wavelength scanning resolution of the microchannel laser-induced fluorescence detection system is improved by 0.4 nm. Aiming at the defects of the traditional parallel factor algorithm which is sensitive to the sample components, slow operation speed and easy to fall into local convergence, the generalized inverse singular value decomposition is used to improve the parallel factor algorithm. Configure different concentrations of diesel, gasoline and kerosene solution samples. The experimental verification shows that the excitation-emission spectra of each component obtained by the improved parallel factor algorithm are highly consistent with the measured spectral characteristics, indicating that the microchannel laser-induced fluorescence detection system combined with the improved parallel factor analysis method can accurately identify oil pollutants.
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