Application of Three-Dimensional Fluorescence Spectra Combined with Algorithm Combination Methodology in Environmental Pollution Monitoring: Oil Identification and Quantitative Analysis
CHEN Zhi-kun1, HUANG Wei1*, CHENG Peng-fei1, SHEN Xiao-wei1, WANG Fu-bin1, WANG Yu-tian2
1. College of Electrical Engineering，North China University of Science and Technology, Tangshan 063210, China
2. Measurement Technology and Instrument Key Lab of Hebei Province, Yanshan University, Qinhuangdao 066004, China
Abstract：In order to solve the problem that the composition of oil pollutants is complex and the spectrum overlap is difficult to identify, qualitative and quantitative analysis of oil pollutants was carried out by three-dimensional fluorescence spectroscopy combined with Algorithm Combination Methodology (ACM). Rayleigh scattering in fluorescence spectra has a great influence on the detection of three-dimensional fluorescence spectrum. In this paper, the missing data recoveryr-principal component analysis (MDR-PCA) method was proposed. The principle is that the single fluorescence spectrum excitation emission matrix conforms to bilinearity and can be analyzed by principal component analysis (PCA). The scattering interference data were first deducted completely, and then the deducted part was repaired by using the remaining effective signal data in the iteration process. This method not only eliminates the scattering interference, but also makes full use of the effective information in the fluorescence spectrum matrix. The three-dimensional data were constructed by using the excitation-emission fluorescence spectra of different concentration mineral oil. The sample data were obtained from carbon tetrachloride solutions of 0# diesel, 95# gasoline and ordinary kerosene solutes. The trilinear decomposition algorithms commonly used for three-dimensional fluorescence spectral data analysis include parallel factor analysis (PARAFAC), alternating trilinear decomposition (ATLD), and self-weighted alternating trilinear decomposition algorithm (SWATLD). PARAFAC is based on the strict principle of least squares and has strong anti-noise ability. Its model is the stablest and the error is expected to be the smallest. It can provide the best fit of 3D data array, but the convergence speed of PARAFAC algorithm is slow and correct. The estimated number of components is more sensitive. The ATLD algorithm is based on the Moore-penrose generalized inverse of singular value decomposition to realize the trilinear model decomposition. By using the inverse diagonal element and the tangent singular value to solve the generalized inverse, the convergence speed of the method is greatly improved, and the sensitivity of the algorithm to the component number is reduced, but the operation of the diagonal element makes the ATLD method more sensitive to noise. SWATLD inherits the advantages of ATLD, which is insensitive to the number of components and fast convergence, and has the characteristics of being insensitive to noise levels. However, the SWATLD algorithm has a slightly lower ability to resist collinearity than ATLD. This paper divides the iteration process according to the change of loss function in the iteration process of trilinear decomposition algorithm, and proposes the algorithm combination method (ACM)-combining ATLD, SWATLD and PARAFAC, giving full play to the advantages of each algorithm, and realizing the complementary advantages of the second-order correction algorithm. The three-dimensional fluorescence spectra of two-component and three-component mineral oil samples were analyzed by ACM algorithm, and the recovery rates of three mineral oil samples were calculated. The recovery rate of diesel was 97.08%, the recovery rate of gasoline was 97.34%. and the recovery rate of kerosene was 97.25%. The analytical spectrum and the recovery rate show that the ACM algorithm can realize species identification and concentration measurement of oil pollutants.
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