Study On Three-Dimension Fluorescence Spectrocopy Detection Technique And Method Of Polycyclic Aromatic Hydrocarbons

The rapid expansion of the global economy has resulted in an increase in the usage of chemical products,which has resulted in an increasingly critical problem of organic contaminants in water.The 14 th Five-Year Plan emphasizes the importance of strengthening our country’s ecological environmental quality and persisting in establishing a green environment in order to adapt to new development stages and concepts and to construct new development modes.PAHs,a common organic contaminant in the aquatic environment,cause irreversible harm to human health and the natural environment.As a result,finding an effective and sensitive method to identify PAHs for environmental protection and management is critical.The purpose of this work is to address the issue that existing experimental instruments and methodologies are incapable of reliably distinguishing and predicting the categories and amounts of polycyclic aromatic hydrocarbons in water.Polycyclic aromatic hydrocarbon concentrations are predicted using three-dimensional fluorescence spectrum detection technology paired with a second-order correction algorithm.To detect the types and quantities of polycyclic aromatic hydrocarbons,the support vector machine model is optimized using several approaches.To perform qualitative and quantitative analyses on polycyclic aromatic hydrocarbons.This paper’s main research contents are as follows:(1)To simulate the pollution status of PAHs in water,we chose NAN,FLU,and NAP as experimental samples.The FS920 fluorescence spectrometer was used to collect spectral data from PAHs solution,and the characteristic peaks and fluorescence intensity of the three-dimensional fluorescence spectrum were analyzed.(2)Due to the high level of redundant noise in the original data collected by the FS920 fluorescence spectrometer,the Savitzky-Galay smoothing algorithm,Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise were used to de-noise the data collected by the spectrometer,laying the groundwork for the subsequent qualitative and quantitative analysis of PAHs mixed solution.(3)The parallel factor algorithm was used to quantitatively analyze the mixed solution of PAHs to solve the problem that it is difficult to predict the concentration of each component due to the high overlap of fluorescence peak position and fluorescence intensity in the three-dimensional fluorescence spectrum when multiple PAHs coexist.To address the issue that the parallel factor approach is sensitive to the mixed system’s group fraction,an alternate penalty trilinear decomposition algorithm for the quantitative analysis of polycyclic aromatic hydrocarbons mixed solution is provided.The advantage of the alternating punishment trilinear decomposition algorithm is demonstrated by comparing experimental outcomes.(4)The detection model was created by optimizing a support vector machine with the bird swarm algorithm to complete a qualitative and quantitative analysis of mixed solution samples of various polycyclic aromatic hydrocarbons,and the prediction results were compared to those of the chicken swarm optimization support vector machine and the genetic algorithm optimization support vector machine models to confirm the feasibility of the detection model optimized by the bird swarm algorithm.