Quantitative Analysis Of Anthracene And Fluoranthene In Soil By Mid-infrared Spectroscopy Combined With Chemometrics

Polycyclic aromatic hydrocarbons?PAHs?are a typical soil organic pollutant with teratogenic and carcinogenic properties.The rapid and accurate analysis of PAHs in soil can provide a theoretical basis and technical support for environmental pollution risk assessment.This thesis takes soil as the research object and carries out quantitative analysis of PAHs in soil.Infrared spectroscopy was used as input variable to construct partial least squares calibration model.The spectral preprocessing,feature variable extraction and data fusion techniques were used to gradually improve the predictive performance of the established model to achieve rapid and accurate analysis of anthracene and fluoranthene in soil.The results show that the PLS calibration model based on infrared spectroscopy has excellent predictive performance,which can realize rapid and accurate quantitative analysis of anthracene and fluoranthene in soil,and provides new ideas and new methods for on-site analysis of soil pollution.The main research contents are as follows:Taking anthracene and fluoranthene in the soil as research objects,a PLS calibration model based on IR spectroscopy was established to achieve rapid quantitative analysis.First,the influence of different spectral preprocessing methods?standard positive transformation and Savitzky-Golay first-order derivation?on the predictive performance of the PLS calibration model was investigated,and then the variation of the predictive performance of the PLS calibration model with different input variables was studied.Finally,the infrared spectrum in the 900-1100 cm^-1 range was used as the optimal input variable to construct PLS calibration model.The results show that the PLS calibration model has an excellent predictive performance:the result of anthracene is R²=0.9628,RMSE=1.107 mg/g;The result of fluoranthene is R²=0.9696,RMSE=1.009 mg/g.The combination of infrared spectroscopy and PLS method is a fast and effective tool for detecting PAHs in soil,which provides new ideas and methods for the accurate analysis and safety warning of PAHs in soil.With the help of hybrid variable selection strategy,a PLS calibration model combining genetic algorithm and si PLS interval selection was established to achieve quantitative analysis of anthracene and fluoranthene in soil.First,si PLS was used to select and optimize the optimal wavelength interval of the PLS calibration model,and then the influence of different variable selection methods?SPA,PSO and GA?combined with si PLS on the predictive performance of the PLS calibration model was studied.The results show that the PLS calibration model based on the si PLS-GA hybrid variable selection strategy shows the best predictive performance,with the R²=0.9830,RMSE=0.5897 mg/g,and R²=0.9849,RMSE=0.4739 mg/g for anthracene and fluoranthene,respectively.Hybrid variable selection can simultaneously take advantage of si PLS and GA,enhance the predictive performance of the model,and show great potential in the quantitative analysis of PAHs in soil.With the aid of data fusion technology,a PLS calibration model based on IR-Raman fusion spectroscopy was established to achieve rapid and accurate quantitative analysis of anthracene and fluoranthene in soil.The full spectrum of infrared and Raman were used for low-level data fusion,and the characteristic wavelengths of the two spectra were used for mid-level data fusion.The effects of different levels of fusion methods on the predictive performance of the PLS calibration model were discussed.The results show that the PLS calibration model based on mid-level data fusion uses fewer variables and has higher predictive performance:for predicting anthracene R²=0.9864,RMSE=0.5757 mg/g;for predicting fluoranthene R²=0.9894,RMSE=0.4018 mg/g.Data fusion technology can make up for the shortage of a single spectrum and improve the prediction performance of the model.Data fusion combined with multiple correction methods is expected to achieve real-time monitoring and on-site analysis of soil pollution.