Search

Abstract Interference-free determination of polycyclic aromatic hydrocarbons (PAHs) in water pollution is proposed based on third-order correction algorithms with quadrilinear component modeling applied to the constructed four way fluorescence excitation–emission-sample data array with higher accuracy and better predictive ability than second-order (three-dimension) correction. Alternating weighted residue constraint quadrilinear decomposition (AWRCQLD), quadrilinear parallel factor analysis (4-PARAFAC), alternate penalty quadrilinear decomposition (APQLD) and alternate penalty trilinear decomposition (APTLD) are applied to acenaphthene (ANA), naphthalene (NAP) and fluorene (FLU) respectively. Fulvic acid affects PAHs determination seriously in real-world situation, so it is simulated as an interfering agent. Excitation-emission fluorescence matrixes (EEMs) of PAHs are measured at different volumes of fulvic acid simulated different interference conditions, to construct a four-way data array. After the four-way spectra data is analyzed by AWRCQLD, 4-PARAFAC, and APQLD, three-way EEMs analyzed by APTLD, results show that, on the one hand, PAHs can be measured more accurately with four-way data combined with third-order calibration than lower-order. On the other hand, AWRCQLD algorithm can reflect the superiority of third-order advantage better with higher recovery rate and smaller root mean square error, than other third-order or second-order correction algorithms. Graphical Abstract Unlabelled Image Highlights • Interference-free determination of PAHs is realised with high-order correction algorithm. • The proposed four-way data array can get satisfactory results with higher precision. • AWRCQLD algorithm with best performance is better than APQLD or 4-PARAFAC algorithm. [ABSTRACT FROM AUTHOR]

Parallel factor analysis is a widely used method to extract qualitative and quantitative information of the analyte of interest from fluorescence emission-excitation matrix containing unknown components. Big amplitude of scattering will influence the results of parallel factor analysis. Many methods of eliminating scattering have been proposed. Each of these methods has its advantages and disadvantages. The combination of symmetrical subtraction and interpolated values has been discussed. The combination refers to both the combination of results and the combination of methods. Nine methods were used for comparison. The results show the combination of results can make a better concentration prediction for all the components. [ABSTRACT FROM AUTHOR]

Qualitative and quantitative analysis of polycyclic aromatic hydrocarbons (PAHs) was carried out by three-dimensional fluorescence spectroscopy combining with Alternating Weighted Residue Constraint Quadrilinear Decomposition (AWRCQLD). The experimental subjects were acenaphthene (ANA) and naphthalene (NAP). Firstly, in order to solve the redundant information of the three-dimensional fluorescence spectral data, the wavelet transform was used to compress data in preprocessing. Then, the four-dimensional data was constructed by using the excitation-emission fluorescence spectra of different concentration PAHs. The sample data was obtained from three solvents that are methanol, ethanol and Ultra-pure water. The four-dimensional spectral data was analyzed by AWRCQLD, then the recovery rate of PAHs was obtained from the three solvents and compared respectively. On one hand, the results showed that PAHs can be measured more accurately by the high-order data, and the recovery rate was higher. On the other hand, the results presented that AWRCQLD can better reflect the superiority of four-dimensional algorithm than the second-order calibration and other third-order calibration algorithms. The recovery rate of ANA was 96.5%~103.3% and the root mean square error of prediction was 0.04 μgL − 1 . The recovery rate of NAP was 96.7%~115.7% and the root mean square error of prediction was 0.06 μgL − 1 . [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a method to detect a mixture of carbamate pesticides using a back propagation network (BP), which is optimized by genetic algorithm (GA) for quantitative analysis. This method aims to combine the advantages of BP and GA to remedy their drawbacks. The training samples were taken as input, some performance indexes such as the predicted values, iteration time, mean squared error, correlation coefficient and recovery rate were compared between BP neural network and the constructed GA-BP model to evaluate the performance of two neural networks. Results show that the optimized GA-BP model can effectively predict the concentrations, the mean squared error and recovery rate are better. In addition, the correlation coefficient has a significant improvement. This study can provide a new way for detection of the pesticides mixture and help to analysis in a reliable way. Unlabelled Image • A method to detect a mixture of carbmate pesticides using a GA-BP network for quantitative analysis • Iteration time and mean squared error is improved, correlation coefficient is better. • Average recovery rate is close to 100% by GA-BP. • GA-BP can prevent the solutions from falling into local optimal and help to converge. [ABSTRACT FROM AUTHOR]