Missing data recovery combined with Parallel factor analysis model for eliminating Rayleigh scattering in the process of detecting pesticide mixture.

Excitation-Emission Matrix (EEM) fluorescence spectroscopy combined with Parallel factor analysis (PARAFAC) provides a widely used method to extract useful information containing unknown components. However, the inherent scattering especially Rayleigh scattering will influence the accuracy of PARAFAC so that appropriate procedure to the scattering becomes an essential problem when processing the EEM data. Many methods have been proposed to solve the problems about eliminating scattering. Missing data recovery combined with PARAFAC model has been discussed in this paper. For EEM data, this method extracted the signal values in the missing area which can effectively correct scattering region. It can eliminate Rayleigh scattering effectively by choosing Gaussian contour constraint. The results of the correlation coefficient (R), the root mean square error of prediction (RMSEP) and average recovery rate (AR) are better which can prove that the combined method is easier to implement and provide better concentration prediction results in detecting pesticide mixture.