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Missing data recovery combined with Parallel factor analysis model for eliminating Rayleigh scattering in the process of detecting pesticide mixture.
Wang, Junzhu; Wang, Shutao; Shang, Fengkai; Sun, Yangyang; Liu, Shiyu.
Afiliación
  • Wang J; Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China.
  • Wang S; Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China. Electronic address: 15032325996@163.com.
  • Shang F; Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China.
  • Sun Y; Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China.
  • Liu S; Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China.
Spectrochim Acta A Mol Biomol Spectrosc ; 232: 118187, 2020 May 05.
Article en En | MEDLINE | ID: mdl-32120299
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Spectrochim Acta A Mol Biomol Spectrosc Asunto de la revista: BIOLOGIA MOLECULAR Año: 2020 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Spectrochim Acta A Mol Biomol Spectrosc Asunto de la revista: BIOLOGIA MOLECULAR Año: 2020 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido