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Getting rid of Rayleigh

Getting rid of Rayleigh. Åsmund Rinnan. Introduction Fluorescence. Emitted from sample. Excites sample. Sample. Light source. Detector. Introduction Fluorescence. Introduction PARAFAC. Can be seen as an expansion of PCA from two-way data to multi-way data. X is the EEM

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Getting rid of Rayleigh

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  1. Getting rid of Rayleigh Åsmund Rinnan

  2. IntroductionFluorescence Emitted from sample Excites sample Sample Light source Detector

  3. IntroductionFluorescence

  4. IntroductionPARAFAC Can be seen as an expansion of PCA from two-way data to multi-way data X is the EEM a are the scores b are the emissionspectra c are the excitationspectra E is the residuals

  5. IntroductionPARAFAC & Fluorescence C = B A

  6. IntroductionPARAFAC & Fluorescence Catechol Hydroquinone

  7. Introduction”Faking” fluorescence

  8. IntroductionLight scatter

  9. IntroductionLight scatter – The trouble maker 1st order Rayleigh Raman 2nd orderRayleigh Excitation Emission

  10. IntroductionLight scatter

  11. IntroductionBi-linearity

  12. IntroductionWhy is this a problem? X X

  13. ExampleFluorescence & PARAFAC

  14. Getting rid of Rayleigh • Cut off and insert missing/ zeros • Subtraction of standard • Weights • Modeling of Rayleigh

  15. Subtracting a standard

  16. Missing values Signal/ Data area Missing values Zeros Thygesen, Rinnan, Barsberg & Møller

  17. Example • 18 wood samples • 4 different levels of p-benzoquinone adsorbed in the fiber cell walls • 30 emission wavelengths x 35 excitation wavelengths Thygesen, Rinnan, Barsberg & Møller

  18. WOW! None Weighted Zeros Non-Negativity

  19. So, now Rayleigh is finished, right? • The data presented so far is a bit simple  Sugar data 1st order Rayleigh Emission Excitation

  20. Weighting - MILES Emission loadings Excitation loadings

  21. Band of missing values

  22. Using a band of missing valuesHard weights Emission loadings Excitation loadings

  23. Using a band of missing valuesMILES weights Emission loadings Excitation loadings

  24. Another method?Why, why, why? • The Rayleigh scatter width has to be estimated quite accurately • The band width of missing data should also be correct • What about an automatic method of removing the Rayleigh scatter, that was not so prone to the estimation of the width of the Rayleigh scatter? • Modeling the Rayleigh is the answer!

  25. Modeling Rayleigh • A Gauss-Lorentz curve fitting method

  26. Modeling Rayleigh Rinnan, Booksh & Bro

  27. Modeling Rayleigh

  28. With constraints even better Emission loadings Excitation loadings Rinnan, Booksh & Bro

  29. Thanks to: Rasmus Bro, Karl Booksh, Lisbeth G Thygesen, Søren Barsberg, Jens K S Møller and Charlotte Andersen • Thank you for your attention

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