1. Removal of the 1st order Rayleigh scatter effect Åsmund Rinnan

2. Fluorescence - EEM Excitation Emission Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

3. PARAFAC X is the EEM a are the scores b are the emissions c are the excitations E is the residual An extension from PCA Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

4. Light scatter in Fluorescence Excitation Emission 2nd order Rayleigh 1st order Rayleigh Raman Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

5. Why is this a problem? X X Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

6. EEM’s with analytes Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

7. Ways of treating scatter Subtraction of standard Cut off and insert missing Weights Modeling of Rayleigh Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

8. Subtraction of standard It is not always possible with a standard Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

9. Why isn’t one method enough!? The data presented so far is a bit simple  Sugar data Excitation Emission 1st order Rayleigh Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

10. Cutting off – inserting zeros Emission loadingsExcitation loadings Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

11. Weighting - MILES Emission loadingsExcitation loadings Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

12. So now everybody says We need a new model to take care of this Hold your horses (a bit longer) Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

13. Band of missing values Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

14. Using a band of missing values Hard weights Emission loadingsExcitation loadings Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

15. Using a band of missing values MILES weights Emission loadingsExcitation loadings Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

16. 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! Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

17. Ways of modeling Rayleigh Rasmus has tested a Gauss-Lorentz curve fitting method Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

18. Modeling Rayleigh Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

19. Fancy doesn’t mean good Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

20. With constraints even better Emission loadingsExcitation loadings Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion

21. Modeling is less sensitive to the estimated Rayleigh peak Give good models, even without constraints or other modifications of the data (band of missing values) The shifting method is relatively fast Introduction Treating scatter Revelation A step back Good reasons Model scatter Conclusion