Removal of the 1st order Rayleigh scatter effect Åsmund Rinnan

Removal of the 1st order Rayleigh scatter effect

Åsmund Rinnan

Fluorescence - EEM

Excita

tion

Emission

IntroductionTreating scatter

Revelation

A step back

Good reasons

Model scatter

Conclusion

PARAFAC

ijk

F

fkfjfifijk ecbax

1

X is the EEMa are the scoresb are the emissionsc are the excitationsE is the residual

An extension from PCA

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F

fjfifij eptx

1

IntroductionTreating scatter

Revelation

A step back

Good reasons

Model scatter

Conclusion

Light scatter in Fluorescence

Excita

tion

Emission

2n

d o

rder

Rayle

igh

1st

ord

er

Rayle

igh

Ram

an

IntroductionTreating scatter

Revelation

A step back

Good reasons

Model scatter

Conclusion

Why is this a problem?X

X

IntroductionTreating scatter

Revelation

A step back

Good reasons

Model scatter

Conclusion

EEM’s with analytes

IntroductionTreating scatter

Revelation

A step back

Good reasons

Model scatter

Conclusion

Ways of treating scatter

•Subtraction of standard

•Cut off and insert missing

•Weights•Modeling of Rayleigh

Introduction

Treating scatterRevelation

A step back

Good reasons

Model scatter

Conclusion

Subtraction of standardIt is not always possible with a standard

Introduction

Treating scatterRevelation

A step back

Good reasons

Model scatter

Conclusion

Why isn’t onemethod enough!?

• The data presented so far is a bit simple Sugar data

Excitation

Emission 1st o

rder

Ray

leig

h

Introduction

Treating scatterRevelation

A step back

Good reasons

Model scatter

Conclusion

Cutting off – inserting zeros

Emission loadings Excitation loadings

Introduction

Treating scatterRevelation

A step back

Good reasons

Model scatter

Conclusion

Weighting - MILES

Emission loadings Excitation loadings

Introduction

Treating scatterRevelation

A step back

Good reasons

Model scatter

Conclusion

So now everybody says

• We need a new model to take care of this

• Hold your horses (a bit longer)

Introduction

Treating scatter

RevelationA step back

Good reasons

Model scatter

Conclusion

Band of missing values

Introduction

Treating scatter

Revelation

A step backGood reasons

Model scatter

Conclusion

Using a band of missing valuesHard weights

Emission loadings Excitation loadings

Introduction

Treating scatter

Revelation

A step backGood reasons

Model scatter

Conclusion

Using a band of missing valuesMILES weights

Emission loadings Excitation loadings

Introduction

Treating scatter

Revelation

A step backGood reasons

Model scatter

Conclusion

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 reasonsModel scatter

Conclusion

Ways of modeling Rayleigh

• Rasmus has tested a Gauss-Lorentz curve fitting method

Introduction

Treating scatter

Revelation

A step back

Good reasons

Model scatterConclusion

Modeling Rayleigh

Introduction

Treating scatter

Revelation

A step back

Good reasons

Model scatterConclusion

Fancy doesn’t mean good

Introduction

Treating scatter

Revelation

A step back

Good reasons

Model scatterConclusion

With constraints even better

Emission loadings Excitation loadings

Introduction

Treating scatter

Revelation

A step back

Good reasons

Model scatterConclusion

Conclusion

• 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