Modeling Fluorescence EEM Data

Course Description

Fluorescence excitation emission spectroscopy is an interesting and useful analytical tool in discplines such as environmental monitoring, food quality assessment, process control and various other fields. The measurements of each sample give rise to an Excitation Emission Matrix (EEM); that is a landscape of data. Ideally such data will follow Beers Law in which case, the so-called PARAFAC model allows one to extract the underlying chemical components directly from mixture measurements. Sometimes, when measuring complex and possibly optically dense samples, artifacts will occur in the fluorescence data. These need to be handled before a meaningful PARAFAC model can be obtained. In this course, the participant will learn how to critically analyze EEM data and handle problems with inner filter effects, Rayleigh and Raman scattering and other common problems. Examples will be given throughout the course including hands-on computer time using MATLAB and PLS_Toolbox software.


Linear Algebra for Chemometricians, MATLAB for Chemometricians and Chemometrics I -- PCA, or equivalent experience.

Course Outline

1.	Introduction to fluorescence EEM spectroscopy

2.	The structure of fluorescence data

3.	The PARAFAC model

4.	Validating and using the PARAFAC model

5.	Handling Rayleigh scatter

6.	Handling Raman scatter

7.	Handling inner filter effects

8.	Practical examples from metabolomics, food quality and process analytical technology