Automatic scatter detection in fluorescence landscapes by means of spherical principal component analysis
Version of Record online: 30 JAN 2013
Copyright © 2013 John Wiley & Sons, Ltd.
Journal of Chemometrics
Volume 27, Issue 1-2, pages 3–11, January-February 2013
How to Cite
Kotwa, E., Jørgensen, B., Brockhoff, P. B. and Frosch, S. (2013), Automatic scatter detection in fluorescence landscapes by means of spherical principal component analysis. J. Chemometrics, 27: 3–11. doi: 10.1002/cem.2485
- Issue online: 20 FEB 2013
- Version of Record online: 30 JAN 2013
- Manuscript Accepted: 30 NOV 2012
- Manuscript Revised: 26 NOV 2012
- Manuscript Received: 17 AUG 2012
- Raman and Rayleigh scatters;
In this paper, we introduce a new method, based on spherical principal component analysis (S-PCA), for the identification of Rayleigh and Raman scatters in fluorescence excitation–emission data. These scatters should be found and eliminated as a prestep before fitting parallel factor analysis models to the data, in order to avoid model degeneracies. The work is inspired and based on a previous research, where scatter removal was automatic (based on a robust version of PCA called ROBPCA) and required no visual data inspection but appeared to be computationally intensive. To overcome this drawback, we implement the fast S-PCA in the scatter identification routine. Moreover, an additional pattern interpolation step that complements the method, based on robust regression, will be applied. In this way, substantial time savings are gained, and the user's engagement is restricted to a minimum, which might be beneficial for certain applications. We conclude that the subsequent parallel factor analysis models fitted to excitation–emission data after scatter identification based on either ROBPCA or S-PCA are comparable; however, the modified method based on S-PCA clearly outperforms the original approach in relation to computational time. Copyright © 2013 John Wiley & Sons, Ltd.