Automatic scatter detection in fluorescence landscapes by means of spherical principal component analysis

Ewelina Katarzyna Kotwa, Bo Munk Jørgensen, Per B. Brockhoff, Stina Frosch

Publikation: Artikel i tidsskrift og konference artikel i tidsskriftTidsskriftartikelForskningpeer review


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.
TidsskriftJournal of Chemometrics
Udgave nummer1-2
Sider (fra-til)3-11
StatusUdgivet - 2013
Udgivet eksterntJa


Dyk ned i forskningsemnerne om 'Automatic scatter detection in fluorescence landscapes by means of spherical principal component analysis'. Sammen danner de et unikt fingeraftryk.