A fully robust PARAFAC method for analyzing fluorescence data

• DOI: 10.1002/cem.1208
• Publication Date: 2009-01-07T15:27:36Z
• Authors: Sanne Engelen; Stina Frosch; Bo Munk Jørgensen
• Keywords: Least-squares function approximation; Data set; Identification

Abstract Parallel factor analysis (PARAFAC) is a widespread method for modeling fluorescence data by means of an alternating least squares procedure. Consequently, the PARAFAC estimates are highly influenced outlying excitation–emission landscapes (EEM) and element‐wise outliers, like example Raman Rayleigh scatter. Recently, robust that circumvents harmful effects samples has been developed. For removing scatter on final model, different techniques exist. Newly, automated identification tool constructed. However, there still exists no handling encountering both EEM In this paper, we present iterative algorithm where alternately performed. A fully obtained in way. The assessed simulations laboratory‐made set. Copyright © 2009 John Wiley & Sons, Ltd.