Analysis of raw EEM fluorescence spectra - ICA and PARAFAC capabilities

Excitation-Emission fluorescence spectroscopy is a versatile technique and has been used to detect, characterize and quantify residual Dissolved Organic Matter (DOM) in aquatic domains. PARAllel FACtor Analysis (PARAFAC) has been extensively used in the analysis of excitation-emission matrices (EEM), allowing for a better identification and quantification of contributions resulting from spectral decomposition. In this work we have adapted Independent Component Analysis (ICA) in order to make it suitable to the analysis of three-way EEM datasets, and tested ICA and PARAFAC performances for the study of three available datasets (Claus, Dorrit and drEEM). Semi-empirical simulation allowed us to assess the impact of (a) sample size, (b) signal sources and (c) composition dependencies, and the presence of (d) unspecific signal contributions (e.g. light scattering) upon both algorithms. PARAFAC and ICA have similar performances in processing ideal three-way EEM datasets but, in the presence of non-trilinear responses, ICA leads to a more realistic approach, yielding a better decomposition of contributing sources and their identification and quantification. This makes this algorithm more suitable for the analysis of real, raw EEM data, without the need of preprocessing to remove any unspecific contributions.