Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/54908
Title: Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components
Author: Ejarque, E.
Butturini, Andrea
Keywords: Ecologia aquàtica
Nutrients (Medi ambient)
Fluorescència
Algorismes
Aquatic ecology
Nutrients (Ecology)
Fluorescence
Algorithms
Issue Date: 6-Jun-2014
Publisher: Public Library of Science (PLoS)
Abstract: Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.
Note: Reproducció del document publicat a: http://dx.doi.org/10.1371/journal.pone.0099618
It is part of: PLoS One, 2014, vol. 9, num. 6, p. e99618
Related resource: http://dx.doi.org/10.1371/journal.pone.0099618
URI: http://hdl.handle.net/2445/54908
ISSN: 1932-6203
Appears in Collections:Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)

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