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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.

Ejarque-Gonzalez E, Butturini A - PLoS ONE (2014)

Bottom Line: SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure.According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics.We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

View Article: PubMed Central - PubMed

Affiliation: Departament d'Ecologia, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalunya, Spain.

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.

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Multivariate analysis of our data set based on the four fluorescence components determined by SOM analysis.A non-metric multidimensional scaling was complemented with a vector fit analysis with the optical indices HIX, SUVA and FI. A) Main stem sites are coloured according to their discharge category. B) Main stem sites are coloured according to their downstream distance. C) Tributary sites are represented according to their source type.
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pone-0099618-g008: Multivariate analysis of our data set based on the four fluorescence components determined by SOM analysis.A non-metric multidimensional scaling was complemented with a vector fit analysis with the optical indices HIX, SUVA and FI. A) Main stem sites are coloured according to their discharge category. B) Main stem sites are coloured according to their downstream distance. C) Tributary sites are represented according to their source type.

Mentions: Finally, we evaluated the capacity of these four fluorescence components to describe patterns in our data set as new independent variables by performing a NMDS. The results are shown in Figure 8. For the sake of simplicity in exploring the distribution of the samples in the NMDS space, panels A and B include only the main stem sites, whereas panel C includes only the tributary sites. However, it should be noted that all three figures come from the same analysis, and therefore the loadings of the variables (i.e. the fluorescence components C1 to C4) and the vector fit analysis of the optical indices is the same in the three panels.


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.

Ejarque-Gonzalez E, Butturini A - PLoS ONE (2014)

Multivariate analysis of our data set based on the four fluorescence components determined by SOM analysis.A non-metric multidimensional scaling was complemented with a vector fit analysis with the optical indices HIX, SUVA and FI. A) Main stem sites are coloured according to their discharge category. B) Main stem sites are coloured according to their downstream distance. C) Tributary sites are represented according to their source type.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4048288&req=5

pone-0099618-g008: Multivariate analysis of our data set based on the four fluorescence components determined by SOM analysis.A non-metric multidimensional scaling was complemented with a vector fit analysis with the optical indices HIX, SUVA and FI. A) Main stem sites are coloured according to their discharge category. B) Main stem sites are coloured according to their downstream distance. C) Tributary sites are represented according to their source type.
Mentions: Finally, we evaluated the capacity of these four fluorescence components to describe patterns in our data set as new independent variables by performing a NMDS. The results are shown in Figure 8. For the sake of simplicity in exploring the distribution of the samples in the NMDS space, panels A and B include only the main stem sites, whereas panel C includes only the tributary sites. However, it should be noted that all three figures come from the same analysis, and therefore the loadings of the variables (i.e. the fluorescence components C1 to C4) and the vector fit analysis of the optical indices is the same in the three panels.

Bottom Line: SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure.According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics.We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

View Article: PubMed Central - PubMed

Affiliation: Departament d'Ecologia, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalunya, Spain.

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.

Show MeSH
Related in: MedlinePlus