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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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Projection of space, discharge, and type of tributary onto the U-matrix.Neuron colour scale indicates, for every neuron, the sum of the euclidean distances to all its immediate neighbours. Samples are projected on the SOM grid and coloured according to A) hydrology: blue represents flood conditions, black represents base flow, and green drought; B) space: blue corresponds to headwater samples, black middle reaches samples, and green are the lowland samples; C) types of tributary: blue are industrial, black are WWTP, and green are natural tributaries.
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pone-0099618-g005: Projection of space, discharge, and type of tributary onto the U-matrix.Neuron colour scale indicates, for every neuron, the sum of the euclidean distances to all its immediate neighbours. Samples are projected on the SOM grid and coloured according to A) hydrology: blue represents flood conditions, black represents base flow, and green drought; B) space: blue corresponds to headwater samples, black middle reaches samples, and green are the lowland samples; C) types of tributary: blue are industrial, black are WWTP, and green are natural tributaries.

Mentions: The samples in our data set were collected along a longitudinal downstream gradient, and under a variety of hydrological conditions. In order to test the influence of space and hydrology on the distributions of EEM spectral shapes, samples were projected onto the SOM grid, and coloured according to their sampling location (“headwaters”, “middle reaches” and “lowland” categories) and hydrology (“flood”, “baseflow” and “drought” categories, Figure 5).


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)

Projection of space, discharge, and type of tributary onto the U-matrix.Neuron colour scale indicates, for every neuron, the sum of the euclidean distances to all its immediate neighbours. Samples are projected on the SOM grid and coloured according to A) hydrology: blue represents flood conditions, black represents base flow, and green drought; B) space: blue corresponds to headwater samples, black middle reaches samples, and green are the lowland samples; C) types of tributary: blue are industrial, black are WWTP, and green are natural tributaries.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0099618-g005: Projection of space, discharge, and type of tributary onto the U-matrix.Neuron colour scale indicates, for every neuron, the sum of the euclidean distances to all its immediate neighbours. Samples are projected on the SOM grid and coloured according to A) hydrology: blue represents flood conditions, black represents base flow, and green drought; B) space: blue corresponds to headwater samples, black middle reaches samples, and green are the lowland samples; C) types of tributary: blue are industrial, black are WWTP, and green are natural tributaries.
Mentions: The samples in our data set were collected along a longitudinal downstream gradient, and under a variety of hydrological conditions. In order to test the influence of space and hydrology on the distributions of EEM spectral shapes, samples were projected onto the SOM grid, and coloured according to their sampling location (“headwaters”, “middle reaches” and “lowland” categories) and hydrology (“flood”, “baseflow” and “drought” categories, Figure 5).

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