A recent approach based on self-organizing maps (SOMs) to extract patterns from three-way data, named MOLMAP, was applied in a four-seasons study on soil pollution and its results compared with three different conventional approaches: Parallel factor analysis (PARAFAC), matrix augmented principal components analysis (MA-PCA) and Procrustes rotation. Each sampling season comprised 92 roadsoil samples and 12 analytical variables (Cd, Co, Cu, Cr, Fe, Mn, Ni, Pb, Zn, loss on ignition, pH and humidity). It was found that all techniques yielded highly similar results as the samples became organized in two major groups, each with a differentiated pollution pattern. This confirmed MOLMAP as a reliable option to handle environmental three-way datasets and to extract accurate pollution patterns. (C) 2010 Elsevier B.V. All rights reserved.
Gomez Carracedo, M., Ballabio, D., Andrade, J., Aires de Sousa, J., Consonni, V. (2010). Comparing roadsoils pollution patterns extracted by MOLMAP and classical three-way decomposition methods. ANALYTICA CHIMICA ACTA, 677(1), 64-71 [10.1016/j.aca.2010.07.044].
Comparing roadsoils pollution patterns extracted by MOLMAP and classical three-way decomposition methods
BALLABIO, DAVIDE;CONSONNI, VIVIANA
2010
Abstract
A recent approach based on self-organizing maps (SOMs) to extract patterns from three-way data, named MOLMAP, was applied in a four-seasons study on soil pollution and its results compared with three different conventional approaches: Parallel factor analysis (PARAFAC), matrix augmented principal components analysis (MA-PCA) and Procrustes rotation. Each sampling season comprised 92 roadsoil samples and 12 analytical variables (Cd, Co, Cu, Cr, Fe, Mn, Ni, Pb, Zn, loss on ignition, pH and humidity). It was found that all techniques yielded highly similar results as the samples became organized in two major groups, each with a differentiated pollution pattern. This confirmed MOLMAP as a reliable option to handle environmental three-way datasets and to extract accurate pollution patterns. (C) 2010 Elsevier B.V. All rights reserved.
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