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Issue 3, 2012
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Practical comparison of multivariate chemometric techniques for pattern recognition used in environmental monitoring

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Abstract

Environmental datasets usually contain multiple and highly correlated variables. The use of multivariate techniques in this case has experienced a significant development. This paper provides a feasible comparison among several methods for pattern recognition: Principal Component Analysis (PCA), Multivariate Curve Resolution (MCR), Parallel Factor Analysis (PARAFAC) and Tucker3. They have been systematically applied on the same dataset, consisting of the concentration of trace elements measured in sediments of the estuary of the Nerbioi-Ibaizabal river (Basque Country). The results obtained have been critically discussed in terms of ease of use, interpretation of the results, and amount of information provided. The most popular PCA is probably enough for a correct and overall interpretation of the results. MCR, however, may lead to slightly different results due to the non-perceptive orthogonality of the principal components. Both PARAFAC and Tucker3 provide simplified graphical outputs that make easier interpretation of the results.

Graphical abstract: Practical comparison of multivariate chemometric techniques for pattern recognition used in environmental monitoring

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Publication details

The article was received on 03 Oct 2011, accepted on 10 Jan 2012 and first published on 21 Feb 2012


Article type: Paper
DOI: 10.1039/C2AY05636D
Citation: Anal. Methods, 2012,4, 676-684
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    Practical comparison of multivariate chemometric techniques for pattern recognition used in environmental monitoring

    A. Gredilla, J. M. Amigo, S. Fdez-Ortiz de Vallejuelo, A. de Diego, R. Bro and J. M. Madariaga, Anal. Methods, 2012, 4, 676
    DOI: 10.1039/C2AY05636D

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