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

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.