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CHEMOMETRICS

Geographical Characterization of Greek Olive Oils Using Rare Earth Elements Content and Supervised Chemometric Techniques

, , , , &
Pages 920-932 | Received 22 Apr 2011, Accepted 28 Sep 2011, Published online: 30 May 2012
 

Abstract

Different ANNs models [Multi-layer Perceptrons (MLPs) and Radial Basis Function (RBF)] were developed and evaluated for the discrimination of olive oils produced in four Greek regions according to their geographical origin. For this purpose, ninety-seven samples were analyzed for 10 rare earth elements (REE) by ICP-MS. Moreover, two additional supervised techniques, discriminant analysis (DA) and classification trees (CTs), were applied to the same set for the data pre-treatment and for comparison purposes. In addition, two approaches were used for models' training and evaluation: the classical random choice of samples for the learning data set and an innovative one, which used the two linear discriminant functions (LDFs) of the preceding DA to choose the most representative learning sample set. The results were very satisfactory for the new ANNs classifiers. Over-fitting phenomena were overcome and the prediction ability was 73%, as evaluated by an independent test sample set. The results are encouraging for the ANNs efficiency even in demanding data bases, as the one under consideration.

[Supplementary materials are available for this article. Go to the publisher's online edition of Analytical Letters for the following free supplemental resources: Additional figures and tables.]

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