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An aqueous solution spectroscopic (Vis and EPR) study of the copper(II) complexes with the Ac-HGGG-NH2 and Ac-PHGGGWGQ-NH2 polypeptides (generically designated as L) suggests square base pyramids ascribable to [Cu(L)H(-2)] complex species, which contain three nitrogen donor atoms, arising from imidazole and peptide groups, in the equatorial plane and for a pseudo-octahedral geometry in the case of [CuLH-3]- and [Cu(L)H-4]2- which have four nitrogen donor atoms in their equatorial plane. The coordination sphere of the copper complex in the [Cu(L)H(-2)] species, which is present at neutral pH values, is completed by two oxygen donor atoms. ESI-MS spectra ascertained that water molecules are not present in the coordination equatorial plane of this latter species, in comparison with other copper(II) complexes with ligands bearing nitrogen and oxygen donor atoms and surely having equatorial water molecules. This indicates the coordination of a carbonyl oxygen atom in the equatorial plane has to be invoked. However, no direct proof about the involvement of a carbonyl group oxygen donor atom apically linked to copper was obtained, due to the flexibility of these structures at room temperature. Additionally, the low A(ll) value leads one to consider another oxygen atom of a carbonyl group being involved in the apical bond to copper in a fast exchange fashion. This apical interaction, which may also involve a water molecule, is more pronounced in the Cu-Ac-HGGG-NH2 than in the analogous Cu-Ac-PHGGGWGQ-NH2 system, probably because of the presence of tryptophan and proline in the polypeptide sequence.

This work has focused on discriminating extra virgin olive oils from Sabina (Lazio, Italy) by olive fruit variety (cultivar). A set of oils from five of the most widespread cultivars (Carboncella, Frantoio, Leccino, Moraiolo, and Pendolino) in this geographical area was analyzed for chemical composition using only the Official Analytical Methods, recognized for the quality control and commercial classification of this product. The obtained data set was converted into a computer-compatible format, and principal component analysis (PCA) and a method based on the Fisher F ratio were used to reduce the number of variables without a significant loss of chemical information. Then, to differentiate these samples, two supervised chemometric procedures were applied to process the experimental data: linear discriminant analysis (LDA) and artificial neural network (ANN) using the back-propagation algorithm. It was found that both of these techniques were able to generalize and correctly predict all of the samples in the test set. However, these results were obtained using 10 variables for LDA and 6 (the major fatty acid percentages, determined by a single gas chromatogram) for ANN, which, in this case, appears to provide a better prediction ability and a simpler chemical analysis. Finally, it is pointed out that, to achieve the correct authentication of all samples, the selected training set must be representative of the whole data set.

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