Giorgio Bianchi

Germplasm resources of Vitellaria paradoxa based on variations in fat composition across the species distribution range

The karité (Vitellaria paradoxa Gaertner) is an economically important African tree with significant but little studied variation across its broad distribution range. Differences in economically important fat characteristics were determined for 42 karité populations in 11 countries. The results showed very high variability in all measured parameters both within and between populations. Kernel fat content range is generally 20–50%. Fatty acid composition is dominated by stearic (25–50%) and oleic (37–62%) acids. The variable relative proportions of these two fatty acids produces major differences in karité butter consistency across the species distribution range. The principal triglycerides are stearic-oleic-stearic (13–46%) and stearic-oleic-oleic (16–31%). Ugandan karité fat is liquid and requires fractionation to obtain a butter. West African karité butter is more variable, with soft and hard consistencies produced within the same local populations. The hardest butters are produced on the Mossi Plateau in Burkina Faso and northern Ghana. The implications of distinctive population characteristics as germplasm resources for the chocolate and cosmetic industries are discussed.

Chemometric criteria for the characterisation of Italian Protected Denomination of Origin (DOP) olive oils from their metabolic profiles

This study was conducted and carried out as a consequence of the European directives n°2081/92 and n°2037/93 in which regulations for the protection of denomination of origin of food commodities were established. The research work was intended to investigate if monovarietal oils may be differentiated by their basic chemical composition. A positive outcome of this pilot study would hopefully permit extension to the characterisation of Italian DOP (Protected Denomination of Origin) olive oils. In the olive, long-chain alcohols, triterpenes and fatty acids, formed in distinct biosynthetic compartments, provide characteristic compositional data of an olive cultivar. The three classes of compounds were qualitatively and quantitatively determined by GC in six Italian olive cultivars, Coratina and Provenzale from Puglia, Frantoio and Moraiolo from Toscana, Bosana from Sardegna and Dritta from Abruzzo. Basic statistics and multivariate methods were first applied to the GC data of each class of compounds and then to the complete set of data with the aim of obtaining a clear discrimination of the cultivars. When Principal Components Analysis (PCA) was applied to the whole set of data consisting of alcohols, triterpenes and acids, the PCA revealed that the compounds (variables) that gave the better class distinction were: cycloartenol for Coratina, acids C20:0, C17:0, C18:0 for Dritta, citrostadienol for Frantoio and β-sitosterol for Moraiolo. Bosana and Provenzale correlated with erythrodiol and uvaol. A correct assignment of each oil sample to its monovarietal group was obtained.

Determination of the geographical origin of Italian extra virgin olive oil using pyrolysis mass spectrometry and artificial neural networks

Abstract Olives were collected from five important regions of Italy, from as many cultivars and locales as possible. For each region a number of samples were produced, representative of the area as a whole. Once collected the olives were washed and processed using standard methods within the ISE olive mill to produce DOC extra virgin olive oils of known region, province and variety (cultivar). These oils were analysed in triplicate by Curie-point pyrolysis mass spectrometry and the spectra collected. Spectra were normalised and sorted according to region. The data-splitting program, Multiplex (A. Jones, D.B. Kell and J. Rowland, Submitted to Analytica Chimica Acta (1996)) was used to sort the spectra into training and test sets split in the ratio 2:1 for Abruzzo:Sardinia and Apulia:Sardinia predictions and a ratio of 1:1 for Lazio:Sicily. Using artificial neural nets with a single output that represented the networks estimation of the geographical provenance as a numeric code, all unknown samples (as triplicates) from an Abruzzo/Sardinia challenge were successfully identified. Samples form a Lazio/Sicily were successfully predicted/separated when the outputs from the network for each triplicates were averaged, and Apulia/Sardinia were predicted with only a single error for each region. This represents the first report in which the precision and discrimination of pyrolysis mass spectrometry has been shown, when combined with artificial neural networks, to allow the discrimination of olive oils by region.