Characterization and Authentication of Olive and Other by María Jesús Lerma García

By María Jesús Lerma García

This thesis offers new tools for the characterization of vegetable oils, with concentration in olive oil, in response to geographical and botanical foundation, genetic kind and different concerns influencing product caliber. a wide selection of analytical suggestions were hired, reminiscent of numerous chromatographic innovations (different fuel and liquid chromatography methods), an digital nostril, infrared spectroscopy and expert-panel review. numerous households of youngster compounds, with curiosity as adulteration markers, were used for technique improvement, together with tocopherols, sterols, phenolics, alcohols, proteins and others. such a lot tools were stronger through the applying of multivariate chemometrics. The proposed analytical thoughts are of curiosity to enquire fraudulent activities and practices that are harmful to product caliber.

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2007). EVOOs are produced from very different genetic varieties (Barranco et al. 2001). The most widespread varieties in Spain, which accounts 95 % of the produced olives, are Arbequina, Hojiblanca and Picual varieties, although the number of other varieties cultivated in Spain is also high and very diverse, depending on the geographic region where the crop is located. The cultivar Picual, also know as Marteña, is the most important and widespread, mainly cultivated in Andalucía, Castilla-La Mancha and Extremadura.

Ramis Ramos and M. Celia García Álvarez-Coque, Quimiometría, from Fig. 12 of p. 192) An artificial neuron contains two algorithms, one of them calculates the weighted sum of the values that come through the inlet connexions, and the other, called transfer function, generates a response or output that is communicated to other neurons. The network is able to ‘‘learn’’ mainly by adjusting the ‘‘weights’’ of the connections between neurons, until the network as a whole provides sufficiently precise predictions.

Signal pre-processing techniques are used to compensate possible deviations from sensor signals, and also to reduce variations between the measure of different samples. The techniques applied in these cases include: • Handling of the baseline, which is a transformation based on the initial values of the sensors. • Normalization of responses. Scaling is able to adjust the values of the responses of the sensors to prevent that some variables have more weight than others in the result obtained. The normalization methods are able to avoid experimental variations and reduce computational errors when applying pattern recognition techniques (Gardner 1993).

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