Composición del aceite de la semilla del pistacho (Pistacia vera): efectos de la situación geográfica y de la variedad

This study was aimed at characterizing four Tunisian pistachio cultivations (Pistacia vera) from the Mateur (North), Nabeul (North-East), Kairouan (Middle) and Sfax (Middle- East) regions and two varieties, Mateur and Ohadi, considering fatty acid composition and main lipid class contents (polar lipids, diacylglycerols, triacylglycerols and free fatty acids) in ripe seeds. Lipid classes were separated using thin layer chromatography, and fatty acid methyl esters (FAMEs) were analyzed by gas chromatography. Oleic acid (C18:1 Δ9) was the major fatty acid for all samples; those of the Mateur region (the northern region) differed significantly with the lowest content in C18:1 (54.2 % of total fatty acids) and the highest in C18:2 (24.1 %). The total amount of fatty acids (TFA) in the seeds of Ohadi variety was significantly lower than that of the Mateur variety from the Sfax region. Triacylglycerols were most abundant in pistachio seeds from Nabeul (98.5% of total glycerolipids).Este estudio tiene como objetivo caracterizar cuatro cultivos de pistacho (Pistacia vera) de Túnez, de las regiones de Mateur (Norte), Nabeul (Nordeste), Kairouan (Centro) y Sfax (Centro-Este) y dos variedades, Mateur and Ohadi, considerando la composición en ácidos grasos y de lípidos mayoritarios (lípidos polares, diglicéridos, triglicéridos y ácidos grasos libres) de semillas maduras. Los lípidos se separaron mediante cromatografía en capa fina, y los ésteres metílicos de los ácidos grasos (FAMEs) se analizaron por cromatografía de gases. El ácido oleico (C18:1 Δ9) fue el mayoritario en todas las muestras; las de la región de Mateur (Norte) fueron significativamente distintas, presentando el contenido más bajo de C18:1 (54.2 % del total de ácidos grasos) y más alto de C18:2 (24.1 %). La cantidad total de ácidos grasos (TFA) en las semillas de la variedad Ohadi fue significativamente más bajo que el de la variedad Mateur de la región de Sfax. Los triglicéridos fueron el componente más importante en la semillas de pistacho de Nabeul (98.5% del total de glicerolípidos)

Prince: An algorithm for generating rule bases without closure computations

The problem of the relevance and the usefulness of extracted association rules is becoming of primary importance, since an overwhelming number of association rules may be derived, even from reasonably sized databases. To overcome such drawback, the extraction of reduced size generic bases of association rules seems to be promising. Using the concept of minimal generator, we propose an algorithm, called Prince, allowing a shrewd extraction of generic bases of rules. To this end, Prince builds the partial order. Its originality is that this partial order is maintained between minimal generators and no more between closed itemsets. A structure called minimal generator lattice is then built, from which the derivation of the generic association rules becomes straightforward. An intensive experimental evaluation, carried out on benchmarking sparse and dense datasets, showed that Prince largely outperforms the pioneer level-wise algorithms, i.e., Close, A-Close and Titanic

Prince : Extraction optimisée des bases génériques de régles sans calcul de fermetures

National audienc

Avoiding the itemset closure computation ''pitfall''

International audienc

Succinct System of Minimal Generators: A thorough study, limitations and new definitions

International audienc

Generic Association rule bases : Are they so succinct ?

International audienc

Tunis

Abstract. Extracting generic bases of association rules seems to be a promising issue in order to present informative and compact user addedvalue knowledge. However, extracting generic bases requires partially ordering costly computed itemset closures. To avoid the nightmarish itemset closure computation cost, specially for sparse contexts, we introduce an algorithm, called Prince, allowing an astute extraction of generic bases of association rules. The Prince algorithm main originality is that the partial order is maintained between frequent minimal generators and no more between frequent closed itemsets. A structure called minimal generator lattice is then built, from which the derivation of itemset closures and generic association rules becomes straightforward. An intensive experimental evaluation, carried out on benchmarking and ”worst case ” datasets, showed that Prince largely outperforms the pioneer algorithms, i.e., Close, A-Close and Titanic