1,720,965 research outputs found

    Il sistema ambiente. Aspetti caratteristici, impatti e sistemi di gestione

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    Il testo presenta una generale descrizione del -Sistema Ambiente – attraverso la descrizione degli Ecosistemi, degli Impatti Ambientali e degli strumenti indicati dalla Comunità Internazionale ed Europea per l’interiorizzazione della variabile ambientale (Sistemi di gestione, norme ISO EN UNI 14000 e regolamento EMAS). Lo schema adottato nell’analisi dell’ambiente è stato quello suggerito dalla EEA (European Environmental Agency ) del modello DIPSIR (Determinanti, Pressioni,Stato, Impatti e Risposte)

    Blends of olive oil and seeds oils: Characterisation and olive oil quantification using fatty acids composition and chemometric tools. Part II

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    A method to verify the percentage of olive oil in a blend, in compliance with the Commission Regulation EU No. 29/2012, was developed by GC–FID analysis of methyl esters of fatty acids, followed by chemometric tools (PCA, TFA, SIMCA and PLS).First of all, binary blends of twelve olive oils and one sunflower oil were studied, in order to evaluate the variability associated to the fatty acids profile of olive oils (Monfreda, Gobbi, & Grippa, 2012). In this study, binary blends of twelve olive oils with four types of seeds oils (peanut, corn, rice and grape seed oils) were evaluated. These four groups of blends were analysed and processed separately, each group consisting of 36 samples with 40%, 50% and 60% of olive oil content. Chemometric tools were also applied to the global data set (180 samples, including those analysed in the previous paper).Outstanding results were achieved, showing that the proposed method would be capable to discriminate blends with a difference in concentration of olive oil lower than 5% (a standard error of prediction of 3.97% was obtained with PLS). Therefore blends containing 45% and 55% of olive oil were also analysed with the current method and added to the data sets for chemometric assessment with supervised tools. SIMCA still provided good models; however the best performance was achieved by processing each group of binary blends (consisting of 60 samples) separately, rather than applying SIMCA to the overall data set (300 samples). On the other hand PLS did not show significant improvements
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