32 research outputs found
Migration and sensory properties of plastics-based nets used as food-contacting materials under ambient and high temperature heating conditions
On-line solid-phase extraction with surfactant accelerated on-column derivatization and micellar liquid chromatographic separation as a tool for the determination of biogenic amines in various food substrates
On-Line Solid-Phase Extraction with Surfactant Accelerated On-Column Derivatization and Micellar Liquid Chromatographic Separation as a Tool for the Determination of Biogenic Amines in Various Food Substrates
Biodiesel Production: Reaction and Process Parameters of Alkali-Catalyzed Transesterification of Waste Frying Oils
Characterization and classification of Western Greek olive oils according to cultivar and geographical origin based on volatile compounds
The aim of the present study was to characterize and classify olive oils from Western Greece according to
cultivar and geographical origin, based on volatile compound composition, by means of Linear Discriminant
Analysis. A total of 51 olive oil samples were collected during the harvesting period 2007–2008 from
six regions of Western Greece and from six local cultivars. Forty-five of the samples were characterized
as extra virgin olive oils. The analysis of volatile compounds was performed by Headspace Solid Phase
Microextraction-Gas Chromatography/Mass Spectrometry (HS-SPME-GC/MS). Fifty-three (53) different
volatile compounds were tentatively identified and semi-quantified. Using selected volatile compound
composition data (selection was based on the application of ANOVA to total volatiles to determine those
variables showing substantial differences among samples of different geographical origin/cultivar), the
olive oil samples were satisfactorily classified according to geographical origin (87.2%) and cultivar (74%)
Smart packaging: definitions, models and packaging as an intermediator between digital and physical product management
Characterisation of the geographical origin of Western Greek virgin olive oils based on instrumental and multivariate statistical analysis
In this work, measurements of free acidity, peroxide content, spectrophotometric parameters,
chlorophyll content, phenols, sterols, fatty acids and triacylglycerol composition, were carried out
on samples of virgin olive oils (VOOs) coming from four different Greek Ionian islands, i.e.
Zakynthos, Kefalonia, Lefkada and Kerkyra. An analysis of variance (ANOVA) highlighted
statistically significant differences (p<0.01) in the values of 26 analytical parameters among the
VOOs produced in the four different geographical regions but a Post-Hoc test showed that no
variable was able to distinguish all four origins. Analogously, a Principal Component Analysis
(PCA) showed a modest grouping of VOOs according to geographical origin except for Kerkyra
samples which were more distinct from others. Applying discriminant function analysis (DFA) a
good separation of the four geographical groups was achieved with classification and prediction
abilities equal to 97.7% and 95.3%, respectively. Moreover, the analysis of the standardized
coefficients showed that the fatty acids and triacylglycerols were the most discriminant variables.
This last outcome was confirmed by comparison of the prediction performances obtained applying
DFA on four subdatasets containing fatty acids (69.8%), triacylglycerols (76.7%), sterols (62.8%),
and remaining parameters (65.1%) together, respectively. As the results showed, the
multidisciplinary approach that combines different types of analytical determinations improved the
discrimination of geographical origin for Greek virgin olive oils
Instrumental and multivariate statistical analyses for the characterisation of the geographical origin of Apulian virgin olive oils
In this paper, virgin olive oils (VOOs) coming from three different geographic origins of Apulia, were analysed for free acidity, peroxide value. spectrophotometric indexes, chlorophyll content. sterol, fatty acid, and triacylglycerol compositions. In order to predict the geographical origin of VOOs, different multivariate approaches were applied. By performing principal component analysis (PCA) a modest natural grouping of the VOOs was observed on the basis of their origin, and consequently three supervised techniques, i.e., general discriminant analysis (GDA), partial least squares-discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA) were used and the results were compared. In particular, the best prediction ability was produced by applying GDA (average prediction ability of 82.5%), even if interesting results were obtained also by applying the other two classification techniques, i.e., 77.2% and 75.5% for PLS-DA and SIMCA, respectively. (C) 2012 Elsevier Ltd. All rights reserved