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      Multidisciplinary Approach to Characterizing the Fingerprint of Italian EVOO

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          Abstract

          Extra virgin olive oil (EVOO) is characterized by its aroma and other sensory attributes. These are determined by the geographical origin of the oil, extraction process, place of cultivation, soil, tree varieties, and storage conditions. In the present work, an array of metal oxide gas sensors (called S3), in combination with the SPME-GC-MS technique, was applied to the discrimination of different types of olive oil (phase 1) and to the identification of four varieties of Garda PDO extra virgin olive oils coming from west and east shores of Lake Garda (phase 2). The chemical analysis method involving SPME-GC-MS provided a complete volatile component of the extra virgin olive oils that was used to relate to the S3 multisensory responses. Furthermore, principal component analysis (PCA) and k-Nearest Neighbors (k-NN) analysis were carried out on the set of data acquired from the sensor array to determine the best sensors for these tasks and to assess the capability of the system to identify various olive oil samples. k-NN classification rates were found to be 94.3% and 94.7% in the two phases, respectively. These first results are encouraging and show a good capability of the S3 instrument to distinguish different oil samples.

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          Rapid direct analysis to discriminate geographic origin of extra virgin olive oils by flash gas chromatography electronic nose and chemometrics.

          At present, the geographical origin of extra virgin olive oils can be ensured by documented traceability, although chemical analysis may add information that is useful for possible confirmation. This preliminary study investigated the effectiveness of flash gas chromatography electronic nose and multivariate data analysis to perform rapid screening of commercial extra virgin olive oils characterized by a different geographical origin declared in the label. A comparison with solid phase micro extraction coupled to gas chromatography mass spectrometry was also performed. The new method is suitable to verify the geographic origin of extra virgin olive oils based on principal components analysis and discriminant analysis applied to the volatile profile of the headspace as a fingerprint. The selected variables were suitable in discriminating between "100% Italian" and "non-100% Italian" oils. Partial least squares discriminant analysis also allowed prediction of the degree of membership of unknown samples to the classes examined.
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            Characterisation of virgin olive oil of Italian olive cultivars: `Frantoio' and `Leccino', grown in Andalusia

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              Influence of volatile compounds on virgin olive oil quality evaluated by analytical approaches and sensor panels

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                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Molecules
                Molecules
                molecules
                Molecules
                MDPI
                1420-3049
                12 April 2019
                April 2019
                : 24
                : 8
                : 1457
                Affiliations
                [1 ]Department of Information Engineering, University of Brescia, Brescia, via Branze, 38, 25123 Brescia, BS, Italy; giorgio.duina@ 123456unibs.it
                [2 ]CNR-IBBR, Institute of Bioscience and Bioresources, via Madonna del Piano, 10, 50019 Sesto Fiorentino, FI, Italy; estefania.nunezcarmona@ 123456ibbr.cnr.it (E.N.-C.); veronica.sberveglieri@ 123456ibbr.cnr.it (V.S.)
                [3 ]NANO SENSOR SYSTEMS, NASYS Spin-Off University of Brescia, Brescia, via Camillo Brozzoni, 9, 25125 Brescia, BS, Italy
                Author notes
                [* ]Correspondence: m.abbatangelo@ 123456unibs.it ; Tel.: +39-34-8842-3503
                Author information
                https://orcid.org/0000-0002-5550-3401
                https://orcid.org/0000-0002-4621-0658
                https://orcid.org/0000-0002-5991-9391
                Article
                molecules-24-01457
                10.3390/molecules24081457
                6515353
                31013836
                9c71f007-e003-4b6d-a9af-c2db8ddc3d6e
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 13 March 2019
                : 11 April 2019
                Categories
                Article

                sensors array,nanowire gas sensors,evoo,gc-ms-spme,pca,k-nn
                sensors array, nanowire gas sensors, evoo, gc-ms-spme, pca, k-nn

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