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Instrumental and multivariate statistical analyses for the characterisation of the geographical origin of Apulian virgin olive oils.
Food Chem. 2012 Jul 15; 133(2):579-84.FC

Abstract

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.

Authors+Show Affiliations

Dipartimento di Chimica, Campus Universitario, Università di Bari, Via Orabona 4, 70126 Bari, Italy. Electronic address: longobardi@chimica.uniba.it.Dipartimento di Chimica, Campus Universitario, Università di Bari, Via Orabona 4, 70126 Bari, Italy.Dipartimento di Chimica, Campus Universitario, Università di Bari, Via Orabona 4, 70126 Bari, Italy.Dipartimento di Chimica, Campus Universitario, Università di Bari, Via Orabona 4, 70126 Bari, Italy.Dipartimento di Chimica, Campus Universitario, Università di Bari, Via Orabona 4, 70126 Bari, Italy; IPCF-CNR, sez. Bari, Via Orabona 4, 70126 Bari, Italy.Dipartimento di Chimica, Campus Universitario, Università di Bari, Via Orabona 4, 70126 Bari, Italy; IPCF-CNR, sez. Bari, Via Orabona 4, 70126 Bari, Italy; INSTM, Via G. Giusti 9, 50121 Firenze, Italy.Laboratory of Food Chemistry and Technology, Department of Chemistry, University of Ioannina, P.O. Box 1186, 45110 Ioannina, Greece.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

25683436

Citation

Longobardi, F, et al. "Instrumental and Multivariate Statistical Analyses for the Characterisation of the Geographical Origin of Apulian Virgin Olive Oils." Food Chemistry, vol. 133, no. 2, 2012, pp. 579-84.
Longobardi F, Ventrella A, Casiello G, et al. Instrumental and multivariate statistical analyses for the characterisation of the geographical origin of Apulian virgin olive oils. Food Chem. 2012;133(2):579-84.
Longobardi, F., Ventrella, A., Casiello, G., Sacco, D., Catucci, L., Agostiano, A., & Kontominas, M. G. (2012). Instrumental and multivariate statistical analyses for the characterisation of the geographical origin of Apulian virgin olive oils. Food Chemistry, 133(2), 579-84. https://doi.org/10.1016/j.foodchem.2012.01.059
Longobardi F, et al. Instrumental and Multivariate Statistical Analyses for the Characterisation of the Geographical Origin of Apulian Virgin Olive Oils. Food Chem. 2012 Jul 15;133(2):579-84. PubMed PMID: 25683436.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Instrumental and multivariate statistical analyses for the characterisation of the geographical origin of Apulian virgin olive oils. AU - Longobardi,F, AU - Ventrella,A, AU - Casiello,G, AU - Sacco,D, AU - Catucci,L, AU - Agostiano,A, AU - Kontominas,M G, Y1 - 2012/01/27/ PY - 2011/10/27/received PY - 2012/01/05/revised PY - 2012/01/19/accepted PY - 2015/2/17/entrez PY - 2012/7/15/pubmed PY - 2015/8/19/medline KW - Geographical origin KW - Multivariate statistical analysis KW - Oil quality parameters KW - Virgin olive oil SP - 579 EP - 84 JF - Food chemistry JO - Food Chem VL - 133 IS - 2 N2 - 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. SN - 1873-7072 UR - https://www.unboundmedicine.com/medline/citation/25683436/Instrumental_and_multivariate_statistical_analyses_for_the_characterisation_of_the_geographical_origin_of_Apulian_virgin_olive_oils_ DB - PRIME DP - Unbound Medicine ER -