Instrumental and multivariate statistical analyses for the characterisation of the geographical origin of Apulian virgin olive oils

• Published in: Food chemistry 2012-07, Vol.133 (2), p.579-584
• Main Authors: Longobardi, F., Ventrella, A., Casiello, G., Sacco, D., Catucci, L., Agostiano, A., Kontominas, M.G.
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Chlorophyll - analysis; Discriminant Analysis; Fat industries; Fatty Acids - analysis; Food industries; Fundamental and applied biological sciences. Psychology; Geographical origin; Geography; Italy; Least-Squares Analysis; Multivariate Analysis; Multivariate statistical analysis; Oil quality parameters; Olive Oil; Plant Oils - chemistry; Principal Component Analysis; Quality Control; Triglycerides - analysis; Virgin olive oil
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2012.01.059

► Apulian virgin olive oils were analysed by different instrumental and statistical techniques. ► The target was the classification of Apulian samples from three different origins. ► Three multivariate statistical techniques were used and results were compared. ► General discriminant analysis gave the best average prediction ability (82.5%). 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.