Geographical origin determination of olive oils based on physical-chemical properties can be a difficult task. Multivariate statistics can provide specific tools for authenticity control and geographical origin determination. CAIMAN (Classification And Influence Matrix ANalysis) is a classification method based on a simple mathematical approach where the results can be easily interpreted by analyzing the leverage values. Moreover, the application of this new classification approach to the geographical origin identification of olive oils seems to offer several advantages. First of all, the asymmetric version of CAIMAN (A-CAIMAN) showed good performance when compared to partial least square discriminant analysis (PLS-DA). Moreover, asymmetric CAIMAN is able to deal in a simple and easily interpretable way classification problems related to tipicity, authenticity, and uniqueness characterization, which are increasingly common in food quality issues. It showed good capabilities in discriminating the geographical origin of olive oil samples and may be an ideal tool for describing olive oil uniqueness based on a chemical fingerprint. © 2010 Copyright © 2010 Elsevier Inc. All rights reserved.

Ballabio, D., Todeschini, R. (2010). Geographical characterization of olive oil by means of multivariate classification: application of CAIMAN. In V.R. Preedy, R.R. Watson (a cura di), Olives and olive oil in health and disease prevention (pp. 129-137). Amsterdam : Elsevier Inc. [10.1016/B978-0-12-374420-3.00016-4].

Geographical characterization of olive oil by means of multivariate classification: application of CAIMAN

BALLABIO, DAVIDE
;
TODESCHINI, ROBERTO
2010

Abstract

Geographical origin determination of olive oils based on physical-chemical properties can be a difficult task. Multivariate statistics can provide specific tools for authenticity control and geographical origin determination. CAIMAN (Classification And Influence Matrix ANalysis) is a classification method based on a simple mathematical approach where the results can be easily interpreted by analyzing the leverage values. Moreover, the application of this new classification approach to the geographical origin identification of olive oils seems to offer several advantages. First of all, the asymmetric version of CAIMAN (A-CAIMAN) showed good performance when compared to partial least square discriminant analysis (PLS-DA). Moreover, asymmetric CAIMAN is able to deal in a simple and easily interpretable way classification problems related to tipicity, authenticity, and uniqueness characterization, which are increasingly common in food quality issues. It showed good capabilities in discriminating the geographical origin of olive oil samples and may be an ideal tool for describing olive oil uniqueness based on a chemical fingerprint. © 2010 Copyright © 2010 Elsevier Inc. All rights reserved.
Capitolo o saggio
classification; olive oil; chemometrics
English
Olives and olive oil in health and disease prevention
2010
978-0-08-092220-1
Ballabio, D., Todeschini, R. (2010). Geographical characterization of olive oil by means of multivariate classification: application of CAIMAN. In V.R. Preedy, R.R. Watson (a cura di), Olives and olive oil in health and disease prevention (pp. 129-137). Amsterdam : Elsevier Inc. [10.1016/B978-0-12-374420-3.00016-4].
Ballabio, D; Todeschini, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/7086
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