Olive oil production represents a big part of the Mediterranean economy, and as such it must be protected from frauds. For this reason, it is necessary to develop alternative low-cost techniques, applicable on large scale, for checking the quality of the product and for detecting adulteration. On such bases, the present work deals with the possibility of adopting microwave reflectometry for obtaining a 'spectral signature' of vegetable oils. For this purpose, time domain reflectometry (TDR) measurements, in combination with specific data processing, are first used for the dielectric characterization of several oil types. Successively, the acquired data are processed through the principal component analysis (for identifying clusters of oil types that exhibit common features) and through the partial least square analysis (for identifying a predictive model for detecting oil adulteration). Results confirm that the proposed procedure holds considerable potential for quality and anti-adulteration control purposes, especially in view of practical applications. (C) 2012 Elsevier Ltd. All rights reserved.
Classification and adulteration control of vegetable oils based on microwave reflectometry analysis / Andrea, Cataldo; Piuzzi, Emanuele; Giuseppe, Cannazza; Egidio De, Benedetto. - In: JOURNAL OF FOOD ENGINEERING. - ISSN 0260-8774. - STAMPA. - 112:4(2012), pp. 338-345. [10.1016/j.jfoodeng.2012.04.012]
Classification and adulteration control of vegetable oils based on microwave reflectometry analysis
PIUZZI, Emanuele;
2012
Abstract
Olive oil production represents a big part of the Mediterranean economy, and as such it must be protected from frauds. For this reason, it is necessary to develop alternative low-cost techniques, applicable on large scale, for checking the quality of the product and for detecting adulteration. On such bases, the present work deals with the possibility of adopting microwave reflectometry for obtaining a 'spectral signature' of vegetable oils. For this purpose, time domain reflectometry (TDR) measurements, in combination with specific data processing, are first used for the dielectric characterization of several oil types. Successively, the acquired data are processed through the principal component analysis (for identifying clusters of oil types that exhibit common features) and through the partial least square analysis (for identifying a predictive model for detecting oil adulteration). Results confirm that the proposed procedure holds considerable potential for quality and anti-adulteration control purposes, especially in view of practical applications. (C) 2012 Elsevier Ltd. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.