An electronic nose and an electronic tongue, in combination with multivariate analysis, have been used to verify the geographical origin and the uniqueness of specific extra virgin olive oils. The olive oil samples belong to a small production, located in the lake of Garda (north of Italy) and distinguished with a European Protected Denomination of Origin trademark since 1998. In order to obtain a complete description of oil samples, free acidity, peroxide value, ultraviolet indices, and phenol content have been also determined. The dataset includes 36 Garda oils and 17 oils from other regions. Two classification models have been built by means of Counterpropagation Artificial Neural Networks in order to separate Garda and not-Garda oils, as follows: first, by using all the chemical variables and sensor signals; second, by using electronic tongue sensors; finally, by using four selected electronic nose sensors. All the models have been also tested with 19 commercial olive oil samples. Neural networks have provided very satisfactory results and have indicated the electronic nose as the most appropriate tool for the characterization of the analyzed oils. These results have suggested how electronic nose, in combination with neural networks, could represent a fast, cheap and functional method to classify and describe extra virgin olive oils from a circumscribed geographical area. (copyright) 2006 Elsevier B.V. All rights reserved.
Geographical origin and authentication of extra virgin olive oils by an electronic nose in combination with artificial neural networks / M.S. Cosio, D. Ballabio, S. Benedetti, C. Gigliotti. - In: ANALYTICA CHIMICA ACTA. - ISSN 0003-2670. - 567:2(2006 May 17), pp. 202-210.
|Titolo:||Geographical origin and authentication of extra virgin olive oils by an electronic nose in combination with artificial neural networks|
COSIO, MARIA STELLA (Primo)
BALLABIO, DAVIDE (Secondo)
BENEDETTI, SIMONA (Penultimo)
GIGLIOTTI, CARMELINA (Ultimo)
|Parole Chiave:||Extra virgin olive oil ; Electronic nose ; Uniqueness ; Neural networks|
|Settore Scientifico Disciplinare:||Settore AGR/15 - Scienze e Tecnologie Alimentari|
|Data di pubblicazione:||17-mag-2006|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.aca.2006.03.035|
|Appare nelle tipologie:||01 - Articolo su periodico|