Discrimination and identification of geographical origin virgin olive oil by an e-nose based on MOS sensors and pattern recognition techniques

Abstract

International audienceIn the present work, the potential of an electronic nose to differentiate the geographical origin of the Moroccan virgin olive oils based on their volatile profile was investigated. An electronic gas sensor array system composed of 6 metal oxide semiconductor sensors was used to generate a chemical fingerprint (pattern) of the volatile compounds present in olive oils. Multivariate statistical approach showed good discrimination between the classes of the 27-sample of the dataset population. The results of this study provide promising perspectives for the use of a low-cost and rapid system for the verification of geographical origin of the olive oils based on their volatile profile

Electronic Nose as a Tool for Monitoring the Authenticity of Food. A Review

“…The geographical origin of Moroccan virgin olive oils was confirmed in the study of Haddi et al (2011) by an e-nose based on six MOS sensors and pattern recognition techniques such as PCA and LDA. They found that especially LDA gives good separation of tested olive oils.…”

“…Actually, most of the applications listed in Table 1 were studied using e-noses based on MOS. E-noses based on conductometric sensors have been used for geographical origin confirmation or adulteration detection of dairy products (Cevoli et al 2011;Pillonel et al 2003;Yu et al 2007), plant oils (Bougrini et al 2014;Cerrato Oliveros et al 2002;Cosio et al 2006;Guadarrama et al 2001;Haddi et al 2011;Hai and Wang 2006;Jeleń 2008, Mildner-Szkudlarz andWei et al 2015), meat and meat products (Laureati et al 2014;Tian et al 2013), honey Pei et al 2015;Subari et al 2012;Subari et al 2014;Zakaria et al 2011), beverages (Aleixandre et al 2008;Berna et al 2009;Hong et al 2014;Lozano et al 2007;Penza and Cassano 2004;Steine et al 2001), coffee (Buratti et al 2015), tea (Kovács et al 2010), and some spices (Banach et al 2012;Heidarbeigi et al 2015).…”

Sensory intensity assessment of olive oils using an electronic tongue

“…So, several gas-, liquid-and mass-spectrometry chromatography, DNA and spectroscopy based methods have been developed to assess olive oil quality and authenticity as well as to detect possible adulterations [3,5,6,[8][9][10][11][12][13][14][15][16]. Electrochemical sensors have also been extensively used, including electronic noses and electronic tongues (E-tongues), individually or in combination, mainly with the aim of identifying possible adulterations or classifying olive oils according to quality level, geographical origin or olive cultivar [16][17][18][19][20][21][22][23][24][25][26][27][28]. Recently, a "magnetic tongue" was used to quantify minor compounds of EVOO that are related to the sensory attributes [29].…”

Discrimination of Olive Oil by Cultivar, Geographical Origin and Quality Using Potentiometric Electronic Tongue Fingerprints

“…The satisfactory overall E-tongue predictive classification performance could be partially attributed to the fact that, in general, for the studied two single-cultivar Tunisian olive oil, different fruity and rancid intensity sensations could be perceived by the panelists depending on the geographical origin. Also, it should be remarked that the predictive geographical origin sensitivities achieved with the proposed E-tongue, used for the first time as an olive oil's origin discrimination tool, are of the same order of those obtained with E-nose systems (correct classification rates greater or equal to 96%) [15,17,18], with voltammetric E-tongue devices (correct classification rates greater or equal to 94%) [15,18] or by fusing E-nose and voltammetric E-tongue (100% of correct classification for LOO-CV) [18].…”

“…For the potentiometric assays, a commercial reference Ag/AgCl electrode was used (Metrohm Ag/ AgCl double junction with SGG sleeve). Each sensor was identified a letter S (for sensor) followed by the number of the array (1 or 2) and the number of the membrane (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20).…”