Combination of an e-nose, an e-tongue and an e-eye for the characterisation of olive oils with different degree of bitterness

Apetrei, Constantin; Apetrei, Irina Mirela; Villanueva, Sergio Menéndez; Saja, J.A. de; Gutiérrez‐Rosales, F.; Rodrı́guez-Méndez, M.L.

doi:10.1016/j.aca.2010.01.034

Cited by 163 publications

(103 citation statements)

References 27 publications

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“…The olive oil extraction procedure used was previously described by Dias et al [26]. Hydro-ethanolic solutions (H 2 O:EtOH, 80:20 v/v) were used to overcome the difficulty of carrying out electrochemical assays in viscous non-conductive liquids [10]. Ethanol was of analytical grade (Panreac, Barcelona) Fig.…”

Section: E-tongue Analysis: Olive Oils Sample Preparation and Potentimentioning

confidence: 99%

“…In each assay, 10.00 g of olive oil were mixed to 100 mL of hydro-ethanolic solution during 5-10 min under strong agitation, using a vortex stirrer (LBX V05 series, lbx instruments), with a constant speed of approximately 500 rpm. This process allowed the extraction of polar compounds, which are responsible for sensory attributes of olive oils [10,13,25,26]. The mixture was left at ambient temperature during 60 min, after which, 40.0 mL of the supernatant solution was carefully removed and immediately analyzed with the Etongue.…”

Section: E-tongue Analysis: Olive Oils Sample Preparation and Potentimentioning

confidence: 99%

“…Several approaches have been proposed as alternatives to human sensory panels to evaluate the quality of olive oil (positive and/ or negative attributes), namely those based on the individual or fused application of electronic noses, tongues and/or eyes (E-nose, E-tongue and/or E-eye), based on different analytical procedures and principles [5,6,[10][11][12][13]. Qualitative and/or quantitative E-tongue based approaches have been successfully reported for olive oils physicochemical and positive sensory sensations assessment [10,[13][14][15][16][17][18][19][20][21]. In which concerns the evaluation of negative sensory attributes, Borràs et al [4] proposed partial least squares discriminant classification models, based on mid-infrared spectra, to differentiate EVOOs (defect absent) from lower quality olive oils (defect present).…”

Section: Introductionmentioning

confidence: 99%

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Perception of olive oils sensory defects using a potentiometric taste device

Veloso

Silva

Rodrigues

et al. 2018

Talanta

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A B S T R A C TThe capability of perceiving olive oils sensory defects and intensities plays a key role on olive oils quality grade classification since olive oils can only be classified as extra-virgin if no defect can be perceived by a human trained sensory panel. Otherwise, olive oils may be classified as virgin or lampante depending on the median intensity of the defect predominantly perceived and on the physicochemical levels. However, sensory analysis is time-consuming and requires an official sensory panel, which can only evaluate a low number of samples per day. In this work, the potential use of an electronic tongue as a taste sensor device to identify the defect predominantly perceived in olive oils was evaluated. The potentiometric profiles recorded showed that intraand inter-day signal drifts could be neglected (i.e., relative standard deviations lower than 25%), being not statistically significant the effect of the analysis day on the overall recorded E-tongue sensor fingerprints (Pvalue = 0.5715, for multivariate analysis of variance using Pillai's trace test), which significantly differ according to the olive oils' sensory defect (P-value = 0.0084, for multivariate analysis of variance using Pillai's trace test). Thus, a linear discriminant model based on 19 potentiometric signal sensors, selected by the simulated annealing algorithm, could be established to correctly predict the olive oil main sensory defect (fusty, rancid, wet-wood or winey-vinegary) with average sensitivity of 75 ± 3% and specificity of 73 ± 4% (repeated K-fold cross-validation variant: 4 folds×10 repeats). Similarly, a linear discriminant model, based on 24 selected sensors, correctly classified 92 ± 3% of the olive oils as virgin or lampante, being an average specificity of 93 ± 3% achieved. The overall satisfactory predictive performances strengthen the feasibility of the developed taste sensor device as a complementary methodology for olive oils' defects analysis and subsequent quality grade classification. Furthermore, the capability of identifying the type of sensory defect of an olive oil may allow establishing helpful insights regarding bad practices of olives or olive oils production, harvesting, transport and storage.

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Section: E-tongue Analysis: Olive Oils Sample Preparation and Potentimentioning

confidence: 99%

Section: E-tongue Analysis: Olive Oils Sample Preparation and Potentimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Perception of olive oils sensory defects using a potentiometric taste device

Veloso

Silva

Rodrigues

et al. 2018

Talanta

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“…It has already been applied to the characterization of red wines [41] and olive oils with different degree of bitterness [42]. The fusion of these three sensory modalities (volatiles, liquids, and color) has given rise to the so-called "electronic panel", which includes the corresponding sensors as well as the electronics and software necessary for combining information from the three modules.…”

Section: Using E-tonguesmentioning

confidence: 99%

On-line monitoring of food fermentation processes using electronic noses and electronic tongues: A review

Peris

Escuder-Gilabert

2013

Analytica Chimica Acta

125

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“…Among the many systems employing multivariate data analysis, particularly relevant for biosensing are those related to electronic tongues and noses [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56]. The latter comprise arrays of chemical sensors, whose response constitutes a taste or odor pattern, respectively.…”

Section: Electronic Tongues and Nosesmentioning

confidence: 99%

Information Visualization to Enhance Sensitivity and Selectivity in Biosensing

Oliveira¹,

Pavinatto²,

Constantino³

et al. 2012

Biointerphases

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An overview is provided of the various methods for analyzing biosensing data, with emphasis on information visualization approaches such as multidimensional projection techniques. Emphasis is placed on the importance of data analysis methods, with a description of traditional techniques, including the advantages and limitations of linear and non-linear methods to generate layouts that emphasize similarity/dissimilarity relationships among data instances. Particularly important are recent methods that allow processing high-dimensional data, thus taking full advantage of the capabilities of modern equipment. In this area, now referred to as e-science, the choice of appropriate data analysis methods is crucial to enhance the sensitivity and selectivity of sensors and biosensors. Two types of systems deserving attention in this context are electronic noses and electronic tongues, which are made of sensor arrays whose electrical or electrochemical responses are combined to provide “finger print” information for aromas and tastes. Examples will also be given of unprecedented detection of tropical diseases, made possible with the use of multidimensional projection techniques. Furthermore, ways of using these techniques along with other information visualization methods to optimize biosensors will be discussed.

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Combination of an e-nose, an e-tongue and an e-eye for the characterisation of olive oils with different degree of bitterness

Cited by 163 publications

References 27 publications

Perception of olive oils sensory defects using a potentiometric taste device

Perception of olive oils sensory defects using a potentiometric taste device

On-line monitoring of food fermentation processes using electronic noses and electronic tongues: A review

Information Visualization to Enhance Sensitivity and Selectivity in Biosensing

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