Perception of olive oils sensory defects using a potentiometric taste device

Abstract

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.

A taste sensor device for unmasking admixing of rancid or winey-vinegary olive oil to extra virgin olive oil

“…1) included two homemade print-screen potentiometric arrays covered with an acrylic resin (PLASTIK 70) for ensuring a waterproof surface. As previously described (Dias et al, 2015;Veloso et al, 2018), each polyvinyl chloride (PVC) board (3 cm × 12 cm) had 20 wells (3.6 mm of diameter and 0.3 mm of thickness), where 20 cross-sensitivity lipid polymeric membranes were applied, using a drop-by-drop technique. The polymeric membranes had different combinations of 4 lipid additives (∼3%: octadecylamine, oleyl alcohol, methyltrioctylammonium chloride or oleic acid), 5 plasticizers (∼32%: bis(1-butylpentyl) adipate, dibutyl sebacate, 2-nitrophenyl-octylether, tris(2-ethylhexyl)phosphate or dioctyl phenylphosphonate) and PVC (∼65%), which were used as chemical sensors (Fluka, minimum purity ≥97%).…”

“…The multi-sensor E-tongue device was stored in a KCl aqueous solution (1 mol/L). The same sensor coding used previously was also adopted and so each sensor was identified with a letter S (for sensor) followed by the number of the array (1 or 2) and the number of the membrane (1-20, corresponding to different combinations of plasticizer and additive used) (Veloso et al, 2018).…”

“…Olive oil sample preparation and potentiometric assays All samples were electrochemically analyzed within the same day avoiding the need of statistical complex signal pre-treatments to overcome possible potential drifts (Dias et al, 2009;Marx et al, 2017b;Veloso et al, 2018). The olive oil samples were extracted using a previous described procedure (Dias et al, 2014).…”

“…The literature survey clearly point out the limited number of works reporting the successful use of E-noses (Oliveros et al, 2002;Jeleń, 2008, 2010;Lerma-García et al, 2010;Santonico et al, 2015) to detect olive oil adulteration with other vegetable oils or lower quality olive oils (possessing or not common sensory defects), as well as the scarce use of voltammetric E-tongues (Apetrei and Apetrei, 2014;Santonico et al, 2015). Recently, the use of a pontentiometric E-tongue device comprising cross-sensitivity lipid polymeric membranes, has demonstrated to be a practical and helpful taste sensor tool for olive oil analysis (Dias et al, 2014Veloso et al, 2016Veloso et al, , 2018Slim et al, 2017;Souayah et al, 2017). It was previously reported by Marx et al (2017b) and Slim et al (2017) the capability of this type of E-tongue to provide quantitative potentiometric responses towards aldehydes, alcohols and esters compounds that mimic positive olive oil sensory attributes namely, 4-hydroxy-3-methoxybenzaldehyde (vanilla sensation), hexyl acetate (sweet, green, grassy, fruity or apple sensations), (Z)-hex-3-en-1-ol (green leaves or banana sensations), (E)-hex-2-enal (green, almonds or apple sensations), (Z)-hex-3-enyl acetate (fruity or green leaves sensations), citric and tartaric acids (acid sensation), caffeine and quinine (bitter sensations) and sodium or potassium chloride (salty sensation).…”

“…Thus, the measured electric potential depends on the membrane surface-charge density changes, and on its permeability to ions altered by the physical adsorption of nonelectrolytes compounds (Iiyama et al, 1986;Kurihara et al, 1986;Hayashi et al, 1989). Recently Veloso et al (2018) reported the capability of using an E-tongue device to classify olive oil according to the main sensory defect perceived. In this work, a pontentiometric E-tongue device was applied, for the first time, for detecting intentionallyadulterated EVOO with known percentages of rancid or winey-vinegary LOO (LOO-R or LOO-WV), which sensory defect and intensities were assessed by trained panelists.…”