Application of an electronic tongue for Tunisian olive oils’ classification according to olive cultivar or physicochemical parameters

Emerging Trends of Electrochemical Sensors in Food Analysis

Food quality and safety pose an increasing threat to human health worldwide [...]

Quantification of phosphatides in sunflower oils using a potentiometric e-tongue

Consisting of two fatty acyl groups, phospholipids are a vital part of vegetable oils and the source of essential fatty acids. Moreover, phospholipids influence oxidative and flavor stability and color evolution of vegetable oils, and their quantification has a significant role in the quality assessment of oils. In this study, we proposed a new highly efficient, affordable, environmentally friendly, and simple approach for the evaluation of phospholipid concentrations based on potentiometric multisensor systems coupled with chemometric data processing. Support vector machines, partial least squares, and multiple linear regressions were used to predict phosphatide concentrations based on potentiometric multisensor system responses. Application of multivariate regression tools yielded the following root mean square errors of prediction: 0.005 mg/100 g of oil in the range 0.0-59.4 mg/100 g for refined oils; 0.008 mg/100 g in the range 0.0-100 mg/100 g for low phosphatide oils and 0.24 mg/100 g in the range 100-2270 mg/100 g for high phosphatide oils. This approach can be considered as a rapid and straightforward method to quantify the phosphatides in sunflower oils.

Impact of the Covering Vegetable Oil on the Sensory Profile of Canned Tuna of Katsuwonus pelamis Species and Tuna’s Taste Evaluation Using an Electronic Tongue

The impact of the covering vegetable oil (sunflower oil, refined olive oil and extra virgin olive oil, EVOO) on the physicochemical and sensory profiles of canned tuna (Katsuwonus pelamis species) was evaluated, using analytical techniques and a sensory panel. The results showed that canned tuna covered with EVOO possesses a higher content of total phenols and an enhanced antioxidant capacity. This covering medium also increased the appreciated redness-yellowness color of the canned tuna, which showed a higher chromatic and intense color. Olfactory and kinesthetic sensations were significantly dependent on the type of oil used as covering medium. Tuna succulence and adhesiveness were promoted by the use of EVOO, which also contributed to decreasing the tuna-related aroma sensations. The tuna sensory data could be successfully used to identify the type of vegetable oil used. Moreover, a potentiometric electronic tongue allowed discriminating between the canned tuna samples according to the vegetable oil used (mean sensitivity of 96 ± 8%; repeated K-fold cross-validation) and the fruity intensity of the EVOO (mean sensitivity of 100%; repeated K-fold cross-validation). Thus, the taste sensor device could be a practical tool to verify the authenticity of the declared covering medium in canned tuna and to perceive the differences in consumers’ taste.

Evaluation of Olive Oil Quality with Electrochemical Sensors and Biosensors: A Review

Electrochemical sensors, sensor arrays and biosensors, alongside chemometric instruments, have progressed remarkably of late, being used on a wide scale in the qualitative and quantitative evaluation of olive oil. Olive oil is a natural product of significant importance, since it is a rich source of bioactive compounds with nutritional and therapeutic properties, and its quality is important both for consumers and for distributors. This review aims at analysing the progress reported in the literature regarding the use of devices based on electrochemical (bio)sensors to evaluate the bioactive compounds in olive oil. The main advantages and limitations of these approaches on construction technique, analysed compounds, calculus models, as well as results obtained, are discussed in view of estimation of future progress related to achieving a portable, practical and rapid miniature device for analysing the quality of virgin olive oil (VOO) at different stages in the manufacturing process.

Instrumental Techniques to Classify Olive Oils according to Their Quality

This review includes an update of the publications on quality classification of olive oils into extra, virgin or lampante olive oil categories. Nowadays, the official method to carry out this classification is time-consuming and, sometimes, it is not systematic and/or objective. It is based on conventional physicochemical analysis and on a sensorial tasting of olive oils carried out by a panel of experts. The aim of this review was to explore and give value to the alternative techniques reported in the bibliography to complement the current official methods established for that classification of olive oils. Specifically considered were non-separation and separation analytical techniques which could contribute to correctly classify olive oils according to their physicochemical and/or sensorial characteristics. An in-depth description has been written on the methods used to differentiate these three types of olive oils and the main advantages and disadvantages of the proposed procedures. The techniques here reviewed could be a real and fast option to complement or even substitute some of the analysis included in the official method. Finally, general trends and detected difficulties found to address this issue have been discussed throughout the article.

A Potentiometric Electronic Tongue as a Discrimination Tool of Water-Food Indicator/Contamination Bacteria

Microorganism assessment plays a key role in food quality and safety control but conventional techniques are costly and/or time consuming. Alternatively, electronic tongues (E-tongues) can fulfill this critical task. Thus, a potentiometric lab-made E-tongue (40 lipid sensor membranes) was used to differentiate four common food contamination bacteria, including two Gram positive (Enterococcus faecalis, Staphylococcus aureus) and two Gram negative (Escherichia coli, Pseudomonas aeruginosa). Principal component analysis and a linear discriminant analysis-simulated annealing algorithm (LDA-SA) showed that the potentiometric signal profiles acquired during the analysis of aqueous solutions containing known amounts of each studied bacteria allowed a satisfactory differentiation of the four bacterial strains. An E-tongue-LDA-SA model (12 non-redundant sensors) correctly classified 98 ± 5% of the samples (repeated K-fold-CV), the satisfactory performance of which can be attributed to the capability of the lipid membranes to establish electrostatic interactions/hydrogen bonds with hydroxyl, amine and/or carbonyl groups, which are comprised in the bacteria outer membranes. Furthermore, multiple linear regression models, based on selected subsets of E-tongue sensors (12–15 sensors), also allowed quantifying the bacteria contents in aqueous solutions (0.993 ± 0.011 ≤ R2 ≤ 0.998 ± 0.005, for repeated K-fold-CV). In conclusion, the E-tongue could be of great value as a preliminary food quality and safety diagnosis tool.

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue-Critical Overview

Amid today's stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.

An electronic tongue as a classifier tool for assessing perfume olfactory family and storage time-period

The identification of more than three perfumes is difficult and no analytical tool can completely replace the human olfactory system for fragrance classification. Indeed, no analytical system can mimic the human fragrance perception, being the recognition of perfume aroma patterns by conventional or sensor-based analytical tools a challenging task. For the perfume sector, the possibility of applying fast, cost-effective and green analytical devices for perfume analysis would represent a huge economic revenue. Since the perfume aroma pattern will depend on the composition of the liquid phase and on the diffusion properties of their volatile components, this work aimed to apply a potentiometric electronic tongue, comprising non-specific cross-sensitive lipid polymeric membranes, combined with chemometric techniques, as a novel perfume classifier. The multisensors device allowed establishing perfumes' unique fingerprints, which were successfully used to discriminate men from women perfumes, to identify the perfume aroma family (Citric-Aromatic, Floral, Floral-Fruity, Floral-Oriental, Floral-Woody, Woody-Oriental and Woody-Spicy) and, assessing the perfume storage time-period (≤ 9 months; 9-24 months; and, ≥ 24 months). The established linear discriminant models were based on single-run potentiometric profiles gathered by sub-sets of sensors selected using the simulated annealing algorithm, which enabled achieving correct classification rates of 93-100% (for leave-one-out cross-validation procedure). The satisfactory performance of the electronic tongue demonstrates the versatility of the proposed approach as a practical perfume preliminary classifier sensor device, which industrial application may be foreseen in a near future, contributing to a green-sustained economic growth of the perfume industry.

Potential use of electronic tongue coupled with chemometrics analysis for early detection of the spoilage of Zygosaccharomyces rouxii in apple juice

Apple juice spoilage by Zygosaccharomyces rouxii could scarcely be identified at early stage. It is crucial to recognize the spoilage at early stage to prevent waste of products. In present study, electronic tongue was applied to detect the spoilage of Z. rouxii in apple juice, using taste evaluation by panelists as reference. Combined with linear discriminant analysis, identification of the contaminated juice was fulfilled after 12 h, equivalent to yeast population of less than 2.0 lg colony forming units/mL. At the level, panelists were not capable of discerning the spoilage. Sensors HA, ZZ, BB and BA were relatively more sensitive to the changes in overall taste of apple juice. Moreover, cell number of Z. rouxii could be properly quantified by partial least squares regression models with high determination coefficient of 0.98-0.99. Electronic tongue appears to be a powerful approach to realize early detection of contamination of Z. rouxii.

Application of an electronic tongue as a single-run tool for olive oils' physicochemical and sensory simultaneous assessment

Olive oil is highly appreciated due to its nutritional and organoleptic characteristics. However, a huge compositional variation is observed between olive oils, requiring the use of diverse analytical techniques for its classification including titration, spectrophotometry and chromatography, as well as sensory analysis. Chemical analysis is usually time-consuming, expensive and require skilled technicians, while the sensorial ones are dependent upon individual subjective evaluations, even if performed by trained panellists. This work evaluated and demonstrated the feasibility of using a potentiometric electronic tongue, comprising non-specific lipid polymeric and cross-sensitive sensor membranes, coupled with chemometric tools based on different sub-sets of sensors (from 11 to 14 sensors), to predict key quality parameters of olive oils based on single-run assays. The multivariate linear models established for 23 centenarian olive trees from different cultivars allowed predicting peroxide value, oxidative stability, total phenols and tocopherols contents, CIELAB scale parameters (L*, a* and b* values), as well as 11 gustatory-retronasal positive attributes (green, sweet, bitter, pungent, tomato and tomato leaves, apple, banana, cabbage, fresh herbs and dry fruits) with satisfactory accuracy (0.90 ± 0.07 ≤ R² ≤ 0.98 ± 0.02 for the repeated K-fold-CV procedure, which ensured that 25% of the data was used for internal-validation purposes). The electronic tongue device had an accuracy statistically similar to that achieved with standard analytical techniques, pointing out the versatility of the device for the fast and simultaneous chemical and sensory analysis of olive oil.

Electrochemical Sensor-Based Devices for Assessing Bioactive Compounds in Olive Oils: A Brief Review

Electrochemical bioinspired sensor devices combined with chemometric tools have experienced great advances in the last years, being extensively used for food qualitative and quantitative evaluation, namely for olive oil analysis. Olive oil plays a key role in the Mediterranean diet, possessing unique and recognized nutritional and health properties as well as highly appreciated organoleptic characteristics. These positive attributes are mainly due to olive oil richness in bioactive compounds such as phenolic compounds. In addition, these compounds enhance their overall sensory quality, being mainly responsible for the usual olive oil pungency and bitterness. This review aims to compile and discuss the main research advances reported in the literature regarding the use of electrochemical sensor based-devices for assessing bioactive compounds in olive oil. The main advantages and limitations of these fast, accurate, bioinspired voltammetric, potentiometric and/or amperometric sensor green-approaches will be addressed, aiming to establish the future challenges for becoming a practical quality analytical tool for industrial and commercial applications.

Olive Oil Total Phenolic Contents and Sensory Sensations Trends during Oven and Microwave Heating Processes and Their Discrimination Using an Electronic Tongue

Olive oil has unique organoleptic attributes and its consumption is associated with nutritional and health benefits, which are mainly related to its rich composition in phenolic and volatile compounds. The use of olive oil in heat-induced cooking leads to deep reduction of phenolic and volatile concentrations and to changes of the sensory profiles. This work confirmed that oven and microwave heating significantly reduced total phenolic contents (P value < 0.0001, one-way ANOVA), more pronounced in the latter, together with a significant reduction of the intensity of fruity, sweet, bitter, pungent, and green attributes (P value < 0.0001, Kruskal-Wallis test), particularly for fruity and green sensations. Besides, bitter, fruity, green, and pungent intensities showed a linear dependency with the total phenolic contents (0.8075≤R-Pearson ≤ 0.9694). Finally, the potentiometric electronic tongue together with linear discriminant analysis-simulated annealing algorithm allowed satisfactory discrimination (sensitivities of 94±4%, for repeated K-fold cross-validation) of olive oils subjected to intense microwave heating (5–10 min, 160–205°C) from those processed under usual cooking conditions (oven heating during 15–60 min or microwave heating during 1.5–3 min, 72–165°C). This could be due to the different responses of the electronic tongue towards olive oils with diverse phenolic and sensory profiles.

Perception of olive oils sensory defects using a potentiometric taste device

The 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 intra- and 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 (P-value = 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.