Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Usefulness of the 1H NMR Multisuppression Approach for the Global Characterization of Monovarietal Extra-Virgin Olive Oils

Extra-virgin olive oil (EVOO) is one of the most appreciated vegetable oils worldwide, but its high price makes it prone to suffer adulteration with lower quality oils. Therefore, it is important to have methodologies able to study EVOO composition as a whole in a simple and fast way, in order to guarantee its quality and safety. For this purpose, in this study, commercial samples of five Spanish olive cultivars (Arbequina, Arroniz, Cornicabra, Hojiblanca, Picual) were studied by Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy, using standard and multisuppression pulses. The aim was to explore the possibility of 1H NMR use to characterize in a single run and in a global way the composition of these monocultivar oils, regarding not only their main components (fatty acids supported on triglycerides) but also minor ones (squalene, sterols, diterpenic wax esters of phytol and geranylgeraniol, phenolic and secoiridoid derivatives, like tyrosol, hydroxytyrosol, oleacein, oleocanthal, and lignans, among others, and aldehydes). The use of univariate and multivariate statistical analyses confirmed the presence of compositional features that were specific to some olive varieties. The Arbequina and Arroniz oils showed the most characteristic features that allowed for clearly differentiating them from the others. In contrast, the discrimination between the Cornicabra, Hojiblanca and Picual oils was not so easily achieved.

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

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.

Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model

As a taste bionic system, electronic tongues can be used to derive taste information for different types of food. On this basis, we have carried forward the work by making it, in addition to the ability of accurately distinguish samples, be more expressive by speaking evaluative language like human beings. Thus, this paper demonstrates the correlation between the qualitative digital output of the taste bionic system and the fuzzy evaluation language that conform to the human perception mode. First, through principal component analysis (PCA), backward cloud generator and forward cloud generator, two-dimensional cloud droplet groups of different flavor information were established by using liquor taste data collected by electronic tongue. Second, the frequency and order of the evaluation words for different flavor of liquor were obtained by counting and analyzing the data appeared in the artificial sensory evaluation experiment. According to the frequency and order of words, the cloud droplet range corresponding to each word was calculated in the cloud drop group. Finally, the fuzzy evaluations that originated from the eight groups of liquor data with different flavor were compared with the artificial sense, and the results indicated that the model developed in this work is capable of outputting fuzzy evaluation that is consistent with human perception rather than digital output. To sum up, this method enabled the electronic tongue system to generate an output, which conforms to human's descriptive language, making food detection technology a step closer to human perception.

Seeking for sensory differentiated olive oils? The urge to preserve old autochthonous olive cultivars

Mediterranean olive heritage richness is poorly characterized. Olive oils from minor cultivars of Northeast-Portugal (Lentisca, Madural, Rebolã, Redondal, Verdeal and Verdeal Transmontana) from centenarian trees were chemical and sensory characterized, aiming to identify autochthonous cultivars capable of producing differentiated olive oils. All oils, produced during two campaigns, were classified as extra virgin. Cv. Redondal showed the highest oxidative stability (OS), total phenols, vitamin E and C_18:1/C_18:2. Contrary, cv. Madural presented the lowest OS and C_18:1/C_18:2 ratios, supporting the importance of fatty acids on OS, while cv. Verdeal had the lowest total phenols and vitamin E contents. Sensory notes of tomato, apple, dry fruits, fresh herbs, tomato leaves and cabbage were predominant on the oils of most cultivars, whilst some attributes were more specific, such as banana and kiwi (Madural), cherry and apricot (cvs. Lentisca and Madural). The chemical and sensory diversity enabled the statistical discrimination of all cultivars and harvesting years.

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.

Varietal Authentication of Extra Virgin Olive Oils by Triacylglycerols and Volatiles Analysis

In recent years, there is an increasing interest in high-quality extra virgin olive oils (EVOOs) produced from local cultivars. They have particular chemical/organoleptic characteristics and are frequently subjected to fraud, whereby the control of quality requires a powerful varietal check. In the present research, triacylglycerols (TAGs) and volatiles have been studied as chemical markers for the authentication of EVOO samples from four Italian varieties of Olea europea (Dolce Agogia, Frantoio, Leccino, and Moraiolo). The monocultivar EVOO samples have been subjected to a chemical⁻enzymatic chromatographic method in order to perform a stereospecific analysis, an important procedure for the characterization of TAG of food products. The results, combined with chemometric analysis (linear discriminant analysis, LDA), were elaborated in order to classify Italian EVOO monocultivar samples. In accordance with the total and intrapositional fatty acid (FA) composition of TAG fraction, the results were allowed to carry out a varietal discrimination. In addition, volatile compounds were also determined by solid-phase micro-extraction gas chromatography⁻mass spectrometry analysis. All EVOO samples were correctly classified when TAG stereospecific data and volatile results were elaborated by the LDA procedure, even if volatile compounds showed a higher discriminant power.

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.

Distinguishment, identification and aroma compound quantification of Portuguese olive oils based on physicochemical attributes, HS-GC/MS analysis and voltammetric electronic tongue

BACKGROUND

In this paper, various extra-virgin and virgin olive oils samples from different Portuguese markets were studied. For this purpose, a voltammetric electronic tongue (VE-tongue), consisting of two kinds of working electrode within the array, together with physicochemical analysis and headspace gas chromatography coupled with mass spectrometry (HS-GC-MS), were applied. In addition, preliminary considerations of relationships between physicochemical parameters and multisensory system were reported.

RESULTS

The physicochemical parameters exhibit significant differences among the analyzed olive oil samples that define its qualities. Regarding the aroma profile, 14 volatile compounds were characterized using HS-GC-MS; among these, hex-2-enal, hexanal, acetic acid, hex-3-ene-1-ol acetate and hex-3-en-1-ol were semi-quantitatively detected as the main aroma compounds in the analyzed samples. Moreover, pattern recognition methods demonstrate the discrimination power of the proposed VE-tongue system. The results reveal the VE-tongue's ability to classify olive oil samples and to identify unknown samples based of built models. In addition, the correlation between VE-tongue and physicochemical analysis exhibits a remarkable prediction model aimed at anticipating carotenoid content.

CONCLUSION

The preliminary results of this investigation indicate that physicochemical and HS-GC-MS analysis, together with multisensory system coupled with chemometric techniques, presented a satisfactory performance regarding olive oil sample discrimination and identification. © 2017 Society of Chemical Industry.

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.