State of the Art in the Field of Electronic and Bioelectronic Tongues – Towards the Analysis of Wines

High-performance bioelectronic tongue for the simultaneous analysis of phenols, sugars and organic acids in wines

BACKGROUND

Electronic tongues have been widely used to analyze wines. However, owing to the complexity of the matrix, the problem is not completely solved and further improvements are required.

RESULTS

A high-performance potentiometric bioelectronic tongue (bio-ET) specifically devoted to the assessment of wine components is presented. The novelty of this system is due to two innovative approaches. First, the improved performance is obtained through the use of potentiometric biosensors based on carboxylated polyvinyl chloride (PVC) membranes, where enzymes (glucose oxidase, tyrosinase, laccase, and lyase) specific to compounds of interest are linked covalently. Second, the performance is further enhanced by introducing electron mediators (gold nanoparticles or copper phthalocyanine) into the PVC membrane to facilitate the electron transfer process. Individual sensors exposed to target analytes (glucose, catechol, cysteine, or tartaric acid) show a linear behavior, with limits of detection in the region of 10-4  mol L-1 for all the compounds analyzed, with excellent reproducibility (coefficient of variation lower than 3%). Sensors combined to form a bio-ET show excellent capabilities. Principal component (PC) analysis can discriminate monovarietal white wines (PC1 77%; PC2 15%) and red wines (PC1 63%; PC2 30%). Using partial least squares, the bio-ET can provide information about chemical parameters, including glucose, total polyphenols, total anthocyanins, free and total sulfur dioxide, total acidity, and pH with R2 between 0.91 and 0.98 in calibration and between 0.89 and 0.98 in validation.

CONCLUSIONS

This advanced instrument is able to assess the levels of seven parameters in a single measurement, providing an advantageous method to the wine industry. © 2023 Society of Chemical Industry.

A chemiluminescence sensor array for discrimination of seven toxicants

A chemiluminescence (CL) sensor array was developed based on 11 CL systems cross-combined by three luminescence reagents and four oxidants. Using the CL sensor array, we measured seven toxicants, including morphine, ketamine, diazepam, chlorpromazine, strychnine, paraquat, and fenpropathrin, which represent psychotropic drugs, sedatives and hypnotics, rodenticides, herbicides, and insecticides, respectively. The CL response pattern or 'fingerprints' were obtained for a given compound on the sensor array and then discriminated through principal component analysis. The established sensor array has been applied to real-life samples and the results showed that it possesses excellent discrimination.

Electrochemical Sensors Coupled with Multivariate Statistical Analysis as Screening Tools for Wine Authentication Issues: A Review

Consumers are increasingly interested in the characteristics of the products they consume, including aroma, taste, and appearance, and hence, scientific research was conducted in order to develop electronic senses devices that mimic the human senses. Thanks to the utilization of electroanalytical techniques that used various sensors modified with different electroactive materials coupled with pattern recognition methods, artificial senses such as electronic tongues (ETs) are widely applied in food analysis for quality and authenticity approaches. This paper summarizes the applications of electrochemical sensors (voltammetric, amperometric, and potentiometric) coupled with unsupervised and supervised pattern recognition methods (principal components analysis (PCA), linear discriminant analysis (LDA), partial least square (PLS) regression, artificial neural network (ANN)) for wine authenticity assessments including the discrimination of varietal and geographical origins, monitoring the ageing processes, vintage year discrimination, and detection of frauds and adulterations. Different wine electrochemical authentication methodologies covering the electrochemical techniques, electrodes types, functionalization sensitive materials and multivariate statistical analysis are emphasized and the main advantages and disadvantages of using the proposed methodologies for real applications were concluded.

Analysis of musts and wines by means of a bio-electronic tongue based on tyrosinase and glucose oxidase using polypyrrole/gold nanoparticles as the electron mediator

A bioelectronic tongue (bioET) based on combinations of enzymes (tyrosinase and glucose oxidase) and polypyrrole (Ppy) or polypyrrole/AuNP (Ppy/AuNP) composites was build up and applied to the analysis and discrimination of musts and wines. Voltammetric responses of the array of sensors demonstrated the effectiveness of polymers as electron mediators and the existence of favorable synergistic effects between Ppy and the AuNPs. Using Principal Component Analysis and Parallel Factor Analysis it was possible to discriminate musts according to the °Brix and TPI (Total Polyphenol Index), and wines according to the alcoholic degree and TPI. Partial Least Squares provided good correlations between the bioET output and traditional chemical parameters. Moreover, Support Vector Machines permitted to predict the TPI and the alcoholic degree of wines, from data provided by the bioET in the corresponding grapes. This result opens the possibility to predict wine characteristics from the beginning of the vinification process.

Solid-Contact Potentiometric Sensors and Multisensors Based on Polyaniline and Thiacalixarene Receptors for the Analysis of Some Beverages and Alcoholic Drinks

Electronic tongue is a sensor array that aims to discriminate and analyze complex media like food and beverages on the base of chemometrics approaches for data mining and pattern recognition. In this review, the concept of electronic tongue comprising of solid-contact potentiometric sensors with polyaniline and thacalix[4]arene derivatives is described. The electrochemical reactions of polyaniline as a background of solid-contact sensors and the characteristics of thiacalixarenes and pillararenes as neutral ionophores are briefly considered. The electronic tongue systems described were successfully applied for assessment of fruit juices, green tea, beer, and alcoholic drinks They were classified in accordance with the origination, brands and styles. Variation of the sensor response resulted from the reactions between Fe(III) ions added and sample components, i.e., antioxidants and complexing agents. The use of principal component analysis and discriminant analysis is shown for multisensor signal treatment and visualization. The discrimination conditions can be optimized by variation of the ionophores, Fe(III) concentration, and sample dilution. The results obtained were compared with other electronic tongue systems reported for the same subjects.

A critical review on the applications of artificial neural networks in winemaking technology

Since their development in 1943, artificial neural networks were extended into applications in many fields. Last twenty years have brought their introduction into winery, where they were applied following four basic purposes: authenticity assurance systems, electronic sensory devices, production optimization methods, and artificial vision in image treatment tools, with successful and promising results. This work reviews the most significant approaches for neural networks in winemaking technologies with the aim of producing a clear and useful review document.

A Voltammetric Electronic Tongue for the Resolution of Ternary Nitrophenol Mixtures

This work reports the applicability of a voltammetric sensor array able to quantify the content of 2,4-dinitrophenol, 4-nitrophenol, and picric acid in artificial samples using the electronic tongue (ET) principles. The ET is based on cyclic voltammetry signals, obtained from an array of metal disk electrodes and a graphite epoxy composite electrode, compressed using discrete wavelet transform with chemometric tools such as artificial neural networks (ANNs). ANNs were employed to build the quantitative prediction model. In this manner, a set of standards based on a full factorial design, ranging from 0 to 300 mg·L^-1, was prepared to build the model; afterward, the model was validated with a completely independent set of standards. The model successfully predicted the concentration of the three considered phenols with a normalized root mean square error of 0.030 and 0.076 for the training and test subsets, respectively, and r ≥ 0.948.

Voltammetric electronic tongue to identify Brett character in wines. On-site quantification of its ethylphenol metabolites

This work reports the applicability of a voltammetric sensor array able to evaluate the content of the metabolites of the Brett defect: 4-ethylphenol, 4-ethylguaiacol and 4-ethylcatechol in spiked wine samples using the electronic tongue (ET) principles. The ET used cyclic voltammetry signals, obtained from an array of six graphite epoxy modified composite electrodes; these were compressed using Discrete Wavelet transform while chemometric tools, among these artificial neural networks (ANNs), were employed to build the quantitative prediction model. In this manner, a set of standards based on a modified full factorial design and ranging from 0 to 25mgL^-1 on each phenol, was prepared to build the model; afterwards, the model was validated with an external test set. The model successfully predicted the concentration of the three considered phenols with a normalized root mean square error of 0.02 and 0.05, for the training and test subsets respectively, and correlation coefficients better than 0.958.

Trained and consumer panel evaluation of sparkling wines sweetened to brut or demi sec residual sugar levels with three different sugars

The dosage liquid, added at the final stage of sparkling wine production, imparts residual sweetness to the wine. No study has yet analyzed the influence of dosage composition on the final wine's sensory profile or consumer acceptance. In this study, dosage composition was altered through the addition of different sugar types (ST; fructose, glucose, or sucrose) to produce seven sparkling wines of varying residual sugar levels (RSL), including no sugar added, brut (5.3-8.4gST/L) or demi sec (34.9-37.8gST/L). As evaluated by a trained panel (n=9), the interaction between ST and RSL influenced the perception of caramelized/vanilla/honey (CVH) flavor, sweet taste, and sour taste attributes (p<0.05). Demi sec wines displayed lower intensities of green flavor, yeasty flavor, and sour taste compared to the no sugar added wine (p<0.05). Consumers (n=126) also evaluated the sparkling wines and ST, RSL, and their interaction influenced consumer acceptance of different attributes, as well as the perception of the "refreshing" aspect of the wine (p<0.05). Overall consumer acceptance of sparkling wines was highly correlated (r²≤0.88) to CVH, floral, and fruity flavors, as well as sweet taste and creamy mouthfeel. External preference mapping revealed two clusters of consumers. Both consumer clusters liked wines sweetened with fructose, but Cluster 1 liked the demi sec sparkling wine sweetened with fructose (32.8g/L fructose) while Cluster 2 preferred the brut wine sweetened with fructose (8.4g/L fructose). These results suggest that consumer preference for sparkling wine was segmented based on sweetness preference. The results of this study offer winemakers knowledge about the influence of dosage composition on the sensory profile of sparkling wine.

Portable Electronic Tongue Based on Microsensors for the Analysis of Cava Wines

Cava is a quality sparkling wine produced in Spain. As a product with a designation of origin, Cava wine has to meet certain quality requirements throughout its production process; therefore, the analysis of several parameters is of great interest. In this work, a portable electronic tongue for the analysis of Cava wine is described. The system is comprised of compact and low-power-consumption electronic equipment and an array of microsensors formed by six ion-selective field effect transistors sensitive to pH, Na⁺, K⁺, Ca²⁺, Cl⁻, and CO₃²⁻, one conductivity sensor, one redox potential sensor, and two amperometric gold microelectrodes. This system, combined with chemometric tools, has been applied to the analysis of 78 Cava wine samples. Results demonstrate that the electronic tongue is able to classify the samples according to the aging time, with a percentage of correct prediction between 80% and 96%, by using linear discriminant analysis, as well as to quantify the total acidity, pH, volumetric alcoholic degree, potassium, conductivity, glycerol, and methanol parameters, with mean relative errors between 2.3% and 6.0%, by using partial least squares regressions.

Electronic Noses and Tongues in Wine Industry

The quality of wines is usually evaluated by a sensory panel formed of trained experts or traditional chemical analysis. Over the last few decades, electronic noses (e-noses) and electronic tongues have been developed to determine the quality of foods and beverages. They consist of arrays of sensors with cross-sensitivity, combined with pattern recognition software, which provide a fingerprint of the samples that can be used to discriminate or classify the samples. This holistic approach is inspired by the method used in mammals to recognize food through their senses. They have been widely applied to the analysis of wines, including quality control, aging control, or the detection of fraudulence, among others. In this paper, the current status of research and development in the field of e-noses and tongues applied to the analysis of wines is reviewed. Their potential applications in the wine industry are described. The review ends with a final comment about expected future developments.

Electronic tongues to assess wine sensory descriptors

This work reports the application of an electronic tongue as a tool towards the analysis of wine in tasks such as its discrimination based on the maturing in barrels or the prediction of the global scores assigned by a sensory panel. To this aim, red wine samples were first analysed with the voltammetric sensor array, without performing any sample pretreatment. Afterwards, obtained responses were preprocessed employing fast Fourier transform (FFT) for the compression and reduction of signal complexity, and obtained coefficients were then used as inputs to build the qualitative and quantitative models employing either linear discriminant analysis (LDA) or partial least squares regression (PLS), respectively. Satisfactory results were obtained overall, with a classification rate of 100% in the discrimination of the type of barrel used during wine maturing, a normalized NRMSE of 0.077 in the estimation of ageing time (months) or 0.11 in the prediction of the scores (0-10) from a trained sensory panel (all for the external test subset).

A High Sensitivity IDC-Electronic Tongue Using Dielectric/Sensing Membranes with Solvatochromic Dyes

In this paper, an electronic tongue/taste sensor array containing different interdigitated capacitor (IDC) sensing elements to detect different types of tastes, such as sweetness (glucose), saltiness (NaCl), sourness (HCl), bitterness (quinine-HCl), and umami (monosodium glutamate) is proposed. We present for the first time an IDC electronic tongue using sensing membranes containing solvatochromic dyes. The proposed highly sensitive (30.64 mV/decade sensitivity) IDC electronic tongue has fast response and recovery times of about 6 s and 5 s, respectively, with extremely stable responses, and is capable of linear sensing performance (R² ≈ 0.985 correlation coefficient) over the wide dynamic range of 1 µM to 1 M. The designed IDC electronic tongue offers excellent reproducibility, with a relative standard deviation (RSD) of about 0.029. The proposed device was found to have better sensing performance than potentiometric-, cascoded compatible lateral bipolar transistor (C-CLBT)-, Electronic Tongue (SA402)-, and fiber-optic-based taste sensing systems in what concerns dynamic range width, response time, sensitivity, and linearity. Finally, we applied principal component analysis (PCA) to distinguish between various kinds of taste in mixed taste compounds.

Thin-film amperometric multibiosensor for simultaneous determination of lactate and glucose in wine

An amperometric multi-biosensor based on lactate and glucose oxidases has been developed for determination of lactate and glucose in wine. Gold thin-film amperometric electrodes were used as multi-transducers. Analytical characteristics of the multi-biosensor developed were studied. The minimum detectable concentration was 5×10(-6) mol/l for both glucose and lactate. High reproducibility and storage stability of the multi-biosensor are demonstrated in this paper. Lactate and glucose were determined in wine, and a good correlation was obtained with concentrations determined using high-performance liquid chromatography (correlation coefficient for glucose R(2)=0.998, for lactate R(2)=0.718).

Highly Sensitive Multi-Channel IDC Sensor Array for Low Concentration Taste Detection

In this study, we designed and developed an interdigitated capacitor (IDC)-based taste sensor array to detect different taste substances. The designed taste sensing array has four IDC sensing elements. The four IDC taste sensing elements of the array are fabricated by incorporating four different types of lipids into the polymer, dioctyl phenylphosphonate (DOPP) and tetrahydrofuran (THF) to make the respective dielectric materials that are individually placed onto an interdigitated electrode (IDE) via spin coating. When the dielectric material of an IDC sensing element comes into contact with a taste substance, its dielectric properties change with the capacitance of the IDC sensing element; this, in turn, changes the voltage across the IDC, as well as the output voltage of each channel of the system. In order to assess the effectiveness of the sensing system, four taste substances, namely sourness (HCl), saltiness (NaCl), sweetness (glucose) and bitterness (quinine-HCl), were tested. The IDC taste sensor array had rapid response and recovery times of about 12.9 s and 13.39 s, respectively, with highly stable response properties. The response property of the proposed IDC taste sensor array was linear, and its correlation coefficient R2 was about 0.9958 over the dynamic range of the taste sensor array as the taste substance concentration was varied from 1 μM to 1 M. The proposed IDC taste sensor array has several other advantages, such as real-time monitoring capabilities, high sensitivity 45.78 mV/decade, good reproducibility with a standard deviation of about 0.029 and compactness, and the circuitry is based on readily available and inexpensive electronic components. The proposed IDC taste sensor array was compared with the potentiometric taste sensor with respect to sensitivity, dynamic range width, linearity and response time. We found that the proposed IDC sensor array has better performance. Finally, principal component analysis (PCA) was applied to discriminate different types of taste of the mixed taste substances.

Hydrogel-Based Molecularly Imprinted Polymers for Biological Detection

Molecularly imprinted polymers (MIPs) have become an important tool in the preparation of artificial and robust recognition materials that are capable of mimicking natural systems. MIPs have been regarded as 'antibody mimics' and have shown clear advantages over real antibodies for sensor technology. Currently, on-site diagnostic (OSD) and point-of-care (POC) biosensor development are heavily dominated by antibody-dependent immuno-sensors such as the lateral flow immuno-assay. Although antibodies exhibit a high degree of selectivity, any biological recognition element is inherently unstable with limited shelf-life, even when stored under optimum conditions. OSD and POC tests are essential for disease screening and treatment monitoring as part of emergency management. Introduced or naturally occurring pathogens can cause significant disruptions, raise panic in the population, and result in significant economic losses. Cheaper, smaller, and smarter devices for early detection of disease or environmental hazards ultimately lead to rapid containment and corrective action. To this end, there has been extensive research on detection platforms based on genetic or immune techniques. MIPs have proven to produce selective biological extractions that rival immunoaffinity-based separations, but without the tediously lengthy time-consuming process. MIPs could provide an alternative to antibodies, and ultimately lead to cheaper, smaller, and smarter biosensors.

Information visualization to enhance sensitivity and selectivity in biosensing

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.

BioElectronic Tongue for the quantification of total polyphenol content in wine

This work reports the application of a BioElectronic Tongue (BioET) in the estimation of polyphenol content in wine. The approach used an array of enzyme biosensors capable of giving a wide and complete response of the analyzed species, plus a chemometric processing tool able to interpret the chemical signals and extract meaningful data from the complex readings. In our case, the proposed BioET was formed by an array of four voltammetric enzymatic biosensors based on epoxy-graphite composites, one blank electrode and the other three bulk-modified with tyrosinase and laccase on one side, and copper nanoparticles on the other; these modifiers were used in order to incorporate differentiated or catalytic response to different polyphenols present in wine and aimed to the determination of its total polyphenol content value. The obtained voltammetric responses were pre-processed employing the Fast Fourier Transform (FFT); this was used to compress the relevant information whereas the obtained coefficients fed an Artificial Neural Network (ANN) model that accomplished the quantification of total polyphenol content. For comparison purposes, obtained polyphenol content was compared against the one assessed by two different reference methods: Folin-Ciocalteu and UV polyphenol index (I(280)); good prediction ability was attained with correlation coefficients higher than 0.949 when comparing against reference methods. Qualitative discrimination of individual polyphenols found in wine was also assessed by means of Principal Component Analysis which allowed the discrimination of the individual polyphenols under study.

A chemiluminescence sensor array based on nanomaterials for discrimination of teas

In recent years, electronic tongue and nose devices have been developed that consist of an array of cross-responsive sensors. In this study, we report a chemiluminescence (CL) sensor array based on oxidation at twelve different catalytic nanomaterial locations for the discrimination of eight teas. CL response patterns or "fingerprints" were obtained for a given compound on the sensor array and then discriminated through linear discriminant analysis. The experiments demonstrate that the sensor array had excellent differentiability and reversibility.