Electronic Tongues Employing Electrochemical Sensors

Electrochemical Sensors in the Food Sector: A Review

In a world that is becoming increasingly concerned with health, safety, and the sustainability of food supply chains, the control and assurance of food quality have become of utmost importance. This review examines the application and potential of electrochemical sensors in the dynamic field of food science to meet these expanding demands. The article introduces electrochemical sensors and describes their operational mechanics and the components contributing to their function. A summary of the most prevalent electrochemical methods outlines the diverse food analysis techniques available. The review shifts to discussing the food science applications of these sensors, highlighting their crucial role in detecting compounds in food samples like meat, fish, juice, and milk for contemporary quality control. This paper showcases electrochemical sensors' utility in food analysis, underscoring their significance as powerful, efficient tools for maintaining food safety and how they could transform our approach to global food quality control and assurance.

Application of a microfluidic electronic tongue based on impedance spectroscopy for coconut water analysis

The food industry has grown with the demands for new products and their authentication, which has not been accompanied by the area of analysis and quality control, thus requiring novel process analytical technologies for food processes. An electronic tongue (e-tongue) is a multisensor system that can characterize complex liquids in a fast and simple way. Here, we tested the efficacy of an impedimetric microfluidic e-tongue setup - comprised by four interdigitated electrodes (IDE) on a printed circuit board (PCB), with four pairs of digits each, being one bare sensor and three coated with different ultrathin nanostructured films with different electrical properties - in the analysis of fresh and industrialized coconut water. Principal Component Analysis (PCA) was applied to observe sample differences, and Partial Least Squares Regression (PLSR) was used to predict sample physicochemical parameters. Linear Discriminant Analysis (LDA) and Partial Least Square - Discriminant Analysis (PLS-DA) were compared to classify samples based on data from the e-tongue device. Results indicate the potential application of the microfluidic e-tongue in the identification of coconut water composition and determination of physicochemical attributes, allowing for classification of samples according to soluble solid content (SSC) and total titratable acidity (TTA) with over 90% accuracy. It was also demonstrated that the microfluidic setup has potential application in the food industry for quality assessment of complex liquid samples.

Critical View on the Qualification of Electronic Tongues Regarding Their Performance in the Development of Peroral Drug Formulations with Bitter Ingredients

In this review, we aim to highlight the advantages, challenges, and limitations of electronic tongues (e-tongues) in pharmaceutical drug development. The authors, therefore, critically evaluated the performance of e-tongues regarding their qualification to assess peroral formulations containing bitter active pharmaceutical ingredients. A literature search using the keywords 'electronic', 'tongue', 'bitter', and 'drug' in a Web of Science search was therefore initially conducted. Reviewing the publications of the past decade, and further literature where necessary, allowed the authors to discuss whether and how e-tongues perform as expected and whether they have the potential to become a standard tool in drug development. Specifically highlighted are the expectations an e-tongue should meet. Further, a brief insight into the technologies of the utilized e-tongues is given. Reliable protocols were found that enable (i) the qualified performance of e-tongue instruments from an analytical perspective, (ii) proper taste-masking assessments, and (iii) under certain circumstances, the evaluation of bitterness.

Electronic Tongues and Noses: A General Overview

As technology advances, electronic tongues and noses are becoming increasingly important in various industries. These devices can accurately detect and identify different substances and gases based on their chemical composition. This can be incredibly useful in fields such as environmental monitoring and industrial food applications, where the quality and safety of products or ecosystems should be ensured through a precise analysis. Traditionally, this task is performed by an expert panel or by using laboratory tests but sometimes becomes a bottleneck because of time and other human factors that can be solved with technologies such as the provided by electronic tongue and nose devices. Additionally, these devices can be used in medical diagnosis, quality monitoring, and even in the automotive industry to detect gas leaks. The possibilities are endless, and as these technologies continue to improve, they will undoubtedly play an increasingly important role in improving our lives and ensuring our safety. Because of the multiple applications and developments in this field in the last years, this work will present an overview of the electronic tongues and noses from the point of view of the approaches developed and the methodologies used in the data analysis and steps to this aim. In the same manner, this work shows some of the applications that can be found in the use of these devices and ends with some conclusions about the current state of these technologies.

Gustation-Inspired Dual-Responsive Hydrogels for Taste Sensing Enabled by Machine Learning

Human gustatory system recognizes salty/sour or sweet tastants based on their different ionic or nonionic natures using two different signaling pathways. This suggests that evolution has selected this detection dualism favorably. Analogically, this work constructs herein bioinspired stimulus-responsive hydrogels to recognize model salty/sour or sweet tastes based on two different responses, that is, electrical and volumetric responsivities. Different compositions of zwitter-ionic sulfobetainic N-(3-sulfopropyl)-N-(methacryloxyethyl)-N,N-dimethylammonium betaine (DMAPS) and nonionic 2-hydroxyethyl methacrylate (HEMA) are co-polymerized to explore conditions for gelation. The hydrogel responses upon adding model tastant molecules are explored using electrical and visual de-swelling observations. Beyond challenging electrochemical impedance spectroscopy measurements, naive multimeter electrical characterizations are performed, toward facile applicability. Ionic model molecules, for example, sodium chloride and acetic acid, interact electrostatically with DMAPS groups, whereas nonionic molecules, for example, D(-)fructose, interact by hydrogen bonding with HEMA. The model tastants induce complex combinations of electrical and volumetric responses, which are then introduced as inputs for machine learning algorithms. The fidelity of such a trained dual response approach is tested for a more general taste identification. This work envisages that the facile dual electric/volumetric hydrogel responses combined with machine learning proposes a generic bioinspired avenue for future bionic designs of artificial taste recognition, amply needed in applications.

Graphene Oxide, Carbon Nanotubes, and Polyelectrolytes-Based Impedanciometric E-Tongue for Estrogen Detection in Complex Matrices

Currently, it is necessary to maintain the quality of aquifers and water bodies, which means the need for sensors that detect molecules as emerging pollutants (EPs) at low concentrations in aqueous complex solutions. In this work, an electronic tongue (e-tongue) prototype was developed to detect 17β-estradiol in tap water. To achieve such a prototype, an array of sensors was prepared. Each sensor consists of a solid support with interdigitated electrodes without or with thin films prepared with graphene oxide, nanotubes, and other polyelectrolytes molecules adsorbed on them. To collect data from each sensor, impedance spectroscopy was used to analyze the electrical characteristics of samples of estrogen solutions with different concentrations. To analyze the collected data from the sensors, principal components analysis (PCA) method was used to create a three-dimensional plane using the calculated principal components, namely PC1 and PC2, and the estrogen concentration values. Then, damped least squares (DLS) was used to find the optimal values for the hyperplane calibration, as the sensitivity of this e-tongue was not represented by a straight line but by a surface. For the collected data, from nanotubes and graphene oxide sensors, a calibration curve for concentration given by the 10PC1×0.492-PC2×0.14-14.5 surface was achieved. This e-tongue presented a detection limit of 10-16 M of 17β-estradiol in tap water.

A Sensor Array Based on Molecularly Imprinted Polymers and Machine Learning for the Analysis of Fluoroquinolone Antibiotics

Fluoroquinolones (FQs) are one of the most important types of antibiotics in the clinical, poultry, and aquaculture industries, and their monitoring is required as the abuse has led to severe issues, such as antibiotic residues and antimicrobial resistance. In this study, we report a voltammetric electronic tongue (ET) for the simultaneous determination of ciprofloxacin, levofloxacin, and moxifloxacin in both pharmaceutical and biological samples. The ET comprises four sensors modified with three different customized molecularly imprinted polymers (MIPs) and a nonimprinted polymer integrated with Au nanoparticle-decorated multiwall carbon nanotubes (Au-fMWCNTs). MWCNTs were first functionalized to serve as a supporting substrate, while the anchored Au nanoparticles acted as a catalyst. Subsequently, MIP films were obtained by electropolymerization of pyrrole in the presence of the different target FQs. The sensors' morphology was characterized by scanning electron microscopy and transmission electron microscopy, while the modification process was followed electrochemically step by step employing [Fe(CN)₆]^3-/4- as the redox probe. Under the optimal conditions, the MIP(FQs)@Au-fMWCNT sensors exhibited different responses, limits of detection of ca. 1 μM, and a wide detection range up to 300 μM for the three FQs. Lastly, the developed ET presents satisfactory agreement between the expected and obtained values when used for the simultaneous determination of mixtures of the three FQs (R² ≥0.960, testing subset), which was also applied to the analysis of FQs in commercial pharmaceuticals and spiked human urine samples.

Application and Progress of Chemometrics in Voltammetric Biosensing

The voltammetric electrochemical sensing method combined with biosensors and multi-sensor systems can quickly, accurately, and reliably analyze the concentration of the main analyte and the overall characteristics of complex samples. Simultaneously, the high-dimensional voltammogram contains the rich electrochemical features of the detected substances. Chemometric methods are important tools for mining valuable information from voltammetric data. Chemometrics can aid voltammetric biosensor calibration and multi-element detection in complex matrix conditions. This review introduces the voltammetric analysis techniques commonly used in the research of voltammetric biosensor and electronic tongues. Then, the research on optimizing voltammetric biosensor results using classical chemometrics is summarized. At the same time, the incorporation of machine learning and deep learning has brought new opportunities to further improve the detection performance of biosensors in complex samples. Finally, smartphones connected with miniaturized voltammetric biosensors and chemometric methods provide a high-quality portable analysis platform that shows great potential in point-of-care testing.

Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy-Application of Sensor Arrays and Machine Learning

In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early detection of these devastating diseases. First, we highlight the social relevance of developing systematic screening programs for such diseases and how sensor-arrays and ML approaches could ease their early diagnosis. Then, we present diverse works related to the colorimetric and electrochemical sensing of biomarkers related to DM and DR with non-invasive sampling (e.g., urine, saliva, breath, tears, and sweat samples), with a special mention to some already-existing sensor arrays and ML approaches. We finally highlight the great potential of the latter approaches for the fast and reliable early diagnosis of DM and DR.

Toward the Development of Combined Artificial Sensing Systems for Food Quality Evaluation: A Review on the Application of Data Fusion of Electronic Noses, Electronic Tongues and Electronic Eyes

Devices known as electronic noses (ENs), electronic tongues (ETs), and electronic eyes (EEs) have been developed in recent years in the in situ study of real matrices with little or no manipulation of the sample at all. The final goal could be the evaluation of overall quality parameters such as sensory features, indicated by the "smell", "taste", and "color" of the sample under investigation or in the quantitative detection of analytes. The output of these sensing systems can be analyzed using multivariate data analysis strategies to relate specific patterns in the signals with the required information. In addition, using suitable data-fusion techniques, the combination of data collected from ETs, ENs, and EEs can provide more accurate information about the sample than any of the individual sensing devices. This review's purpose is to collect recent advances in the development of combined ET, EN, and EE systems for assessing food quality, paying particular attention to the different data-fusion strategies applied.

Dummy Molecularly Imprinted Polymers Using DNP as a Template Molecule for Explosive Sensing and Nitroaromatic Compound Discrimination

This work reports a rapid, simple and low-cost voltammetric sensor based on a dummy molecularly imprinted polymer (MIP) that uses 2,4-dinitrophenol (DNP) as a template for the quantification of 2,4,6-trinitrotoluene (TNT) and DNP, and the identification of related substances. Once the polymer was synthesised by thermal precipitation polymerisation, it was integrated onto a graphite epoxy composite (GEC) electrode via sol–gel immobilisation. Scanning electron microscopy (SEM) was performed in order to characterise the polymer and the sensor surface. Responses towards DNP and TNT were evaluated, displaying a linear response range of 1.5 to 8.0 µmol L−1 for DNP and 1.3 to 6.5 µmol L−1 for TNT; the estimated limits of detection were 0.59 µmol L−1 and 0.29 µmol L−1, for DNP and TNT, respectively. Chemometric tools, in particular principal component analysis (PCA), demonstrated the possibilities of the MIP-modified electrodes in nitroaromatic and potential interfering species discrimination with multiple potential applications in the environmental field.

E-Tongues/Noses Based on Conducting Polymers and Composite Materials: Expanding the Possibilities in Complex Analytical Sensing

Conducting polymers (CPs) are extensively studied due to their high versatility and electrical properties, as well as their high environmental stability. Based on the above, their applications as electronic devices are promoted and constitute an interesting matter of research. This review summarizes their application in common electronic devices and their implementation in electronic tongues and noses systems (E-tongues and E-noses, respectively). The monitoring of diverse factors with these devices by multivariate calibration methods for different applications is also included. Lastly, a critical discussion about the enclosed analytical potential of several conducting polymer-based devices in electronic systems reported in literature will be offered.

Mathematical Modelling of Biosensing Platforms Applied for Environmental Monitoring

In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.

Calibration Transfer for LED-Based Optical Multisensor Systems

Multivariate calibration transfer is widely used to expand the applicability of the existing regression model to new analytical devices of the same or similar type. The present research proves the feasibility of calibration model transfer between a full-scale laboratory spectrometer and an optical multisensor system based on only four light-emitting diodes with different wavelengths. The model transfer between two multisensor systems of this kind has also been studied. Both possibilities were successfully performed without any significant loss of precision using a designed set of training and transfer samples. Direct standardization and slope and bias correction protocols for model transfer were tested and compared. The best model transfer between two optical multisensor systems was obtained using direct standardization.

Nonlinear Feature Extraction Through Manifold Learning in an Electronic Tongue Classification Task

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.

Optimization of Sensors to be Used in a Voltammetric Electronic Tongue Based on Clustering Metrics

Herein we investigate the usage of principal component analysis (PCA) and canonical variate analysis (CVA), in combination with the F factor clustering metric, for the a priori tailored selection of the optimal sensor array for a given electronic tongue (ET) application. The former allows us to visually compare the performance of the different sensors, while the latter allows us to numerically assess the impact that the inclusion/removal of the different sensors has on the discrimination ability of the ET. The proposed methodology is based on the measurement of a pure stock solution of each of the compounds under study, and the posterior analysis by PCA/CVA with stepwise iterative removal of the sensors that demote the clustering when retained as part of the array. To illustrate and assess the potential of such an approach, the quantification of paracetamol, ascorbic acid, and uric acid mixtures were chosen as the study case. Initially, an array of eight different electrodes was considered, from which an optimal array of four sensors was derived to build the quantitative ANN model. Finally, the performance of the optimized ET was benchmarked against the results previously reported for the analysis of the same mixtures, showing improved performance.

Application of Chemometrics in Biosensing: A Review

The field of biosensing is rapidly developing, and the number of novel sensor architectures and different sensing elements is growing fast. One of the most important features of all biosensors is their very high selectivity stemming from the use of bioreceptor recognition elements. The typical calibration of a biosensor requires simple univariate regression to relate a response value with an analyte concentration. Nevertheless, dealing with complex real-world sample matrices may sometimes lead to undesired interference effects from various components. This is where chemometric tools can do a good job in extracting relevant information, improving selectivity, circumventing a non-linearity in a response. This brief review aims to discuss the motivation for the application of chemometric tools in biosensing and provide some examples of such applications from the recent literature.

Polyelectrolyte Based Sensors as Key to Achieve Quantitative Electronic Tongues: Detection of Triclosan on Aqueous Environmental Matrices

Triclosan (TCS) is a bacteriostatic used in household items that promotes antimicrobial resistance and endocrine disruption effects both to humans and biota, raising health concerns. In this sense, new devices for its continuous monitoring in complex matrices are needed. In this work, sensors, based on polyelectrolyte layer-by-layer (LbL) films prepared onto gold interdigitated electrodes (IDE), were studied. An electronic tongue array, composed of (polyethyleneimine (PEI)/polysodium 4-styrenesulfonate (PSS))₅ and (poly(allylamine hydrochloride/graphene oxide)₅ LbL films together with gold IDE without coating were used to detect TCS concentrations (10⁻¹⁵–10⁻⁵ M). Electrical impedance spectroscopy was used as means of transduction and the obtained data was analyzed by principal component analysis (PCA). The electronic tongue was tested in deionized water, mineral water and wastewater matrices showing its ability to (1) distinguish between TCS doped and non-doped solutions and (2) sort out the TCS range of concentrations. Regarding film stability, strong polyelectrolytes, as (PEI/PSS)_n, presented more firmness and no significant desorption when immersed in wastewater. Finally, the PCA data of gold IDE and (PEI/PSS)₅ sensors, for the mineral water and wastewater matrices, respectively, showed the ability to distinguish both matrices. A sensitivity value of 0.19 ± 0.02 per decade to TCS concentration and a resolution of 0.13 pM were found through the PCA second principal component.

Analytical Figures of Merit for Multisensor Arrays

Multisensor arrays employing various sensing principles are a rapidly developing field of research as they allow simple and inexpensive quantification of various parameters in complex samples. Quantitative analysis with such systems is based on multivariate regression techniques, and deriving of traditional analytical figures of merit (e.g., sensitivity, selectivity, limit of detection, and limit of quantitation) for such systems is not obvious and straightforward. Nevertheless, it is absolutely needed for further development of the multisensor research field and for introducing these instruments into the general context of analytical chemistry. Here, we report on the protocol for calculation of sensitivity, selectivity, and detection limits for multisensor arrays. The results are provided and discussed in detail for several real-world data sets.

Critical Evaluation of Laboratory Potentiometric Electronic Tongues for Pharmaceutical Analysis-An Overview

Electronic tongue systems equipped with cross-sensitive potentiometric sensors have been applied to pharmaceutical analysis, due to the possibility of various applications and developing new formulations. Many studies already proved the complementarity between the electronic tongue and classical analysis such as dissolution tests indicated by Pharmacopeias. However, as a new approach to study pharmaceuticals, electronic tongues lack strict testing protocols and specification limits; therefore, their results can be improperly interpreted and inconsistent with the reference studies. Therefore, all aspects of the development, measurement conditions, data analysis, and interpretation of electronic tongue results were discussed in this overview. The critical evaluation of the effectiveness and reliability of constructed devices may be helpful for a better understanding of electronic tongue systems development and for providing strict testing protocols.

Taste Masking of Nizatidine Using Ion-Exchange Resins

The purpose of this study was to mask the bitter taste of nizatidine (NZD) using cation-exchange resins. Amberlite IRP-69 and Dowex-50 containing cross-linked polystyrene backbones were used. The drug resin complexes were prepared by batch process using drug: resin ratios of 1:1, 1:3, and 1:5. The optimum drug: resin ratio and the time required for maximum percentage drug loading into the complexes were determined. The selected drug-resin complexes were evaluated for morphology, drug release, and taste. The NZD-Dowex complex was obtained at a drug: resin ratio of 1:5 using a stirring time of 1 h in order to get 100% loading of NZD. The NZD-Dowex complex had a spherical shape and smooth texture similar to Dowex resin. The NZD-Dowex complex with a ratio of 1:5 showed that in vitro drug release of 4.27% at 5 min in simulated salivary fluid of pH 6.8 and 99.67% at 1 h in simulated gastric fluid of pH 1.2. NZD’s bitter taste was effectively masked when it formed a complex with Dowex at a ratio of 1:5. This was proved by an electronic tongue and human test panel.

Voltammetric Electronic Tongues in Food Analysis

A critical revision is made on recent applications of voltammetric electronic tongues in the field of food analysis. Relevant works are discussed dealing with the discrimination of food samples of different type, origin, age and quality and with the prediction of the concentration of key substances and significant indexes related to food quality.

Modified Diamide and Phosphine Oxide Extracting Compounds as Membrane Components for Cross-Sensitive Chemical Sensors

This research is devoted to the development and study of novel cross-sensitive sensors based on modified extracting ligands. According to the previous results of liquid extraction studies, the chemical modification of membrane active components would change the analytical characteristics of a sensor comprising them. The sensing elements of the studied sensors consisted of various derivatives of N,N,N′,N′-tetraoctyldiamide of diglycolic acid (TODGA) and di-phenyl-N,N-di-i-sobutylcarbamoylmethylen phoshine oxide (CMPO) used as neutral carriers, CCD (chlorinated cobalt dicarbollide) as a lipophilic additive, different plasticizers, and poly(vinyl chloride) (PVC) as a polymer. TODGA-based sensors demonstrated a stable and reproducible response towards rare earth cations in acidic media (pH = 2). Changing the concentrations and ratio of neutral carriers and the lipophilic additive, it is possible to modify the sensitivity and selectivity of the sensors towards the same target ions. Bonded ligands, such as cobalt dicarbollide covalently attached to TODGA and CMPO, exhibited lower selectivity and sensitivity to rare earth cations. A possibility to vary the cross-sensitivity patterns of the sensors in a wide range might be of great interest for the development of multisensor systems allowing the simultaneous determination of several analytes in multicomponent solutions.

A Simple Procedure to Assess Limit of Detection for Multisensor Systems

Currently, there are no established procedures for limit of detection (LOD) evaluation in multisensor system studies, which complicates their correct comparison with other analytical techniques and hinders further development of the method. In this study we propose a simple and visually comprehensible approach for LOD estimation in multisensor analysis. The suggested approach is based on the assessment of evolution of mean relative error values in calibration series with growing analyte concentration. The LOD value is estimated as the concentration starting from which MRE values become stable from sample to sample. This intuitive procedure was successfully tested with a variety of real data from potentiometric multisensor systems.

Chemical Sensing at the Robot Fingertips: Toward Automated Taste Discrimination in Food Samples

The development of robotic sensors that mimic the human sensing capabilities is critical for the interaction and cognitive abilities of modern robots. Though robotic skin with embedded pressure or temperature sensors has received recent attention, robotic chemical sensors have long been unnoticed due to the challenges associated with realizing chemical sensing modalities on robotic platforms. For realizing such chemically sensitive robotic skin, we exploit here the recent advances in wearable chemical sensor technology and flexible electronics, and describe chemical sensing robotic fingers for rapid screening of food flavors and additives. The stretchable taste-sensing finger electrochemical devices are printed on the robotic glove, which simulates the soft skin, and are integrated with a wireless electronic board for real-time data transmission. The printed middle, index, and ring robotic fingers allow accurate discrimination between sweetness, sourness, and spiciness, via direct electrochemical detection of glucose, ascorbic acid, and capsaicin. The sweet-sensing ability has been coupled with a caffeine-sensing robotic finger for rapid screening of the presence of sugar and caffeine in common beverages. The "sense of taste" chemically sensitive robotic technology thus enables accurate discrimination between different flavors, as was illustrated in numerous tests involving a wide range of liquid and solid food samples. Such realization of advanced wearable taste-sensing systems at the robot fingertips should pave the way to automated chemical sensing machinery, facilitating robotic decision for practical food assistance applications, with broad implications to a wide range of robotic sensing applications.

Characterization of Sicilian Honeys Pollen Profiles Using a Commercial E-Tongue and Melissopalynological Analysis for Rapid Screening: A Pilot Study

Honey is usually classified as "unifloral" or "multifloral", depending on whether a dominating pollen grain, originating from only one particular plant, or no dominant pollen type in the sample is found. Unifloral honeys are usually more expensive and appreciated than multifloral honeys, which highlights the importance of honey authenticity. Melissopalynological analysis is used to identify the botanical origin of honey, counting down the number of pollens grains of a honey sample, and calculating the respective percentages of the nectariferous pollens. In addition, sensory properties are also very important for honey characterization, and electronic senses emerged as useful tools for honey authentication. In this work, a comparison of the results obtained from melissopalynological analysis with those provided by a potentiometric electronic tongue is given, resulting in a 100% match between the two techniques.

Electronic Tongue for Brand Uniformity Control: A Case Study of Apulian Red Wines Recognition and Defects Evaluation †

The potentiometric electronic tongue system has been tested as a potential analytical tool for brand uniformity control of monoculture Apulian red wines (Primitivo and Negroamaro). The sensor array was composed of eight porphyrin coatings obtained by electrochemical polymerization process and was employed for both wines discrimination and quantitative detection of wine defect compounds: "off-odour" 3-(methylthio)-propanol; isoamyl alcohol fusel oil; benzaldehyde (marker of the yeast activity) and acetic acid (marker of vinegar formation). PLS-DA applied to Electronic tongue output data has permitted a correct discrimination of more than 70% of analysed wines in respect to the original brand affiliation. Satisfactory PLS1 predictions were obtained in real wine samples; with R² = 0.989 for isoamyl alcohol and R² = 0.732 for acetic acid. Moreover; the possibility to distinguish wine samples on the base of permitted levels of fault compounds content was shown.

Fingerprinting Green Curry: An Electrochemical Approach to Food Quality Control

The detection and identification of multiple components in a complex sample such as food in a cost-effective way is an ongoing challenge. The development of on-site and rapid detection methods to ensure food quality and composition is of significant interest to the food industry. Here we report that an electrochemical method can be used with an unmodified glassy carbon electrode for the identification of the key ingredients found within Thai green curries. It was found that green curry presents a fingerprint electrochemical response that contains four distinct peaks when differential pulse voltammetry is performed. The reproducibility of the sensor is excellent as no surface modification is required and therefore storage is not an issue. By employing particle swarm optimization algorithms the identification of ingredients within a green curry could be obtained. In addition, the quality and freshness of the sample could be monitored by detecting a change in the intensity of the peaks in the fingerprint response.

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.

Electronic Tongue-A Tool for All Tastes?

Electronic tongue systems are traditionally used to analyse: food products, water samples and taste masking technologies for pharmaceuticals. In principle, their applications are almost limitless, as they are able to almost completely reduce the impact of interferents and can be applied to distinguish samples of extreme complexity as for example broths from different stages of fermentation. Nevertheless, their applications outside the three principal sample types are, in comparison, rather scarce. In this review, we would like to take a closer look on what are real capabilities of electronic tongue systems, what can be achieved using mixed sensor arrays and by introduction of biosensors or molecularly imprinted polymers in the matrix. We will discuss future directions both in the sense of applications as well as system development in the ever-growing trend of low cost analysis.

Improved Durability and Sensitivity of Bitterness-Sensing Membrane for Medicines

This paper reports the improvement of a bitterness sensor based on a lipid polymer membrane consisting of phosphoric acid di-n-decyl ester (PADE) as a lipid and bis(1-butylpentyl) adipate (BBPA) and tributyl o-acetylcitrate (TBAC) as plasticizers. Although the commercialized bitterness sensor (BT0) has high sensitivity and selectivity to the bitterness of medicines, the sensor response gradually decreases to almost zero after two years at room temperature and humidity in a laboratory. To reveal the reason for the deterioration of the response, we investigated sensor membranes by measuring the membrane potential, contact angle, and adsorption amount, as well as by performing gas chromatography-mass spectrometry (GC-MS), liquid chromatography-tandem mass spectrometry (LC-MS/MS). We found that the change in the surface charge density caused by the hydrolysis of TBAC led to the deterioration of the response. The acidic environment generated by PADE promoted TBAC hydrolysis. Finally, we succeeded in fabricating a new membrane for sensing the bitterness of medicines with higher durability and sensitivity by adjusting the proportions of the lipid and plasticizers.

Functionalization-Free Microfluidic Electronic Tongue Based on a Single Response

Electronic tongues (e-tongues) are promising analytical devices for a variety of applications to address the challenges of quality control in water monitoring and industries of foods, beverages, and pharmaceuticals. A crucial drawback in the current e-tongues is the need to recalibrate the device when one or more sensing units (usually with modified surface) are replaced. Another downside is the necessity to perform subsequent surface modifications and analyses to each of the diverse sensing units, undermining the simplicity and velocity of the method. These features have prevented widespread commercial use of the e-tongues. In this paper, we introduce a microfluidic e-tongue that overcomes all such limitations. The key principle of global selectivity of the e-tongue was achieved by recording only a single response, namely, the equivalent admittance spectrum of an association of resistors in parallel. Such resistors consisted of five nonfunctionalized stainless steel microwires (sensing units), which were short-circuited and coated with gold, platinum, nickel, iron, and aluminum oxide films. The microwires were inserted in a chip composed of a single piece of polydimethylsiloxane (PDMS). Using impedance spectroscopy, the e-tongue was successfully applied in classification of basic tastes at a concentration below the threshold for the human tongue. In addition, our chip allowed the distinction of various chemicals used in oil industry. Finally, our cleanroom-free prototyping allows the mass production of chips with easily replaceable and reproducible sensing units. Hence, one can now envisage the widespread dissemination of e-tongues with fast and reproducible data.

A Versatile Electronic Tongue Based on Surface Plasmon Resonance Imaging and Cross-Reactive Sensor Arrays-A Mini-Review

Nowadays, there is a strong demand for the development of new analytical devices with novel performances to improve the quality of our daily lives. In this context, multisensor systems such as electronic tongues (eTs) have emerged as promising alternatives. Recently, we have developed a new versatile eT system by coupling surface plasmon resonance imaging (SPRi) with cross-reactive sensor arrays. In order to largely simplify the preparation of sensing materials with a great diversity, an innovative combinatorial approach was proposed by combining and mixing a small number of easily accessible molecules displaying different physicochemical properties. The obtained eT was able to generate 2D continuous evolution profile (CEP) and 3D continuous evolution landscape (CEL), which is also called 3D image, with valuable kinetic information, for the discrimination and classification of samples. Here, diverse applications of such a versatile eT have been summarized. It is not only effective for pure protein analysis, capable of differentiating protein isoforms such as chemokines CXCL12α and CXCL12γ, but can also be generalized for the analysis of complex mixtures, such as milk samples, with promising potential for monitoring the deterioration of milk.

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.

Sensor Arrays and Electronic Tongue Systems

This paper describes recent work performed with electronic tongue systems utilizing electrochemical sensors. The electronic tongues concept is a new trend in sensors that uses arrays of sensors together with chemometric tools to unravel the complex information generated. Initial contributions and also the most used variant employ conventional ion selective electrodes, in which it is named potentiometric electronic tongue. The second important variant is the one that employs voltammetry for its operation. As chemometric processing tool, the use of artificial neural networks as the preferred data processing variant will be described. The use of the sensor arrays inserted in flow injection or sequential injection systems will exemplify attempts made to automate the operation of electronic tongues. Significant use of biosensors, mainly enzyme-based, to form what is already named bioelectronic tongue will be also presented. Application examples will be illustrated with selected study cases from the Sensors and Biosensors Group at the Autonomous University of Barcelona.