Search Results (36)

Low-cost electrochemical methodologies for the determination of L-ascorbic acid (vitamin C) and the analysis of juices are developed based on its electro-oxidation on carbon screen-printed electrodes. A novel chronoamperometric methodology is developed for the quantification of L-ascorbic acid in fruit juices. The proposed method stands out for its simplicity and rapidity, demonstrating its efficacy in determining L-ascorbic acid content in various fruit juices. Notably, the results obtained with this chronoamperometric approach are compared with those yielded by chromatography, with no significant differences between the two methods being found. Additionally, an electronic tongue is developed for the differentiation of juices based on the square wave voltammetric signals. Full article

Due to the lucrative coffee market, this product is often subject to adulteration, as inferior or non-coffee materials or varieties are mixed in, negatively affecting its quality. Traditional sensory evaluations by expert tasters and chemical analysis methods, although effective, are time-consuming, costly, and require skilled personnel. The aim of this work was to evaluate the capacity of a smart electronic tongue (e-tongue) based on a polypyrrole sensor array as a tool for the rapid analysis of coffees elaborated from beans of different varieties. The smart e-tongue device was developed with a polypyrrole-based voltammetric sensor array and portable multi-potentiostat operated via smartphone. The sensor array comprised seven electrodes, each doped with distinct counterions to enhance cross-selectivity. The smart e-tongue was tested on five Arabica coffee varieties (Typica, Bourbon, Maragogype, Tabi, and Caturra). The resulting voltammetric signals were analyzed using principal component analysis assisted by neural networks (PCNN) and cluster analysis (CA), enabling clear discrimination among the coffee samples. The results demonstrate that the polypyrrole sensors can generate distinct electrochemical patterns, serving as “fingerprints” for each coffee variety. This study highlights the potential of polypyrrole-based smart e-tongues as a rapid, cost-effective, and portable alternative for coffee quality assessment and adulteration detection, with broader applications in the food and beverage industry. Full article

This paper reports the use of nanoparticles (NPs)-modified voltammetric sensors for the rapid determination of glycerol in the presence of ethanol and methanol, which are used in the transesterification reaction of biodiesel production. Two different modified electrodes have been prepared to form the electronic tongue (ET): copper hexacyanoferrate NPs obtained by chemical synthesis and mixed into graphite/epoxy (GEC) electrode, and nickel hydroxide NPs electrodeposited in reduced graphene oxide onto a GEC electrode. The response characteristics of these electrodes were first evaluated by building the respective calibration against glycerol, ethanol, and methanol. The electrodes demonstrated good stability during their analytical characterization, while principal component analysis confirmed the differentiated response against the different alcohols. Finally, the quantification of mixtures of these substances was achieved by a genetic algorithm-artificial neural networks (GA-ANNs) model, showing satisfactory agreement between expected and obtained values. Full article

Water quality monitoring has become critical in modern societies in multiple areas and at different stages. In this regard, chemical oxygen demand (COD) has become a key index in water testing, as it readily allows the determination of its overall quality and the presence of organic contaminants. However, conventional COD determination presents several drawbacks in view of the use of toxic reagents and possible interferences. The electrochemical determination of COD can be an alternative with many advantages, especially if using an array of sensors. Herein, the use of an electronic tongue (ET) for the estimation of COD was explored. The proposed ET was formed by an array of five voltammetric electrodes modified with different metal nanoparticles. An artificial neural network (ANN) model was built based on the responses of the array towards glucose and glycine as standards. This model was then used with real and spiked water samples, and the results compared to the electrochemical calibration and the commercial COD colorimetric methods. While the COD values of the real samples were low and outside the range of the ANN model, a satisfactory prediction for the spiked samples was achieved, showing a good agreement with the reference colorimetric method, that was better than the performance of the conventional electrochemical calibration method. Full article

Electronic tongues and artificial gustation for crucial analytes in the environment, such as metal ions, are becoming increasingly important. In this contribution, we propose a multi-level fusion framework for a hybrid impedimetric and voltammetric electronic tongue to enhance the accuracy of K⁺, Mg²⁺, and Ca²⁺ detection in an extensive concentration range (100.0 nM–1.0 mM). The proposed framework extracts electrochemical-based features and separately fuses, in the first step, impedimetric features, which are characteristic points and fixed frequency features, and the voltammetric features, which are current and potential features, for data reduction by LDA and classification by kNN. Then, in a second step, a decision fusion is carried out to combine the results for both measurement methods based on Dempster–Shafer (DS) evidence theory. The classification results reach an accuracy of 80.98% and 81.48% for voltammetric measurements and impedimetric measurements, respectively. The decision fusion based on DS evidence theory improves the total recognition accuracy to 91.60%, thus realizing significantly high accuracy in comparison to the state-of-the-art. In comparison, the feature fusion for both voltammetric and impedimetric features in one step reaches an accuracy of only 89.13%. The proposed hierarchical framework considers for the first time the fusion of impedimetric and voltammetric data and features from multiple electrochemical sensor arrays. The developed approach can be implemented for several further applications of pattern fusion, e.g., for electronic noses, measurement of environmental contaminants such as heavy metal ions, pesticides, explosives, and measurement of biomarkers, such as for the detection of cancers and diabetes. Full article

A novel voltammetric electronic tongue (VE-tongue) system based on three nanocomposites modified working electrodes was used for the discrimination of red wine from different geographical origins. The three types of modified working electrodes were fabricated to detect glucose (Glu), tartaric acid (TA), and non-specific flavor information in a red wine sample, respectively. The electrochemical properties of three electrodes were tested by cyclic voltammetric method, and pH, accumulation time, and scan rates were optimized for Glu and TA sensors. Scanning electron microscopy (SEM), X-ray proton spectrum (XPS), and X-ray diffraction (XRD) were used for the characterization of modified materials. This sensor array was then applied to identify four kinds of red wines from different geographical origins, and the multi-frequency and potential steps (STEP) method was used to obtain flavor information regarding rice wines. The classification ability of this VE-tongue system was evaluated by using partial least squares (PLS) regression and principal component analysis (PCA), while back propagation neural network (BPNN), random forest (RF), support vector machines (SVM), deep neural network (DNN), and K-nearest neighbor (KNN) were used for the prediction. The results showed that PCA could explain about the 95.7% of the total variance, and BPNN performed best in the prediction work (the prediction accuracy was 95.8%). Therefore, the VE-tongue system with BPNN was chosen to effectively discriminate red wines from different geographical origins, and the novel VE-tongue aiming at red wine discrimination with high accuracy and lower cost was established. Full article

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. Full article

Tea, after water, is the most frequently consumed beverage in the world. The fermentation of tea leaves has a pivotal role in its quality and is usually monitored using the laboratory analytical instruments and olfactory perception of tea tasters. Developing electronic sensing platforms (ESPs), in terms of an electronic nose (e-nose), electronic tongue (e-tongue), and electronic eye (e-eye) equipped with progressive data processing algorithms, not only can accurately accelerate the consumer-based sensory quality assessment of tea, but also can define new standards for this bioactive product, to meet worldwide market demand. Using the complex data sets from electronic signals integrated with multivariate statistics can, thus, contribute to quality prediction and discrimination. The latest achievements and available solutions, to solve future problems and for easy and accurate real-time analysis of the sensory-chemical properties of tea and its products, are reviewed using bio-mimicking ESPs. These advanced sensing technologies, which measure the aroma, taste, and color profiles and input the data into mathematical classification algorithms, can discriminate different teas based on their price, geographical origins, harvest, fermentation, storage times, quality grades, and adulteration ratio. Although voltammetric and fluorescent sensor arrays are emerging for designing e-tongue systems, potentiometric electrodes are more often employed to monitor the taste profiles of tea. The use of a feature-level fusion strategy can significantly improve the efficiency and accuracy of prediction models, accompanied by the pattern recognition associations between the sensory properties and biochemical profiles of tea. Full article

Sweat is a promising biofluid in allowing for non-invasive sampling. Here, we investigate the use of a voltammetric electronic tongue, combining different metal electrodes, for the purpose of non-invasive sample assessment, specifically focusing on sweat. A wearable electronic tongue is presented by incorporating metal electrodes on a flexible circuit board and used to non-invasively monitor sweat on the body. The data obtained from the measurements were treated by multivariate data processing. Using principal component analysis to analyze the data collected by the wearable electronic tongue enabled differentiation of sweat samples of different chemical composition, and when combined with ¹H-NMR sample differentiation could be attributed to changing analyte concentrations. Full article

The integration of silver nanomaterials as electron mediators in electrochemical biosensors can be crucial to improve the affinity with biomolecules and the electrochemical response. In this work, two voltammetric bioelectronics tongues (bioET) formed by biosensors based on the combination of enzymes with silver nanoparticles (AgNPs) (bioET-1) or silver nanowires (AgNWs) (bioET-2) have been developed and used to analyze milks. Each array was formed by four biosensors formed by enzymes (glucose oxidase, galactose oxidase, β-galactosidase and a blank), capable to detect compounds usually found in milks. Principal component analysis (PCA) has revealed the ability of both biosensor systems to discriminate between milk samples with different fat contents, but with some differences, attributed to the structure employed in the detection. Full article

Chemical Oxygen Demand (COD) is a widely used parameter in analyzing and controlling the degree of pollution in water. COD is defined as the amount of molecular oxygen (in milligrams of O₂) required to decompose all the organic compounds in 1 L of aqueous solution to carbon dioxide and water. There are many methods reported for COD determination, such as the conventional dichromate titration method. Electro-oxidizing the organic contaminants to completely transform them into CO₂ and H₂O using sensors is considered the best method for COD estimation. Increasing attention has been paid to electrochemical methods because they are highly sensitive, time-saving, low-cost, and easy to operate. In this sense, copper electrodes have been reported based on the fact that copper in alkaline media acts as a powerful electrocatalyst for the oxidation of aminoacids and carbohydrates, which are believed to be the major culprits for organic pollution. Cyclic voltammetry was the technique used to obtain the voltammetric responses. It is common for different organic compounds to show different cyclic voltammogram shapes and current intensities in different concentrations. In this work, four kinds of electrodes modified with copper (Cu)/copper oxide (CuO)/nickel copper alloy (Ni Cu alloy) nanoparticles were studied for COD analysis; this was done by employing the cyclic voltammetry technique, which involved a Nafion film-covered electrodeposited CuO/Cu nanoparticle electrode (E1), a Cu nanoparticle–graphite composite electrode (E2), a CuO nanoparticle–graphite composite electrode (E3), and a Ni Cu alloy nanoparticle–graphite composite electrode (E4). The COD values were determined via the plotted calibration of COD values vs. the current intensity. Glucose, glycine, potassium hydrogen phthalate (KHP), and ethylene glycol—which show different reducibilities—were chosen as the standard substances to play the role of organic contaminants with different degradation difficulties. From the obtained cyclic voltammograms, we can see that glucose is very easily oxidized by those four electrodes, with electrode E1 displaying the best performance, with a linear range of 19.2~1120.8 mg/L and limit of detection of 27.5 mg/L (calculated based on the formula 3σ/k). In contrast, it is very difficult for the compound KHP to be oxidized by these four electrodes. Nevertheless, the obtained voltammetric profiles presented different shapes with the tested organic compounds, suggesting these four electrodes can compose an electronic tongue array for multivariate analysis. As a result, the main component of river samples—whose degradation could be easy or difficult—can be evaluated via the PCA technique. This evaluation is very helpful for the accuracy of COD detection. The resulting sensor-based method demonstrates great potential not only for estimating the precise value of COD, but for predicting the difficulty of its degradation; this represents a simple, fast, and clean methodology, which is perfectly suited to the present demands of green techniques. Full article

Codeine and diclofenac overdoses have been widely reported. Here, a biomimetic sensor (bi-MIP) was devised, and an electronic tongue was used to analyze water samples simultaneously containing both these drugs. The bi-MIP sensor limits of detection for diclofenac and codeine taken individually were 0.01 µg/mL and 0.16 µg/mL, respectively. Due to a cross-reactivity effect when using the bi-MIP sensor, the electronic tongue was shown to differentiate samples containing both analytes. The results confirm the feasibility of simultaneous detection of two target analytes via a bi-MIP sensor. Additionally, they demonstrate the ability of a multi-sensor to classify different water samples. Full article

Electronic tongues are devices used in the analysis of aqueous matrices for classification or quantification tasks. These systems are composed of several sensors of different materials, a data acquisition unit, and a pattern recognition system. Voltammetric sensors have been used in electronic tongues using the cyclic voltammetry method. By using this method, each sensor yields a voltammogram that relates the response in current to the change in voltage applied to the working electrode. A great amount of data is obtained in the experimental procedure which allows handling the analysis as a pattern recognition application; however, the development of efficient machine-learning-based methodologies is still an open research interest topic. As a contribution, this work presents a novel data processing methodology to classify signals acquired by a cyclic voltammetric electronic tongue. This methodology is composed of several stages such as data normalization through group scaling method and a nonlinear feature extraction step with locally linear embedding (LLE) technique. The reduced-size feature vector input to a k-Nearest Neighbors (k-NN) supervised classifier algorithm. A leave-one-out cross-validation (LOOCV) procedure is performed to obtain the final classification accuracy. The methodology is validated with a data set of five different juices as liquid substances.Two screen-printed electrodes voltametric sensors were used in the electronic tongue. Specifically the materials of their working electrodes were platinum and graphite. The results reached an 80% classification accuracy after applying the developed methodology. Full article

Biogenic amines (BAs), which are produced naturally due to the decomposition of amino acids, are crucial for the food industry because its formation is directly related to improper storage and the presence of bacteria. High concentrations of BAs can be easily related with the quality and spoilage of the products of this sector. The necessity to quickly and efficiently quantify these targets makes mandatory the use of alternatives to conventional analytical methods used up to now. For example, the combination of sensors with chemometric tools (known as electronic tongue) are a promising alternative for quick and informative analysis in the food sector. Chemometric tools allow us to develop models for the quantification of specific compounds in a complex matrix, making it a feasible tool for the development of more user-friendly methods than the traditional ones. In this context, the work presents an electronic tongue created for the detection of histamine, cadaverine and tyramine using a set of five modified GEC (graphite epoxy composite) electrodes: ZnO NPs, CuO NPs, SnO₂ NPs, Bi₂O₃ NPs, and polypyrrole, as the voltammetric multisensor array. The chemometric model was obtained with an Artificial Neural Network (ANN) with 51 input neurons, five neurons in the hidden layer and three neurons in the output layer. The functions used for the hidden and output layers were tansig and purelin, respectively. The results show slopes near to 1 and intercepts close to 0, indicating the feasibility of the model. Full article

This work attempts the identification of the production year, the cultivar’s region and the aging method used in the elaboration of different Spanish red wines, all from the “tempranillo” grape variety. The identification of such characteristics relies on the use of a voltammetric electronic tongue (ET) system formed by modified graphite-epoxy electrodes (GEC) and metallic electrodes to collect a set of six voltammograms per sample, and different chemometric tools to accomplish the final identifications. A large sample set that included 199 different wine samples from commercial and own elaboration origin were analysed with the electronic tongue system, using the cyclic voltammetry technique and without any sample pre-treatment. To process the extremely complex and high-dimensionality generated data, a compression strategy was used for the acquired voltammograms, using discrete wavelet transform (DWT). This treatment reduced the information to ca. 10%, preserving significant features from the voltammetric signals. Compressed data was evaluated firstly by unsupervised methods, i.e., principal component analysis (PCA), without much success as it was found that such methods were unable to unravel the patterns contained within such complex data samples. Finally, the processed electrochemical information was evaluated by supervised methods to accomplish the proper identification; among those methods were linear discriminant analysis (LDA), supported vector machines (SVM) or artificial neural networks (ANN). The best results were obtained using artificial neural networks (ANNs), achieving 96.1% of correct classification for bottled year, 86.8% for elaboration region (protected designation of origin) and 98.6% for maturation type with or without use of wood barrel. Full article