Cava Wine Authentication Employing a Voltammetric Electronic Tongue

The application of an Electronic Tongue for the classification of cava samples based on their different ageing times is reported. As such, voltammetric responses were obtained from an array of six bulk‐modified graphite‐epoxy electrodes, which exhibited marked mix‐responses towards the different samples. Obtained responses were then preprocessed employing FFT and the resulting coefficients were input to a LDA model which allowed the classification of the samples according to its vintage time. Besides, a quantitative model employing ANNs was built for the prediction of the total amount of sugar present in the samples, a parameter also used to classify cava samples.     

A Miniaturized Voltammetric Electronic Tongue

Abstract

A miniaturized electronic tongue based on pulsed voltammetry has been developed. It was made by inserting three types of wires acting as working electrodes (gold, platinum, and rhodium; diameter 0.25 mm) into a platinum tube acting as a counter electrode (diameter 2 mm; length 4 mm). The arrangement was connected to a potentiostat controlled by a computer. Due to the small size of the miniaturized electronic tongue, and since no reference electrode is used, the setup is very simple and convenient. In order to characterize the analytical possibilities of the miniaturized electronic tongue, some initial experiments were performed. These include the determination of trace amounts of cadmium and lead (in the µM range) in 5 µL samples. Furthermore, the setup was placed under the real tongue of a volunteer to follow saliva composition during exercise.     

Data Compression for a Voltammetric Electronic Tongue Modelled with Artificial Neural Networks

Abstract

In the study of voltammetric electronic tongues, a key point is the preprocessing of the departure information, the voltammograms which form the response of the sensor array, prior to classification or modeling with advanced chemometric tools. This work demonstrates the use of the discrete wavelet transform (DWT) for compacting these voltammograms prior to modeling. After compression, a system based on artificial neural networks (ANNs) was used for the quantification of the electroactive substances present, using the obtained wavelet decomposition coefficients as their inputs. The Daubechies wavelet of fourth order permitted an effective compression up to 16 coefficients, reducing the original dimension by ca. 10 times. The case studied is a mixture of three oxidizable amino acids:tryptophan, cysteine, and tyrosine. With the reduced information, one ANN per specie was trained using the Bayesian regularization algorithm. The proposed procedure was compared with the more conventional treatments of downsampling the voltammogram, or its feature extraction employing principal component analysis prior to ANNs.     

A Voltammetric Electronic Tongue for the Quantitative Analysis of Quality Parameters in Wastewater

The use of a voltammetric electronic tongue for the quantitative analysis of quality parameters in influent wastewater from a wastewater treatment plant (WWTP) that treats domestic and industrial wastewater is proposed. The electronic voltammetric tongue consists of a set of four noble electrodes (iridium, rhodium, platinum and gold) housed inside a stainless steel cylinder. These noble metals have high durability and are low maintenance‐demanding, as required for developing future automated equipment. A pulse voltammetry study was conducted in 35 wastewater samples to determine ammonia (NH₄⁺‐N), nitrates (NO₃⁻‐N), total phosphate (tot‐P), soluble chemical oxygen demand (CODs) and conductivity. These parameters were also determined in these samples by routine analytical methods in the WWTP laboratory. A partial least squares (PLS) analysis was run to obtain a model to predict each parameter. Twenty‐five samples were included in the calibration set and 10 in the validation set. Calibration and validation sets were selected randomly, except for the extreme values of each parameter, which were included in the calibration set. Variable selection was performed on the voltammetric data using Genetic Algorithms in the calibration data set for each parameter. The electronic tongue showed good predictive power to determine the concentrations of NH₄⁺‐N, NO₃⁻‐N and tot‐P and CODs.     

Sequential injection system with higher dimensional electrochemical sensor signals Part 1. Voltammetric e-tongue for the determination of oxidizable compounds

A sequential injection analysis (SIA) system was developed with the aim of obtaining an automatic and versatile way to prepare standards needed in the study of systems with higher dimensional sensor signals. To illustrate this, different analytical techniques were used in determinations of several analytes. Automated potentiometric calibrations of different potentiometric sensors, with and without interference, were carried out. Useful determinations of selectivity coefficients with two degrees of freedom were obtained. Simultaneous voltammetric determinations have also been done. Firstly, simultaneous determinations of lead and cadmium, using epoxy-graphite composite as the working electrode, have enabled a separate calibration for each metal to be obtained. Next, a voltammetric electronic tongue was designed and applied to the determination of oxidizable species. The use of artificial neural networks has solved the overlapped signal of ascorbic acid, 4-aminophenol and 4-acetamidophenol (paracetamol). A set of 63 data points was prepared automatically and has facilitated the training of an electronic tongue for these three analytes. Accurate predictions of test solutions, in the range of 12–410 μM for ascorbic acid, 17–530 μM for 4-aminophenol and 10–420 μM for paracetamol, have been achieved with RMSEs lower than 0.10 μM.     

Use of a Voltammetric Electronic Tongue for Detection and Classification of Nerve Agent Mimics

An electronic tongue (ET) based on pulse voltammetry has been used to predict the presence of nerve agent mimics in aqueous environments. The electronic tongue array consists of eight working electrodes (Au, Pt, Ir, Rh, Cu, Co, Ni and Ag) encapsulated on a stainless steel cylinder. Studies including principal component analysis (PCA), artificial neural networks (fuzzy ARTMAP) and partial least square techniques (PLS) have been applied for data management and prediction models. For instance the electronic tongue is able to discriminate the presence of the nerve agent simulants diethyl chlorophosphate (DCP) and diethyl cyanophosphate (DCNP) from the presence of other organophosphorous derivatives in water. Finally, PLS data analysis using a system of 3 compounds and 3 concentration levels shows a good accuracy in concentration prediction for DCP and DCNP in aqueous environments.     

Ammonium and Phosphate Quantification in Wastewater by Using a Voltammetric Electronic Tongue

Ammonia (NH₄? N) and orthophosphate (PO₄? P) quantification in wastewater treatment plants is important due to their implication in the eutrophication process. A voltammetric electronic tongue as a tool for the prediction of ammonia and orthophosphate concentrations from influent and effluent wastewater is proposed herein. An electrochemical study of the response of the ammonium and orthophosphate ions was performed in order to design a suitable waveform for each electrode. Partial Least Squares analysis was used to obtain a correlation between the data from the tongue and the concentrations of ammonia and orthophosphate measured in the laboratory showing good predictive power.     

Electronic Tongue Applied to Phenolic Compounds Analysis

Abstract

A new voltammetric electronic tongue has been developed for the simultaneous determination of p-aminophenol, p-chlorophenol, and p-chloro-m-methylphenol. The three analytes are high environmental pollutants. Three epoxy-graphite composite electrodes have been used as detectors, with two of them being modified by the addition of metal catalysts in their composition. Simple fixed potential voltammetry has been used in order to simplify measurements. Artificial neural networks were used as chemometric tools for learning and prediction of the system. Good correlations in both modeling and predicting capacities have been obtained for the range of 1 to 28 µM of the three phenolic compounds.     

An electronic tongue for gliadins semi-quantitative detection in foodstuffs

An all-solid-state potentiometric electronic tongue with 36 polymeric membranes has been used for the first time to detect gliadins, which are primarily responsible for gluten intolerance in people suffering from celiac disease. A linear discriminant model, based on the signals of 11 polymeric membranes, selected from the 36 above using a stepwise procedure, was used to semi-quantitatively classify samples of a “Gluten-free” foodstuff (baby milked flour), previously contaminated with known amounts of gliadins (<10, 20-50 or >50 mg/kg), as “Gluten-free”, “Low-Gluten content” or “Gluten-containing”. For this food matrix, the device had sensitivity towards gliadins of 1-2 mg/kg and overall sensitivity and specificity of 77% and 78%, respectively. Moreover, the device never identified an ethanolic extract containing gliadins as “Gluten-free”. Finally, the system also allowed distinguishing “Gluten-free” and “Gluten-containing” foodstuffs (15 foods, including breads, flours, baby milked flours, cookies and breakfast cereals) with an overall sensitivity and specificity greater than 83%, using the signals of only 4 selected polymeric membranes (selected using a stepwise procedure). Since only one “Gluten-containing” foodstuff was misclassified as “Gluten-free”, the device could be used as a preliminary tool for quality control of foods for celiac patients.     

Integration of Commercial Screen‐printed Electrodes into a Voltammetric Electronic Tongue for the Analysis of Aminothiols

A voltammetric sensor array (or electronic tongue) is developed for the simultaneous quantification of cysteine, glutathione and homocysteine without need of previous separation. It is based on the integration of three commercial screen‐printed electrodes (gold curated at high and low temperature and carbon modified with carbon nanotubes). Linear sweep voltammograms measured simultaneously by all three sensors are processed by Partial Least Squares (PLS) regression and different variables selection algorithms such as Genetic Algorithm and interval‐Partial Least Squares. The method was applied to synthetic mixtures and successfully validated, with correlation coefficients of prediction (R_p²) of 0.9542, 0.9429 and 0.9589 for cysteine, glutathione, and homocysteine respectively.     

Electronic Tongue Using an Enzyme Inhibition Biosensor Array for the Resolution of Pesticide Mixtures

An electronic tongue has been developed, employing an array of inhibition biosensors and Artificial Neural Networks (ANNs). The array of biosensors was formed by three amperometric pesticide biosensors that used different acetylcholinesterase (AChE) enzymes: the wild type from electric eel (EE) and two different genetically modified enzymes (B1 and B394). In order to model the response to dichlorvos and carbofuran mixtures, a total amount of 22 solutions were prepared, with random concentrations. Chronoamperometric responses of the biosensor array were used in order to obtain the inhibition bioelectronic tongue. Mean values of concentration of pesticides evaluated were 0.79 nM for dichlorvos and 4.1 nM for carbofuran. Good prediction ability was obtained with correlation coefficients better than 0.918 when the obtained values were compared with those expected for a set of 6 external test samples not used for training.     

Classification of Tieguanyin Tea with an Electronic Tongue and Pattern Recognition

Analysis of four Tieguanyin teas from different origins were performed using an electronic tongue, which has significant advantages in terms of accuracy and precision for pattern recognition. Hierarchical cluster analysis and principal component analysis were then applied to identify origins of these teas, and a distinct separation was observed. The back propagation neural network (BPNN) and the back propagation neural network with the Levenberg-Marquardt training algorithm (LMBP) were applied to build identification models. The Levenberg-Marquardt training algorithm model outperformed the back propagation neural network, as the identification performances of the former model were 100% in the training and prediction sets when four principal components were used. The results demonstrate that an electronic tongue with pattern recognition is suitable to classify Tieguanyin tea and shows broad potential in food inspection and quality control.     

Voltammetric Electronic Tongue Based on Carbon Paste Electrodes Modified with Biochar for Phenolic Compounds Stripping Detection

Biochar is a charcoal produced from the biomass pyrolysis process that presents a highly porous and functionalized surface. In the present work an array of carbon paste electrodes (CPE) made of different forms of carbon (graphite, carbon nanotubes and activated biochar) was evaluated in the development of an electronic tongue for discrimination and stripping voltammetric determination of catechol (CAT), 4‐ethylcatechol (4‐EC) and 4‐ethylguaiacol (4‐EG) phenolic compounds. Morphological characterization of carbon materials and electrodes surfaces was performed by scanning electron microscopy (SEM) and semi‐quantitative elemental composition by energy dispersive spectroscopy (EDS). Electrochemical Impedance Spectroscopy (EIS) measurements were used for electrochemical characterization of electrodes. Cyclic voltammetry measurements were performed for the phenolic compounds evaluated using different concentrations. Principal component analysis (PCA) was performed to evaluate the qualitative analysis. Quantitative data modeling was done using artificial neural networks (ANN). The proposed sensor array presented analytical potentiality allowing the distinction and determination of CAT, 4‐EC and 4‐EG by using chemometric processing. The method showed sensibility, reproducibility and a good linearity (R²>0.9940) for three compounds evaluated. Spontaneous preconcentration of three compounds was possible using all three sensors, which can allow the application of these as passive samplers for remote determinations of phenolic compounds in wine and food samples.     

Hybrid Electronic Tongue as a Tool for the Monitoring of Wine Fermentation and Storage Process

Hybrid electronic tongue based on potentiometric and voltammetric sensors was applied for the monitoring of wine production process. The sensor array formed by miniaturized ion‐selective electrodes and glassy carbon electrodes provided the analysis of the progress and correctness of wine fermentation and storage process, detection of the presence of disturbing factors and evaluation of the quality of final product. The efficiency of the proposed approach was compared with the monitoring of wine production carried out using standard reference methods. The results indicated that hybrid electronic tongue could be used as simple and reliable analytical tool dedicated to qualitative and quantitative assessment of wine production.     

A Voltammetric Electronic Tongue Based on Commercial Screen‐printed Electrodes for the Analysis of Aminothiols by Differential Pulse Voltammetry

A voltammetric electronic tongue has been designed as a proof of concept for the analysis of aminothiols by differential pulse voltammetry and has been tested in ternary mixtures of cysteine (Cys), homocysteine (hCys) and glutathione (GSH). It consists of three screen‐printed electrodes of carbon, carbon nanofibers and gold cured at low temperature. A preliminary calibration study carried out separately for each aminothiol confirmed that, working at an optimal pH value of 7.4, every electrode produces differentiated responses for every analyte (cross‐response). As for the tongue, it was applied to calibration and validation mixtures of Cys, hCys and GSH and provided voltammograms that, baseline‐corrected, normalized and combined in different ways were submitted to partial least squares (PLS) calibration. The calibration models produced good predictions of the concentrations of all three analytes, which suggest that the proposed voltammetric tongue improves the performance of a previous design based on linear sweep voltammetric measurements under acidic conditions.     

Electronic tongue based on an array of metallic potentiometric sensors

An electronic tongue system based on the array of six metallic potentiometric sensors (metallic wires) was developed and utilized for discrimination of foodstuffs: several types of vinegar and fruit juices. Copper, tin, iron, aluminum, brass and stainless steel wires were included in the array and supplemented by pH glass electrode. The response of potentiometric metallic sensors towards various organic acids has been studied and possible sensitivity mechanisms were discussed. Overall potential changes of metallic sensors were exanimate as complex mixed signals influenced by several components presenting in analyte employing chemometric approach. The multisensor array of such a type can be useful for several applications since of simplicity in handling, low cost of sensors and easy measure procedure.     

Feasibility study on the use of infrared spectroscopy for the direct authentication of Iberian pig fattening diet

The feasibility of using both middle- and near-infrared spectroscopy for discrimination between subcutaneous fat of Iberian pigs reared on different fattening diets has been evaluated. The sample set was formed by subcutaneous fat of pigs fattened outdoors (extensively) with natural resources (montanera) and pigs fattened on commercial feeds, either with standard feed or with especial formulations with higher content in oleic acid (HO-formulated feed). Linear discriminant analysis was used to classify the samples according to the fattening diet using the scores obtained from principal component analysis of near- and middle-infrared spectra as variables to construct the discriminant functions. The most influential variables were identified using a stepwise procedure. The discriminant potential of each spectral region was investigated. Best results were obtained with the combination of both regions with 91.7% of the standard feed and 100% of montanera and HO-formulated feed samples correctly classified. Chemical explanations are provided based on the correlation of these variables with fatty acid content in the samples.     

不同产地啤酒中无机元素的因子分析与聚类分析

啤酒样品经硝酸消解后,用微波等离子体炬原子发射光谱法(MPT-AES)测定其中无机元素的含量。所得结果用SPSS 17.0科学统计软件进行因子分析和聚类分析。因子分析的结果表明:总方差(52.803%)的贡献来自于代表钴、锶、锰元素的因子1和代表锌、铁、铜元素的因子2,所以这些元素是啤酒中的特征元素。聚类分析的结果表明:9种产地的啤酒可以很好地彼此区别,并以此进行啤酒产地的归属辨别。     

Determination of total polyphenol index in wines employing a voltammetric electronic tongue

This work reports the application of a voltammetric electronic tongue system (ET) made from an array of modified graphite-epoxy composites plus a gold microelectrode in the qualitative and quantitative analysis of polyphenols found in wine. Wine samples were analyzed using cyclic voltammetry without any sample pretreatment. The obtained responses were preprocessed employing discrete wavelet transform (DWT) in order to compress and extract significant features from the voltammetric signals, and the obtained approximation coefficients fed a multivariate calibration method (artificial neural network-ANN-or partial least squares-PLS-) which accomplished the quantification of total polyphenol content. External test subset samples results were compared with the ones obtained with the Folin–Ciocalteu (FC) method and UV absorbance polyphenol index (I₂₈₀) as reference values, with highly significant correlation coefficients of 0.979 and 0.963 in the range from 50 to 2400 mg L⁻¹ gallic acid equivalents, respectively. In a separate experiment, qualitative discrimination of different polyphenols found in wine was also assessed by principal component analysis (PCA).     

Hybrid E‐Tongue for the Evaluation of Sweetness and Bitterness of Soft Drinks Fortified with Epigallocatechin Gallate

A home‐made hybrid electronic tongue was set up, validated and applied to discriminate soft drinks fortified with plant extracts of green tea. The e‐tongue consists of a flow injection system equipped with two electrochemical and one optical sensors. Different formulations of soft drinks composed of glucose and epigallocatechin gallate were then discriminated by principal component analysis. Furthermore, two partial least squares regression models were developed to estimate the “sweetness” (r² of 0.992) and “bitterness” (r² of 0.993) of the model solutions and commercial soft drinks, before and after their fortification with epigallocatechin gallate.