Voltammetric Electronic Tongue for the Simultaneous Determination of Three Benzodiazepines.

The presented manuscript reports the simultaneous detection of a ternary mixture of the benzodiazepines diazepam, lorazepam, and flunitrazepam using an array of voltammetric sensors and the electronic tongue principle. The electrodes used in the array were selected from a set of differently modified graphite epoxy composite electrodes; specifically, six electrodes were used incorporating metallic nanoparticles of Cu and Pt, oxide nanoparticles of CuO and WO3, plus pristine electrodes of epoxy-graphite and metallic Pt disk. Cyclic voltammetry was the technique used to obtain the voltammetric responses. Multivariate examination using Principal Component Analysis (PCA) justified the choice of sensors in order to get the proper discrimination of the benzodiazepines. Next, a quantitative model to predict the concentrations of mixtures of the three benzodiazepines was built employing the set of voltammograms, and was first processed with the Discrete Wavelet Transform, which fed an artificial neural network response model. The developed model successfully predicted the concentration of the three compounds with a normalized root mean square error (NRMSE) of 0.034 and 0.106 for the training and test subsets, respectively, and coefficient of correlation R ≥ 0.938 in the predicted vs. expected concentrations comparison graph.

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.

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).

Electronic tongue for nitro and peroxide explosive sensing.

This work reports the application of a voltammetric electronic tongue (ET) towards the simultaneous determination of both nitro-containing and peroxide-based explosive compounds, two families that represent the vast majority of compounds employed either in commercial mixtures or in improvised explosive devices. The multielectrode array was formed by graphite, gold and platinum electrodes, which exhibited marked mix-responses towards the compounds examined; namely, 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX), pentaerythritol tetranitrate (PETN), 2,4,6-trinitrotoluene (TNT), N-methyl-N,2,4,6-tetranitroaniline (Tetryl) and triacetone triperoxide (TATP). Departure information was the set of voltammograms, which were first analyzed by means of principal component analysis (PCA) allowing the discrimination of the different individual compounds, while artificial neural networks (ANNs) were used for the resolution and individual quantification of some of their mixtures (total normalized root mean square error for the external test set of 0.108 and correlation of the obtained vs. expected concentrations comparison graphs r>0.929).

Independent comparison study of six different electronic tongues applied for pharmaceutical analysis.

Electronic tongue technology based on arrays of cross-sensitive chemical sensors and chemometric data processing has attracted a lot of researchers' attention through the last years. Several so far reported applications dealing with pharmaceutical related tasks employed different e-tongue systems to address different objectives. In this situation, it is hard to judge on the benefits and drawbacks of particular e-tongue implementations for R&D in pharmaceutics. The objective of this study was to compare the performance of six different e-tongues applied to the same set of pharmaceutical samples. For this purpose, two commercially available systems (from Insent and AlphaMOS) and four laboratory prototype systems (two potentiometric systems from Warsaw operating in flow and static modes, one potentiometric system from St. Petersburg, one voltammetric system from Barcelona) were employed. The sample set addressed in the study comprised nine different formulations based on caffeine citrate, lactose monohydrate, maltodextrine, saccharin sodium and citric acid in various combinations. To provide for the fair and unbiased comparison, samples were evaluated under blind conditions and data processing from all the systems was performed in a uniform way. Different mathematical methods were applied to judge on similarity of the e-tongues response from the samples. These were principal component analysis (PCA), RV' matrix correlation coefficients and Tuckers congruency coefficients.

Crown ether-modified electrodes for the simultaneous stripping voltammetric determination of Cd(II), Pb(II) and Cu(II).

This work describes the immobilization of 4-carboxybenzo-18-crown-6 (CB-18-crown-6) and 4-carboxybenzo-15-crown-5 (CB-15-crown-5) assisted by lysine on aryl diazonium salt monolayers anchored to the surface of graphite-epoxy composite electrodes (GEC), and their use for the simultaneous determination of Cd(II), Pb(II) and Cu(II) by differential pulse anodic stripping voltammetry (DPASV). These modified electrodes display a good repeatability and reproducibility with detection and quantification limits at levels of µg L(-1) (ppb), confirming their suitability for the determination of Cd(II), Pb(II) and Cu(II) ions in environmental samples. The overlapped nature of the multimetal stripping measurements was resolved by employing the two-sensor array CB-15-crown-5-GEC and CB-18-crown-6-GEC, since the metal complex selectivity exhibited by the considered ligands could add some discrimination power. For the processing of the voltammograms, Discrete Wavelet Transform and Causal Index were selected as preprocessing tools for data compression coupled with an artificial neural network for the modeling of the obtained responses, allowing the resolution of mixtures of these metals with good prediction of their concentrations (correlation with expected values for an external test subset better than 0.942).