Manel del Valle

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 (I(280)) 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).

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).

Comparison of methods for the processing of voltammetric electronic tongues data

AbstractWe are making a numerical comparison of various preprocessing strategies for dealing with data from voltammetric electronic tongues in order to reduce the high dimensionality of the response matrices. Different modelling tools are presented and briefly described. We then compare combinations of four preprocessing strategies (principal component analysis, fast Fourier transform, discrete wavelet transform, voltammogram-windowed slicing integral) with four modelling alternatives (principal component regression, partial least squares regression, multi-way partial least squares regression, artificial neural networks) by employing data from a voltammetric bioelectronic tongue, an array formed by enzyme-modified biosensors and applied to the discrimination and quantification of phenolic compounds. FigureWe are making a numerical comparison of various preprocessing strategies for dealing with data from voltammetric electronic tongues in order to reduce the high dimensionality of the response matrices

Beer classification by means of a potentiometric electronic tongue

In this work, an electronic tongue (ET) system based on an array of potentiometric ion-selective electrodes (ISEs) for the discrimination of different commercial beer types is presented. The array was formed by 21 ISEs combining both cationic and anionic sensors with others with generic response. For this purpose beer samples were analyzed with the ET without any pretreatment rather than the smooth agitation of the samples with a magnetic stirrer in order to reduce the foaming of samples, which could interfere into the measurements. Then, the obtained responses were evaluated using two different pattern recognition methods, principal component analysis (PCA), which allowed identifying some initial patterns, and linear discriminant analysis (LDA) in order to achieve the correct recognition of sample varieties (81.9% accuracy). In the case of LDA, a stepwise inclusion method for variable selection based on Mahalanobis distance criteria was used to select the most discriminating variables. In this respect, the results showed that the use of supervised pattern recognition methods such as LDA is a good alternative for the resolution of complex identification situations. In addition, in order to show an ET quantitative application, beer alcohol content was predicted from the array data employing an artificial neural network model (root mean square error for testing subset was 0.131 abv).

BioElectronic Tongue for the quantification of total polyphenol content in wine

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

Simultaneous identification and quantification of nitro-containing explosives by advanced chemometric data treatment of cyclic voltammetry at screen-printed electrodes

The simultaneous determination of three nitro-containing compounds found in the majority of explosive mixtures, namely hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), 2,4,6-trinitrotoluene (TNT) and pentaerythritol tetranitrate (PETN), is demonstrated using both qualitative and quantitative approaches involving the coupling of electrochemical measurements and advanced chemometric data processing. Voltammetric responses were obtained from a single bare screen-printed carbon electrode (SPCE), which exhibited marked mix-responses towards the compounds examined. The responses obtained were then preprocessed employing discrete wavelet transform (DWT) and the resulting coefficients were input to an artificial neural network (ANN) model. Subsequently, meaningful data was extracted from the complex voltammetric readings, achieving either the correct discrimination of the different commercial mixtures (100% of accuracy, sensitivity and specificity) or the individual quantification of each of the compounds under study (total NRMSE of 0.162 for the external test subset).

Signal amplification for thrombin impedimetric aptasensor: Sandwich protocol and use of gold-streptavidin nanoparticles

In this work, we report a highly specific amplification strategy demonstrated for the ultrasensitive biosensing of thrombin with the use of gold-streptavidin nanoparticles (strep-AuNPs) and silver reduction enhancement. The biotinylated aptamer of thrombin was immobilized onto an avidin-graphite epoxy composite (AvGEC) electrode surface by affinity interaction between biotin and avidin; electrochemical impedance measurements were performed in a solution containing the redox marker ferrocyanide/ferricyanide. The change in interfacial charge transfer resistance (Rct) experimented by the redox marker, was recorded to confirm aptamer complex formation with target protein, thrombin (Thr), in a label-free first stage. A biotinylated second thrombin aptamer, with complementary recognition properties was then used in a sandwich approach. The addition of strep-AuNPs and silver enhancement treatment led to a further increment of Rct thus obtaining significant signal amplification. The AptThrBio1-Thr-AptThrBio2 sandwich formation was inspected by confocal microcopy after incubation with streptavidin quantum dots. In order to visualize the presence of gold nanoparticles, the same silver enhancement treatment was applied to electrodes already modified with the nanoparticle-sandwich conjugate, allowing direct observation by scanning electron microscopy (SEM). Results showed high sensitivity and selectivity for thrombin detection, with an improvement from ca. 4.7 pM in a simple assay to 0.3 pM in the amplified reported scheme.

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.

Array of peptide-modified electrodes for the simultaneous determination of Pb(II), Cd(II) and Zn(II)

This paper reports the development of three peptide modified sensors in which glutathione (GSH) and its fragments Cys-Gly and γ-Glu-Cys were immobilized respectively through aryl diazonium electrochemical grafting onto the surface of graphite-epoxy composite electrodes (GEC), and used for the simultaneous determination of Cd(II), Pb(II) and Zn(II). The concentration interval ranged from 0.1 to 1.5 μmol L(-1) for each metal, and the technique used was differential pulse adsorptive stripping voltammetry. This study aimed to the comparison of the information provided by one single modified electrode at both fixed and multiple pH values (pH 6.8, 7.5 and 8.2) for the simultaneous determination of the three metals, with those supplied by the three-sensor array at multiple pH values. For the processing of the voltammograms, the fast Fourier transform was selected as the preprocessing tool for data compression coupled with an artificial neural network for the modeling of the obtained responses.

Label free aptasensor for Lysozyme detection: A comparison of the analytical performance of two aptamers

This work presents a comparison of two different aptamers (Apts) (COX and TRAN) for the detection of a ubiquitous protein Lysozyme (Lys) using Apt-based biosensors. The detection is based on the specific recognition by the Apt immobilized on screen printed carbon electrodes (SPCEs) via diazonium coupling reaction. The quantitative detection of Lys protein was achieved by electrochemical impedance spectroscopy (EIS). A very good linearity and detection limits for the quantitation of Lys were obtained from 0.1 to 0.8 μM and 100 nM using Apt COX and from 0.025 to 0.8 μM and 25 nM using Apt TRAN respectively. The obtained results showed that the developed aptasensors exhibit good specificity, stability and reproducibility for Lys detection. For real application, the aptasensors were tested in wine samples and good recovery rates were recorded in the range from 94.2 to 102% for Lys detection. The obtained recovery rates confirm the reliability and suitability of the developed method in wine matrix. The developed method could be a useful and promising platform for detection of Lys in different applications.