Xavier Cetó

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).

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.

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.

Application of an electronic tongue towards the analysis of brandies

This work reports the application of a voltammetric Electronic Tongue (ET) in the analysis of brandies, specifically in their classification according to the scores given by a skilled sensory panel and in the discrimination of different ageing methods. For this purpose, spirits were analyzed with no other pretreatment than their dilution with a saline solution to ensure enough conductivity. Recorded voltammetric signals produced by an array of six modified epoxy-composite sensors were preprocessed employing Fast Fourier Transform in order to reduce the complexity of the input signals while preserving the relevant information. Then, using the obtained coefficients, responses were evaluated using Linear Discriminant Analysis (LDA) as the pattern recognition model used to carry out the classification tasks. In both cases, good prediction ability was attained by the ET (classification rates of 100% and 97%, respectively), therefore permitting the correct classification of the different samples under study. Furthermore, two Artificial Neural Network models were also trained for the semi-quantitative identification of some undesired compound markers of some brandy defects above certain levels (namely butan-2-ol, ethyl acetate, acetaldehyde and butan-1-ol; r > 0.975) and the quantification of polyphenol index I280 (r = 0.977).

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).

Synergistic influence of collagen I and BMP 2 drives osteogenic differentiation of mesenchymal stem cells: a cell microarray analysis

Cell microarrays are a novel platform for the high throughput discovery of new biomaterials. By re-creating a multitude of cell microenvironments on a single slide, this approach can identify the optimal surface composition to drive a desired cell response. To systematically study the effects of molecular microenvironments on stem cell fate, we designed a cell microarray based on parallel exposure of mesenchymal stem cells (MSCs) to surface-immobilised collagen I (Coll I) and bone morphogenetic protein 2 (BMP 2). This was achieved by means of a reactive coating on a slide surface, enabling the covalent anchoring of Coll I and BMP 2 as microscale spots printed by a robotic contact printer. The surface between the printed protein spots was passivated using poly (ethylene glycol) bisamine 10,000Da (A-PEG). MSCs were then captured and cultured on array spots composed of binary mixtures of Coll I and BMP 2, followed by automated image acquisition and quantitative, multi-parameter analysis of cellular responses. Surface compositions that gave the highest osteogenic differentiation were determined using Runx2 expression and calcium deposition. Quantitative single cell analysis revealed subtle concentration-dependent effects of surface-immobilised proteins on the extent of osteogenic differentiation obscured using conventional analysis. In particular, the synergistic interaction of Coll I and BMP 2 in supporting osteogenic differentiation was confirmed. Our studies demonstrate the value of cell microarray platforms to decipher the combinatorial interactions at play in stem cell niche microenvironments.

Antibacterial properties of silver dendrite decorated silicon nanowires

In this work, we report on the antibacterial properties of silicon nanowires (SiNWs) generated by via metal-assisted chemical etching (MACE) against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) bacteria strains. The results demonstrate that the antibacterial action can be attributed to the layer of silver (Ag) dendrites found on the surface of the SiNWs as a natural by-product of the MACE reaction, thus eliminating the need for a second surface modification step with an antibacterial agent. Furthermore, a 100 fold increase in bacterial adherence to SiNWs by virtue of their unique morphology is also demonstrated compared to flat silicon. We observed negligible toxicity exhibited by the SiNWs towards mammalian cells, in addition to very low rates of attachment of the mammalian cells to the SiNWs. This combination of characteristics makes these nanowire substrates an interesting alternative to other biomaterials for use in medical implants and wound dressings to combat bacterial infections.

A new amperometric bienzymatic biosensor based on biocomposites for the determination of gluconic acid in wines

A new amperometric bienzymatic biosensor for gluconic acid based on the coimmobilization of gluconokinase (EC 2.7.1.12) and phosphogluconate dehydrogenase (EC 1.1.1.44) by polysulfone membrane entrapment onto the surface of a graphite-epoxy composite is reported. This biosensor represents an alternative to gluconate dehydrogenase (EC 1.1.99.3) based methods, which is no longer commercially available. Measurements were done at an applied potential of +0.800 V, room temperature and phosphate buffer pH 7.50; obtaining a linear response range for gluconic acid extended from 7.0 × 10(-6) to 2.5 × 10(-4)M. Constructed biosensors showed good reproducibility for calibrations using different electrodes (RSD of 1.74%). Finally, biosensor was applied to real wine samples, and the results obtained were validated by comparison with those provided by a reference laboratory. Good correlation was found when the biosensor results were plotted vs. the reference values (slope=1.03 ± 0.04, intercept=0.01 ± 0.02, r(2)=0.995).