Manuel Gutiérrez

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).

Hybrid electronic tongue based on optical and electrochemical microsensors for quality control of wine.

A multiparametric system able to classify red and white wines according to the grape varieties and for analysing some specific parameters is presented. The system, known as hybrid electronic tongue, consists of an array of electrochemical microsensors and a colorimetric optofluidic system. The array of electrochemical sensors is composed of six ISFETs based sensors, a conductivity sensor, a redox potential sensor and two amperometric electrodes, an Au microelectrode and a microelectrode for sensing electrochemical oxygen demand. The optofluidic system is entirely fabricated in polymer technology and comprises a hollow structure, air mirrors, microlenses and self-alignment structures. The data obtained from these sensors has been treated with multivariate advanced tools; Principal Component Analysis (PCA), for the patterning recognition and classification of wine samples, and Partial-Least Squares (PLS) regression, for quantification of several chemical and optical parameters of interest in wine quality. The results have demonstrated the utility of this system for distinguishing the samples according to the grape variety and year vintage and for quantifying several sample parameters of interest in wine quality control.

Application of an E-Tongue to the Analysis of Monovarietal and Blends of White Wines

This work presents a multiparametric system capable of characterizing and classifying white wines according to the grape variety and geographical origin. Besides, it quantifies specific parameters of interest for quality control in wine. The system, known as a hybrid electronic tongue, consists of an array of electrochemical microsensors—six ISFET based sensors, a conductivity sensor, a redox potential sensor and two amperometric electrodes, a gold microelectrode and a microelectrode for sensing electrochemical oxygen demand—and a miniaturized optofluidic system. The test sample set comprised eighteen Catalan monovarietal white wines from four different grape varieties, two Croatian monovarietal white wines and seven bi- and trivarietal mixtures prepared from the Catalan varieties. Different chemometric tools were used to characterize (i.e., Principal Component Analysis), classify (i.e., Soft Independent Modeling Class Analogy) and quantify (i.e., Partial-Least Squares) some parameters of interest. The results demonstrate the usefulness of the multisensor system for analysis of wine.

Microelectromechanical resonators based on an all polymer/carbon nanotube composite structural material

Carboxylated multi-wall carbon-nanotubes (CNTs) monolayers are integrated on microfabricated all-polymer micro-electromechanical systems (pMEMS) resonator bridges on glass substrates. The structural layer of the MEMS bridges is a multilayer blended conductive polymer based on poly(3,4-ethylenedioxythiophene):poly (styrenesulfonate) (PEDOT:PSS) to which functionalized CNT monolayers are electrostatically attached. The resonance frequency (fres) of electrostatically actuated pMEMS bridges was measured as a function of their length (32–67 lm) for different multilayer compositions. A significant increase in fres and quality factor (Q) with the addition of CNT monolayers to the PEDOT:PSS structural material is observed, demonstrating that CNT monolayers can be used to modulate pMEMS resonator properties.

Procedure 45 An electronic tongue made of coated wire potentiometric sensors for the determination of alkaline ions: Use of artificial neural networks for its response model

Publisher Summary This chapter presents a procedure to prepare an array of potentiometric sensors (i.e., coated-wire type) for potassium and ammonium ions based on valinomycin and nonactin, respectively; and a third sensor with generic response to both ions based on dibenzo. The three electrodes form the sensor array for the electronic tongue. It also discusses the steps for preparation and measurement of a number of calibration standards that contain the ammonium ion, the potassium ion or both ions simultaneously. These two-dimensional combined standards are generated systematically, trying to provide the maximum variability to the system. The analytical information is obtained using the array of three potentiometric poly vinyl chloride membrane sensors, one for the ammonium ion, one for the potassium ion and one for the generic response potassium and ammonium, gathering at the same time crossed information because of interfering effects. The response surface to both ions may be fitted according to Nicolsky–Eisenman for each constructed electrode. Additionally, the construction of the response model is presented based on artificial neural networks, using the shareware program EasyNN-plus.