Hybrid Electronic Tongue based on Multisensor Data Fusion for Discrimination of Beers

This paper reports the use of a hybrid Electronic Tongue based on data fusion of two different sensor families, applied in the recognition of beer types. Six modifiedgraphite- epoxy voltammetric sensors plus 15 potentiometric sensors formed the sensor array. The different samples were analyzed using cyclic voltammetry and direct potentiometry without any sample pretreatment in both cases. The sensor array coupled with feature extraction and pattern recognition methods, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), was trained to classify the data clusters related to different beer varieties. PCA was used to visualize the different categories of taste profiles and LDA with leave-one-out cross-validation approach permitted the qualitative classification. The aim of this work is to improve performance of existing electronic tongue systems by exploiting the new approach of data fusion of different sensor types

Use of Sequential Injection Analysis to construct a potentiometric electronic tongue: Application to the multidetermination of heavy metals

7 páginas, 5 figuras, 6 tablas.-- Trabajo presentado a: "The 13th International Symposium on Olfaction and Electronic Nose — ISOEN 2009".An automated potentiometric electronic tongue (ET) was developed for the quantitative determination of Cd2+, Cu2+ and Pb2+ heavy metal mixtures. The Sequential Injection Analysis (SIA) technique was used in order to automate the obtaining of input data. The combined response was modelled by means of Artificial Neural Networks (ANNs). The sensor array was formed by four Ion Selective Electrode (ISE) sensors: two based on chalcogenide glasses, Cd sensor and Cu sensor, and the rest on poly(vinyl chloride) membranes, Pb sensor and Zn sensor. The sensors were first characterized with respect to one and two analytes, by means of high-dimensionality calibrations, aided by the use of the SIA flow system; this characterization enabled an interference study of great practical utility. To take profit of the dynamic nature of the sensors response, each kinetic profile was compacted by Fast Fourier Transform (FFT) and the extracted coefficients used as inputs for the ANN in the multidetermination application. Finally, analyses were performed employing synthetic samples to validate obtained results.This work was supported by European Community project FP6- IST No. 034472, “WARMER: Water risk management in Europe” and by Spanish Ministry of Science and Innovation, project TEC2007- 68012-C03-02/MIC.Peer reviewe

Discrimination of soils and assessment of soil fertility using information from an ion selective electrodes array and artificial neural networks

Multichannel sensor measurements combined with advanced treatment is the departure point for a new concept in sensorics, the electronic tongue. Our setup worked with an array of 20 ion selective electrodes plus an artificial neural network used as a pattern recognition method applied to soil analysis. With this design, we got a versatile tool which was able to perform qualitative and quantitative determinations. As first application, the qualitative discrimination between six distinct soil types based on their extractable components was attempted. The procedure was simplified to a single extraction step before measurements. Water, a BaCl₂ saline solution and an acetic acid extract were evaluated as extracting agents. The best performance was reached with the acetic acid extraction method with a correct classification rate and sensitivity both of 94%, and a specificity of 100%. In addition, a quantitative determination of several physicochemical properties of agricultural interest, such as organic carbon content and selected cations (like K⁺ or Mg2⁺) and anions (like NO₃¯ or Cl¯) was also demonstrated, showing satisfactory agreement with the reference methods

Hybrid Electronic Tongue based on Multisensor Data Fusion for Discrimination of Beers

This paper reports the use of a hybrid Electronic Tongue based on data fusion of two different sensor families, applied in the recognition of beer types. Six modifiedgraphite- epoxy voltammetric sensors plus 15 potentiometric sensors formed the sensor array. The different samples were analyzed using cyclic voltammetry and direct potentiometry without any sample pretreatment in both cases. The sensor array coupled with feature extraction and pattern recognition methods, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), was trained to classify the data clusters related to different beer varieties. PCA was used to visualize the different categories of taste profiles and LDA with leave-one-out cross-validation approach permitted the qualitative classification. The aim of this work is to improve performance of existing electronic tongue systems by exploiting the new approach of data fusion of different sensor types