Electronic tongue metal sensors

The paper describes the different chemical sensors and mathematical methods applied and presents the review of electronic tongue application for quantitative analysis (heavy metals and other impurities in river water, uranium in former mines, metal impurities in exhaust gases, ets) and for classification and taste determination of some beverages (coffee, bear, juice, wines), vegetable oil, milk, etc. [1]. 

An integrated electronic tongue consisting of a multiple light-addressable potentiometric sensor (MLAPS) and two sets of electrochemical electrodes was developed to simultaneously detect Fe (111) and Cr (VI) ions and other heavy metals, respectively [64], The MLAPS was based on chalcogenide thin film while the electrochemical electrodes used stripping voltammetry. It has been suggested by the authors that these methods are suitable for the detection of heavy metals in wastewater or seawater. 

Similarly, electronic noses were developed in order to take a picture of the complex gaseous mixtures, typically on the basis to their effect on the electrical conductivity of semiconducting metal oxides. A similar approach is followed in the so-called electronic tongues, which are stUl much less established than noses. In particular, potentiometric and amperometric sensors for blind analysis of liquid samples have been proposed. The more easily controlled environment consisting of a solution could constitute a favorable element with respect to gaseous mixtures, even tested in natural environment, with so many uncontrolled variables. 

Electronic tongue systems for remote environmental monitoring applications have been presented in several applications. A new approach in the chemical sensor field consists in the use of an array of nonspecific sensors coupled with a multivariate calibration tool which may form a node of a sensor network. The proposed arrays were made up of potentiometric sensors based on polymeric membranes, and the subsequent cross-response processing was based on a multilayer artificial neural network model as proposed by Mimendia et al. who described environmental monitoring of ammonium as a pollutant plus alkaline ions at different measuring sites in the states of Mexico and Hidalgo (Mexico), and monitoring of heavy metals (Cu ", Pb ", Zn ", and Cd " ) in open-air waste streams and rivers. 

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