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. © 2009 Elsevier B.V. All rights reserved.
|Journal||Sensors and Actuators B: Chemical|
|Publication status||Published - 29 Apr 2010|
- Artificial Neural Network
- Automated electronic tongue
- Heavy metals
- Potentiometric sensors
- Sequential Injection Analysis