A. Gutés

Sequential injection system with higher dimensional electrochemical sensor signals: Part 1. Voltammetric e-tongue for the determination of oxidizable compounds

A sequential injection analysis (SIA) system was developed with the aim of obtaining an automatic and versatile way to prepare standards needed in the study of systems with higher dimensional sensor signals. To illustrate this, different analytical techniques were used in determinations of several analytes. Automated potentiometric calibrations of different potentiometric sensors, with and without interference, were carried out. Useful determinations of selectivity coefficients with two degrees of freedom were obtained. Simultaneous voltammetric determinations have also been done. Firstly, simultaneous determinations of lead and cadmium, using epoxy-graphite composite as the working electrode, have enabled a separate calibration for each metal to be obtained. Next, a voltammetric electronic tongue was designed and applied to the determination of oxidizable species. The use of artificial neural networks has solved the overlapped signal of ascorbic acid, 4-aminophenol and 4-acetamidophenol (paracetamol). A set of 63 data points was prepared automatically and has facilitated the training of an electronic tongue for these three analytes. Accurate predictions of test solutions, in the range of 12-410muM for ascorbic acid, 17-530muM for 4-aminophenol and 10-420muM for paracetamol, have been achieved with RMSEs lower than 0.10muM.

Sequential injection system with higher dimensional electrochemical sensor signals Part 2. Potentiometric e-tongue for the determination of alkaline ions.

An intelligent, automatic system based on an array of non-specific-response chemical sensors was developed. As a great amount of information is required for its correct modelling, we propose a system generating it itself. The sequential injection analysis (SIA) technique was chosen as it enables the processes of training, calibration, validation and operation to be automated simply. Detection was carried out using an array of potentiometric sensors based on PVC membranes of different selectivity. The diluted standard solutions needed for system learning and response modelling are automatically prepared from more concentrated standards. The electrodes used were characterised with respect to one and two analytes, by means of high-dimensionality calibrations, and the response surface of each was represented; this characterisation enabled an interference study of great practical utility. The combined response was modelled by means of artificial neural networks (ANNs), and thus it was possible to obtain an automated electronic tongue based on SIA. In order to identify the ANN which provided the best model of the electrode responses, some of the networks parameters were optimised and its usefulness in determining NH(4)(+), K(+) and Na(+) ions in synthetic samples was then tested. Finally, it was used to determine these ions in commercial fertilisers, the obtained results being compared with reference methods.

Wavelet neural networks to resolve the overlapping signal in the voltammetric determination of phenolic compounds

Three phenolic compounds, i.e. phenol, catechol and 4-acetamidophenol, were simultaneously determined by voltammetric detection of its oxidation reaction at the surface of an epoxy-graphite transducer. Because of strong signal overlapping, Wavelet Neural Networks (WNN) were used in data treatment, in a combination of chemometrics and electrochemical sensors, already known as the electronic tongue concept. To facilitate calibration, a set of samples (concentration of each phenol ranging from 0.25 to 2.5mM) was prepared automatically by employing a Sequential Injection System. Phenolic compounds could be resolved with good prediction ability, showing correlation coefficients greater than 0.929 when the obtained values were compared with those expected for a set of samples not employed for training.