An automated electronic tongue consisting of an array of potentiometric sensors and an artificial neural network (ANN) has been developed to resolve mixtures of anionic surfactants. The sensor array was formed by five different flow-through sensors for anionic surfactants, based on poly(vinyl chloride) membranes having cross-sensitivity features. Feedforward multilayer neural networks were used to predict surfactant concentrations. As a great amount of information is required for the correct modelling of the sensors response, a sequential injection analysis (SIA) system was used to automatically provide it. Dodecylsulfate (DS-), dodecylbenzenesulfonate (DBS-) and α-alkene sulfonate (ALF-) formed the three-analyte study case resolved in this work. Their concentrations varied from 0.2 to 4 mM for ALF- and DBS- and from 0.2 to 5 mM for DS-. Good prediction ability was obtained with correlation coefficients better than 0.933 when the obtained values were compared with those expected for a set of 16 external test samples not used for training. © 2007 Elsevier B.V. All rights reserved.
|Journal||Journal of Pharmaceutical and Biomedical Analysis|
|Publication status||Published - 22 Jan 2008|
- Anionic surfactants
- Artificial neural networks
- Electronic tongue
- Ion-selective electrodes
- Sequential injection analysis