Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
