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 alpha-alkene sulfonate (ALF(-)) formed the three-analyte study case resolved in this work. Their concentrations varied from 0.2 to 4mM for ALF(-) and DBS(-) and from 0.2 to 5mM 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.
journal_name
J Pharm Biomed Analjournal_title
Journal of pharmaceutical and biomedical analysisauthors
Cortina M,Ecker C,Calvo D,del Valle Mdoi
10.1016/j.jpba.2007.09.013subject
Has Abstractpub_date
2008-01-22 00:00:00pages
213-8issue
2eissn
0731-7085issn
1873-264Xpii
S0731-7085(07)00524-9journal_volume
46pub_type
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