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With proper training procedures, including the use of a sufficiently large training sample set, ISE array with Back-Propagation Neural Networks (BPNNs) can be used to simultaneously determine the concentrations for multiple ions in environmental water. However, it is not practical for end-users to re-measure the whole training sample set – dozens of samples – to re-build the BPNN model every time after a long storage or measurement period, since both the sensitivity and selectivity of ISEs would be gradually degraded over time. In this study, instead of using BPNNs, we developed and patented some mathematical models to describe the based characteristics of the ISE array's response, including the sensitivity and selectivity. The developed mathematical equations can be used to simulate the response values of ISE array, and to determine the analytes' concentrations from unknown samples. By measuring only a couple of standard solutions, which are practical to the end users, the ISE array can be automatically recalibrated using genetic algorithm (GA). This research is based on an array of Prussian blue modified Carbon Paste Electrodes (PB-CPEs), and the interferences between nitrate (NO 3 ) and chloride (Cl) was used as a case study. [ABSTRACT FROM AUTHOR]

A method for determining ammonium ion concentration from complex aqueous samples is presented in this work. It does not need to eliminate chemical interferences, mainly sodium and potassium, because an array of potentiometric sensors with intrinsic responses is used. The measurements taken from the array are processed with a multicomponent data treatment. This approach is already known as electronic tongue. Multivariable calibration was implemented with an artificial neural network, trained under the rules of the Bayesian regularization. The developed system has been applied to water samples from rivers and wastewaters with ammonium content in the range 1 × 10−4–5 × 10−2 mol L−1. Results are similar to those obtained with other reference methods. [ABSTRACT FROM AUTHOR]