Processing of signals from an ion-elective electrode array by a neural network

Neural network software is described for processing the signals of arrays of ion-selective electrodes. The performance of the software was tested in the simultaneous determination of calcium and copper(II) ions in binary mixtures of copper(II) nitrate and calcium chloride and the simultaneous determination of potassium, calcium, nitrate and chloride in mixtures of potassium and calcium chlorides and ammonium nitrate. The measurements for the Ca2+/Cu2+ determinations were done with a pH-glass electrode and calcium and copper ion-selective electrodes; results were accurate to ±8%. For the K+/Ca2+NO−3/Cl− determinations, the measurements were made with the relevant ion-selective electrodes and a glass electrode; the mean relative error was ±6%, and for the worst cases the error did not exceed 20%

The development of a fully computerized system for sampled d.c. polarography with standard interfacing

A complete system, based on the online PDP-11 computer (Digital Equipment Corporation) was developed for computerized sampled d.c. polarography with direct digital control. The system includes compensation of ohmic cell resistance and processing of the polarographic data. The accuracy of the system in the determination of the various polarographic parameters is: diffusion current ± 2 %, half-wave potential ± 2 mV, and slope of the log plot ± 2 mV

Kalman filtering for the evaluation of the current-time function in d.c. polarography

Kalman filtering was applied to the current vs. time data obtained at the growing mercury drop of a DME under d.c. polarographic conditions, to separate the faradaic and capacitive components of the electrode current. Polarograms consisting of the pure faradaic current vs. applied d.c. potential were subjected to a four-parameter curve-fitting procedure to obtain the polarographic characteristics, viz. half-wave potential, limiting current and slope of the log plot together with the baseline current. The method was tested with cadmium and zinc in the 10−6–10−5 M range. The standard deviations of the half-wave potentials and the limiting current/concentration ratios were found to be 1.0 mV and 0.04 respectively

Computerized kalousek polarography

A versatile online computer-based system for Kalousek polarography features simultaneous recording of oxidation and reduction currents, averaging over successive scans, and processing of the polarographic data by curve-fitting. The accuracy for determinations of cadmium, potassium and lithium down to 10-5 M is ± 5% for pulse rates up to 25 Hz

The computerized determination of double-layer capacitance with the use of kalousek-type waveforms and its application in titrimetry

A method for the rapid determination of double-layer capacitance—potential curves of electrodes is described. An on-line computer is used to apply Kalousek-type waveforms to the electrochemical cell and to measure the accompanying current response. The capacitances are determined from the slope of the plots of log current against time. For 0.1 M KCl, the computerized method agrees well with the bridge method, except for the potential range of 0 to –0.15 V. The method is very useful for automating titrations with tensammetric detection of the end-point. The method is applied to the titration of barium with a macrocydic compound (kryptofix 222) and the titration of cetyl-trimethyl-ammonium bromide with bromocresol purple. The accuracy of the titrations is ±2%

A multivariate calibration procedure for the tensammetric determination of detergents

A multivariate calibration procedure based on singular value decomposition (SVD) and the Ho-Kashyap algorithm is used for the tensammetric determination of the cationic detergents Hyamine 1622, benzalkonium chloride (BACl), N-cetyl-N,N,N-trimethylammonium bromide (CTABr) and mixtures of CTABr and BACl. The sensitivity and accuracy depend strongly on the nature of the detergent. Acceptable accuracy is obtained with a two-step calculation procedure in which calibration constants for the total concentration range of interest are used to guide the choice of a more specific set of calibration constants which are valid for a much smaller concentration span. For Hyamine 1622, concentrations in the range 5 × 10−6−2 × 10−4 M could be determined with an accuracy of ± 10−6 M. For CTABr, these numbers were 3 × 10−6−2 × 10−4 M and ± 5 × 10−7 M; for BACl, they were 2 × 10−3−9 × 10−2 g l−1 and ± 1 × 10−3 g l−1. In the mixtures of CTABr and BACl, the accuracies were ± 3 × 10−6 M and × 1 × 10−3 g l−1, respectively

Multivariate data analysis for x-ray fluorescence spectrometry

A multivariate data analysis procedure that uses singular value decomposition and the Ho-Kashyap algorithm is proposed to obtain calibration constants for x-ray fluorescence spectrometry. These calibration constants can be used to obtain results from experimental data by means of a simple dot product calculation. The method was tested on experimental data from the literature. Comparison of results showed that the method performs at least as well or better than the Rasberry-Heinrich method or its modifications. The method can be used to express calibration results obtained with a theoretically based program in such a way that they can be used conveniently in routine applications

Artificial neural networks as a multivariate calibration tool: modelling the Fe-Cr-Ni system in X-ray fluorescence spectroscopy

The performance of artificial neural networks (ANNs) for modeling the Cr---Ni---Fe system in quantitative x-ray fluorescence spectroscopy was compared with the classical Rasberry-Heinrich model and a previously published method applying the linear learning machine in combination with singular value decomposition. Apart from determining if ANNs were capable of modeling the desired non-linear relationships, also the effects of using non-ideal and noisy data were studied. For this goal, more than a hundred steel samples with large variations in composition were measured at their primary and secondary K¿ and Kß lines. The optimal calibration parameters for the Rasberry-Heinrich model were found from this dataset by use of a genetic algorithm. ANNs were found to be robust and to perform generally better than the other two methods in calibrating over large ranges