Application of neural network calibrations to an halide ISE array

Baret, Monique

doi:10.1016/s0039-9140(99)00334-3

Cited by 33 publications

(18 citation statements)

References 17 publications

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“…Ion‐selective electrodes (ISEs) are normally based on ionophores designed for complexing, interacting, and holding the analyte in a molecular cavity . These interactions are nonspecific so that interference from other undesired analytes of a similar size and charge commonly appear . In the last decade, approaches using an ISE array with artificial neural networks (ANNs) to solve the problem of cross‐interference have been widely studied .…”

Section: Introductionmentioning

confidence: 99%

Novel recalibration methodologies for ion‐selective electrode arrays in the multi‐ion interference scenario

Wang

Cheng

Lamb

et al. 2016

Journal of Chemometrics

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Because of the deterioration of sensitivity and selectivity of ion‐selective electrode (ISE) arrays after a long period of storage or measurement, the recalibration of ISE arrays is a major challenge, which significantly affects the practical application of ISE arrays. Although approaches using artificial neural networks (ANN) to solve the problem have been widely studied, it is impractical for end‐users to frequently remeasure dozens of samples to rebuild an ANN model for measuring of ISE array. In this study, a system of equations was developed to simulate the ISE array response for the scenario of multiple interfering ions. By fitting the coefficients for the system of equations using optimization algorithms, such as genetic algorithms (GA), the ISE array can be recalibrated with only a couple of selected samples. As a case study, a solid‐state ISE array for the detection of 4 exchangeable cations was applied. The deterioration of sensitivity and selectivity of the ISE array over time was investigated. The simplified methodology for calibrating this ISE array was applied and demonstrated in this study.

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Section: Introductionmentioning

confidence: 99%

Novel recalibration methodologies for ion‐selective electrode arrays in the multi‐ion interference scenario

Wang

Cheng

Lamb

et al. 2016

Journal of Chemometrics

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“…Principal Component Analysis (PCA) is also of current use to visualize the data structure obtained from sensor arrays [11]. A superior performance of ANNs with respect to PLS has been previously noticed [15] for the determination of Cl À /Br À mixtures using a potentiometric sensor array formed by four polycrystalline sensors.…”

Section: Introductionmentioning

confidence: 99%

Automated SIA System Using an Array of Potentiometric Sensors for Determining Alkaline‐Earth Ions in Water

et al. 2007

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This work deals with the simultaneous determination of Mg 2þ , Ca 2þ, and Ba 2þ in water using a PVC-membrane potentiometric sensor array and multivariate calibration, in what is known as electronic tongue. The subsequent processing of the data was based on the use of a multilayer artificial neural network (ANN). The information needed for training or generation of the model was obtained with the aid of an automated analytical system based on the Sequential Injection Analysis (SIA) technique. The modeling ability was verified with an external set of standards, and next the determinations were performed in real samples of mineral waters, where close results for Mg 2þ and Ca 2þ were obtained to those obtained with reference methods. The determination of Ba 2þ can be considered as semiquantitative for synthetic samples -due to the absence of Ba 2þ in mineral waters, its concentration in real samples was not measured.

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“…The superior performance of ANNs than that of PLS has been previously claimed by various other authors (Baret 2000;Gutiérrez 2007).…”

Section: Validation Of the Proposed Methodologymentioning

confidence: 69%

Multicomponent Titration of Calcium + Magnesium Mixtures Employing a Potentiometric Electronic‐Tongue

2007

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A fast novel potentiometric titration procedure is proposed, in which the detection system is formed by a potentiometric electronic tongue. The titration consists in a reduced number of fixed titrant additions to the sample and the recording of the potentials of an array of Ion Selective Electrodes. The obtained data matrix is entered to an Artificial Neural Network response model, previously trained to furnish concentrations of a multicomponent mixture. The principle is demonstrated with automated EDTA titration of mixtures of Ca 2þ and Mg 2þ at fixed pH 8.5. In this case, five 2 ml fixed volume additions plus the readings of four sensors were adequate for the resolution of the mixture. In the conditions used, titration was feasible up to 3.3 mM total ion concentration, the precision was estimated as 3.20% RSD for Ca 2þ and 2.76% RSD for Mg 2þ (n ¼ 5), and the detection limits 0.16 mM Ca 2þ and 0.26 mM Mg 2þ . The procedure was applied to mineral waters and compared with reference methods (correlation ¼ 0.92 for Ca 2þ and correlation ¼ 0.89 for Mg 2þ , n ¼ 14).

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Application of neural network calibrations to an halide ISE array

Cited by 33 publications

References 17 publications

Novel recalibration methodologies for ion‐selective electrode arrays in the multi‐ion interference scenario

Novel recalibration methodologies for ion‐selective electrode arrays in the multi‐ion interference scenario

Automated SIA System Using an Array of Potentiometric Sensors for Determining Alkaline‐Earth Ions in Water

Multicomponent Titration of Calcium + Magnesium Mixtures Employing a Potentiometric Electronic‐Tongue

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