Volume 24, Issue 2
Full Paper

Bayesian Methods for Ion Selective Electrodes

Peter W. Dillingham

George Perkins Marsh Institute, Clark University, 950 Main Street, Worcester, Massachusetts 01610, USA

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Tanja Radu

CLARITY Centre for Sensor Web Technologies, National Centre for Sensor Research, Dublin City University, Dublin, Ireland tel.:+44(0)2392‐84‐213

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Dermot Diamond

CLARITY Centre for Sensor Web Technologies, National Centre for Sensor Research, Dublin City University, Dublin, Ireland tel.:+44(0)2392‐84‐213

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Aleksandar Radu

Corresponding Author

E-mail address: a.radu@chem.keele.ac.uk

CLARITY Centre for Sensor Web Technologies, National Centre for Sensor Research, Dublin City University, Dublin, Ireland tel.:+44(0)2392‐84‐213

School of Physical and Geographical Sciences, Keele University, Keele, ST5 5BG, UK

Aleksandar Radu, CLARITY Centre for Sensor Web Technologies, National Centre for Sensor Research, Dublin City University, Dublin, Ireland tel.:+44(0)2392‐84‐213

Christina M. McGraw, Gustaf H. Carlson School of Chemistry, 950 Main Street, Clark University, Worcester, MA, USA tel: +1‐508‐421‐3897

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Christina M. McGraw

Corresponding Author

E-mail address: cmcgraw@clarku.edu

George Perkins Marsh Institute, Clark University, 950 Main Street, Worcester, Massachusetts 01610, USA

Aleksandar Radu, CLARITY Centre for Sensor Web Technologies, National Centre for Sensor Research, Dublin City University, Dublin, Ireland tel.:+44(0)2392‐84‐213

Christina M. McGraw, Gustaf H. Carlson School of Chemistry, 950 Main Street, Clark University, Worcester, MA, USA tel: +1‐508‐421‐3897

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First published: 12 January 2012
Citations: 9

Abstract

With the increasing use of ion‐selective electrodes in environmental and health applications, it is important to know the precision of estimated concentrations. A Bayesian model for non‐linear calibration is introduced which provides estimates of measurement precision by incorporating uncertainty in calibration parameters and inherent random noise in emf response. The analysis of lead in 17 soil samples demonstrates that large gains in precision are possible when calibrations are extended to include multiple electrodes and standard addition data. The results highlight the need for improved calibration and routine use of standard addition as ion selective electrodes become increasingly popular for demanding, real world applications.

Number of times cited according to CrossRef: 9

  • Multivariate limit of detection for non-linear sensor arrays, Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2020.104016, (104016), (2020).
  • Semi-Automated Data Analysis for Ion-Selective Electrodes and Arrays Using the R Package ISEtools, Sensors, 10.3390/s19204544, 19, 20, (4544), (2019).
  • Establishing meaningful limits of detection for ion-selective electrodes and other non-linear sensors, ACS Sensors, 10.1021/acssensors.9b02133, (2019).
  • Simultaneous Detection of Ammonium and Nitrate in Environmental Samples Using on Ion-Selective Electrode and Comparison with Portable Colorimetric Assays, Sensors, 10.3390/s18103555, 18, 10, (3555), (2018).
  • undefined, 2017 IEEE SENSORS, 10.1109/ICSENS.2017.8233898, (1-3), (2017).
  • Statistical generation of training sets for measuring NO 3 − , NH 4 + and major ions in natural waters using an ion selective electrode array , Environmental Science: Processes & Impacts, 10.1039/C6EM00043F, 18, 5, (590-599), (2016).
  • Hand-Held Transistor Based Electrical and Multiplexed Chemical Sensing System, ACS Sensors, 10.1021/acssensors.6b00520, 1, 12, (1423-1431), (2016).
  • Robust and Ultrasensitive Polymer Membrane-Based Carbonate-Selective Electrodes, Analytical Chemistry, 10.1021/acs.analchem.5b01756, 87, 15, (7515-7518), (2015).
  • Calibration of potentiometric sensor arrays with a reduced number of standards, Talanta, 10.1016/j.talanta.2012.06.030, 98, (28-33), (2012).

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