Characterising uncertainty in instrumental limits of detection when sensor response is non-linear

Dillingham, Peter W.; Alsaedi, Basim S. O.; McGraw, Christina M.

doi:10.1109/icsens.2017.8233898

Cited by 4 publications

(14 citation statements)

References 8 publications

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“…From this, we see that ISE #1 had close to the ideal Nernstian slope, estimated as b = 29.5 mV/decade (95% CI 25.6–34.3 mV/decade), with an LOD near 10 -6 . This information can be used when developing new ISEs and when testing whether they are fit for purpose [13]. For example, lead activity for some of the experimental soil samples (Figure 4) was near this LOD, indicating that, by itself, ISE #1 would struggle to distinguish the lower activities at this site from a blank.…”

Section: Resultsmentioning

confidence: 99%

“…ISEtools implements Bayesian methods described by [3,13], and operates within R [1] or the related RStudio [17], interfacing with an additional programme to run the Bayesian analyses, OpenBUGS [18] or jags [19], both based on the BUGS [20] language. Users with basic familiarity of R or other scripting languages will have an advantage getting started, but will not need familiarity with the Bayesian programmes.…”

Section: Methodsmentioning

confidence: 99%

“…If all data are in the Nernstian region, these both simplify to linear regression results. However, in other contexts the Bayesian approach has clear advantages including supporting complex sampling distributions, non-standard data sources, and multivariate data from sensor arrays [3,10,13].…”

Section: Methodsmentioning

confidence: 99%

“…Similar issues arise when estimating the activities of experimental samples, compounded by asymmetric sampling distributions in some regions of the response curve or when using standard addition techniques. Similarly, if defined in a probabilistic manner in accordance with IUPAC recommendations [12], the LOD is a highly non-linear function of three parameters resulting in a skewed distribution that may have substantial uncertainty [13].…”

Section: Introductionmentioning

confidence: 99%

“…The goal of ISEtools is to make implementing best practices [8,12,14] as simple as possible, for as wide a range of data as possible, and for as many researchers as possible. The version introduced here (Version 3.1.1) implements statistical methods described by [3,13] for single ISEs or ISE arrays of redundant sensors, allowing the estimation of model parameters, experimental activities, and LODs. Additional functionality will be introduced in future versions (e.g., current projects include developing methods to accommodate sensor arrays measuring multiple analytes, estimating LOD for an entire sensor array, and improving statistical methods for estimating selectivity coefficients).…”

Section: Introductionmentioning

confidence: 99%

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Semi-Automated Data Analysis for Ion-Selective Electrodes and Arrays Using the R Package ISEtools

Dillingham

Alsaedi

Radu

et al. 2019

Sensors

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A new software package, ISEtools, is introduced for use within the popular open-source programming language R that allows Bayesian statistical data analysis techniques to be implemented in a straightforward manner. Incorporating all collected data simultaneously, this Bayesian approach naturally accommodates sensor arrays and provides improved limit of detection estimates, including providing appropriate uncertainty estimates. Utilising >1500 lines of code, ISEtools provides a set of three core functions—, , and — for analysing ion-selective electrode data using the Nikolskii–Eisenman equation. The functions call, fit, and extract results from Bayesian models, automatically determining data structures, applying appropriate models, and returning results in an easily interpretable manner and with publication-ready figures. Importantly, while advanced statistical and computationally intensive methods are employed, the functions are designed to be accessible to non-specialists. Here we describe basic features of the package, demonstrated through a worked environmental application.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semi-Automated Data Analysis for Ion-Selective Electrodes and Arrays Using the R Package ISEtools

Dillingham

Alsaedi

Radu

et al. 2019

Sensors

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Multivariate limit of detection for non-linear sensor arrays

Alsaedi

McGraw

Schaerf

et al. 2020

Chemometrics and Intelligent Laboratory Systems

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Establishing Meaningful Limits of Detection for Ion-Selective Electrodes and Other Nonlinear Sensors

et al. 2019

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Although IUPAC has recommended a probabilistic approach to determining limit of detection (LOD) based on false-positive and false-negative rates for more than 20 years, the LOD definition for ion-selective electrodes (ISEs) long predates these recommendations and conflicts substantively with them. Although it is well known that the ISE LOD definition does not follow best practice, it continues to be used due to simplicity and a lack of available methods for estimating LOD for nonlinear sensors. Here, we use ISEs as a model system for estimation of LOD for nonlinear sensors that is consistent with broad IUPAC recommendations and justified using statistical theory. Using freely available software, the new approach and updated definition is demonstrated through theory, simulation, and an environmental application. The results show that the current LOD definition for ISEs performs substantially worse than the proposed definition when assessed against IUPAC recommendations, including ignoring sensor noise and LOD uncertainty, leading to bias of an order of magnitude or more. Further, the environmental application shows that the new definition, which includes estimates of LOD uncertainty, allows more objective assessment of sensor response and fitness for purpose. The growing demand for ultrasensitive sensors that operate in complex matrices has pushed the boundaries of traditional calibration approaches. These sensors often operate near their limit of detection (LOD), with additional challenges created if their response is nonlinear. These challenges are amplified when assessing new sensors, since they may be less reproducible and noisier than benchmark techniques.

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Characterising uncertainty in instrumental limits of detection when sensor response is non-linear

Cited by 4 publications

References 8 publications

Semi-Automated Data Analysis for Ion-Selective Electrodes and Arrays Using the R Package ISEtools

Semi-Automated Data Analysis for Ion-Selective Electrodes and Arrays Using the R Package ISEtools

Multivariate limit of detection for non-linear sensor arrays

Establishing Meaningful Limits of Detection for Ion-Selective Electrodes and Other Nonlinear Sensors

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