Establishing Meaningful Limits of Detection for Ion-Selective Electrodes and Other Nonlinear Sensors.
ACS Sens
; 5(1): 250-257, 2020 01 24.
Article
en En
| MEDLINE
| ID: mdl-31845574
ABSTRACT
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.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Electrodos de Iones Selectos
Tipo de estudio:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Año:
2020
Tipo del documento:
Article