Threshold-Based Biosensor Correction for Interference
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Solution Overview
Problem
Existing biosensors face challenges in achieving accurate analyte concentration measurements due to interference from secondary effects like temperature and hematocrit, which can lead to inaccuracies in glucose determination in body fluids.
Innovation Solution
A threshold-based correction method and apparatus for biosensors that uses secondary measurements, such as temperature and hematocrit levels, to apply correction functions, including linear and non-linear curves, to improve the accuracy of analyte concentration readings by minimizing interference effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If biosensors are used to measure analyte concentration, then measurement speed and quantitative accuracy are improved, but measurement precision deteriorates due to interference from secondary effects like temperature and hematocrit
Solution Approach 1:
The patent introduces intermediary correction functions that act as mediators between the raw sensor signal and the final analyte concentration result. These correction functions, based on secondary measurements of temperature and hematocrit, compensate for interference effects without requiring changes to the fundamental biosensor measurement process, thus maintaining measurement speed while improving precision.
Solution Approach 2:
The patent applies parameter changes by measuring secondary parameters (temperature and hematocrit levels) and using these to dynamically adjust the interpretation of the primary analyte signal. By changing the correction parameters based on measured conditions, the system maintains accurate analyte concentration determination across varying environmental and sample conditions.
2Measurement precision
If correction functions are applied to account for secondary effects, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the correction process into distinct, independent correction functions for different interference sources (temperature correction and hematocrit correction). Each correction function operates independently on the raw signal, allowing for modular implementation that improves precision through systematic correction while keeping individual correction components relatively simple and manageable.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method enhances the accuracy of diagnostic chemistry tests by effectively correcting for secondary effects, such as temperature and hematocrit interference, resulting in more reliable analyte concentration measurements.
Implementation Method 1
the reaction with glucose oxidase and oxygen is represented by equation (A)
Implementation Method 2
the electron flow is then converted to the electrical signal which directly correlates to the glucose concentration
Implementation Method 3
direct electron transfer to the surface of a conventional electrode does not occur to any measurable degree
Implementation Method 4
A thermistor 114 provides a temperature signal input
Implementation Method 5
A correction function is identified responsive to the compared values. The correction function is applied to the primary measurement of the analyte value to provide a corrected analyte value
Data Source
AI summary
A biosensor system, method and apparatus are provided for implementing threshold based correction functions for biosensors. A primary measurement of an analyte value is obtained. A secondary measurement of a secondary effect is obtained and is compared with a threshold value. A correction function is identified responsive to the compared values. The correction function is applied to the primary measurement of the analyte value to provide a corrected analyte value. The correction method uses correction curves that are provided to correct for an interference effect. The correction curves can be linear or non-linear. The correction method provides different correction functions above and below the threshold value. The correction functions may be dependent or independent of the primary measurement that is being corrected. The correction functions may be either linear or nonlinear.