Biosensor Error Compensation Using Segmented Primary and Residual Functions
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Solution Overview
Problem
Biosensor systems face challenges in accurately determining analyte concentrations due to errors from operating conditions, particularly in user self-testing, which are difficult to reproduce and compensate for using conventional methods.
Innovation Solution
A method involving a primary function and a residual function is used to compensate for errors, where the primary function corrects for hematocrit and temperature, and the residual function addresses remaining errors, improving measurement performance by reducing total error and increasing the percentage of results within a specified bias limit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional error compensation methods are used, then device complexity is reduced, but measurement precision deteriorates due to inability to compensate for operating condition errors in user self-testing
Solution Approach 1:
The error compensation function is divided into two separate functions: a primary compensation function that corrects for hematocrit and temperature effects, and a residual compensation function that corrects for remaining errors including underfill errors. This segmentation allows each function to be optimized for its specific purpose while keeping the overall system manageable.
Solution Approach 2:
The primary compensation function is applied first to correct for the major error sources (hematocrit and temperature) before the residual compensation function is applied. This preliminary action approach allows the residual function to focus only on the remaining smaller errors, improving overall precision without requiring the residual function to handle all error types.
2Measurement precision
If a comprehensive error compensation model is implemented, then measurement precision improves, but device complexity increases due to multiple compensation functions
Solution Approach 1:
The comprehensive error compensation is segmented into primary and residual components, where the primary function handles major error sources and the residual function handles remaining errors. This segmentation reduces the complexity burden on any single function while achieving comprehensive compensation.
Solution Approach 2:
The residual compensation function acts as an intermediary that bridges the gap between primary compensation and the final measured value. It handles the residual errors that the primary function cannot correct, including underfill errors, and provides a complete compensation solution without requiring a single overly complex function.
3Measurement precision
If residual compensation function is added, then measurement precision improves by compensating for underfill errors, but loss of time increases due to additional compensation steps
Solution Approach 1:
The primary compensation function is executed first as a preliminary step, correcting for the major error sources quickly. Then the residual compensation function is applied to correct remaining errors. This preliminary action structure allows the system to achieve comprehensive compensation while minimizing total time by handling the largest corrections first.
Solution Approach 2:
The residual compensation function extracts and specifically targets only the remaining errors after primary compensation, including underfill errors. By taking out only the necessary residual corrections rather than re-compensating all parameters, the system improves precision without adding excessive time.
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
This approach significantly enhances the accuracy and precision of analyte concentration measurements, especially in user self-testing, by compensating for a substantial portion of total errors, thereby improving measurement performance and reducing the need for repeated analyses.
Implementation Method 1
In other optical systems, a chemical indicator fluoresces or emits light in response to the analyte when illuminated by an excitation beam
Implementation Method 2
The chemical indicator produces a reaction product that absorbs light. As the incident beam passes through the sample, the reaction product absorbs a portion of the incident beam, thus attenuating or reducing the intensity of the incident beam
Implementation Method 3
In electrochemical biosensor systems, the analyte concentration is determined from an electrical signal generated by an oxidation/reduction or redox reaction of the analyte or a species responsive to the analyte
Data Source
AI summary
A biosensor system determines analyte concentration from an output signal generated from a light-identifiable species or a redox reaction of the analyte. The biosensor system compensates at least 50% of the total error in the output signal with a primary function and compensates a portion of the remaining error with a residual function. The amount of error compensation provided by the primary and residual functions may be adjusted with a weighing coefficient. The compensation method including a primary function and a residual function may be used to determine analyte concentrations having improved accuracy from output signals including components attributable to error.