Virtual Calibration for Biosensor Surface Drift Compensation
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Solution Overview
Problem
Analytical sensor systems face challenges in maintaining the binding capacity of ligand-supporting sensor surfaces, particularly with unstable ligands like virus antigens, leading to decreased throughput and increased costs due to frequent calibrations needed to compensate for surface drift.
Innovation Solution
A method that uses virtual calibration data calculated from real calibration data to determine analyte concentrations in each analysis cycle, minimizing the need for frequent calibrations by creating specific calibration curves or coefficients for each cycle, even when the sensor surface's binding capacity decreases over multiple analysis cycles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequent calibrations are performed to compensate for surface drift, then measurement accuracy is maintained, but throughput decreases and costs increase
Solution Approach 1:
The invention changes the parameter of calibration frequency from frequent (to maintain accuracy) to reduced (to improve throughput). This is achieved by introducing a drift compensation parameter that mathematically adjusts for surface degradation, allowing accurate measurements without frequent recalibration
Solution Approach 2:
The invention creates a virtual copy of the calibration curve that is mathematically adjusted to compensate for surface drift. Instead of repeatedly performing physical calibrations, a computational model replicates and adjusts the calibration relationship, reducing the need for actual recalibration events
2Measurement precision
If frequent calibrations are performed to compensate for surface drift, then measurement accuracy is maintained, but reagent consumption increases
Solution Approach 1:
The invention changes the parameter of calibration frequency, which directly reduces reagent consumption. By reducing how often calibrations are performed, less analyte and other reagents are consumed in the calibration process itself
Solution Approach 2:
The virtual calibration curve serves as a computational substitute that eliminates the need for physical reagent-based recalibrations, thereby reducing reagent consumption while maintaining measurement accuracy through mathematical compensation
3Productivity
If the sensor surface is used for multiple analysis cycles, then productivity increases, but binding capacity decreases due to ligand instability
Solution Approach 1:
The invention implements a feedback mechanism where the measured response is adjusted based on the known drift characteristics of the ligand. The system continuously compensates for binding capacity loss by applying a drift factor derived from calibration data, allowing the surface to be reused without losing measurement reliability
Solution Approach 2:
The invention changes the interpretation parameter of the binding response by applying a drift compensation factor. This allows the same physical binding event to be accurately quantified even though the absolute binding capacity has decreased due to ligand instability over 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 improves the quality of quantitative measurements and reduces the frequency of calibrations, maintaining measurement accuracy and efficiency even with unstable ligands, thereby enhancing the overall performance of biosensor systems.
Implementation Method 1
A representative such biosensor system is the BIACORETM instrumentation sold by GE Healthcare Biosciences AB (Uppsala, Sweden) which uses surface plasmon resonance (SPR) for detecting interactions between molecules in a sample and molecular structures immobilized on a sensing surface.
Data Source
AI summary
A method of determining the concentration of at least one analyte in a plurality of samples by sequentially subjecting each sample to an analysis cycle comprises contacting the sample or a sample-derived solution with a sensor surface supporting a species capable of specifically binding the analyte or an analyte-binding species, detecting the amount of binding to the sensor surface, and regenerating the sensor surface to prepare it for the next analytical cycle, and based on the detected binding to the sensor surface determining the concentration of analyte in each sample using virtual calibration data calculated for each analysis cycle from real calibration data obtained by contacting the solid phase with samples containing known concentrations of analyte.