Predicting Sensor Endpoint Response for Faster Sample Throughput
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Solution Overview
Problem
Conventional clinical analyzers face delays in providing end point responses for analyte measurements in body fluids, which slows down diagnosis and therapeutic intervention due to the time required for sensor response and analysis.
Innovation Solution
A system and method that predict the end point response time of electrochemical sensors by determining a curve fitting equation from data signals, specifically a second-degree logarithmic polynomial, and improve reliability by removing outliers, allowing for faster analyte concentration calculation without waiting for the full sensor response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the sensor waits for the full end point response time to provide an accurate analyte measurement, then measurement precision is improved, but sample throughput decreases due to longer analysis time
Solution Approach 1:
The system performs preliminary data collection and curve fitting during the sensor response phase, then uses the fitted equation to predict the end point response value before the sensor actually reaches endpoint. This preliminary calculation of the final value allows the system to report results faster without sacrificing accuracy, as the prediction is based on the established kinetic curve rather than waiting for the actual endpoint to occur
Solution Approach 2:
The invention replaces the mechanical waiting process (physically waiting for the sensor to reach endpoint) with a mathematical prediction system. Instead of timing out until the sensor stabilizes, the system uses curve fitting equations to calculate what the endpoint value will be, substituting computational prediction for temporal waiting
2Productivity
If the sensor response time is reduced to increase sample throughput, then productivity is improved, but measurement precision deteriorates due to insufficient response time
Solution Approach 1:
The system collects data points during the sensor response phase (before endpoint is reached) and performs curve fitting to predict the final endpoint value. This preliminary data collection and mathematical modeling allows the system to determine analyte concentration faster than traditional endpoint waiting, improving throughput while maintaining precision through the accuracy of the curve fitting prediction
Solution Approach 2:
The invention changes the parameter used for measurement from the actual endpoint value (which requires waiting) to a predicted endpoint value derived from kinetic curve fitting. By transforming the measurement parameter from a time-dependent endpoint reading to a mathematically predicted value based on response kinetics, the system achieves faster results without sacrificing accuracy
3Reliability
If conventional endpoint waiting is used to ensure reliable measurements, then measurement reliability is improved, but loss of time increases due to prolonged sensor exposure
Solution Approach 1:
The system performs preliminary curve fitting and prediction calculations during the sensor response phase, establishing a reliable mathematical model before the endpoint is reached. This preliminary action of modeling the response curve allows the system to predict the endpoint value with high reliability without actually waiting for the endpoint, thereby reducing the time the sensor must be exposed to the sample
Solution Approach 2:
The invention substitutes the mechanical process of waiting for endpoint stabilization with a mathematical prediction process. Instead of timing out until the sensor signal stabilizes (which consumes time), the system uses curve fitting equations to calculate the expected endpoint value, replacing temporal waiting with computational prediction that achieves the same reliability goal faster
Data Source
AI summary
Technologies for increasing sample throughput by predicting the end point response time of a sensor for the analysis of an analyte in a sample are disclosed. In one aspect, a system includes a sensor that generates data signals associated with the measurement of an analyte within the sample. A processor records appropriate data points corresponding to the signals, converts them to a logarithmic function of time scale, and plots the converted data points. The processor then determines a curve that fits the plotted data points and determines a curve fitting equation for the curve. Once the equation is determined, the processor extrapolates an end point response of the sensor using the equation. A value, such as analyte concentration, is then calculated using the extrapolated end point response.