Analyte Concentration Measurement Using Multivariate Curve Evaluation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining analyte concentrations in body fluids, such as blood glucose, are hindered by complexity, high resource consumption, susceptibility to disturbances like hematocrit and temperature variations, and inability to account for particulate components, leading to imprecise measurements.
Innovation Solution
A method involving the use of a first variable indicating the end value of a measurement curve and a second variable derived from an exponential characteristic of the curve, combined through a multivariate evaluation algorithm to accurately determine analyte concentrations while accounting for disturbance variables like hematocrit and temperature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex evaluation algorithms are used to account for disturbance variables, then measurement precision is improved, but device complexity and resource consumption increase
Solution Approach 1:
The patent segments the measurement curve evaluation into distinct phases: initial evaluation phase using a first algorithm for rapid assessment, and refined evaluation phase using a second algorithm for precise correction. This segmentation allows the system to achieve high measurement precision while managing computational complexity by applying different levels of processing to different portions of the measurement data.
Solution Approach 2:
The patent applies preliminary correction factors based on disturbance variables (hematocrit, temperature) before performing the final analyte concentration calculation. By pre-processing the measurement data to account for known disturbances, the system reduces the computational burden on the final evaluation algorithm while maintaining high measurement precision.
2Measurement precision
If multiple measurement parameters are analyzed to correct for disturbance variables, then measurement precision is improved, but resource consumption increases
Solution Approach 1:
The patent implements a tiered evaluation approach where a basic measurement is performed first, and additional correction calculations are applied only when disturbance variables are detected or when the measurement conditions warrant refinement. This partial application of complex processing reduces overall energy consumption while maintaining precision when needed.
3Measurement precision
If disturbance variables are accounted for in the evaluation, then measurement precision is improved, but the evaluation becomes more complex
Solution Approach 1:
The patent segments the evaluation into an initial phase that produces a preliminary result, and a correction phase that adjusts for disturbance variables. This segmentation allows the system to maintain relatively simple base algorithms while adding complexity only where necessary for precision correction.
Solution Approach 2:
The patent introduces correction factors as intermediary elements that mediate between the raw measurement data and the final analyte concentration result. These correction factors account for disturbance variables without requiring the main evaluation algorithm to be fundamentally complex, thereby improving precision while managing overall system complexity.
Data Source
AI summary
Methods are provided for deriving/determining an analyte concentration that include recording measurement values during a time development indicating a progress of a detection reaction of at least one test substance and a body fluid sample and providing at least one measurement curve F(t) containing the measurement values, where the detection reaction is known to be influenced by the analyte concentration and at least one disturbance variable Y. The methods also include deriving an end value of the measurement curve to form a first variable x1, and deriving at least one fit parameter by taking into account an exponential characteristic of the measurement curve, and where the fit parameter forms at least one second variable x2. The methods further include deriving/determining the analyte concentration by using at least one multivariate evaluation algorithm adapted to combine x1 and x2. Also provided are computer programs and devices that incorporate the same.