Analyte Measurement via Simulated Spectral Calibration
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Solution Overview
Problem
Current methods for measuring analytes in biological samples are inefficient and prone to variability, requiring extensive data collection from human subjects and struggling to separate correlations between different analytes, leading to inaccurate calibration equations.
Innovation Solution
The development of methods and systems that generate simulated spectral data to create calibration equations, using partial least squares regression and spectral correction factors to determine analyte values, reducing the need for direct measurement from biological samples and minimizing variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral data is collected from human or animal subjects, then calibration equations can be generated for analyte determination, but the process is time-consuming, expensive, and prone to variability
Solution Approach 1:
The patent creates a computational model that copies the light attenuation behavior of biological samples through mathematical equations. Instead of collecting spectral data from actual human or animal subjects, the system generates synthetic spectral data by solving the diffusion equation with various parameter combinations, thereby eliminating the need for extensive in vivo data collection while maintaining accuracy
Solution Approach 2:
The patent performs preliminary action by pre-calculating a comprehensive database of spectral data covering the full range of possible analyte concentrations and tissue optical properties. This pre-computed database is then used to generate calibration equations without requiring new data collection from subjects, significantly reducing the time and variability associated with traditional calibration methods
2Reliability
If extensive spectral data is collected from multiple human subjects to account for variability, then more accurate calibration equations can be obtained, but the cost and complexity of the measurement system increases
Solution Approach 1:
The patent systematically varies the parameters in the light diffusion model (such as absorption coefficients, scattering coefficients, and analyte concentrations) to generate spectral data that encompasses the full range of biological variability. This approach captures the effects of different tissue types, concentrations, and optical properties without requiring physical samples from multiple subjects, thereby reducing device complexity while maintaining reliability
Solution Approach 2:
The computational model serves multiple functions: it generates calibration data, simulates various biological conditions, and provides a framework for understanding light-tissue interactions. This single multi-functional approach replaces the need for multiple specialized measurement systems and extensive subject recruitment, reducing overall system complexity while improving reliability
3Adaptability or versatility
If mathematical generation of spectral data is used instead of measured data, then the range of parameters can be systematically varied to encompass all relevant sample properties, but the model must accurately represent complex light attenuation mechanisms
Solution Approach 1:
The patent introduces a computational diffusion equation as an intermediary that bridges the complex physical light attenuation process and the simplified spectral data generation. This mathematical mediator accurately represents the complex interactions of light with tissue while allowing systematic variation of parameters, making the system both versatile and manageable without requiring direct measurement from diverse biological samples
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 allows for rapid and accurate determination of analyte values such as pH, oxygen tension, and hemoglobin concentration by using mathematically generated spectral data, reducing the number of measurements needed from human subjects and improving measurement accuracy.
Implementation Method 1
The at least two different sources of light attenuation can include light scattering and absorption. The light scattering can include contributions from light scattering by one or more fat layers and light scattering by muscle tissue.
Implementation Method 2
The absorption can include contributions from light absorption by at least two different analytes.
Data Source
AI summary
Methods are disclosed for determining a value associated with an analyte in a sample. The methods include: determining a set of spectra from a model for light attenuation in the sample, where the model includes contributions from at least two different sources of light attenuation in the sample; determining a set of spectral correction factors associated with the analyte in the sample based on the set of spectra; and using the set of spectral correction factors to determine the value associated with the analyte of interest.