Non-Invasive Blood Glucose Estimation via Raman Spectral Segmentation
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Solution Overview
Problem
Current methods for monitoring blood glucose levels in diabetes patients are invasive, causing pain and infection risks, and non-invasive methods lack accuracy.
Innovation Solution
A non-invasive apparatus and method using Raman spectroscopy to estimate analyte concentrations by obtaining Raman spectra, extracting analyte and non-analyte spectra, removing background signals, and calculating areas under the curves to estimate concentrations, with a concentration estimation model generated through regression analysis or machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If invasive finger pricking method is used to measure blood glucose levels, then measurement reliability is improved, but patient comfort and safety deteriorate due to pain and infection risk
Solution Approach 1:
The patent replaces the mechanical invasive sampling method (finger pricking) with an optical measurement system. A spectrometer detects Raman scattering signals from blood glucose molecules through the skin, eliminating the need for physical penetration while maintaining measurement capability.
Solution Approach 2:
The patent introduces Raman scattering as an intermediary physical phenomenon to enable non-invasive measurement. Light interacts with glucose molecules in the skin, producing Raman-shifted signals that carry concentration information, serving as a mediator between the measurement device and the analyte without direct contact.
2Ease of operation
If non-invasive spectrometer method is used to measure blood glucose levels, then patient comfort is improved, but measurement accuracy deteriorates
Solution Approach 1:
The patent segments the complex spectral data into distinct Raman bands corresponding to different glucose molecular vibrations. By isolating specific frequency ranges (e.g., 911 cm⁻1, 1060 cm⁻1, 1125 cm⁻1), the system extracts meaningful concentration information while filtering out interfering signals from other biological components.
Solution Approach 2:
The patent utilizes the frequency-shifted Raman scattering parameters to identify and quantify glucose concentration. The Raman shift values and intensity ratios serve as characteristic parameters that change with glucose concentration, enabling accurate measurement through optical property changes rather than direct sampling.
3Ease of operation
If Raman spectra are analyzed to estimate analyte concentration, then non-invasive measurement is achieved, but signal complexity increases due to overlapping spectra from multiple analytes and biological components
Solution Approach 1:
The patent divides the Raman spectrum into distinct regions corresponding to different molecular vibrations. By segmenting the spectral data into specific bands (e.g., 911 cm⁻1 for glucose, 1003 cm⁻1 for water, 1450 cm⁻1 for lipids), the system can independently analyze each component and reduce the complexity of multi-component analysis.
Solution Approach 2:
The patent extracts and isolates specific Raman bands corresponding to target analytes from the complex spectral background. By selectively extracting and analyzing only the relevant frequency ranges, the system simplifies the measurement process and reduces interference from other biological components.
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
The method provides accurate and pain-free estimation of blood glucose levels, improving upon the limitations of invasive techniques and enhancing the accuracy of non-invasive Raman spectroscopy methods.
Implementation Method 1
measure the Raman spectra by emitting light onto the object and receiving Raman-scattered light returning from the object
Data Source
AI summary
A apparatus for estimating concentration may include: a spectrum obtainer configured to obtain Raman spectra of an object; and a processor configured to extract, from the Raman spectra, at least one analyte spectrum related to an analyte and at least one non-analyte spectrum related to a biological component other than the analyte, and estimate concentration of the analyte based on a first area under a curve of the at least one analyte spectrum and a second area under a curve of the at least one non-analyte spectrum.