Spectrometer Wavelength Selection for Faster Bio-Information Estimation
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Solution Overview
Problem
Existing bio-information estimation methods using Raman Spectroscopy or near-infrared spectrometers face challenges in selecting optimal wavelengths for accurate measurement within limited time, leading to decreased selectivity and limit of detection.
Innovation Solution
A spectrometer system that calculates signal-to-noise ratio (SNR) values for each wavelength, generates simulated absorbance spectra, and determines an optimal wavelength combination using noise equivalent absorbance and prediction values to enhance measurement performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple wavelengths are measured to improve measurement accuracy, then bio-information estimation precision improves, but measurement time increases
Solution Approach 1:
The system performs preliminary simulation of absorbance spectra and calculation of prediction values for multiple wavelength combinations before actual measurement. By pre-evaluating which wavelength combinations yield the best prediction accuracy for target components, the system can select only the optimal wavelengths for actual measurement, thereby reducing measurement time while maintaining high precision
Solution Approach 2:
The spectrum is divided into multiple wavelength regions, and the system evaluates different wavelength combinations by segmenting the full spectrum. This allows systematic comparison of different wavelength subsets to identify the optimal combination that provides maximum measurement accuracy with minimum wavelengths
2Reliability
If optimal wavelength combination is determined through simulation and analysis, then selectivity and limit of detection improve, but computational complexity increases
Solution Approach 1:
The system uses the measured spectrum itself to generate simulated absorbance spectra and calculate prediction values, rather than requiring external reference data or complex calibration procedures. The SNR values and noise equivalent absorbance are derived directly from the measured spectrum, enabling the system to self-optimize wavelength selection without additional complex equipment or procedures
Solution Approach 2:
The system transforms the measured spectrum into simulated absorbance spectra by applying mathematical transformations based on SNR values and noise equivalent absorbance. This parameter transformation allows evaluation of different wavelength combinations in the absorbance domain, which provides better correlation with concentration and improves selectivity without requiring physical changes to the measurement system
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 system maximizes selectivity and limit of detection by identifying the optimal wavelength combination for bio-information estimation, improving measurement accuracy and efficiency.
Implementation Method 1
a detector configured to detect an optical signal by receiving light scattered or reflected from the object
Implementation Method 2
a detector configured to detect an optical signal by receiving light scattered or reflected from the object
Implementation Method 3
generate a plurality of simulated absorbance spectra based on the SNR values for each wavelength of the spectrum
Data Source
AI summary
An apparatus and method for estimating bio-information are provided. The apparatus for estimating bio-information includes: a spectrometer configured to measure a spectrum from an object according to measurement conditions; and a processor configured to obtain signal-to-noise ratio (SNR) values for each wavelength of the spectrum measured by the spectrometer, generate a plurality of simulated absorbance spectra based on the SNR values for each wavelength of the spectrum, and determine an optimal wavelength combination for use in measuring the bio-information based on the plurality of simulated absorbance spectra.