Spectroscopic Analysis Using Generalized Inverse Matrix for Noise
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Solution Overview
Problem
Existing spectroscopic analysis methods face challenges in accurately analyzing samples due to noise components in spectral data, which hinder precise substance concentration determination.
Innovation Solution
A spectroscopic analysis apparatus and method that includes a light source, spectrometer, detector, and processor, which generates and disperses light on labeled substances, detects and analyzes spectral data using a generalized inverse matrix incorporating reference spectral data and noise components to accurately quantify substances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional spectroscopic analysis methods are used to measure spectral data, then the analysis process is simple, but noise components in the spectral data reduce measurement precision and analysis accuracy
Solution Approach 1:
The patent applies preliminary action by pre-calculating the generalized inverse matrix using reference spectral data and noise components before actual measurement. This pre-computed inverse matrix is then used to quickly and accurately determine substance concentrations from measured spectral data, improving measurement precision while maintaining efficient data processing.
Solution Approach 2:
The patent introduces an intermediary element - the generalized inverse matrix - that mediates between the raw spectral data (containing noise) and the final concentration values. This inverse matrix acts as a mathematical filter that separates signal from noise, enabling accurate concentration determination even when spectral data contains noise components.
2Reliability
If noise components are included in the spectral data, then the data reflects real measurement conditions, but accurate analysis cannot be performed
Solution Approach 1:
The patent converts the harmful noise components into a beneficial element by incorporating them into the generalized inverse matrix calculation. By using reference spectral data that includes noise characteristics, the method learns to distinguish between signal and noise, transforming the previously harmful noise into a factor that improves the robustness and reliability of the analysis under real measurement conditions.
Solution Approach 2:
The patent changes the parameter representation by using a generalized inverse matrix that accounts for noise components rather than a simple inverse matrix. This parameter transformation allows the system to handle noisy spectral data effectively, improving analysis reliability while maintaining the ability to process real-world measurement data.
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
Enables accurate analysis of samples by effectively accounting for noise components, improving measurement precision and reducing errors in substance concentration determination.
Implementation Method 1
a sample including a plurality of substances labeled by a plurality of labeled substances... observed light generated in the sample by the light incident on the sample
Implementation Method 2
a spectrometer configured to disperse observed light generated in the sample by the light incident on the sample
Implementation Method 3
a detector configured to detect the observed light dispersed by the spectrometer to output observed spectral data
Data Source
AI summary
A spectroscopic analysis apparatus, a spectroscopic analysis method, and a program capable of appropriately analyzing a sample are provided. The spectroscopic analysis apparatus according to an embodiment includes: a light source (13) generates light to be incident on a sample including a plurality of substances labeled by a plurality of labeled substances; a spectrometer (14) disperse observed light generated in the sample by the light incident on the sample; a detector (15) detects the observed light dispersed by the spectrometer (14) to output observed spectral data; and a processor (16) analyzes the plurality of substances included in the sample based on the observed spectral data output from the detector (15), the processor (16) analyzing the substances included in the sample using a generalized inverse of a matrix having, as elements, reference spectral data set for the plurality of labeled substances and data of a noise component.