ICA Calibration Using Component Amount Independence
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Solution Overview
Problem
Existing independent component analysis methods struggle with accurately estimating component amounts when the optical spectra derived from multiple components are not statistically independent, leading to inaccurate calibration of target components.
Innovation Solution
A calibration apparatus and method that perform independent component analysis by treating component amounts as independent components, extracting subsets from optical spectra, and using inner product values to determine a target component calibration spectrum, enabling accurate calibration even when components are not statistically independent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If normal independent component analysis is performed treating optical spectra as independent components, then the analysis can be applied to calibration problems, but the accuracy deteriorates when the statistical independence condition is not satisfied
Solution Approach 1:
The patent inverts the traditional ICA approach by treating component amounts (quantities) as the independent components rather than treating optical spectra as independent components. This inversion allows the method to work accurately even when spectra from different components are not statistically independent, as the component amounts themselves are used as the independent variables in the analysis.
Solution Approach 2:
The patent changes the fundamental parameter being analyzed from optical spectrum characteristics to component amount characteristics. By transforming the analysis to operate on component amounts rather than spectra, the method adapts to situations where spectral independence does not hold, maintaining measurement precision through this parameter transformation.
2Measurement precision
If component amounts are treated as independent components, then measurement precision improves, but device complexity increases due to additional processing steps
Solution Approach 1:
The patent creates a universal calibration approach that can handle multiple components and samples through a single integrated analysis framework. By treating component amounts as independent components, the same analysis procedure applies to different components and sample types, reducing the need for separate calibration procedures for each component and thereby managing system complexity.
Solution Approach 2:
The patent segments the calibration process into distinct functional modules: acquiring optical spectra, extracting subsets of spectra, performing ICA on component amounts, calculating inner products, and determining calibration curves. This segmentation allows each module to be optimized independently and facilitates easier implementation and maintenance of the overall 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
This approach improves the accuracy of independent component analysis and calibration by enhancing independency in component amount distributions, allowing for precise estimation of target component amounts.
Implementation Method 1
an optical spectrum acquisition unit that acquires an optical spectrum obtained through spectrometry on the test object
Implementation Method 2
an inner product value calculation unit that computes an inner product value between the optical spectrum acquired for the test object and the target component calibration spectrum
Data Source
AI summary
A calibration data acquisition unit (a) acquires Q optical spectra and S evaluation spectra, (b) extracts R subsets from a set of the Q optical spectra, (c) performs independent component analysis in which component amounts in each sample treated as independent components on each of R subsets so as to acquire R×N component calibration spectra, (d) obtains an inner product value between the R×N component calibration spectrum and an evaluation spectrum, (e) selects a component calibration spectrum for which a correlation degree between a component amount for the target component and the inner product value is the maximum as the target component calibration spectrum from among the R×N component calibration spectra, and (f) creates a calibration curve by using the target component calibration spectrum.