Spectral Component Identification via Regression Residual Analysis
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Solution Overview
Problem
Existing spectral analysis methods are ineffective when comparing sample spectra of mixtures to reference spectra, especially when the components are present at different concentrations or when identifying unknown components, as they fail to accurately determine the presence of target components due to high residual similarity and correlation coefficients.
Innovation Solution
A computer-implemented method that performs regression using a target spectrum and known component spectra to extract a residual spectrum, which is then compared to the target spectrum using metrics like correlation coefficient or Euclidean distance, allowing for the identification of target components even at low concentrations and in complex mixtures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing spectral comparison metrics (Euclidian distance, maximum distance, correlation coefficient) are used to compare sample spectra to reference spectra, then the comparison can be performed using standard methods, but the accuracy deteriorates when components are present at different concentrations or when identifying unknown components due to high residual similarity
Solution Approach 1:
The patent segments the spectral analysis process into distinct phases: first performing regression to extract the target component spectrum from the mixture, then separately comparing this extracted spectrum to reference spectra. This segmentation isolates the target component's spectral features from interfering components, enabling accurate identification even when concentration ratios differ significantly between sample and reference spectra.
Solution Approach 2:
The patent applies preliminary regression analysis to the sample spectrum before performing the actual spectral comparison. By first extracting the target component's contribution through regression against known interferents, the method prepares a purified spectral signature that can then be reliably compared to reference spectra, overcoming the limitation of direct comparison methods.
2Productivity
If direct comparison of sample spectrum to reference spectrum is performed, then the process is simple and fast, but the ability to identify target components at low concentrations deteriorates due to masking by other components
Solution Approach 1:
The patent extracts the target component's spectral contribution from the mixture spectrum through regression analysis. By mathematically separating the target component's signal from the combined mixture spectrum, the method isolates weak spectral features of low-concentration components that would otherwise be masked by dominant components, enabling their detection without sacrificing analysis speed.
3Adaptability or versatility
If standard spectral comparison methods are used, then the methodology remains simple and widely applicable, but the reliability deteriorates when sample and reference spectra contain components at different concentrations
Solution Approach 1:
The patent transforms the spectral analysis by changing the parameter being compared: instead of comparing the entire sample spectrum to the reference spectrum, it compares only the extracted target component spectrum (obtained through regression) to the reference spectrum. This parameter change makes the comparison invariant to concentration differences of other components, maintaining high accuracy across varying concentration scenarios.
Data Source
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AI summary
The present invention discloses a system and method for detecting the spectra of unknown components in the spectrum of a mixture and/or for verifying the presence of suspected components in the spectrum of a mixture. The system and method involves using the algorithm to perform a regression that includes the target and known spectra in a mixture, calculating a residual where the coefficient for the target spectrum is zero, called the extracted spectrum, and comparing the extracted spectrum and the target spectrum. The system and method may be used with chemometrics algorithms, multiple known spectra, and/or multiple target spectra.