Iterative Spectral Search Algorithm for Mixture Component Identification
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Solution Overview
Problem
Traditional spectral searching methods are ineffective for identifying components of unknown mixtures, as they struggle with matching mixture spectra to those of pure components, especially when there is no dominant component, and subtraction-based methods can lead to incorrect results due to reliance on top matches.
Innovation Solution
A search algorithm that compares the unknown mixture spectrum with library compounds, generates candidate mixture combinations, fits spectra, computes residual spectra, and iteratively adds potential compounds to refine the identification, using metrics like similarity measures and spectral error parameters to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional spectral searching methods are used to identify components of unknown mixtures, then the method is simple to operate, but the matching accuracy is poor when there is no dominant component in the mixture
Solution Approach 1:
The patent segments the mixture identification process into iterative steps: initial spectrum comparison to identify potential components, subtraction of identified component spectra from the mixture spectrum, and repeated identification on the residual spectrum. This segmentation allows systematic decomposition of complex mixtures without requiring a dominant component, thereby improving matching accuracy while maintaining operational simplicity.
Solution Approach 2:
The patent performs preliminary actions by first comparing the mixture spectrum with library spectra to identify potential components before proceeding to subtraction and residual analysis. This preliminary identification step guides subsequent processing and improves overall accuracy by establishing a foundation for iterative refinement.
2Measurement precision
If spectra of mixtures are incorporated into the library to improve mixture analysis, then the matching accuracy improves, but the device complexity and data storage requirements increase exponentially
Solution Approach 1:
The patent extracts individual component spectra from the mixture spectrum through iterative subtraction processes. Instead of storing and comparing against pre-collected mixture spectra, the method extracts and identifies pure component spectra mathematically, thereby avoiding the exponential increase in library size while maintaining accurate mixture analysis capability.
Solution Approach 2:
The patent creates computational models (fitted spectra) of mixture compositions based on identified pure components rather than storing actual mixture spectra. This copying approach using mathematical representations achieves accurate mixture identification without the storage and complexity burden of maintaining extensive mixture libraries.
3Adaptability or versatility
If subtraction based mixture analysis is used to identify components, then the method can handle mixtures, but the reliability decreases when the top match is not actually present in the mixture
Solution Approach 1:
The patent implements feedback mechanisms where the identified components and their fitted spectra are continuously compared against the residual spectrum. The process includes validation steps where the reliability of identifications is assessed, and the iterative subtraction continues only when confident identifications are made. This feedback loop significantly improves reliability by preventing erroneous identifications from propagating through the analysis.
Solution Approach 2:
The patent employs dynamic adjustment of the identification process based on spectral matching quality and residual analysis. The method adapts its behavior by adjusting subtraction weights, selecting different library compounds based on evolving residual patterns, and modulating the identification confidence thresholds dynamically during the iterative process, thereby maintaining high reliability across diverse mixture compositions.
Data Source
AI summary
Methods, systems and computer program products for identifying components of an unknown mixture using spectral analysis techniques. The method includes comparing the spectrum of the unknown mixture with the spectra of library compounds to obtain candidate mixture combinations. A model is generated for each of the candidate mixture combinations based on a modeling metric. A residual spectrum is computed corresponding to each of the candidate mixture combinations by removing the spectrum of each of the compounds of the candidate mixture combination from the spectrum of the unknown mixture. One or more potential compounds are identified by comparing the residual spectrum with the spectrum of library compounds. The potential compounds are added to the candidate mixture combinations to generate an updated list of the candidate mixture combinations. The search algorithm repeats the steps described above on the updated candidate mixture combinations, until a first termination condition is satisfied.