Multi-component Regression Spectral Analysis Algorithm
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Solution Overview
Problem
Current spectral analysis methods for evolving samples are time-consuming and require significant computational resources, especially when dealing with large reference libraries and the need for both qualitative and quantitative analysis of time-series spectra, often taking hours or days to complete even with high-speed processors.
Innovation Solution
The method employs Multi-component Regression (MCR) to extract linearly independent spectra, which are then processed using a Multi-component Search (MCS) algorithm to deconvolute and identify components, significantly accelerating the analysis by iteratively correlating estimated components with spectral libraries, reducing the need for manual expertise and initial knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive spectral matching is performed to achieve complete qualitative and quantitative analysis, then analysis accuracy is improved, but computational time increases significantly (hours to days)
Solution Approach 1:
The patent segments the exhaustive spectral matching process into two distinct phases: (1) Multi-component Regression (MCR) that quickly extracts independent spectra and estimates component proportions, and (2) Multi-component Search (MCS) that performs detailed matching only on the extracted components. This segmentation avoids comparing all possible combinations of reference spectra, reducing computational time from hours/days to minutes while maintaining analysis accuracy through the subsequent MCS refinement step.
Solution Approach 2:
The MCR algorithm performs preliminary action by extracting independent spectra and estimating component proportions before the detailed MCS matching occurs. This preliminary extraction identifies the key components present in the time-series spectra, allowing the subsequent MCS step to focus computational resources only on matching these specific components against the reference library, rather than performing exhaustive comparisons of all possible spectral combinations.
2Measurement precision
If a large reference library is used to improve component identification accuracy, then qualitative analysis capability is improved, but computational complexity increases
Solution Approach 1:
The MCR algorithm extracts only the independent spectra and component proportions that are actually present in the sample data, separating these from the large reference library. This extraction reduces the computational problem from comparing all reference spectra against all possible combinations to matching only the extracted independent components against the reference library, thereby reducing computational complexity while maintaining the ability to use large reference libraries for accurate identification.
3Loss of information
If quantitative analysis is performed to determine relative proportions of components, then analytical information is improved, but computational time increases
Solution Approach 1:
The patent merges the qualitative identification function and quantitative analysis function into a unified MCR-MCS workflow. The MCR algorithm simultaneously performs both tasks by extracting independent spectra (qualitative) and estimating component proportions (quantitative) in a single computational pass. This merging eliminates the need for separate quantitative analysis steps, providing both types of information efficiently without the additional computational time that would result from sequential processing.
Data Source
AI summary
MCR provided estimated pure component time series spectra as extracted from infrared or other spectroscopy is capable of being compared to spectra in a reference library to find the best matches. The best match spectra can then each in turn be combined with the reference spectra, with the combinations also being screened for best matches versus any one of the estimated pure component time series spectra. These resulting best matches can then also undergo the foregoing combination and comparison steps. The process can repeat in this manner in an unbounded fashion if desired until an appropriate stopping point is reached, for example, when a desired number of best matches are identified, when some predetermined number of iterations has been performed, etc. This methodology is able to return best-match spectra with far fewer computational steps and greater speed than if all possible combinations of reference spectra are considered.