Iterative Spectral Correction for Substance Identification
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Solution Overview
Problem
Current spectrographic analysis methods face challenges in accurately identifying substances due to similarities between reference spectra, shifts in baselines, and differences in experimental conditions, leading to insufficient corrections that fail to identify the correct substance.
Innovation Solution
An iterative process is employed to optimize correction parameters for sample and reference spectra, allowing for dynamic adjustment of clipping, horizontal shift, ATR-IR, vertical offset, and baseline corrections to achieve an optimal similarity score, enabling more accurate identification of matching substances by iteratively improving the similarity score until convergence criteria are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard spectral comparison methods are used, then the process is simple and fast, but the accuracy of substance identification is insufficient due to baseline shifts and experimental condition differences
Solution Approach 1:
The patent applies preliminary correction actions to spectra before comparison by detecting peak positions and intensities, then applying corrections for baseline shifts, horizontal shifts, and vertical scaling based on reference peak characteristics. This preliminary processing prepares the spectra for accurate comparison while maintaining a structured approach to complexity.
Solution Approach 2:
The patent changes multiple spectral parameters simultaneously including baseline offset, horizontal shift amount, and vertical scaling factor. By optimizing these parameters based on peak position and intensity matching, the system achieves accurate substance identification despite initial spectral differences caused by experimental conditions.
2Measurement precision
If multiple correction parameters are applied to spectra, then the accuracy of matching improves, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary identification of corresponding peaks between sample and reference spectra before applying corrections. By pre-selecting peak pairs and determining their relationships, the system establishes a framework that guides subsequent correction parameter optimization, reducing unnecessary computational iterations.
Solution Approach 2:
The patent optimizes correction parameters (baseline shift, horizontal shift, vertical scaling) by comparing peak positions and intensities. This parameter optimization approach focuses computational effort on the most critical transformations needed to achieve accurate matching, rather than exhaustive searching of all possible corrections.
3Measurement precision
If iterative optimization of correction parameters is performed, then the similarity score reaches optimal levels, but the computational resources and processing steps increase
Solution Approach 1:
The system performs preliminary detection of peak positions and intensities in both sample and reference spectra before entering iterative optimization. This preliminary analysis provides initial estimates for correction parameters and establishes correspondence relationships that guide the iterative process, reducing the search space and computational burden.
Solution Approach 2:
The patent iteratively optimizes correction parameters by comparing peak positions and intensities, adjusting baseline shifts, horizontal shifts, and vertical scaling factors. The iteration continues until convergence criteria are met, ensuring optimal similarity scores while systematically reducing the number of processing steps through targeted parameter adjustments.
Data Source
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Figure 3A~3B
AI summary
Systems, methods, and apparatuses are provided for identifying an optimal spectral match and potentially display the compared spectra. A sample spectrum of a sample substance can be compared to reference spectra to identify matches, thereby determining possibilities for what the sample substance is. Correction parameter(s) may be used for the sample spectrum and/or the reference spectrum. Initial value(s) for the correction parameter(s) can be applied to the sample spectrum and/or a reference spectrum, and a similarity score can be determined. Value(s) for the correction parameter(s) can be updated and iteratively improved to provide an optimal similarity score that satisfies a convergence criterion. Data about the reference substances having optimal similarity scores that are above a threshold can be output to a user, e.g., the reference spectra can overlay the sample spectrum. A user can then make a final determination of which reference substance corresponds to the sample substance.