Adaptive Mass Spectral Matching for Unknown Substance Identification
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Solution Overview
Problem
Mass spectroscopy faces challenges in identifying unknown substances due to the complexity of molecular fragments with similar masses and systematic variations in reference spectra, leading to non-definitive identifications.
Innovation Solution
An adaptive search method for mass spectrometer analysis that compares sample spectra to reference spectra by determining mass differences and performing group exchanges to adjust peak positions, improving the fit value and identifying potential matches through similarity metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spectral matching is used to identify unknown substances, then the identification process is simple and fast, but the accuracy is insufficient due to molecular fragments with similar masses and systematic variations in reference spectra
Solution Approach 1:
The patent segments the spectral analysis process into multiple stages: initial matching based on retention time windows, followed by adaptive peak searching and matching within those windows, and finally group exchange analysis. This segmentation allows the system to focus computational resources on relevant portions of the spectrum, improving accuracy without requiring complete analysis of all possible fragments.
Solution Approach 2:
The patent implements dynamic adaptive peak searching where the search parameters and windows are adjusted based on the specific sample being analyzed. The system dynamically modifies the matching criteria and search strategy based on initial results, allowing the analysis method to adapt to different molecular structures and spectral patterns, thereby improving identification accuracy.
2Measurement precision
If a comprehensive library of reference spectra is used to improve identification accuracy, then more potential matches can be considered, but the complexity of selecting and comparing spectra increases
Solution Approach 1:
The patent applies local quality by focusing the spectral comparison on specific regions of interest rather than the entire spectrum. By using retention time windows and adaptive peak searching, the system concentrates computational effort on matching specific molecular fragments and their characteristic spectral patterns, reducing the overall complexity of the comparison process while maintaining high accuracy.
Solution Approach 2:
The patent changes key parameters such as retention time windows, peak intensity thresholds, and mass tolerance ranges adaptively based on the sample characteristics. This allows the system to optimize the spectral comparison process for different types of samples, managing the complexity of comprehensive library searching by adjusting parameters to focus on the most relevant comparisons.
3Reliability
If systematic variations in reference spectra are accounted for to improve match definitiveness, then the analysis becomes more rigorous, but the processing time and computational effort increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing reference spectra to account for systematic variations before actual sample matching occurs. Retention time calibration and spectral normalization are performed in advance, creating corrected reference data that can be quickly compared against sample spectra, thereby maintaining high reliability without excessive processing time during actual analysis.
Solution Approach 2:
The patent implements a hierarchical matching approach that skips detailed analysis for obviously mismatched samples. By using quick initial filters based on retention time and overall spectral similarity, the system rapidly eliminates non-matching candidates from the comprehensive library, allowing thorough analysis to be focused only on promising matches, thus maintaining definitiveness while reducing overall processing time.
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
Enhances the accuracy of substance identification by accounting for mass differences and chemical composition variations, providing a more definitive match between sample and reference spectra.
Implementation Method 1
A downstream mass spectrometer (MS) system captures the molecules or fragments thereof from the upstream GC or LC system, and subjects them to an electron beam, with energy and intensity selected to ionize the different molecules
Implementation Method 2
subjects them to an electron beam, with energy and intensity selected to ionize the different molecules, or (more commonly) break them into ionized fragments
Implementation Method 3
The ionized fragments are electromagnetically accelerated and then subjected to a powerful magnetic field in a mass analyzer, bending the travel path of the molecules (if any) and fragments along different pathways, based on their different mass-to-charge ratios
Implementation Method 4
bending the travel path of the molecules (if any) and fragments along different pathways, based on their different mass-to-charge ratios
Data Source
AI summary
A method for analyzing spectra comprises identifying a set of sample peaks in a sample spectrum, where the sample peaks are associated with fragments of a sample, each having a sample fragment mass. A reference spectrum is selected with one or more reference peaks corresponding to fragments of a reference, each having a reference fragment mass. A mass difference can be determined between selected sample and reference peaks, and a group exchange can be selected based on the mass difference; e.g., where the group exchange represents a change in the sample or reference fragment masses associated with the selected peaks. The selected peaks can be shifted by the mass difference, and a fit value can be determined with respect to the reference spectrum. The fit value characterizes similarity between the respective sets of sample and reference peaks, responsive to the group exchange and corresponding peak shift.