Spectral Distance Algorithm for Mass Spectrometry Formula Assignment
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Solution Overview
Problem
Ultra-high resolution mass spectrometry struggles with identifying molecular formulas due to the exponential increase in candidate formulae with mass, leading to time-consuming and inaccurate molecular determination, despite advances in mass accuracy.
Innovation Solution
A computer-implemented method calculates spectral distance (SD) and pattern spectral distance (PSD) to identify corresponding peaks by measuring position and intensity differences between theoretical and measured isotopic peaks, incorporating expected errors and weighting by peak abundance, to improve the accuracy and specificity of elemental composition assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If accurate mass measurement by mass spectrometry is used to determine elemental composition, then mass accuracy is improved, but the number of candidate formulae increases exponentially with mass making identification challenging
Solution Approach 1:
The patent segments the identification process into multiple filtering stages: initial mass-based filtering, isotopic pattern matching, and spectral distance calculation. This divides the overwhelming task of evaluating all candidate formulae into manageable segments that progressively eliminate incorrect candidates.
Solution Approach 2:
The patent introduces additional dimensions beyond mass accuracy by incorporating isotopic abundance ratios and spectral distance metrics. This transforms the problem from a one-dimensional mass matching task to a multi-dimensional analysis that uniquely identifies molecular formulas even when multiple candidates exist at the same mass.
2Productivity
If automated procedures are used for analysis of mass spectrometric data, then productivity is improved, but the complexity of data processing increases
Solution Approach 1:
The patent implements automated self-service through computer-implemented methods that automatically calculate spectral distances, compare isotopic patterns, and assign molecular formulas without manual intervention. The system serves itself by processing large datasets efficiently through algorithmic approaches.
Solution Approach 2:
The patent manages processing complexity by dynamically adjusting parameters such as mass tolerance thresholds, isotopic abundance tolerances, and spectral distance cutoffs. These parameter changes allow the automated system to balance between thoroughness and computational efficiency.
3Measurement precision
If molecular formula assignment is performed manually to ensure accuracy, then measurement precision is improved, but the time required for sample characterization increases
Solution Approach 1:
The patent incorporates feedback mechanisms where the automated system calculates spectral distances for candidate formulae, compares predicted versus observed isotopic patterns, and uses this feedback to rank and select the most likely molecular formula. This automated feedback loop maintains accuracy while eliminating manual review time.
Solution Approach 2:
The patent replaces the mechanical process of manual spectral analysis with computer-implemented algorithms that automatically calculate spectral distances and evaluate isotopic patterns. This substitution maintains or improves accuracy while dramatically reducing the time required for molecular formula assignment.
Data Source
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AI summary
A method of characterising a sample from spectrometric data using calculation of spectral distance values is disclosed, for use in the field of mass spectrometry. Molecular formula assignment of peaks in mass spectral data is difficult and time-consuming, and the invention provides a computer implemented method of finding a most likely elemental composition of a measured spectral peak of interest. The method analyses isotopic peaks in a portion of the spectrum, using both their mass positions and intensities, to determine a spectral distance between those peaks and isotopic peaks of a candidate composition, finding peaks that match (140). A pattern spectral distance is determined (150) to provide a measure of the correspondence between a set of those peaks in the measured spectrum and peaks of each of a number of candidate compositions. The spectral fit is used to determine a most likely candidate composition (160).