Ion Fingerprint Library Search for LC-MS Peak Annotation
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Solution Overview
Problem
Liquid chromatography-mass spectrometry (LC-MS) faces challenges in accurately identifying ions and determining molecular weights due to the presence of numerous related species, leading to complex spectral interpretation and inefficient analysis of complex samples, where thousands of features correspond to a smaller number of actual analytes.
Innovation Solution
A system and method that utilize a mass spectrometer and computing device to analyze mass spectra by annotating peaks, assigning ion types, grouping peaks by common neutral masses, and applying machine learning models to predict analyte identities, thereby improving data processing and identification of analytes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mass spectrometry analysis is used to identify ions and determine molecular weights, then the analysis can be performed with standard equipment and procedures, but the spectral interpretation becomes complicated and inaccurate due to the presence of numerous related species including protonated peaks, adducts, and isotopic peaks
Solution Approach 1:
The patent segments the complex mass spectrum into distinct components by separating peaks into different categories (protonated peaks, adducts, isotopic peaks, neutral losses) based on their mass relationships. This segmentation allows each type of peak to be processed and interpreted independently, reducing the overall complexity of spectral analysis while improving identification accuracy.
Solution Approach 2:
The patent introduces an intermediary computational framework that acts as a mediator between the raw mass spectrum data and the final ion identification results. This framework uses mass difference relationships and neutral mass calculations to bridge the gap between observed peaks and underlying analytes, simplifying the interpretation process while maintaining high measurement precision.
2Measurement precision
If comprehensive peak annotation and ion type assignment are performed to accurately identify analytes, then the identification accuracy improves, but the data processing time and computational resources increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing mass difference relationships between different ion types (protonated peaks, adducts, isotopic peaks) and their corresponding neutral masses. This pre-processing creates a reference framework that accelerates the actual peak annotation process, allowing rapid matching of observed peaks to known mass relationships without performing exhaustive computations during analysis.
Solution Approach 2:
The patent changes the analytical parameters by shifting from direct peak-by-peak analysis to a neutral mass-centered approach. By calculating and using neutral mass values as the reference parameter, the system can efficiently group and evaluate multiple peaks belonging to the same analyte, reducing computational complexity while maintaining high identification accuracy.
3Loss of information
If all detected features are treated as separate analytes, then no information is lost, but the result shows thousands of features corresponding to a much smaller number of actual analytes, reducing the usefulness of the analysis
Solution Approach 1:
The patent merges multiple detected features (peaks) that correspond to the same actual analyte by grouping them based on their mass relationships and neutral mass calculations. Peaks representing different ion types (protonated, adducts, isotopes) of the same analyte are combined into a single unified identification result, eliminating redundancy while preserving all relevant information about the analyte's presence and characteristics.
Solution Approach 2:
The patent creates a simplified copy or representation of the complex spectral data in terms of neutral masses and their corresponding peak groups. This copied representation maintains the essential information about analyte identities and relationships while presenting it in a more manageable and interpretable format, improving productivity without losing critical analytical information.
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
This approach enables efficient and accurate identification of analytes by resolving isobaric signals and correctly grouping MS peaks, reducing noise, and improving the annotation and assignment of MS peaks, leading to more precise analyte identification and molecular weight determination.
Implementation Method 1
analyte ions are frequently formed by the addition or removal of protons, or addition of a metal ion such as sodium ion, potassium ion, or calcium ions, to generate molecular ions in positive mode and/or in negative mode
Data Source
AI summary
Methods and systems for building and using an analyte library are provided. One aspect is a method for building an analyte library, the method comprising receiving mass spectrum data from analysis of a sample using mass spectrometry, mass spectrum data including a mass spectrum and a sample matrix, and the sample including an analyte, identifying peaks in the mass spectrum, assigning at least one ion type to the peaks, annotating the peaks for the analyte based on the sample matrix, extracting an ion fingerprint for the analyte based on the annotated peaks and storing an analyte identification entry including the ion fingerprint for the analyte.