3D Chemical Peak Finding for Neutral Mass Identification in LC-MS
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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, necessitating improved methods for data processing and annotation.
Innovation Solution
A system and method involving a mass spectrometer and computing device that processes mass spectra by annotating peaks, assigning ion types, grouping peaks by common neutral masses, and scoring peaks to identify analytes, utilizing machine learning models for predicting analyte identities and building libraries for efficient analyte identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional mass spectrometry analysis is used to detect all ions in complex samples, then comprehensive ion detection is achieved, but spectral interpretation complexity increases and identification accuracy decreases
Solution Approach 1:
The patent segments the complex mass spectral data by grouping peaks that share common neutral masses across multiple cycles. This divides the overwhelming total dataset into manageable subsets organized by analyte, allowing systematic interpretation without being overwhelmed by the complete spectrum of thousands of features.
Solution Approach 2:
The patent introduces an intermediary computational workflow that acts as a bridge between raw mass spectral data and analyte identification. This intermediary processing layer performs peak annotation, neutral mass calculation, and feature grouping, transforming complex spectral data into organized analyte-level information that is interpretable and actionable.
2Quantity of substance
If multiple ionization processes are used to analyze analytes, then more ion species are detected, but determining true molecular ions becomes more difficult
Solution Approach 1:
The patent employs feedback mechanisms where the system iteratively refines peak annotations and neutral mass assignments by evaluating relationships across multiple mass spectral cycles. The computational workflow uses scoring systems and consistency checks to feedback-correct identifications, progressively improving the accuracy of molecular ion determination despite the presence of multiple ion species from various ionization processes.
3Reliability
If comprehensive peak annotation is performed on all mass spectral features, then complete analyte coverage is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-grouping peaks into neutral mass families and pre-annotating peak relationships before comprehensive analyte identification. This preliminary organization of data structures and peak groupings is established upfront, enabling faster subsequent processing and reducing the computational burden during actual analyte identification while maintaining complete coverage.
4Quantity of substance
If traditional peak picking methods are used to reduce features, then data volume is reduced, but false discovery rate increases
Solution Approach 1:
The patent replaces traditional mechanical peak picking thresholding methods with a computational substitution approach based on neutral mass grouping and inter-peak relationship analysis. Instead of using fixed intensity thresholds to filter peaks, the system uses computational algorithms to identify peaks belonging to the same analyte through neutral mass consistency and relationship scoring, thereby reducing false discoveries while maintaining sensitivity.
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 accurate and efficient identification of analytes by resolving isobaric signals, correctly grouping MS peaks, and reducing noise, thereby improving the accuracy and efficiency of LC-MS data analysis and analyte identification in complex samples.
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 identifying analytes in a sample using mass spectrometry are provided. A method for identifying analytes in mass spectrometry data comprises: introducing a sample to a mass spectrometer; analyzing the sample with the mass spectrometer in a plurality of cycles; generating, for each cycle, a mass spectrum comprising at least one peak; annotating peaks in the mass spectrum based on their relationships; assigning best ion types to each peak; processing each cycle of the mass spectrum to assign a score to each of the at least one peak thereof with respect to the likely neutral mass related to the peak; grouping peaks that share a common neutral mass; and outputting the analyte neutral mass.