Real-Time Isotope Identification Using Hash Table Lookup
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Solution Overview
Problem
Real-time mass spectral analysis is challenging due to the high processing requirements for accurate spectral deconvolution, particularly in techniques like electrospray ionization and laser spray ionization, which produce ions with multiple charge states, limiting the ability to identify materials from a large pool of candidates during mass spectral analysis.
Innovation Solution
A method and system utilizing a hash table data structure for rapid retrieval and fitting of isotope profiles, pre-calculating models to minimize runtime calculations, and employing intensity correction factors for accurate material identification and quantification, enabling faster processing and real-time material identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spectral deconvolution methods are used, then accurate material identification is achieved, but processing time is excessively long
Solution Approach 1:
The patent pre-calculates isotope profiles for multiple charge states and stores them in lookup tables before actual spectral analysis. During real-time analysis, the system retrieves pre-computed profiles matching the observed m/z range and charge states, then performs deconvolution by comparing observed spectra against these stored profiles. This preliminary preparation eliminates the need for time-consuming calculations during actual measurement, reducing processing time while maintaining deconvolution accuracy.
2Productivity
If real-time analysis is implemented, then processing speed is improved, but measurement precision deteriorates
Solution Approach 1:
The system performs comprehensive pre-computation of isotope profiles covering a range of masses and charge states before analysis. These pre-computed profiles are stored in lookup tables with high precision. During real-time analysis, the system retrieves the appropriate pre-computed profiles based on observed m/z values and charge states, ensuring that even rapid analysis uses accurate reference data for deconvolution.
Solution Approach 2:
The patent dynamically adjusts the search parameters and charge state ranges based on the observed spectral data. The system identifies the m/z range and estimated charge states from the input spectrum, then retrieves only the relevant pre-computed profiles matching those parameters. This parameter-based filtering optimizes the matching process while maintaining accuracy by using appropriate reference profiles for the specific analysis conditions.
Data Source
AI summary
An embodiment of a method for real time material identification is described that comprises determining an approximate mass value for an unknown material from spectral information derived from mass spectral analysis of the unknown material; retrieving profile models that correspond to a known material from a data structure using the approximate mass value; fitting a sample profile for the unknown material from the spectral information to the profile models to generate a fit score for each fit, wherein the lowest fit score corresponds to the best fit; calculating a mass value from the best fitting profile model and the sample profile.