Mass Spectral Analysis Using Full Profile Regression
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Solution Overview
Problem
Current mass spectrometry methods face challenges in accurately determining the elemental composition of small and large molecules due to issues with monoisotope abundance, peak shape, charge states, and interference from co-existing ions, leading to inaccurate mass measurements and incorrect identification of molecular structures, especially for larger molecules and complex biological samples.
Innovation Solution
A method for mass spectral analysis using full spectral profile data, incorporating all significant isotopes and accounting for charge states, with spectral accuracy as the objective function in a constrained nonlinear optimization process, allowing for precise determination of elemental compositions and modifications, even in the presence of interfering ions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If monoisotope mass measurement is used for elemental composition determination, then measurement simplicity is improved, but measurement precision deteriorates for large molecules where monoisotope abundance is low
Solution Approach 1:
The patent transitions from single-point monoisotope mass measurement to full spectral profile analysis, adding the dimension of spectral shape and intensity distribution across multiple isotopes. This allows determination of elemental composition using the complete isotopic pattern rather than relying solely on the monoisotope peak, thereby maintaining measurement simplicity while improving precision for large molecules.
Solution Approach 2:
The patent changes the measurement parameter from single monoisotope mass to full spectral accuracy incorporating all significant isotopes (A, A+1, A+2, etc.). By utilizing the complete isotopic distribution and spectral shape information, the method achieves accurate elemental composition determination even when monoisotope abundance is low, as the combined information from multiple isotopes compensates for individual peak intensity limitations.
2Measurement precision
If full spectral profile analysis incorporating all significant isotopes is used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements an iterative optimization process where the spectral accuracy is calculated based on the match between observed and theoretical isotopic patterns. The algorithm adjusts elemental composition hypotheses and refines the analysis through feedback from spectral fitting quality, convergence criteria, and goodness-of-fit metrics, thereby managing computational complexity through structured iterative refinement rather than exhaustive analysis.
Solution Approach 2:
The patent performs preliminary spectral calibration and establishes theoretical isotopic patterns based on candidate elemental compositions before conducting full spectral fitting. By pre-calculating expected isotopic distributions and preparing reference spectra in advance, the method reduces the complexity of real-time full spectral analysis while maintaining measurement precision.
3Measurement precision
If spectral accuracy optimization is performed for full spectral profile data, then measurement precision is improved, but loss of time increases due to computational optimization requirements
Solution Approach 1:
The patent applies partial optimization by focusing spectral accuracy improvement on the most significant isotopes (A, A+1, A+2) that contribute most to the isotopic pattern, rather than exhaustively optimizing all possible isotopic contributions. This selective approach achieves sufficient spectral accuracy for elemental composition determination while reducing computational processing time compared to complete spectral optimization.
Solution Approach 2:
The patent performs preliminary spectral calibration and pre-processing of mass spectral data before full spectral analysis. By preparing the data in advance, including initial parameter estimation and baseline correction, the method reduces the computational burden during the actual spectral accuracy optimization, thereby decreasing overall processing time while maintaining measurement precision.
Data Source
AI summary
A method for mass spectral analysis of molecules based on full mass spectral profile or raw scan mode data, comprising the steps of specifying the basic building blocks for the molecule; estimating initial values including trial numbers of building blocks, charge states, and possible modifications; calculating discrete isotope distributions based on elemental compositions; calculating a profile mode theoretical mass spectrum using a target mass spectrum peak shape function; performing regression analysis between acquired profile mode mass spectrum data and calculated theoretical mass spectrum data and reporting regression statistics; using regression statistics as feedbacks to update initially estimated values including trial numbers of building blocks, charge states, and possible modifications; and repeating selected step to optimize the regression statistics. A mass spectrometer operating in accordance with the method. A medium having computer readable program instructions for causing a mass spectrometer associated with a computer to operate in accordance with the method.