Mass Spectrometer Calibration Using Isotope Peak Location Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing quadrupole mass spectrometers face challenges in accurately calibrating due to high scan rates and low resolution, leading to misalignment of isotope peaks and potential defects, which are exacerbated by noise and fragments, resulting in inaccurate mass measurements.
Innovation Solution
A method involving location matching and adaptive peak-matching schemes to determine candidate locations in the mass spectrum data, using a likelihood function and error bars to refine candidate locations, and iteratively adjusting the calibration process until a target tolerance is reached, thereby improving the accuracy of mass measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high scan rates are used in quadrupole mass spectrometers, then productivity is improved, but measurement precision deteriorates due to misalignment of isotope peaks
Solution Approach 1:
The patent applies feedback by using the determined offset information from isotope pattern matching to adjust and recalibrate the mass spectrometer. The system continuously monitors the alignment between expected and actual isotope patterns, and uses this feedback to correct calibration drift caused by high scan rates, thereby maintaining measurement precision while operating at high productivity.
Solution Approach 2:
The patent performs preliminary calibration and offset determination before actual measurements. By establishing the relationship between expected and observed isotope patterns in advance, the system prepares correction factors that compensate for the effects of high scan rates, enabling both high productivity and maintained precision during subsequent operations.
2Device complexity
If low resolution is used in quadrupole mass spectrometers, then device complexity is reduced, but measurement precision deteriorates due to difficulty in detecting and resolving peaks
Solution Approach 1:
The patent introduces an intermediary computational process - the isotope pattern matching algorithm - that bridges the gap between low-resolution hardware and high-precision measurement requirements. By using the known isotope pattern structure as an intermediary reference, the system can accurately identify peak locations and determine offsets even when the hardware resolution is limited, thus achieving high measurement precision without increasing device complexity.
3Ease of operation
If noise and fragments are present in mass spectrum data, then ease of operation is maintained, but measurement precision deteriorates due to inaccurate candidate location identification
Solution Approach 1:
The patent applies local quality by focusing the analysis on specific local features - the isotope patterns - rather than attempting to process the entire mass spectrum uniformly. By concentrating on the characteristic isotope cluster structures and their expected patterns, the system can accurately identify candidate locations even in the presence of noise and fragments, maintaining measurement precision without complicating the overall operation.
Data Source
AI summary
We provide a method of assessing an acquired mass spectrum, the method including the steps of: obtaining a model isotope pattern for a chemical compound of known elemental composition, providing mass spectrum data obtained from a mass spectrometer in analysis of the chemical compound, and performing location matching by comparing the mass spectrum data to the isotope pattern to match a reference location in the isotope pattern with respective corresponding candidate locations in the mass spectrum data, the location matching including: determining a likelihood function over the mass spectrum data, representing the likelihood that each location in the mass spectrum data correctly corresponds to the reference location of the isotope pattern, determining a set of candidate locations each corresponding to a local maxima in the determined likelihood function, and at each candidate location, determining an associated error bar based on the curvature of the likelihood function at that location.