Spectral Data Selection via Dynamic Parameter Variation
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Solution Overview
Problem
Current mass spectrometry methods often result in poorly optimized spectral data due to fixed or compromised operational parameters, leading to suboptimal signal sensitivity and increased noise, especially when analyzing multiple species simultaneously.
Innovation Solution
A method involving sequential acquisition periods with varying operational parameters allows for the selection of optimal spectral data for a target ion by storing and interrogating data from different acquisition periods, optimizing signal-to-noise ratios and discarding less optimal data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a fixed operational parameter is used for simultaneous analysis of multiple species, then the analysis speed is improved, but the signal-to-noise ratio deteriorates
Solution Approach 1:
The patent applies dynamics by making the operational parameter variable rather than fixed. The parameter is dynamically adjusted across different acquisition periods to match the optimal values for different species being analyzed, allowing the system to adapt to varying requirements while maintaining high analysis speed through automated post-processing selection of optimal data subsets.
Solution Approach 2:
The patent segments the spectral data into multiple acquisition periods, each acquired at a different operational parameter value. This segmentation allows independent evaluation and selection of the best data subsets for each species, improving signal-to-noise ratio while maintaining efficient analysis through parallel data collection across segments.
2Adaptability or versatility
If a compromised operational parameter value is selected for simultaneous analysis, then the adaptability to multiple species is improved, but the measurement precision deteriorates
Solution Approach 1:
The system dynamically varies the operational parameter across different acquisition periods to cover the optimal ranges for multiple species. This dynamic approach replaces the static compromised value with a time-varying parameter that adapts to each species' requirements, achieving both versatility and high precision through post-processing selection.
Solution Approach 2:
The patent employs periodic variation of the operational parameter through sequential acquisition periods at different parameter values. This periodic action ensures that each species receives optimal parameter conditions during specific periods, with the best data subsets subsequently selected through automated interrogation, combining adaptability with measurement precision.
3Productivity
If spectral data from all acquisition periods is combined, then the productivity is improved, but the measurement precision deteriorates due to inclusion of non-optimal data
Solution Approach 1:
The patent extracts only the optimal subsets of spectral data from the total acquired data through automated interrogation and selection based on predefined criteria. This extraction process removes non-optimal data that would degrade quality, while retaining the high productivity benefit of acquiring data across multiple acquisition periods at different parameter values.
Solution Approach 2:
The system changes operational parameters across different acquisition periods and then selects data subsets based on the parameter values that yielded optimal results for each species. This parameter-based selection approach maintains productivity by utilizing multiple acquisition periods while ensuring precision by excluding data acquired at non-optimal parameter settings.
Data Source
AI summary
A method of mass spectrometry or ion mobility spectrometry is disclosed. The method comprises: providing a plurality of species of ions; analyzing the ions during a plurality of sequential acquisition periods so as to obtain spectral data relating to the ions; varying the value of an operational parameter of the spectrometer such that it has different values during the different acquisition periods, wherein the spectral data obtained for a given ion varies depending on the value of the operational parameter; storing the spectral data obtained during the different acquisition periods separately; selecting a target ion; and then interrogating the spectral data so as to identify a set of first acquisition periods that include data corresponding to said target ion. Selecting spectral data from only a subset of the first acquisition periods allows the selection of the optimal spectral data for the target ion, while discarding less optimal data.