Mass Spectrometry Peak Selection for Accurate Substance Quantification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current mass spectrometry data analysis methods face challenges in accurately quantifying substances, particularly when samples are separated into constituent analytes over a time parameter, as they often rely on single raw data samples and struggle with complex Data Independent Acquisition (DIA) data, leading to inefficiencies in identifying and quantifying chemical compositions.
Innovation Solution
The method involves using a Peak Quality Factor (PQF) algorithm to objectively evaluate peak quality over a selected time parameter range, iteratively adjusting the range to meet criteria, and selecting a subset of peaks for quantification based on their quality and alignment, allowing for improved substance quantification without relying on correlation with product ions or precursor masses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional DIA data analysis algorithms are used, then sample identification can be achieved, but quantification accuracy deteriorates due to reliance on single raw data samples and inability to handle complex separated analyte data
Solution Approach 1:
The patent segments the complex DIA data analysis process into distinct stages: peak detection, peak quality factor calculation, and quantification. By dividing the data processing into manageable segments with specific quality criteria, the system achieves accurate quantification without being overwhelmed by the overall data complexity.
Solution Approach 2:
The patent performs preliminary actions by calculating peak quality factors and establishing quality criteria before final quantification. This preliminary evaluation of peak quality ensures that only reliable peaks are used for quantification, improving accuracy while maintaining systematic control over the complex analysis process.
2Reliability
If multiple product ions are correlated across retention time for identification, then identification reliability improves, but quantification becomes more complex and less accurate due to interference from co-eluting substances
Solution Approach 1:
The patent extracts the peak quality factor as a separate, independent metric from the identification process. By taking out the quality evaluation as a distinct step that can be applied to individual peaks regardless of their identification status, the system maintains identification reliability while enabling accurate quantification of reliable peaks without being affected by identification uncertainties or co-eluting substance interference.
3Ease of operation
If a single retention time window is used for product ion matching, then analysis simplicity is maintained, but quantification accuracy deteriorates due to inclusion of interfering peaks from co-eluting analytes
Solution Approach 1:
The patent introduces dynamic peak quality factor calculation that adapts to each individual peak's characteristics rather than applying a static retention time window to all peaks. This dynamic approach maintains operational simplicity by using automated calculations while improving accuracy by evaluating each peak's quality based on its specific context and shape, automatically excluding interfering peaks.
4Productivity
If conventional peak integration methods are used, then processing speed is maintained, but quantification reliability deteriorates due to inability to objectively evaluate peak quality and select appropriate peaks
Solution Approach 1:
The patent implements self-service through automated peak quality factor calculation and objective criterion-based peak selection. The system automatically evaluates each peak's quality and selects appropriate peaks for quantification without requiring manual intervention, maintaining high processing speed while significantly improving quantification reliability through objective quality assessment.
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 enhances the accuracy and consistency of substance quantification by objectively determining the optimal time parameter range and selecting relevant peaks, improving the reliability of mass spectrometry data analysis across multiple samples.
Implementation Method 1
The sample is separated by a first separator (for example, a chromatographic or ion mobility separator) into constituent analytes over a time parameter
Data Source
AI summary
A sample is separated by a first separator into constituent analytes over a time parameter. The constituent analytes are analysed by a mass spectrometer, which provides intensity measurements against mass-to-charge ratio for each constituent analyte. A relationship of the measured intensity at a mass-to-charge ratio over the time parameter for each constituent analyte defines a respective peak. A peak quality factor may be determined for each of the peaks and/or a common peak position in respect of the time parameter may be determined for at least some of the peaks. A specific range for a time parameter to be used for quantifying the substance may be based on the peak quality factor (or factors). Alternatively, a subset of the peaks may be selected for quantification of the substance, based on the determined peak quality factors and/or the determined common peak position.