Mass Spectrometer Peak Extrapolation for Overlapping Ion Events
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Solution Overview
Problem
Existing methods for processing data streams from mass spectrometers, which involve setting thresholds to filter out noise, lead to inaccuracies in peak detection and area calculation, especially when dealing with overlapping ion events, as they inadvertently include or exclude parts of peaks based on their separation status.
Innovation Solution
A method that detects peaks with amplitudes above a threshold and extrapolates the segments below the threshold based on shape characteristics, using growth and decay functions to estimate the amplitude of these segments, thereby producing a more accurate filtered and compensated data stream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If a threshold is applied to filter out noise from the data stream, then noise reduction is improved, but measurement precision deteriorates because parts of genuine peaks are inadvertently excluded
Solution Approach 1:
The method performs preliminary identification of peak regions based on amplitude thresholds, then applies shape-based extrapolation to recover the portions of peaks that fall below the threshold. This two-stage approach first filters noise efficiently, then compensates for the inevitable loss of peak segments through mathematical extrapolation based on characteristic peak shapes.
Solution Approach 2:
The patent introduces shape characteristics (such as growth and decay functions) as an intermediary mechanism between the threshold filtering process and the final peak measurement. These shape models act as mediators that allow the system to infer the complete peak structure from only the threshold-exceeding portions, thereby recovering information that would otherwise be lost.
2Object-affected harmful factors
If a threshold is set high enough to ignore background noise, then noise filtering is improved, but manufacturing precision deteriorates because segments of genuine peaks are excluded from analysis
Solution Approach 1:
The method performs preliminary identification of peak regions based on amplitude thresholds, then applies shape-based extrapolation to recover the portions of peaks that fall below the threshold. This two-stage approach first filters noise efficiently, then compensates for the inevitable loss of peak segments through mathematical extrapolation based on characteristic peak shapes.
Solution Approach 2:
The patent employs feedback through iterative refinement of peak parameters. By using the detected peak apex and shape characteristics to extrapolate sub-threshold segments, the system continuously refines its estimate of the complete peak area, feeding back corrections that improve the accuracy of integrated measurements.
3Ease of operation
If conventional thresholding is used to process overlapping peaks, then processing simplicity is maintained, but measurement precision deteriorates because overlapping peaks cannot be accurately distinguished
Solution Approach 1:
The patent segments the analysis process into distinct stages: threshold-based peak identification, shape characteristic extraction, extrapolation of sub-threshold portions, and integration. This segmentation allows each stage to be optimized independently while maintaining overall simplicity, particularly through the use of standardized growth and decay functions for common peak types.
Solution Approach 2:
The patent introduces shape characteristics (such as growth and decay functions) as an intermediary mechanism between the threshold filtering process and the final peak measurement. These shape models act as mediators that allow the system to infer the complete peak structure from only the threshold-exceeding portions, thereby recovering information that would otherwise be lost.
Data Source
AI summary
A method of processing an input data stream including at least one data peak (2), comprising: detecting at least one peak (2) in the input data stream having an apex with an amplitude above a predetermined threshold (4); and extrapolating (30) the segment of the peak which has an amplitude above the predetermined threshold (7, 8), based on a shape characteristic of the peak (2), to estimate the amplitude of the segments of the peak which have an amplitude less than said threshold (15, 16).


