Peak Predictor for Mass Spectrometry Saturation Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The dynamic range of a mass spectrometer is limited by detector saturation, causing peak flattening and distortion, which results in inaccurate peak measurement and potential false detection of multiple peaks.
Innovation Solution
A computer-implemented peak predictor system that uses confidence values to differentiate between reliable and unreliable data points, applying a peak model to predict intensities in saturated regions and correct for detector saturation, employing a combination of system and predictor confidence values to produce accurate peak reconstructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ion flux is increased to improve detection sensitivity, then measurement precision is improved, but detector saturation occurs causing peak flattening and measurement accuracy degradation
Solution Approach 1:
The patent applies preliminary action by using a peak predictor to model and predict the true peak shape and intensity before detector saturation distorts the signal. The system pre-establishes a relationship between observed saturated peaks and expected unsaturated peaks through confidence-based modeling, allowing correction of saturation effects before they completely degrade the measurement.
Solution Approach 2:
The patent implements feedback through an iterative confidence-based correction process. The system continuously compares predicted peak intensities with observed saturated peak data, adjusts confidence values, and refines the peak prediction to account for detector saturation effects. This feedback loop enables real-time correction of saturation-induced measurement errors.
2Productivity
If detector dynamic range is extended to accommodate higher ion flux, then productivity is improved, but detector saturation and peak distortion still occur at high intensities
Solution Approach 1:
The patent introduces an intermediary peak predictor model that mediates between the raw saturated detector signal and the true peak characteristics. This intermediary computational model translates distorted detector responses into accurate peak intensity and shape information, effectively extending the usable dynamic range beyond the detector's physical limitations.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting confidence values and prediction parameters based on the degree of detector saturation detected. The system modifies its analysis parameters in real-time to optimize peak reconstruction accuracy across different intensity ranges, allowing accurate measurement from low to high ion flux conditions.
3Measurement precision
If internal standard is used to correct ion source effects, then measurement precision is improved, but detector saturation cannot be corrected since only the analyte is affected
Solution Approach 1:
The patent applies segmentation by separating the correction of ion source effects (handled by internal standards) from the correction of detector saturation effects (handled by the peak predictor). This segmentation allows each correction mechanism to operate independently on its specific problem, with the peak predictor specifically addressing saturation distortion that internal standards cannot correct.
Data Source
AI summary
Systems and methods are used to predict intensities for points not measured or not measured with a high degree of confidence of a peak using a peak predictor. A set of data is selected from the plurality of intensity measurements that includes a peak. Confidence values are assigned to each data point in the set of data producing a plurality of confidence value weighted data points. A peak predictor is selected. The peak predictor is applied to the plurality of confidence value weighted data points of the peak that have confidence values greater than a first threshold level using the prediction module, producing predicted intensities for data points of the peak not measured and/or measured data points of the peak that have confidence values less than or equal to a second threshold level. The confidence values can include system confidence values, predictor confidence values, or any combination of the two.