Mass Spectrometer Composition Quantification via Analytical Model Optimization
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Solution Overview
Problem
Existing methods for quantifying gas mixtures using mass spectrometers face challenges with noise, estimation errors, and random variations, which affect accuracy in determining composition and identifying threshold levels.
Innovation Solution
A system and method that utilize a target composition estimation system with a reference database, custom database, and modules for creating and optimizing an analytical model based on sensor data, including fragmentation, ionization, and peak shape analysis, to estimate molecular fractions and composition of gas mixtures without explicit deconvolution of peaks, using techniques like non-negative least squares optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mass spectrometer is used to quantify gas mixture composition, then measurement capability is provided, but noise and estimation errors reduce measurement precision
Solution Approach 1:
The patent applies preliminary action by creating an optimized analytical model before actual measurement. The system pre-processes reference spectra, performs peak deconvolution, and establishes correction factors for instrumental non-idealities before analyzing the target sample. This preliminary modeling reduces estimation errors and noise during the actual quantification process.
Solution Approach 2:
The patent introduces an intermediary analytical model that mediates between the raw mass spectrometer data and the final composition quantification. This model includes reference spectra databases, peak shape functions, and fragmentation patterns that act as intermediaries to filter noise and improve measurement precision by transforming raw signals into accurate compositional data.
2Measurement precision
If deconvolution of convoluted spectrum is performed, then peak separation is achieved, but noise removal is not optimal and random variations remain
Solution Approach 1:
The patent applies parameter changes by optimizing multiple parameters in the analytical model simultaneously. This includes adjusting peak shape parameters, fragmentation ratios, ionization efficiency factors, and reference spectrum parameters. By optimizing these parameters together rather than separately, the system achieves better peak separation while simultaneously reducing noise and random variations through coordinated parameter adjustment.
3Ease of operation
If theoretical isotropic mass spectrum is generated for each candidate, then composition estimation is performed, but estimation error is not reduced
Solution Approach 1:
The patent applies dynamics by transitioning from static theoretical isotropic spectra to dynamic, instrument-specific analytical models. The system adapts the analytical model to match the specific characteristics of the mass spectrometer being used, including its peak shapes, resolution, and fragmentation patterns. This dynamic adaptation significantly reduces estimation error compared to using generic theoretical spectra.
4Ease of operation
If ratios of molecular peaks are used to overlap peaks, then peak identification is facilitated, but effective noise floor is not reduced
Solution Approach 1:
The patent applies segmentation by dividing the complex spectral analysis into multiple independent components. Instead of relying solely on peak ratios, the system segments the analysis into peak detection, deconvolution, reference matching, and quantification steps. Each segment is optimized independently, allowing for more effective noise reduction at each stage while maintaining ease of peak identification through systematic processing.
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 accuracy by optimizing the analytical model and prioritizing mass-to-charge ratios, improving noise robustness and quantifying low-concentration gases, thereby addressing the limitations of existing methods.
Implementation Method 1
The mass spectrometer may ionize different gases at different relative rates
Implementation Method 2
each ion appearing exactly corresponding to its mass to charge ratio (i.e. m/z value)
Data Source
AI summary
System for quantifying a composition of a target sample based on a scan output of a first type of sensor includes a reference database, a custom database and a set of modules. The set of modules includes an analytical model creation module that creates an analytical model of the first type of sensor, a sample processing module that processes samples that include accurately known compositions using the first type of sensor under a standard pressure condition, a molecular fraction estimation module that estimates molecular fraction of the samples using an estimation method and the analytical model, an analytical model optimization module that optimizes the analytical model by comparing the molecular fraction of the samples with a pre-determined molecular fraction of the samples, and a target composition estimation module that estimates a composition of the target sample based on the scan output using the estimation method with the optimized analytical model.