Mass Spectrum Processing Using Learned Simulated Spectra
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Solution Overview
Problem
Current mass spectrometry techniques face challenges in accurately distinguishing between sample peaks and background noise peaks, leading to increased analyst workload and reduced precision in identifying minute components within samples.
Innovation Solution
A mass spectrum processing apparatus and method that utilizes a learned model to generate a simulated spectrum, which is used to filter out background noise peaks from the mass spectrum, thereby enabling precise discrimination of sample peaks through a peak filter, reducing analyst burden and improving discrimination accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If peaks are discriminated based simply on intensity, then the discrimination process is simple, but it is difficult to accurately distinguish between sample peaks and background noise peaks
Solution Approach 1:
The patent transforms the peak discrimination task from simple intensity-based comparison to a comprehensive parameter analysis by generating simulated spectra that incorporate multiple characteristics including peak shape, width, position, and intensity patterns. This allows the system to maintain operational simplicity while achieving accurate discrimination through multi-parameter matching rather than single-parameter comparison.
Solution Approach 2:
The patent creates simulated spectra that serve as templates or copies of expected sample peak patterns. These simulated spectra are generated based on known sample characteristics and are then compared against the actual mass spectrum to identify and extract true sample peaks. This copying approach enables accurate discrimination without requiring complex manual analysis.
2Measurement precision
If all peaks are discriminated by visual inspection by the analyst, then discrimination accuracy can be maintained, but a large burden is placed on the analyst
Solution Approach 1:
The patent implements a self-service system where the mass spectrum processing apparatus automatically performs peak discrimination through simulated spectrum generation and comparison. The system serves itself by autonomously identifying sample peaks without requiring analyst intervention for each peak evaluation, thereby maintaining high accuracy while eliminating the burden of manual inspection and significantly improving productivity.
Solution Approach 2:
The patent replaces the mechanical process of manual visual inspection with an automated computational system. Instead of analysts visually examining and discriminating peaks, the system uses algorithmic generation of simulated spectra and automated comparison processes to perform the discrimination task, substituting human mechanical analysis with computational automation while maintaining or improving accuracy.
3Quantity of substance
If background noise peaks are not effectively removed, then all peaks remain for analysis, but mass spectrometry of minute-amount components becomes difficult
Solution Approach 1:
The patent effectively extracts and removes background noise peaks from the mass spectrum by comparing actual peaks against simulated spectra. Through this extraction process, true sample peaks are identified and separated from background noise, allowing the system to maintain a sufficient number of analyzable peaks while eliminating interference from noise peaks that would otherwise obscure minute-amount components.
Data Source
AI summary
A preprocessor extracts a plurality of spectra to be processed, from an overall mass spectrum. A simulated spectrum generator having a learned model generates a simulated spectrum having a peak discriminating action, from each mass spectrum. A postprocessor generates a combined simulated spectrum based on the plurality of simulated spectra. A peak filter executes peak discrimination on a peak list using the combined simulated spectrum.