Mass Spectrum Noise Reduction via Frequency Domain Filtering
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Solution Overview
Problem
Mass spectrometers face challenges in accurately interpreting mass spectra due to background noise, which obscures real signal data.
Innovation Solution
A method that transforms the original mass spectrum into the frequency domain, identifies dominant frequencies, generates a noise frequency spectrum by filtering these frequencies, and then transforms it back into the mass domain to create a noise mass spectrum, which is subtracted from the original to produce a corrected mass spectrum.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If background noise is present in mass spectra, then the mass spectrum can be obtained, but the real signal is obscured and measurement precision deteriorates
Solution Approach 1:
The mass spectrum is segmented into signal components and noise components through frequency domain analysis. The method divides the spectrum into discrete frequency components, identifies dominant frequencies corresponding to noise, and separates them from the actual signal components for targeted removal.
Solution Approach 2:
The noise component is extracted from the mass spectrum by transforming to the frequency domain, identifying and isolating dominant frequency components that represent background noise, then removing these extracted noise components while preserving the signal.
2Measurement precision
If frequency domain transformation and filtering are applied to remove noise, then signal clarity improves, but processing complexity increases
Solution Approach 1:
The method replaces complex iterative noise removal procedures with a direct frequency domain transformation approach. By using Fourier transformation to convert the mass spectrum into frequency components, the system substitutes mathematical transformation for complex mechanical or procedural noise filtering steps.
Solution Approach 2:
The method changes the representation parameters of the mass spectrum by transforming from the mass domain to the frequency domain. This parameter transformation allows noise identification and removal through frequency analysis, simplifying the overall processing by working in a more suitable parameter space.
Data Source
AI summary
Systems and methods for reducing background noise in a mass spectrum. The method includes the following steps of: (a) obtaining an original mass spectrum; (b) determining a noise mass spectrum corresponding to background noise in the original mass spectrum; and (c) determining a corrected mass spectrum by subtracting the noise mass spectrum from the original mass spectrum. Step (b) of the method may include the steps of: A) effecting a transformation of the original mass spectrum into the frequency domain to obtain an original frequency spectrum; B) identifying at least one dominant frequency in the original frequency spectrum; C) generating a noise frequency spectrum by selectively filtering for said dominant frequencies; and D) determining the noise mass spectrum by effecting a transformation of the noise frequency spectrum into the mass domain. Preferably for each correlated pair of original and noise intensity data points, the minimum value is determined and the noise mass spectrum is modified by making the noise intensity data point equal to the minimum value.