Mass Spectrometry Signal Decomposition Using Iterative Harmonic Modeling
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Solution Overview
Problem
Current mass spectrometry techniques, particularly Fourier Transform Mass Spectrometry (FTMS), face limitations in resolution and accuracy due to frequency resolution uncertainty and the presence of noise, leading to spurious peaks and instability in peak identification.
Innovation Solution
A method that iteratively determines the number of harmonic component signals (K) by minimizing the difference between measured data and a model data set, ensuring this difference falls within a noise range, thereby reducing noise-related artefacts and improving resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Fourier Transform (FT) is used to process mass spectrometry signals, then the method is computationally efficient and provides a complete frequency spectrum, but the resolution and precision are limited by the uncertainty principle (Δf = 1/T) and produce spurious peaks due to noise
Solution Approach 1:
The patent segments the frequency spectrum into multiple frequency windows, each processed independently by the iterative reweighted least squares (IRLS) algorithm. This segmentation allows the method to achieve super-resolution in each window while maintaining computational feasibility, overcoming the fundamental resolution limit of conventional FT processing.
Solution Approach 2:
The patent replaces the conventional Fourier Transform mathematical operation with an iterative signal processing algorithm (IRLS) that models the signal as a sum of damped sinusoids. This substitution enables resolution beyond the Fourier uncertainty limit by using a different mathematical approach that explicitly models signal decay and interference patterns.
2Measurement precision
If the number of harmonic components K is increased to improve signal modeling accuracy, then the resolution improves, but the computational complexity and susceptibility to noise increase
Solution Approach 1:
The patent employs an iterative reweighted least squares algorithm that continuously refines the estimate of K (number of harmonic components) by comparing the modeled signal against the actual signal and adjusting the weights of each component. This feedback mechanism automatically determines the optimal K without requiring manual specification or exhaustive computational search, balancing resolution and complexity.
Solution Approach 2:
The patent changes the parameter K (number of harmonic components) dynamically during the iterative processing of each frequency window. By adjusting K based on the signal characteristics in each window and using reweighted least squares, the method adapts the model complexity to match the actual signal complexity, achieving high resolution without unnecessary computational burden.
3Loss of information
If conventional FT processing is used, then the complete frequency spectrum is obtained, but spurious peaks and artefacts appear due to noise and the equidistant frequency grid limitation
Solution Approach 1:
The patent uses a dynamic model of damped sinusoids with time-varying amplitudes and frequencies, rather than the static equidistant frequency grid of conventional FT. This dynamic approach allows the signal model to adapt to the actual physical behavior of ions in the mass analyzer, reducing spurious peaks while maintaining complete spectral information through iterative refinement.
Solution Approach 2:
The patent introduces an intermediary iterative processing step between signal acquisition and final spectrum generation. The IRLS algorithm acts as an intermediary that filters out noise and spurious components by repeatedly refining the signal model, thereby improving peak identification stability while preserving the complete frequency spectrum information.
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 mass spectral resolution and stability, reducing spurious peaks and artefacts, while maintaining computational efficiency, even in noisy conditions.
Implementation Method 1
The trapped ions oscillate and induce alternating image charge on detector electrodes with specific frequencies dependent on their respective mass-to-charge ratios (m/z)
Implementation Method 2
the trapped ions oscillate and induce alternating image charge on detector electrodes
Implementation Method 3
trapping ions, for instance in a combination of electrostatic and magnetic fields in the case of Fourier Transform Ion Cyclotron Resonance MS (FT-ICR MS)
Implementation Method 4
trapping ions, for instance in a combination of electrostatic and magnetic fields in the case of Fourier Transform Ion Cyclotron Resonance MS (FT-ICR MS)
Data Source
AI summary
A method comprising decomposing mass spectrometry data, especially of ion species that undergo multiple direction changes in a periodic manner, the data comprising signal and noise measured over time, into a sum of K harmonic component signals and a noise component, wherein the harmonic component signals and their number K are derived from the data and a determined quantity representative of the noise. The harmonic component signals and their number K may be determined iteratively on the basis of: using an initial value of K to calculate a minimised non-negative measure of difference R(K) between the measured and model data comprising data sets of K-harmonic component signals, and if R(K) does not lie within a noise range based on the quantity representative of the noise, changing the value of K and recalculating R(K) until R(K) lies within the noise range. Mass spectral information may be derived from the model data set.