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Miniature mass spectrometer signal processing based on ensemble empirical mode decomposition feature enhancement
Ming Li1, Chenrui Zhan1, Yueguang Lv2
1School of Electrical and Control Engineering, North China University of Technology, Beijing, China.
Rapid Communications in Mass Spectrometry : RCM
|May 9, 2023
Summary
Ensemble empirical mode decomposition (EEMD) enhances miniature mass spectrometry signals, improving accuracy and stability for on-site detection. This method offers a new approach for processing mass spectrometry data.
Area of Science:
- Analytical Chemistry
- Spectroscopy
Background:
- Miniature mass spectrometers are crucial for rapid on-site detection.
- Analysis accuracy and stability are limited by sample pretreatment and environment.
- Feature enhancement is needed to improve miniature mass spectrometer performance.
Purpose of the Study:
- To investigate the impact of ensemble empirical mode decomposition (EEMD) on caffeine mass spectrometry (MS) signal analysis.
- To evaluate EEMD's effectiveness in improving determination coefficient (R²) and relative standard deviation (RSD).
Main Methods:
- Applied EEMD combined with polynomial curve fitting to caffeine MS signals.
- Analyzed signals with varying and constant concentrations.
- Used wavelet analysis for comparative verification.
Main Results:
- EEMD adaptively decomposed caffeine MS signals more effectively than wavelet analysis.
- EEMD enhanced features yielded R² > 0.999 and RSD < 2%.
- EEMD significantly outperformed wavelet analysis in both R² and RSD.
Conclusions:
- EEMD feature enhancement substantially improved R² and RSD for caffeine MS signals.
- This method enhances data accuracy and stability for miniature mass spectrometers.
- EEMD provides a novel signal processing technique for miniature mass spectrometry.
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