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Miniature Mass Spectrometer Signal Processing Based on EEMD Feature Enhancement
  • +5
  • Ming Li,
  • Chenrui Zhan,
  • Yueguang Lv,
  • Jiwen Chen,
  • Yutian Wang,
  • Sixian Lu,
  • Yingqi Wan,
  • Qiang Ma
Ming Li
North China University of Technology
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Chenrui Zhan
North China University of Technology
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Yueguang Lv
Chinese Academy of Inspection and Quarantine
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Jiwen Chen
North China University of Technology
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Yutian Wang
North China University of Technology
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Sixian Lu
North China University of Technology
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Yingqi Wan
North China University of Technology
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Qiang Ma
Chinese Academy of Inspection and Quarantine

Corresponding Author:maqiang@caiq.org.cn

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Abstract

RATIONALE: The high sensitivity of the miniature mass spectrometer plays an irreplaceable role in rapid on-site detection. However, its analysis accuracy and stability should be improved due to the influence of sample pretreatment and use environment. The present study investigates the processing effects of EEMD feature enhancement methods on the determination coefficient and relative standard deviation of caffeine mass spectrometry signals. METHODS: This paper employs the EEMD method combined with polynomial curve fitting to enhance the characteristics of seven Caffeine mass spectrum signals with different concentrations and fifteen groups of Caffeine mass spectrum signals with the same concentration, and the wavelet analysis method was used for comparative verification. The determination coefficient and relative standard deviation of the two methods were compared. RESULTS: We found the EEMD method’s capability in adaptively decomposing Caffeine mass spectrum signals is better than wavelet analysis method. The determination coefficient of the EEMD enhanced feature is better than 0.999, and the relative standard deviation is better than 2%, and both are better than wavelet analysis methods. CONCLUSIONS: The feature enhancement processing using the EEMD method has significantly improved the determination coefficient and relative standard deviation of the sample curve, improving the accuracy and stability of the data and providing a new way for miniature mass spectrometer signal processing.
07 Apr 2023Submitted to Rapid Communications in Mass Spectrometry
08 Apr 2023Submission Checks Completed
08 Apr 2023Assigned to Editor
08 Apr 2023Review(s) Completed, Editorial Evaluation Pending
08 Apr 2023Reviewer(s) Assigned
28 Apr 2023Editorial Decision: Revise Major
06 May 20231st Revision Received
07 May 2023Submission Checks Completed
07 May 2023Assigned to Editor
07 May 2023Review(s) Completed, Editorial Evaluation Pending
07 May 2023Editorial Decision: Accept