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Mass Spectrometry: Complex Analysis01:21

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Wavelet Transform for Frequency-Division Multiplex Mass Spectrometry Combined With High-Performance Liquid

Hiroki Suzuki1, Yoshinori Iiguni1, Shinya Kitagawa1

  • 1Department of Life Science and Applied Chemistry, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, Japan.

Journal of Separation Science
|March 26, 2026
PubMed
Summary
This summary is machine-generated.

Frequency-division multiplexing mass spectrometry (FDM-MS) with wavelet transform (WT) processing enhances analytical throughput. This method enables simultaneous sample analysis by effectively separating multiplexed chromatograms, improving efficiency in HPLC-MS workflows.

Keywords:
HPLC–MSfrequency domain multiplexingsimultaneous analyseswavelet transform

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Area of Science:

  • Analytical Chemistry
  • Spectrometry
  • Chromatography

Background:

  • Frequency-division multiplexing mass spectrometry (FDM-MS) combined with multiple high-performance liquid chromatographs (HPLCs) increases analytical throughput by enabling simultaneous analysis of multiple samples.
  • In FDM-MS, chromatograms from individual HPLCs are modulated at specific frequencies and observed as a mixed chromatogram in a single mass spectrometer, necessitating effective signal extraction methods.

Purpose of the Study:

  • To investigate a novel signal extraction approach using wavelet transform (WT) for FDM HPLC-MS, replacing the traditional Fourier transform (FT) method.
  • To evaluate the effectiveness of WT in resolving individual chromatograms from mixed signals while preserving analyte elution times and suppressing interference.

Main Methods:

  • Mixed chromatograms from FDM HPLC-MS were processed using wavelet transform (WT), generating two-dimensional scalograms.
  • Individual chromatograms were extracted by analyzing signal intensity profiles along lines corresponding to modulation frequencies within the scalograms.
  • Complex Morlet wavelet parameters were optimized (B = 3.5, C = 1.5) to mitigate interference peaks, and internal standards were employed for quantitative analysis.

Main Results:

  • WT processing successfully resolved individual chromatograms based on modulation frequencies, closely matching original chromatograms.
  • Optimized WT parameters effectively suppressed interference peaks arising from broad spectra.
  • Signal intensity dependency on modulation frequency was observed, but the introduction of internal standards enabled accurate quantitative comparisons across simultaneously analyzed samples.

Conclusions:

  • Wavelet transform-based signal processing is an effective method for FDM HPLC-MS, enabling the simultaneous analysis of multiple samples and significantly improving analytical throughput.
  • The WT approach offers a viable alternative to FT for chromatogram extraction in multiplexed HPLC-MS systems.
  • The successful implementation of WT processing, coupled with internal standards, validates its utility for high-throughput quantitative analysis in complex chromatographic separations.