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.
None:
Frequency-division multiplexing mass spectrometry (FDM-MS) combined with multiple high-performance liquid chromatographs (HPLCs) is a method for increasing analytical throughput in HPLC-MS by enabling the simultaneous analysis of multiple samples (Analyst, 2019, 144, 2922-2928). In FDM HPLC-MS, chromatograms from each HPLC, modulated at individual frequencies, are observed in a single mass spectrometer as a mixed chromatogram; therefore, extraction processing is essential to obtain each chromatogram. In this study, we investigated a novel signal extraction approach based on the wavelet transform (WT) instead of the previous Fourier transform-based protocol. Application of WT processing to mixed chromatograms produced two-dimensional scalograms, which enabled the resolution of individual chromatograms according to their modulation frequencies while preserving the analytes' elution times. Chromatograms were extracted by tracing signal intensity profiles along selected lines corresponding to each modulation frequency. Although the extracted chromatograms closely matched the original ones, interference peaks were observed in some cases due to broad spectra. Optimization of the complex Morlet wavelet parameters (B = 3.5, C = 1.5) effectively suppressed such interference. It was also found that the signal intensity of chromatograms extracted from the scalogram depended on the modulation frequency. This dependency suggests that quantitative comparison among simultaneously analyzed HPLC results is difficult. However, this issue was resolved by introducing internal standards into each HPLC-MS system, thereby enabling quantitative comparison. Consequently, this fundamental study demonstrated that combining FDM HPLC-MS with WT-based signal processing is effective for the simultaneous analysis of samples, thereby improving analytical throughput.
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