Optimization of the Entropy-Based Wavelet Method for Removing Strong RF and AC Interferences in a Charge Detection Linear Ion Trap Mass Spectrometer.

We developed an entropy-based wavelet method to effectively remove interference from strong radio frequency (RF) and auxiliary alternating current (AC) fields in a linear ion trap (LIT) mass spectrometer coupled to a charge sensing particle detector (CSPD). By optimizing the energy-to-Shannon entropy, we identified the optimal mother wavelet family and decomposition level and determined suitable threshold values based on the median of sub-band coefficients at each decomposition level. These thresholds were applied as rigid criteria across all decomposition levels to eliminate noise interferences and avoid the arbitrary choice of the threshold. This entropy wavelet-based method successfully denoised high-mass protein mass spectra, achieving significant improvements in signal-to-noise ratio (S/N) for immunoglobulin G (IgG) and alpha-2-macroglobulin (A2M) ions, with increases of 68.03% and 81.73%, respectively. Our method surpasses previously reported baseline correction techniques, such as orthogonal wavelet packet decomposition (OWPD) filtering, and enhances the sensitivity of LIT mass spectrometry (LIT-MS) in analyzing high-mass protein ions.