Rationale Field asymmetric waveform ion mobility spectrometry (FAIMS) has a great potential to become a portable technology for rapid detection of chemical and biological agents. However, the ion current signals, measured at the exit of the planar FAIMS directly, may contain different types of noises. The peak information in the FAIMS spectrum, such as the compensation voltage (CV) value at the maximum peak intensity (CVP) and the peak width at half maximum (Wh), could not be accurately determined under the weak signal condition, which significantly limits the achievable instrument sensitivity, and there are no existing solutions to the problem. Methods This study analyzed the noise type of FAIMS signal in detail, and three different signal processing algorithms, such as median filtering (MF), discrete wavelet transform (DWT), and zero‐phase digital filtering (ZDF), were evaluated for their performance in denoising the FAIMS signal. Results The results show that the standard deviation of CVp obtained from the signal denoised using ZDF algorithm is at least 31.82% smaller as compared to using MF and DWT algorithms. The standard deviation of Wh is at least 45.45% smaller using ZDF algorithm. Moreover, only ZDF algorithm can keep the percentage error for the CV value of the denoised signal to be within 0.50 ± 0.47% of the true CV value, implying the effectiveness of ZDF algorithm in denoising while retaining the integrity of the signal. Conclusions The ZDF algorithm greatly reduces the analyte peak extraction error and improves the limit of detection in FAIMS measurements.

The study and design of high-resolution mass analyzer is a very important task in mass spectrometry. Planar electrostatic ion trap (PEIT) mass analyzer with image charge detection and FT-based data...

Rationale Because of its powerful analytical ability, ion mobility spectrometry (IMS) plays an important role in the field of mass spectrometry. However, one of the main defects of IMS is its low structural resolution, which leads to the phenomenon of peak overlap in the analysis of compounds with similar mass charge ratio. Methods A multiobjective dynamic teaching‐learning‐based optimization (MDTLBO) method was proposed to separate IMS overlapping peaks. This method prevents local optimization and identifies peak model coefficients efficiently. In addition, the position information of particles largely reflects the half‐peak width of IMS, which makes single peaks difficult to appear and coefficient identification easier. Results The performance comparison of MDTLBO with other deconvolution methods (genetic algorithm, improved particle swarm optimization algorithm, and dynamic inertia weight particle swarm optimization algorithm) shows that the maximum deconvolution error of MDTLBO is only 0.7%, which is much lower than that for the other three methods. In addition, robustness is a performance index that reflects the advantages and disadvantages of the algorithm. Conclusion MBTLBO is more robust than other algorithms for separating overlapping peaks. The algorithm can separate the heavily overlapped mobility peaks, produce better analysis results, and improve the resolution of IMS.