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Rationale: Ion mobility spectrometry (IMS), as a promising analytical tool, has been widely employed in the structural characterization of biomolecules. Nevertheless, the inherent limitation in the structural resolution of IMS frequently results in peak overlap during the analysis of isomers exhibiting comparable structures. Methods: The radial basis function (RBF) neural network optimization algorithm based on dynamic inertial weight particle swarm optimization (DIWPSO) was proposed for separating overlapping peaks in IMS. The RBF network structure and parameters were optimized using the DIWPSO algorithm. By extensively training using a large dataset, an adaptive model was developed to effectively separate overlapping peaks in IMS data. This approach successfully overcomes issues related to local optima, ensuring efficient and precise separation of overlapping peaks. Results: The method's performance was evaluated using experimental validation and analysis of overlapping peaks in the IMS spectra of two sets of isomers: 3′/6′‐sialyllactose; fructose‐6‐phosphate, glucose‐1‐phosphate, and glucose‐6‐phosphate. A comparative analysis was conducted using other algorithms, including the sparrow search algorithm, DIWPSO algorithm, and multi‐objective dynamic teaching‐learning‐based optimization algorithm. The comparison results show that the DIWPSO‐RBF algorithm achieved remarkably low maximum relative errors of only 0.42%, 0.092%, and 0.41% for ion height, mobility, and half peak width, respectively. These error rates are significantly lower than those obtained using the other three algorithms. Conclusions: The experimental results convincingly demonstrate that this method can adaptively, rapidly, and accurately separate overlapping peaks of multiple components, improving the structural resolution of IMS. [ABSTRACT FROM AUTHOR]

Despite the popularity of ion mobility spectrometry (IMS) for glycan analysis, its limited structural resolution hinders the effective separation of many glycan isomers. This leads to the overlap of IMS peaks, consequently impacting the accurate identification of glycan compositions. To this end, an improved algorithm, namely second-order differentiation combined with a simulated annealing particle swarm optimization algorithm based on sine adaptive weights (DWSA-PSO), was proposed for the separation of overlapping IMS peaks formed by glycan isomers. DWSA-PSO first performed second-order differentiation to automatically determine the number of components in overlapping peaks and exclude impossible single-peak combinations. It then introduced sinusoidal adaptive weights and a simulated annealing mechanism to improve the algorithm's search capability and global optimization performance, thereby enabling accurate and efficient separation of individual peaks. To evaluate the performance of DWSA-PSO and its application to the separation of glycan isomers, multiple sets of overlapping peaks with different degrees of overlap were simulated, and various types of multi-component overlapping peaks were formed using six disaccharide and four trisaccharide isomers. The experimental results consistently demonstrated that the DWSA-PSO algorithm outperformed both the improved particle swarm optimization (IPSO) algorithm and the dynamic inertia weight particle swarm optimization (DIWPSO) algorithm in terms of separation accuracy, running time, and fitness values. In addition, the DWSA-PSO algorithm was successfully applied to the separation of glycan isomers in malt milk beverage. All these results reveal the capability of the DWSA-PSO algorithm to facilitate the accurate identification of glycan isomers. [ABSTRACT FROM AUTHOR]

Rationale: In the field of separation science, ion mobility spectrometry (IMS) plays an important role as an analytical tool. However, the lack of sufficient structural resolution is a common problem in qualitative and quantitative analysis using IMS. A method is needed to solve the problem of overlapping peaks caused by insufficient resolution. Methods: The method uses multiple strategies to more effectively use population information to balance exploration and exploitation capabilities, prevent local optimization, accurately resolve overlapping peaks, quickly obtain optimal spectral peak model coefficients, and accurately identify compounds. Results: Multistrategy JAYA algorithm's (MSJAYA) performance is compared with improved particle swarm optimization (IPSO), dynamic inertia weight particle swarm optimization (DIWPSO), and multiobjective dynamic teaching‐learning‐based optimization (MDTLBO). The analysis shows that MSJAYA's maximum separation error is within 0.6%, a level of accuracy not guaranteed by the other algorithms. In addition, the separation error fluctuates within a much smaller range, demonstrating MSJAYA's superior robustness. Conclusions: Compared with other overlapping peak separation algorithms, MSJAYA is more applicable because no special parameters are used. The method allows fast deconvolution analysis of strong overlapping peaks with multiple components, which greatly improves the resolution of IMS. [ABSTRACT FROM AUTHOR]

Rationale: The existing particle swarm optimization (PSO) algorithms are only effective in deconvoluting the overlapping peaks in ion mobility spectra with fewer than four component peaks, which limits the applicability of these algorithms. Methods: A high‐performance two‐step particle swarm optimization (TSPSO) algorithm was developed. Compared to the existing PSO algorithms, TSPSO can narrow the search ranges of all coefficients for the overlapping peaks through Gaussian model calculation, and thus can deconvolute various overlapping peaks with high accuracy, even for 30‐component overlapping peaks. In addition, the TSPSO could be further applied to enhance the resolution of the spectra by narrowing the peak widths after the peak deconvolution. Results: Simulated overlapping peaks were first used to evaluate the performance of TSPSO as compared to the dynamic inertia weight particle swarm optimization (DIWPSO) algorithm. The results showed that the profiles of the peaks deconvoluted by using TSPSO were more consistent with the original ones. The fitness values and the standard deviations of the fitness values from TSPSO were also at least an order of magnitude less than those from DIWPSO. By applying TSPSO, the overlapping peaks from both mass spectrometry (MS) and field asymmetric waveform ion mobility spectrometry (FAIMS) spectra can also be well deconvoluted. In addition, the resolutions of the MS and FAIMS spectra can be effectively enhanced after peak deconvolution. The enhanced spectra matched excellently with the experimental ones acquired at high‐resolution modes. Conclusions: The experiment results convincingly demonstrate that the TSPSO algorithm is capable of both deconvoluting complex overlapping peaks and enhancing the spectrum resolution with high accuracy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A detailed experimental characterization on the ion storage biases in an ion funnel trap, related to ion structure, charge state and RF voltage applied to the ion funnel trap, is reported by using both cytochrome C and ubiquitin samples. It was first observed experimentally that an unavoidable ion overflow would occur when the incoming ions exceeded the capacity of ion funnel trap. The conformers with extended structures would lose preferentially in the ion overflow process. Accordingly, a significant structural bias in the ion mobility spectrometry/time of flight mass spectrometry (IMS-TOF MS) spectrum was created, as the peak intensities for conformers with compact structures and extended structures would continuously increase and decrease, respectively, when the ion overflow time of the ion funnel trap was increased. Furthermore, the experimental results also showed that the effect of this ion structural bias was more significant when the RF voltage applied to the ion funnel trap was increased. In addition, an ion charge state bias in the ion funnel trap was also observed. The effect of the ion structural bias depends significantly on the specific charge state of the ions. For a given analyte, its lower charge state ions show a greater sensitivity to the ion structural bias than the higher charge state ones under the same ion funnel trap operating conditions. Therefore, it is extremely important to set a reasonable operation condition for the ion funnel trap to avoid ion storage biases in IMS-TOF MS. [Display omitted] • An ion storage bias in the ion funnel trap of an IMS-QTOF MS was observed. • Ion storage bias was proved to be related to ion structure, charge state and so on. • The method to eliminate the ion storage bias in ion funnel trap was reported. • The optimum operation condition for the ion funnel trap was reported. [ABSTRACT FROM AUTHOR]

A high-performance standalone planar field asymmetric waveform ion mobility spectrometry (p-FAIMS) system with a deconvolution algorithm (two-step particle swarm optimization algorithm, TSPSO) for overlapping peaks was developed to effectively detect chemical warfare agents (CWAs). Four CWA simulants were applied in this study to systemically evaluate the performance of the standalone p-FAIMS system. The experimental results showed that each CWA simulant in the mixture can be positively identified by carefully comparing the compensation voltage (CV) value of each peak in the FAIMS spectra for the mixture to the ones in the spectra acquired by using the same FAIMS system for the pure CWA simulant standards. The FAIMS spectrum of the CWA simulant mixture might consist of multiple overlapping peaks, which would be difficult to accurately determine the CV value for each CWA simulant peak. This problem has been effectively resolved in this study by deconvoluting the overlapping peaks via the TSPSO algorithm. As the effective peak deconvolution via TSPSO requires the degree of overlap between each FAIMS peak to be lower than a specific value, the flow rate of FAIMS carrier gas was decreased to further improve the resolution of the p-FAIMS system. After the accurate deconvolution, the resolution of original FAIMS spectrum can also be enhanced to achieve baseline separation by using TSPSO algorithm to narrow the peak width of each peak. The experimental results in this study demonstrated the possibility of using TSPSO algorithm to achieve high-resolution on a typically low-resolution standalone FAIMS. The concept in this study can potentially be applied to any low-resolution instruments to achieve high-resolution results. [Display omitted] • A high-performance standalone FAIMS system was developed in this study. • The chemical warfare agent mixtures were effectively analyzed by using FAIMS. • The overlaid peaks in FAIMS spectra can be effectively deconvoluted via TSPSO algorithm. • The FAIMS spectra can be enhanced to achieve baseline separation using TSPSO algorithm. [ABSTRACT FROM AUTHOR]

Glycosylated proteins are an essential class of molecules playing critical roles in complex biological systems. Understanding their biological functions remains extremely difficult due to the extremely broad compositions and structure variations of glycans. Although the combination of ion mobility spectrometry and mass spectrometry (IMS-MS) has become a promising technique in glycan structure characterization and composition identification, the insufficient resolving power of most IMS-MS instruments has limited its utility in performing the comprehensive structure characterization of glycans. To mitigate the low IMS resolving power, metal ion incorporation has been employed to enhance the separation of isomeric glycans. Here, we present a systematic investigation of many different glycan-metal ion complexes in an attempt to optimize the IMS separation of different isomeric glycans. By selecting optimum glycan-metal ion complexes, partial IMS separation was realized for all the 21 isomeric glycan pairs used in the experimental study. Baseline IMS separation was achieved for 76% of these isomeric glycan pairs. The best IMS separation of isomeric glycans was achieved in some cases by incorporating multiple ions with a glycan, such as the complex [glycan + Ca + Cl]+. In addition, the well-known IMS-MS measurement trendlines, often used to identify specific compound classes, were preserved for glycans even for all the 270 glycan-metal ion complexes observed in IMS-MS spectra. Image 1 • The structure and conformational diversity of glycans were greatly changed by the incorporation of metal ions in gas phase. • The optimum separation of glycan isomers was achieved in some cases by incorporating multiple ions with a glycan. • Baseline IMS separation was realized for 76% of all isomeric glycan pairs used in the experimental study. • The well-known IMS-MS measurement trendlines were preserved for all the 270 glycan-metal ion complexes. [ABSTRACT FROM AUTHOR]