Racetrack FAIMS for High-Resolution and High-Sensitivity Characterization of Peptide Conformers.

A racetrack field asymmetric waveform ion mobility spectrometry (r-FAIMS) device, which consists of both cylindrical FAIMS (c-FAIMS) and planar FAIMS (p-FAIMS) sections with a 1 mm gap width, was developed and applied for high-resolution and high-sensitivity exploration of conformational diversity for peptides. The optimal operating conditions of r-FAIMS were systemically studied, and the performance of the fully optimized r-FAIMS was compared to a previously developed p-FAIMS in detail by using pure nitrogen as the FAIMS carrier gas. Relying on the ion focusing effect in the c-FAIMS section, the intensity of the FAIMS spectrum for doubly charged bradykinin ions acquired by using r-FAIMS is ∼8.5-fold higher than that acquired by using p-FAIMS under the same resolving power/resolution condition, implying about an order of magnitude better sensitivity of r-FAIMS. In addition, the peak separation resolution of r-FAIMS was ∼1.70-fold higher than p-FAIMS under a similar sensitivity condition for doubly charged bradykinin ions. Due to a reduced gap width of the newly designed r-FAIMS (1 mm) as compared to the previously developed p-FAIMS (1.88 mm), r-FAIMS can operate at a much higher separation field with a similar FAIMS dispersion voltage (DV) to gain significantly higher resolving power. For triply charged syntide 2 ions, the resolving power of r-FAIMS can easily exceed 120 at -3.5 kV DV by using pure nitrogen as the FAIMS carrier gas as compared to 44.2 resolving power obtained by using p-FAIMS at -4.0 kV DV. All of the experimental results have confirmed that r-FAIMS can perform structural characterization of biomolecules with both high resolution and high sensitivity.

Regioselective Glycosylation of Mannoside and Galactoside Acceptors Containing 2,4-OH Achieved by Altering Protecting Groups at the 1,3,6-Positions.

In this study, we systematically investigated the regioselective glycosylation of 2,4-OH mannoside and galactoside acceptors since regioselective protection of their 3- and 6-OHs is readily achieved. By altering the protecting groups at 1-, 3-, and 6-positions of such acceptors, we finally screened p-methoxyphenyl 3-OBn, 6-OTBDPS, α-mannoside, and ß-galactoside acceptors whose 2-OHs exhibited excellent selectivity for glycosylation with various glycosyl donors, leading to 1,2-linked products in 70-82% yields. By utilizing such acceptors, a series of 2,4-linked trisaccharide products (53-65% yields over two steps) have been highly efficiently synthesized without the need for complex protection/deprotection operations at the 2- and 4-positions of these acceptors.

Radial basis function neural network optimization algorithm based on dynamic inertial weight particle swarm optimization for separating overlapping peaks in ion mobility spectrometry.

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.

Electron ionization mass spectrometry feature peak relationships combined with deep classification model to assist similarity algorithm for fast and accurate identification of compounds.

RATIONALE: Gas chromatography-mass spectrometry (GC-MS) combines chromatography and MS, providing full play to the advantages of high separation efficiency of GC, strong qualitative ability of MS, and high sensitivity of detector. In GC-MS data processing, determining the experimental compounds is one of the most important analytical steps, which is usually realized by one-to-one similarity calculations between the experimental mass spectrum and the standard mass spectrum library. Although the accuracy of the algorithm has been improved in recent years, it is still difficult to distinguish structurally similar mass spectra, especially isomers. At the same time, the library capacity is very large and increasing every year, and the algorithm needs to perform large numbers of calculations with irrelevant compounds in the library to recognize unknown compounds, which leads to a significant reduction in efficiency. METHODS: This work proposed to exclude a large number of irrelevant mass spectra by presearching, perform preliminary similarity calculations using similarity algorithms, and finally improve the accuracy of similarity calculations using deep classification models. The replica library of NIST17 is used as the query data, and the master library is used as the reference database. RESULTS: Compared with the traditional recognition algorithm, the preprocessing algorithm has reduced the time by 4.2 h, and by adding the deep learning models 1 and 2 as the final determination, the recognition accuracy has been improved by 1.9% and 6.5%, respectively, based on the original algorithm. CONCLUSIONS: This method improves the recognition efficiency compared to conventional algorithms and at the same time has better recognition accuracy for structurally similar mass spectra and isomers.

An efficient strategy with a synergistic effect of hydrophilic and electrostatic interactions for simultaneous enrichment of N- and O-glycopeptides.

N- and O-glycosylation modifications of proteins are closely linked to the onset and development of many diseases and have gained widespread attention as potential targets for therapy and diagnosis. However, the low abundance and low ionization efficiency of glycopeptides as well as the high heterogeneity make glycosylation analysis challenging. Here, an enrichment strategy, using Knoevenagel copolymers modified with polydopamine-adenosine (denoted as PDA-ADE@KCP), was firstly proposed for simultaneous enrichment of N- and O-glycopeptides through the synergistic effects of hydrophilic and electrostatic interactions. The adjustable charged surface and hydrophilic properties endow the material with the capability to achieve effective enrichment of intact N- and O-glycopeptides. The experimental results exhibited excellent selectivity (1 : 5000) and sensitivity (0.1 fmol µL-1) of the prepared material for N-glycopeptides from standard protein digest samples. Moreover, it was further applied to simultaneous capturing of N- and O-glycopeptides from mouse liver protein digests. Compared to the commercially available zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) material, the number of glycoproteins corresponding to all N- and O-glycopeptides enriched with PDA-ADE@KCP was much more than that with ZIC-HILIC. Furthermore, PDA-ADE@KCP captured more O-glycopeptides than ZIC-HILIC, revealing its superior performance in O-glycopeptide enrichment. All these results indicated that the strategy holds immense potential in characterizing N- and O-intact glycopeptides in the field of proteomics.

Lipidomic study and diagnosis of hepatocellular carcinoma tumor with rapid evaporative ionization mass spectrometry.

Liver cancer is generally considered the leading cause of cancer deaths worldwide, and hepatocellular carcinoma (HCC) contributes to more than 90% of liver cancers. The altered lipid metabolism for rapid cancer cell growth and tumor formation has been frequently proven. In this study, an ambient ionization mass spectrometry technique, rapid evaporative ionization mass spectrometry (REIMS) using a monopolar electric knife, called iKnife, was systematically optimized and employed for ex vivo analysis of 12 human HCC tumor tissue specimens together with the paired paracancerous tissue (PT) and noncancerous liver tissue (NCT) specimens. Nine free fatty acids and 34 phospholipids were tentatively identified according to their extract masses and/or tandem mass spectra. With the help of statistical methods, 7 free fatty acids and 10 phospholipids were distributed differently in 3 types of liver tissue specimens (95% confidence interval). The box plots showed these characterized lipid metabolites varied in PT, HCC, and NCT. Compared with PT and NCT, the upregulations of four common fatty acids FA 18:0, FA 20:4, FA 16:0, and FA 18:1, together with phospholipids PC 36:1, PE 38:3, PE (18:0/20:4), PA (O-36:1), PC (32:1), PC 32:0, PE 34:0, and PC (16:0/18:1), were found in HCC specimens. The sensitivity and specificity of the established statistic model for real-time HCC tumor diagnosis were 100% and 90.5%, respectively. This study demonstrated that the described REIMS technique is a potential method for rapid lipidomic analysis and characterization of HCC tumor tissue.

Two-step particle swarm optimization algorithm for effective deconvolution and resolution enhancement of various overlapping peaks.

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.

Overlapping peak separation algorithm for ion mobility spectra based on multistrategy JAYA.

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.

An improved algorithm for resolving overlapping peaks in ion mobility spectrometry and its application to the separation of glycan isomers.

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.

Two-Dimensional FAIMS-IMS Characterization of Peptide Conformers with Resolution Exceeding 1000.

A high-performance field asymmetric waveform ion mobility spectrometry (FAIMS)-IMS-MS platform was developed and applied to explore the conformational diversity of the singly and doubly charged bradykinin (BK + H+)+ and (BK + 2H+)2+ ions. With pure N2 as the FAIMS carrier gas, more than ten conformers of (BK + H+)+ can be resolved using FAIMS-IMS, as compared to only four conformers resolved using either FAIMS or IMS alone. Interestingly, multiple conformers of (BK + H+)+ were found to have completely different values of FAIMS compensation voltage (CV), while their IMS drift times were essentially the same, which were also proven experimentally to not result from the structural annealing by the collisional heating in the ion funnel. The separations in the FAIMS and IMS dimensions are substantially orthogonal, and the overall resolving power of two-dimensional FAIMS-IMS separation is largely proportional to the product of the separation resolving powers of FAIMS and IMS. Using a gas mixture of N2/He to further improve the resolving power of the FAIMS separation, the total resolving powers of the combined FAIMS and IMS separation were estimated to be about 1020 and 1400 for (BK + H+)+ and (BK + 2H+)2+ ions, respectively, which are significantly higher than the resolving power of any ion mobility-based separation techniques demonstrated so far. The combined FAIMS-IMS can thus be a much more powerful technique to explore the structural diversity of biomolecules.

Combination of continuous wavelet transform and genetic algorithm-based Otsu for efficient mass spectrometry peak detection.

Mass spectrometry (MS) data is susceptible to random noises and alternating baseline, posing great challenges to spectral peak detection, especially for weak peaks and overlapping peaks. Herein, an efficient peak detection algorithm combining continuous wavelet transform (CWT) and genetic algorithm-based threshold segmentation (denoted as WSTGA) for mass spectrometry was proposed. Firstly, Mexican Hat wavelet was selected as the mother wavelet by comparing the matching degree between the difference of Gaussian (DOG) and different wavelets. Subsequently, the ridges and valleys were identified from 2D wavelet coefficient matrix. Afterward, an improved threshold segmentation method, Otsu method based on genetic algorithm, was introduced to find optimal segmentation threshold and achieve better image segmentation, overcoming the deficiency of traditional Otsu method that cannot handle long-tailed unimodal histograms. Finally, the characteristic peaks were successfully identified by utilizing the ridge-valley lines in wavelet space and original spectrum. Receiver operating characteristic (ROC) curve, area under curve (AUC) and F1 measure are used as criterions to evaluate performance of peak detection algorithms. Compared with multi-scale peak detection (MSPD) and CWT and image segmentation (CWT-IS) methods, all the results showed that WSTGA can achieve better peak detection. More importantly, the experimental results from MALDI-TOF spectra demonstrated that WSTGA can effectively detect more weak peaks and overlapping peaks while maintaining a lower false peak detection rate than MSPD and CWT-IS methods, indicating its great advantages in characteristic peak identification.

Detection of EGFR gene with a droplet digital PCR chip integrating a double-layer glass reservoir.

The lack of reliable and practical method for detecting rare hot mutation of epidermal growth factor receptor (EGFR) in circulating tumor DNA (ctDNA) for lung cancer has remained a challenge for general clinical application due to excess wild type DNA in clinical samples. In this study, we developed a droplet digital PCR (ddPCR) platform, integrating a PDMS chip and double-layer glass reservoir. The duplex T-junction droplet generators in PDMS chip can produce about one million uniform droplets of 4.187 pL within ∼10 min, which were then stored in the glass reservoir. The double-layer glass reservoir can protect droplets from evaporation and breaking, solving the problem of instability during thermal-cycling. The quantitative capabilities of the ddPCR chip were evaluated by testing EGFR exon gene 21, with a good linear correlation in the wide range of 101 to 106 copies/µL (R2 = 0.9998). We then demonstrated that the proposed ddPCR device can recognize rare EGFR L858R mutation under a background of 106 copies/µL wild-type DNA at a sensitivity of 0.0001%. Finally, we demonstrated this ddPCR platform could identify low amount of EGFR L858R mutation in ctDNA and CTCs of patients with lung cancer.

Deep learning under mass-to-charge ratio pre-retrieval to realize electron ionization mass spectrometry library retrieval.

RATIONALE: Gas chromatography-mass spectrometry (GC-MS) is an analytical technique widely used in materials science, biomedicine, and other fields. The target compound in the experiment is identified by searching for its mass spectrum in a large mass spectrum database using some algorithms. This work introduces the use of deep learning ranking for the identification of small molecules using low-resolution electron ionization MS. Because different spectra are often very similar, the algorithm produces wrong search results, and the search accuracy needs improvement. Due to the library's large amount of data, the algorithm sometimes requires a large amount of calculation and is very time consuming. METHODS: Given these two problems, this work aims to develop a model for ranking based on mass-to-charge ratio (m/z) pre-retrieval method combined with deep learning to improve search accuracy and reduce the algorithm's computational time. The master spectral library maintained by the National Institute of Standards and Technology is used as the reference library for all the experiments, and the replicate library is used as the query library to evaluate the method's performance. RESULTS: Compared with non-machine learning algorithms, the combination of m/z matching pre-retrieval and deep learning significantly improves library retrieval accuracy by about 4%. Moreover, compared with the deep learning sorting algorithm that does not use the pre-retrieval process, it improves the accuracy of spectral library retrieval by about 0.1% and reduces the computational time of the algorithm by more than 2 h. CONCLUSIONS: This method identifies compounds more efficiently and accurately than non-machine learning and deep learning algorithms without a pre-retrieval process.

Application of zero-phase digital filtering for effective denoising of field asymmetric waveform ion mobility spectrometry signal.

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.

An overlapping peaks separation algorithm for ion mobility spectrometry based on second-order differentiation and dynamic inertia weight particle swarm optimization algorithm.

RATIONALE: Ion mobility spectrometry (IMS) is a powerful analytical tool extensively applied in numerous domains. However, there still exists the phenomenon of peaks overlapping in the analysis of isomers with similar structures due to the limited resolution of IMS. In this paper, a dynamic inertia weight particle swarm optimization (DIWPSO) algorithm combined with second-order differentiation is proposed to separate the IMS overlapping peaks efficiently and precisely. METHODS: It can identify the component number of overlapping peaks and limit those parameters (ion mobility, intensity, and full-width at half maximum of each single peak) of the peak model in a small range using second-order differentiation. Based on this, DIWPSO that has been set the best operating parameters is capable of accurately separating IMS overlapping peaks to identify the compound within a short time. RESULTS: A comparison between the performance of DIWPSO and the improved particle swarm optimization (IPSO) found that DIWPSO with separation errors less than 2.34% overall outperforms IPSO whose maximum error is up to 5.58%. Moreover, the running time of DIWPSO is 30-80 times less than that of IPSO, and DIWPSO exhibits stronger robustness. CONCLUSIONS: This method can automatically identify the component number of IMS overlapping peaks and resolve them with muticomponents and different overlapped degrees rapidly and accurately, which further improves the structural resolution of IMS.

A novel hydrophilic hydrogel with a 3D network structure for the highly efficient enrichment of N-glycopeptides.

Protein glycosylation is of great significance in various physiological processes. Nevertheless, it remains a huge challenge to identify glycopeptides in complex biosamples by the direct mass spectrometry analysis due to the low ion efficiency and low abundance of glycopeptides. In this study, a novel hydrogel (denoted as ZIF-8/SAP) with a stable three-dimensional (3D) network structure and excellent hydrophilicity was successfully fabricated to capture glycopeptides with high efficiency. Owing to the unique characteristics, ZIF-8/SAP exhibited great selectivity (1 : 1000), great sensitivity (1 fmol µL-1), large binding capacity (300 mg g-1) and satisfactory reusability (seven cycles). Inspired by the great enrichment performance of the as-prepared material toward glycopeptides, ZIF-8/SAP was further applied to capture glycopeptides from a real human serum sample. The experimental results demonstrated that 217 N-glycosylation sites were identified in 283 N-glycopeptides, corresponding to 95 glycoproteins identified from 10 µL human serum by the nano-LC-MS/MS analysis, revealing the great potential of the novel ZIF-8/SAP hydrogel for glycopeptide enrichment and glycoproteomic research.

A novel graphene oxide/chitosan foam incorporated with metal-organic framework stationary phase for simultaneous enrichment of glycopeptide and phosphopeptide with high efficiency.

A novel hydrophilic porous biocomposite was fabricated by incorporating graphene oxide (GO) @chitosan (CS) foam substrate (GO@CS@ZIF-8 foam) with ZIF-8 crystals in situ via a facile stirring method for simultaneous enrichment of glycopeptides and phosphopeptides from complex biological samples. The experimental results demonstrated that GO@CS@ZIF-8 foam exhibited favorable specificity for simultaneous enrichment of N-glycopeptides and phosphopeptides under the same condition for HRP and ß-casein tryptic digest mixtures. The novel material was further applied to enriching both glycopeptides and phosphopeptides simultaneously from 4 µL complex human serum, and 423 N-glycopeptides and 40 phosphopeptides corresponding to 133 glycoproteins and 29 phosphoproteins were identified, respectively.

Effective Chiral Discrimination of Amino Acids through Oligosaccharide Incorporation by Trapped Ion Mobility Spectrometry.

Chiral analysis is critical to many research fields due to different biological functions of enantiomers in living systems. Although the use of ion mobility spectrometry (IMS) has become an alternative technology in the area of chiral measurements, there is still a lack of a general chiral selector for IMS-based chiral recognition, especially for small chiral molecules. Here, a new method using oligosaccharides as the chiral selector has been developed to discriminate chiral amino acids (AAs) by trapped ion mobility spectrometry-mass spectrometry (TIMS-MS). We analyzed 21 chiral amino acids, including small molecules (e.g., alanine and cysteine). Our data showed that the use of nonreducing tetrasaccharides was effective for the separation of chiral AAs, which differentiated 21 chiral AAs without using metal ions. By further incorporating a copper ion, the separation resolution could be improved to 1.64 on average, which accounts for an additional 52% improvement on top of the already achieved separation in metal-free analysis. These results indicate that the use of tetrasaccharides is an effective strategy for the separation of AA enantiomers by TIMS. The method developed in this study may open up a new strategy for effective IMS-based chiral analysis.

Resolution enhancement of overlapping peaks of ion mobility spectrometry based on improved particle swarm optimization algorithm.

RATIONALE: Ion mobility spectrometry (IMS) is a powerful analytical tool that has been widely applied in many fields. However, the limited structural resolution of IMS results in peak overlapping in the analysis of samples with similar structures. We propose a novel method, improved particle swarm optimization (IPSO), for the separation of IMS overlapping peaks. METHODS: This method, which prevents local optimization, is used to identify the peak model coefficients of IMS. Moreover, we use the half-peak width characteristics of IMS to determine the particle position range, which eliminates impossible combinations of single peaks and reduces the difficulty of identification of coefficients. RESULTS: During a comparison in performance between IPSO and the genetic algorithm (GA), the results show that the maximum separation error of IPSO is only 1.45%, while the error of the GA is up to 17.43%. Moreover, the time consumed by IPSO is 95% less than that of the GA, and IPSO has a greater robustness under the same separation error conditions. CONCLUSIONS: The method proposed provides accurate analytical results in separating overlapping IMS peaks even in cases of severe overlaps, which greatly enhances the structural resolution of IMS.

Hollow electrode capillary plasma ionization source for rapid online detection of gaseous samples.

RATIONALE: Gas chromatography mass spectrometry (GC-MS) with electron ionization (EI) is the most widely used analysis technique of gaseous samples, but it may be time-consuming for online monitoring of mixtures whose concentrations relatively change rapidly. On the contrary, current ionization methods, such as chemical ionization (CI) and proton transfer reaction (PTR), also have some disadvantages such as selectivity. Therefore, appropriate soft ionization sources are searched for rapid online detection. METHODS: Hollow electrode capillary plasma ionization (HECPI) is based on single electrode plasma. A hollow capillary was placed as both the electrode and the inlet of the gaseous samples. The ionization source is coupled with a mass spectrometer for performance evaluation. RESULTS: Several typical compounds have been tested with HECPI-mass spectrometer. In this process, the dominant ion peaks of all compounds can be indexed as molecular ion peaks, and the product ions of HECPI are less than that of dielectric barrier discharge ionization (DBDI). Three gaseous samples (linalool, triethylamine, and styrene) with various concentrations have been used to further confirm the performance of this source, and the detection limit of linalool is as low as 10 ppb. CONCLUSIONS: HECPI is simple in structure and shows good performance. The results also show that HECPI has the potential to be an effective tool for detecting online gaseous samples rapidly.