Ion Mobility Spectrometry Data Research Articles

Geographical authentication of saffron by chemometrics applied to the ion mobility spectrometry data

There is a lack of a reliable tool for quickly determining the geographical origins of saffron (SFR). Ion mobility spectrometry (IMS) has emerged as a promising method for rapid authentication. In this study, 232 Iranian SFR samples harvested in five distinct areas (Khorasan, Azerbaijan, Golestan, Fars, and Isfahan) were analyzed by IMS coupled with chemometric methods. The principal component analysis (PCA) was applied for analyzing the collected IMS data, utilizing three principle components (PCs) that accounted for 81 % of the explained variance. Moreover, the partial least squares-discriminant analysis (PLS-DA) demonstrated the average sensitivity and specificity rates, of 72.3 % to 92.5 % for the exernal test set and 75.5 % to 94.3 % for training set. The accuracy values were ≥ 85.0 % for the prediction set for all classes of samples. The results of this study revealed a successful application of IMS and chemometric methods for rapid geographical authentication of saffron samples in Iran.

Study on the Ion Mobility Spectrometry Data Classification and Application of Port Container Narcotics Using Machine Learning Algorithm

Narcotics should be strictly controlled as they can cause great disruption to society. Narcotics mostly flow into ports from major narcotic makers via transit points and through cargo containers. To prevent narcotic entry through smuggling, airports use animals or detect narcotics through X-rays. However, the use of animals in ports is not practical, and the method using X-rays sometimes does not detect substance narcotics with low atomic numbers. In this paper, we aimed to detect and classify narcotics using ion mobility spectrometry (IMS) data generated by inhaling air inside the container. To classify narcotic IMS data consisting of time-series data, the performance was improved using a time-series classification machine learning algorithm instead of the threshold method previously used. To this end, K-nearest neighbor, time-series forest, and random convolutional kernel algorithms were applied to the proposed algorithm considering the features of narcotic IMS data. The results demonstrate that the proposed algorithm outperforms the existing algorithm, and it reduces the classification performance processing time up to 5 s with more than 0.9 accuracy level.

A comparison of online methods for change point detection in ion-mobility spectrometry data

When on-site classification of volatile organic compounds (VOCs) is required, a portable ion mobility spectrometer (IMS) is a suitable choice. However, the IMS readings often show transient phases before they stabilize. Even so the importance of transient phase and features extracted from it has been highlighted in the literature, it has not, to our knowledge, been used for IMS-based classification so far. This paper analyzes whether change point detection algorithms with low computational complexity can separate transient and stable phases in IMS readings. The algorithms were tested on IMS data from different types of mushrooms. All algorithms successfully detected switches from transient to stable phase. The most accurate results were provided by the previously proposed multivariate max-CUSUM algorithm and the matrix form CUSUM algorithm, which is developed in this paper. • A dataset of IMS measurements from 2 types of mushrooms is presented and shared. • A modification of the CUSUM change point detection algorithm is proposed. • The proposed algorithm is compared to existing change point detection algorithms.

Native Mass Spectrometry and Nucleic Acid G-Quadruplex Biophysics: Advancing Hand in Hand.

While studying nucleic acids to reveal the weak interactions responsible for their three-dimensional structure and for their interactions with drugs, we also contributed to the field of biomolecular mass spectrometry, both in terms of fundamental understanding and with new methodological developments. A first goal was to develop mass spectrometry approaches to detect noncovalent interactions between antitumor drugs and their DNA target. Twenty years ago, our attention turned toward specific DNA structures such as the G-quadruplex (a structure formed by guanine-rich strands). Mass spectrometry allows one to discern which molecules interact with one another by measuring the masses of the complexes, and quantify the affinities by measuring their abundance. The most important findings came from unexpected masses. For example, we showed the formation of higher- or lower-order structures by G-quadruplexes used in traditional biophysical assays. We also derived complete thermodynamic and kinetic description of G-quadruplex folding pathways by measuring cation binding, one at a time. Getting quantitative information requires accounting for nonspecific adduct formation and for the response factors of the different molecular forms. With these caveats in mind, the approach is now mature enough for routine biophysical characterization of nucleic acids. A second goal is to obtain more detailed structural information on each of the complexes separated by the mass spectrometer. One such approach is ion mobility spectrometry, and even today the challenge lies in the structural interpretation of the measurements. We showed that, although structures such as G-quadruplexes are well-preserved in the MS conditions, double helices actually get more compact in the gas phase. These major rearrangements forced us to challenge comfortable assumptions. Further work is still needed to generalize how to deduce structures in solution from ion mobility spectrometry data and, in particular, how to account for the electrospray charging mechanisms and for ion internal energy effects. These studies also called for complementary approaches to ion mobility spectrometry. Recently, we applied isotope exchange labeling mass spectrometry to characterize nucleic acid structures for the first time, and we reported the first ever circular dichroism ion spectroscopy measurement on mass-selected trapped ions. Circular dichroism plays a key role in assigning the stacking topology, and our new method now opens the door to characterizing a wide variety of chiral molecules by mass spectrometry. In summary, advanced mass spectrometry approaches to characterize gas-phase structures work well for G-quadruplexes because they are stiffened by inner cations. The next objective will be to generalize these methodologies to a wider range of nucleic acid structures.

Volatile organic compounds sensing based on Bennet doubler-inspired triboelectric nanogenerator and machine learning-assisted ion mobility analysis

Ion mobility analysis is a well-known analytical technique for identifying gas-phase compounds in fast-response gas-monitoring systems. However, the conventional plasma discharge system is bulky, operates at a high temperature, and inappropriate for volatile organic compounds (VOCs) concentration detection. Therefore, we report a machine learning (ML)-enhanced ion mobility analyzer with a triboelectric-based ionizer, which offers good ion mobility selectivity and VOC recognition ability with a small-sized device and non-strict operating environment. Based on the charge accumulation mechanism, a multi-switched manipulation triboelectric nanogenerator (SM-TENG) can provide a direct current (DC) bias at the order of a few hundred, which can be further leveraged as the power source to obtain a unique and repeatable discharge characteristic of different VOCs, and their mixtures, with a special tip-plate electrode configuration. Aiming to tackle the grand challenge in the detection of multiple VOCs, the ML-enhanced ion mobility analysis method was successfully demonstrated by extracting specific features automatically from ion mobility spectrometry data with ML algorithms, which significantly enhance the detection ability of the SM-TENG based VOC analyzer, showing a portable real-time VOC monitoring solution with rapid response and low power consumption for future internet of things based environmental monitoring applications.

Physicochemical Characterization, Glycosylation Pattern and Biosimilarity Assessment of the Fusion Protein Etanercept.

Etanercept is a soluble fusion protein of the tumor necrosis factor receptor (TNFR) extracellular domain, linked to an Fc part of IgG1. It possesses three N- and 13 O-glycosylation sites. Due to its complex structure, an analytical challenge is facing the development and approval of biosimilars. In the current study, physicochemical characterization using state-of-the-art analytics was performed to analyze intact and subunit masses, post-translational modifications (PTMs), higher order structure and potency of Etanercept originator Enbrel® and its biosimilar Altebrel™ (AryoGen Pharmed) in accordance to critical quality attributes of biopharmaceuticals. Intact mass and subunit analysis revealed a size of about 126kDa for both biologicals. Similar glycoprotein species for the complete monomer and the Fc domain of originator and follow-on product were observed, however, small differences in lysine variants and oxidation were found. N-Glycopeptide analysis with UHPLC-QTOF-MSE confirmed the N-glycosylation sites (N149, N171 and N317) as well as Fc-specific glycosylation on N317, and TNFR-specific highly sialylated glycans on N149 and N171 on both investigated products. Small quantitative variations in the N-glycan profile were detected, although the N-glycans were qualitatively similar. Four different O-glycopeptides bearing core 1-type glycans were detected. For both, N- and O-glycopeptide analysis, determination was achieved without prior cleavage of the sialic acid residues for the first time. In addition, ion mobility spectrometry data confirmed close similarity of higher-order structure of both biologics. Furthermore, a neutralization assay, investigating the impact of altered PTMs on potency, indicated that the differences within all batches are still in the acceptable range for biosimilarity.

Dynamics of flexible peptides under the action of an electrostatic field in the gas phase

The Ion Mobility Spectrometry data of flexible peptides have been found to depend on the temperature of the buffer gas. It has been observed that the number of the peaks of the arrival time distribution may vary, while the position of the peaks may shift with temperature. Such changes depend on the emerging conformers at the experimental conditions. The motion and the dynamics of the peptide are reproduced here through a Nonequilibrium Molecular Dynamics Simulation procedure that depends on the structure of the bending macromolecule. A specific molecular model of one bending mode is considered that depends on an angular interaction potential. The observed conformations are introduced through the consideration of local minima in the model potential. As the molecular dynamics simulation proceeds, the population of the metastable structures is changing in time due to energy exchange during the ion-atom interactions. The corresponding angle distributions depend on the temperature and the field strength. We find that the observed motion of the peptide conformers can be reproduced accurately with the use of empirical model potentials and further predict the behavior of the ions at strong electric fields. The procedure can produce mean properties, such as the ion velocity and energy, as well as molecular distributions and dynamic properties, such as velocity correlation functions. We expect the method to apply to similar flexible macromolecules that acquire one bending mode.

Two-sample homogeneity tests based on divergence measures

The concept of f-divergences introduced by Ali and Silvey (J R Stat Soc (B) 28:131---142, 1996) provides a rich set of distance like measures between pairs of distributions. Divergences do not focus on certain moments of random variables, but rather consider discrepancies between the corresponding probability density functions. Thus, two-sample tests based on these measures can detect arbitrary alternatives when testing the equality of the distributions. We treat the problem of divergence estimation as well as the subsequent testing for the homogeneity of two-samples. In particular, we propose a nonparametric estimator for f-divergences in the case of continuous distributions, which is based on kernel density estimation and spline smoothing. As we show in extensive simulations, the new method performs stable and quite well in comparison to several existing non- and semiparametric divergence estimators. Furthermore, we tackle the two-sample homogeneity problem using permutation tests based on various divergence estimators. The methods are compared to an asymptotic divergence test as well as to several traditional parametric and nonparametric procedures under different distributional assumptions and alternatives in simulations. It turns out that divergence based methods detect discrepancies between distributions more often than traditional methods if the distributions do not differ in location only. The findings are illustrated on ion mobility spectrometry data.

Chemometrics for ion mobility spectrometry data: recent advances and future prospects.

Historically, advances in the field of ion mobility spectrometry have been hindered by the variation in measured signals between instruments developed by different research laboratories or manufacturers. This has triggered the development and application of chemometric techniques able to reveal and analyze precious information content of ion mobility spectra. Recent advances in multidimensional coupling of ion mobility spectrometry to chromatography and mass spectrometry has created new, unique challenges for data processing, yielding high-dimensional, megavariate datasets. In this paper, a complete overview of available chemometric techniques used in the analysis of ion mobility spectrometry data is given. We describe the current state-of-the-art of ion mobility spectrometry data analysis comprising datasets with different complexities and two different scopes of data analysis, i.e. targeted and non-targeted analyte analyses. Two main steps of data analysis are considered: data preprocessing and pattern recognition. A detailed description of recent advances in chemometric techniques is provided for these steps, together with a list of interesting applications. We demonstrate that chemometric techniques have a significant contribution to the recent and great expansion of ion mobility spectrometry technology into different application fields. We conclude that well-thought out, comprehensive data analysis strategies are currently emerging, including several chemometric techniques and addressing different data challenges. In our opinion, this trend will continue in the near future, stimulating developments in ion mobility spectrometry instrumentation even further.

Molecular Dynamics Simulation for the Dynamics and Kinetics of Folding Peptides in the Gas Phase.

The conformations of flexible molecular species, such as oligomers and oligopeptides, and their interconversion in the gas phase have been probed by ion mobility spectrometry measurements. The ion motion is interpreted through the calculation of effective cross sections in the case of stable conformations of the macromolecules. However, when the molecular structures transform to each other as the ions collide with gas atoms during their flight through the drift tube, the introduction of an average cross section is required. To provide a direct way for the reproduction of the ion motion, we employ a nonequilibrium molecular dynamics simulation method and consider a molecular model that consists of two connected stiff cylindrical bodies interacting through an intramolecular model potential. With this procedure we have calculated the ion mobility as a function of temperature for a prototype peptide that converts between a helical and an extended globular form. The results are in good agreement with ion mobility spectrometry data confirming that an angular vibration coordinate can be used for the interpretation of the shifting of the drift-time distributions at high temperatures. The approach produces mean kinetic energies as well as various combined distributions of the ion degrees of freedom. It is easily applied to flexible macromolecular ions and can be extended to include additional degrees of freedom.

Letter: Analytical potential of the in-electrospray ionization source hydrogen/deuterium exchange for the investigation of oligonucleotides.

It has previously been reported that different conformations of oligonucleotides may be detected using a gas-phase hydrogen/deuterium (H/D) exchange performed in the collision cell of a mass spectrometer. The presence of different conformers was postulated based on the bimodal shape of the deuterium distribution and on the ion mobility spectrometry data. Here we implement an in-electrospray ionization source H/D exchange to detect the different conformations of oligonucleotides in the region of ion formation. We observed that the number of H/D exchanges depends considerably on the temperature of the desolvating capillary and varies from 25% at 50°C to 80% at 450°C, but no bimodality in the shape of the deuterium distribution was observed. Such results indicate that in the region of ion formation different conformations of oligonucleotide ions rapidly interconvert one into another.

From solution to the gas phase: factors that influence kinetic trapping of substance P in the gas phase.

Substance P (RPKPQQFFGLM-NH2) [M + 3H](3+) ions have been shown to exist as two conformers: one that is kinetically trapped and one that is thermodynamically more stable and therefore energetically preferred. Molecular dynamics (MD) simulations suggested that the kinetically trapped population is stabilized by interactions between the charge sites and the polar side chains of glutamine (Q) located at positions 5 and 6 and phenylalanine (F) located at positions 7 and 8. Here, the individual contributions of these specific intramolecular interactions are systematically probed through site-directed alanine mutations of the native amino acid sequence. Ion mobility spectrometry data for the mutant peptide ions confirm that interactions between the charge sites and glutamine/phenylalanine (Q/F) side chains afford stabilization of the kinetically trapped ion population. In addition, experimental data for proline-to-alanine mutations at positions 2 and 4 clearly show that interactions involving the charge sites and the Q/F side chains are altered by the cis/trans orientations of the proline residues and that mutation of glycine to proline at position 9 supports results from MD simulations suggesting that the C-terminus also provides stabilization of the kinetically trapped conformation.

Computational methods for metabolomic data analysis of ion mobility spectrometry data-reviewing the state of the art.

Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain.

The Polydispersity of αB-Crystallin Is Rationalized by an Interconverting Polyhedral Architecture

We report structural models for the most abundant oligomers populated by the polydisperse molecular chaperone αB-crystallin. Subunit connectivity is determined by using restraints obtained from nuclear magnetic resonance spectroscopy and mass spectrometry measurements, enabling the construction of various oligomeric models. These candidate structures are filtered according to their correspondence with ion-mobility spectrometry data and cross-validated by using electron microscopy. The ensuing best-fit structures reveal the polyhedral architecture of αB-crystallin oligomers, and provide a rationale for their polydispersity and facile interconversion.

IMS2 – An integrated medical software system for early lung cancer detection using ion mobility spectrometry data of human breath

Abstract IMS2 is an Integrated Medical Software system for the analysis of Ion Mobility Spectrometry (IMS) data. It assists medical staff with the following IMS data processing steps: acquisition, visualization, classification, and annotation. IMS2 provides data analysis and interpretation features on the one hand, and also helps to improve the classification by increasing the number of the pre-classified datasets on the other hand. It is designed to facilitate early detection of lung cancer, one of the most common cancer types with one million deaths each year around the world.After reviewing the IMS technology, we first describe the software architecture of IMS2 and then the integrated classification module, including necessary pre-processing steps and different classification methods. The Lung Hospital Hemer (Germany) provided IMS data of 35 patients suffering from lung cancer and 72 samples of healthy persons. IMS2 correctly classifies 99% of the samples, evaluated using 10-fold cross-validation.

Reduction of ion mobility spectrometry data by clustering characteristic peak structures

AbstractSpectrometric methods generate a large amount of data that are difficult to process due to high dimensionality and considerable variability. Therefore, a method for the detection of characteristic peak structures was established on the base of clustering techniques and a merging regions algorithm that enabled the reduction of data in the size of one million to about one hundred meaningful values with respect to fluctuating axis allocations. The effectiveness of the method was exemplified considering a set of ion mobility spectrometry measurements of human breath. In questions of comparative nature, the reduction even could be upgraded by multiple test procedures. Afterwards statistical analyses could be arranged easily for various targets. Copyright © 2007 John Wiley & Sons, Ltd.

SIMPLISMA applied to two-dimensional wavelet compressed ion mobility spectrometry data

A modified SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) algorithm, referred to as real-time (RT) SIMPLISMA has been combined with two-dimensional (2D) wavelet compression (WC 2). This tool was evaluated with datasets of drugs and bacteria that were acquired from two different ion mobility spectrometers and published reference data that comprised Raman, FTIR microscopy, near-infrared (NIR) and mass spectral data. RTSIMPLISMA is amenable for real-time modeling and is able to determine the number of components automatically. The 2D wavelet compression, which compresses both acquisition and drift time dimensions of measurement, was applied to the datasets prior to RTSIMPLISMA modeling. RTSIMPLISMA models obtained from the compressed data were wavelet transformed back to the uncompressed representation. The effects of wavelet filter types and compression levels were investigated. The relative root-mean-square errors (RRMSE) of reconstruction, which calculate the relative difference between the extracted models with and without 2D compressions, were used to evaluate the effects of compression on self-modeling. The results showed that satisfactory models could be obtained when a data was compressed to 1/256 of its size.

JCAMP-DX. A Standard Format for the Exchange of Ion Mobility Spectrometry Data (IUPAC Recommendations 2001)

JCAMP-DX. A Standard Format for the Exchange of Ion Mobility Spectrometry Data (IUPAC Recommendations 2001)

Monitoring Odorous Sulfur Emissions Using Self-Organizing Maps for Handling Ion Mobility Spectrometry Data

ABSTRACT Possibilities for monitoring emissions of reduced sulfur compounds in pulp and paper mills were investigated using ion mobility spectrometry (IMS) and a self-organizing map (SOM) algorithm. The reduced sulfur compounds measured were hydrogen sulfide (H2S), dimethyl sulfide (DMS), and methyl mercaptan (MM). Attention was paid to momentary concentrations because there is no monitoring device able to measure peak concentrations of reduced sulfur compounds under field conditions. These methods were evaluated by measuring the reduced sulfur compounds first in the laboratory and then at a process monitoring site at a pulp factory. The aim was to find out whether it would be possible to use the laboratory measurements to recognize the same reduced sulfur compounds at the monitoring site. Data collection was followed by analysis using the SOM algorithm and Sammon's mapping. The results showed that the IMS spectra of reduced sulfur compounds and their mixtures can be distinguished from each other by computationally intelligent methods and that the spectra from the process monitoring site corresponded with the laboratory measurements to a certain extent.