Gas Chromatography-differential Mobility Spectrometry Research Articles

Non-destructive method to classify walnut kernel freshness from volatile organic compound (VOC) emissions using gas chromatography-differential mobility spectrometry (GC-DMS) and machine learning analysis

Analysis of volatile organic compounds (VOCs) can be an effective strategy to inspect the quality of horticultural commodities and following their degradation. In this work, we report that VOCs emitted by walnuts can be studied using gas chromatography-differential mobility spectrometry (GC-DMS), and those GC-DMS data can be analyzed to predict the rancidity of walnuts, i.e., classify walnuts into grades of freshness. Walnut kernels were assigned a class n depending on their level of freshness as determined by a peroxide assay. VOC samples were analyzed using GC-DMS. From these VOC data, a partial least square regression (PLSR) model provided a freshness prediction value m, which corresponded to the rancid class n when m=n±0.5. The PLSR model had an accuracy of 80% to predict walnut grade and demonstrated a minimal root mean squared error of 0.42 for the m response variables (representative of walnut grade) with the GC-DMS data. We also conducted gas chromatography-mass spectrometry (GC–MS) experiments to identify volatiles that emerged or were enhanced with more rancid walnuts. The findings of the GC–MS study of walnut VOCs align excellently with the GC-DMS study. Based on our results, we conclude that a GC-DMS device deployed with a pre-trained machine learning model can be a very effective device for classifying walnut grades in the industry.

Peak detection and random forests classification software for gas chromatography/differential mobility spectrometry (GC/DMS) data

Gas Chromatography/Differential Mobility Spectrometry (GC/DMS) is an effective tool to discern volatile chemicals. The process of correlating GC/DMS data outputs to chemical identities requires time and effort from trained chemists due to lack of commercially available software and the lack of appropriate libraries. This paper describes the coupling of computer vision techniques to develop models for peak detection and can align chemical signatures across datasets. The result is an automatically generated peak table that provides integrated peak areas for the inputted samples. The software was tested against a simulated dataset, whereby the number of detected features highly correlated to the number of actual features (r2 ​= ​0.95). This software has also been developed to include random forests, a discriminant analysis technique that generates prediction models for application to unknown samples with different chemical signatures. In an example dataset described herein, the model achieves 3% classification error with 12 trees and 0% classification error with 48 trees. The number of trees can be optimized based on the computational resources available. We expect the public release of this software can provide other GC/DMS researchers with a tool for automated featured extraction and discriminant analysis capabilities.

Modular and reconfigurable gas chromatography / differential mobility spectrometry (GC/DMS) package for detection of volatile organic compounds (VOCs).

Due to the versatility of present day microcontroller boards and open source development environments, new analytical chemistry devices can now be built outside of large industry and instead within smaller individual groups. While there are a wide range of commercial devices available for detecting and identifying volatile organic compounds (VOCs), most of these devices use their own proprietary software and complex custom electronics, making modifications or reconfiguration of the systems challenging. The development of microprocessors for general use, such as the Arduino prototyping platform, now enables custom chemical analysis instrumentation. We have created an example system using commercially available parts, centered around on differential mobility spectrometer (DMS) device. The Modular Reconfigurable Gas Chromatography - Differential Mobility Spectrometry package (MR-GC-DMS) has swappable components allowing it to be quickly reconfigured for specific application purposes as well as broad, generic use. The MR-GC-DMS has a custom user-friendly graphical user interface (GUI) and precisely tuned proportional-integral-derivative controller (PID) feedback control system managing individual temperature-sensitive components. Accurate temperature control programmed into the microcontroller greatly increases repeatability and system performance. Together, this open-source platform enables researchers to quickly combine DMS devices in customized configurations for new chemical sensing applications.

Supervised Semi-Automated Data Analysis Software for Gas Chromatography / Differential Mobility Spectrometry (GC/DMS) Metabolomics Applications.

Modern differential mobility spectrometers (DMS) produce complex and multi-dimensional data streams that allow for near-real-time or post-hoc chemical detection for a variety of applications. An active area of interest for this technology is metabolite monitoring for biological applications, and these data sets regularly have unique technical and data analysis end user requirements. While there are initial publications on how investigators have individually processed and analyzed their DMS metabolomic data, there are no user-ready commercial or open source software packages that are easily used for this purpose. We have created custom software uniquely suited to analyze gas chromatograph / differential mobility spectrometry (GC/DMS) data from biological sources. Here we explain the implementation of the software, describe the user features that are available, and provide an example of how this software functions using a previously-published data set. The software is compatible with many commercial or home-made DMS systems. Because the software is versatile, it can also potentially be used for other similarly structured data sets, such as GC/GC and other IMS modalities.

Fast detection of coliform bacteria by means of gas chromatography-differential mobility spectrometry.

In this study, we demonstrate that the combination of an enzymatic method (based on Colilert-18 medium) and gas chromatography-differential mobility spectrometry (GC-DMS) can reduce the time required for detection of coliform bacteria (including Escherichia coli) from 18 to 2.5h. The presented method includes the incubation (~2.5h) of the sample containing coliform bacteria in Colilert-18 medium. The incubation time of 2.5h is required for the activation of the β-galactosidase enzyme. Produced during the incubation biomarker o-nitrophenol (ONP) can be detected by means of GC-DMS within just 200s. The detection limit for ONP was 45ng (on-column). The method developed in this work provides significantly shorter analysis time compared with standard methods, and can be potentially adapted to the field conditions. Therefore, this method is a promising tool for an early detection of coliform bacteria (including E. coli). Graphical Abstract Fast detection of coliform bacteria by means of GC-DMS.

A rapid and non-invasive method to determine toxic levels of alcohols and γ-hydroxybutyric acid in saliva samples by gas chromatography–differential mobility spectrometry

A polydimethylsiloxane oral sampler was used to extract methanol, ethanol, ethylene glycol, 1,3-propandiol and γ-hydroxybutyric acid from samples of human saliva obtained using a passive drool approach. The extracted compounds were recovered by thermal desorption, isolated by gas chromatography and detected with differential mobility spectrometry, operating with a programmed dispersion field.Complex signal behaviours were also observed that were consistent with hitherto unobserved fragmentation behaviours in differential mobility spectrometry. These yielded high-mobility fragments obscured within the envelope of the water-based reactant ion peak. Further, compensation field maxima shifts were also observed which were attributable to transport gas modification phenomena. Nevertheless, the responses obtained indicated that in vivo saliva sampling with thermal desorption gas chromatography may be used to provide a semi-quantitative diagnostic screen over the toxicity threshold concentration ranges of 100 mg dm−3 to 3 g dm−3. A candidate method suitable for use in low resource settings for the non-invasive screening of patients intoxicated by alcohols and volatile sedatives has been demonstrated.

Coupling a branch enclosure with differential mobility spectrometry to isolate and measure plant volatiles in contained greenhouse settings

Volatile organic compounds (VOCs) are off-gassed from all living organisms and represent end products of metabolic pathways within the system. In agricultural systems, these VOCs can provide important information on plant health and can ordinarily be measured non-invasively without harvesting tissue from the plants. Previously we reported a portable gas chromatography/differential mobility spectrometry (GC/DMS) system that could distinguish VOC profiles of pathogen-infected citrus from healthy trees before visual symptoms of disease were present. These measurements were taken directly from canopies in the field, but the sampling and analysis protocol did not readily transfer to a controlled greenhouse study where the ambient background air was saturated with volatiles contained in the facility. In this study, we describe for the first time a branch enclosure uniquely coupled with GC/DMS to isolate and measure plant volatiles. To test our system, we sought to replicate our field experiment within a contained greenhouse and distinguish the VOC profiles of healthy versus citrus infected with Candidatus Liberibacter asiaticus. We indeed confirm the ability to track infection-related trace biogenic VOCs using our sampling system and method and we now show this difference in Lisbon lemons (Citrus×limon L. Burm. f.), a varietal not previously reported. Furthermore, the system differentiates the volatile profiles of Lisbon lemons from Washington navels [Citrus sinensis (L.) Osbeck] and also from Tango mandarins (Citrus reticulata Blanco). Based on this evidence, we believe this enclosure-GC/DMS system is adaptable to other volatile-based investigations of plant diseases in greenhouses or other contained settings, and this system may be helpful for basic science research studies of infection mechanisms.

Gas Chromatography–Differential Mobility Spectrometry and Gas Chromatography–Mass Spectrometry for the detection of coliform bacteria

The use of miniaturized Gas Chromatography–Differential Mobility Spectrometry (GC–DMS) is shown for the detection and identification of coliform bacteria (including Escherichia coli) grown in five different media: Colilert®-18, glucose broth, M9-medium, tryptophan broth, and tryptic soy broth. After incubation in the different media, headspace containing the volatile compounds was analyzed by the GC–DMS and the results were validated by Gas Chromatography–Mass Spectrometry (GC–MS). Results showed that the GC–DMS and GC–MS were able to detect o-nitrophenol released by coliform bacteria incubated in Colilert®-18. In addition to that, GC–MS was able to detect indole compound released by coliform bacteria grown in all media. Neither GC–DMS nor GC–MS could detect 4-methylumbelliferone from the headspace of E. coli grown in media containing 4-methylumbelliferyl-β-d-glucoronide (MUG) substrate, which was available in Colilert®-18. With the miniaturized GC–DMS being portable and can be operated using ambient pressure, this method offers a potential on-site detection of coliform bacteria.

Electrothermal Vaporization Sample Introduction for Spaceflight Water Quality Monitoring via Gas Chromatography-Differential Mobility Spectrometry.

In the history of manned spaceflight, environmental monitoring has relied heavily on archival sampling. However, with the construction of the International Space Station (ISS) and the subsequent extension in mission duration up to one year, an enhanced, real-time method for environmental monitoring is necessary. The station air is currently monitored for trace volatile organic compounds (VOCs) using gas chromatography-differential mobility spectrometry (GC-DMS) via the Air Quality Monitor (AQM), while water is analyzed to measure total organic carbon and biocide concentrations using the Total Organic Carbon Analyzer (TOCA) and the Colorimetric Water Quality Monitoring Kit (CWQMK), respectively. As mission scenarios extend beyond low Earth orbit, a convergence in analytical instrumentation to analyze both air and water samples is highly desirable. Since the AQM currently provides quantitative, compound-specific information for air samples and many of the targets in air are also common to water, this platform is a logical starting point for developing a multimatrix monitor. Here, we report on the interfacing of an electrothermal vaporization (ETV) sample introduction unit with a ground-based AQM for monitoring target analytes in water. The results show that each of the compounds tested from water have similar GC-DMS parameters as the compounds tested in air. Moreover, the ETV enabled AQM detection of dimethlsilanediol (DMSD), a compound whose analysis had proven challenging using other sample introduction methods. Analysis of authentic ISS water samples using the ETV-AQM showed that DMSD could be successfully quantified, while the concentrations obtained for the other compounds also agreed well with laboratory results.

Detection of Huanglongbing Disease Using Differential Mobility Spectrometry

The viability of the multibillion dollar global citrus industry is threatened by the "green menace", citrus greening disease (Huanglongbing, HLB), caused by the bacterial pathogen Candidatus Liberibacter. The long asymptomatic stage of HLB makes it challenging to detect emerging regional infections early to limit disease spread. We have established a novel method of disease detection based on chemical analysis of released volatile organic compounds (VOCs) that emanate from infected trees. We found that the biomarkers "fingerprint" is specific to the causal pathogen and could be interpreted using analytical methods such as gas chromatography/mass spectrometry (GC/MS) and gas chromatography/differential mobility spectrometry (GC/DMS). This VOC-based disease detection method has a high accuracy of ∼90% throughout the year, approaching 100% under optimal testing conditions, even at very early stages of infection where other methods are not adequate. Detecting early infection based on VOCs precedes visual symptoms and DNA-based detection techniques (real-time polymerase chain reaction, RT-PCR) and can be performed at a substantially lower cost and with rapid field deployment.

Volatile Organic Compounds in Headspace over Electrical Components at 75 to 200?C Part 2. Analytical Response with Gas Chromatography-Differential Mobility Spectrometry for Airborne Vapor Monitoring.

Volatile organic compounds (VOCs) emitted into headspace over discrete electronic components including resistors, capacitors, diodes, transistors, and transformers were identified and quantitatively determined in Part 1 of this series using gas chromatography/mass spectrometry. Vapors emissions of VOCs were characteristic of each component and headspace concentrations increased with temperature and persisted with time in most instances. Technology suitable for routine continuous monitoring of air quality based on gas chromatography/differential mobility spectrometry (GC/DMS) was evaluated with the same electronic components here, Part 2 of this series. Distinctive patterns in plots of ion peak intensity, retention time, and compensation voltage were obtained from VOCs from resistors, capacitors, and insulation from wires of a transformer held at 200°C for ten minutes. Intensity of response and analytically rich information produced by GC/DMS suggest further utility also as an air quality monitor or smart smoke alarm with electronics-dense habitats in spacecraft or offices and industrial venues.

Detection of 1,4-dioxane in water by membrane extraction-gas chromiatgraphy/differential mobility spectrometry

The method of detecting trace 1,4-dioxane in water using membrane extraction coupled with gas chromatography/differential mobility spectrometry (GC/DMS) was developed. The parameters including radio frequency voltage, sampling flow rate, permeation time and trapping time were optimized to 1000 V, 50 mL/min, 30 min and 150 s, respectively. The linear range for dioxane was obtained from 2.0 μg/L to 20.0 μg/L. The LOD was found to be 0. 67 μg/L. The specificity towards 1,4-dioxane in the presence of five chlorinated hydrocarbons was improved by using two-dimensional GC separation with optimized DMS compensation voltage. This method paves a way for developing field-deployable sensors for real-time monitoring contaminants in groundwater.

Volatile Organic Compounds in Headspace over Electrical Components at 75 to 200?C Part 2. Analytical Response with Gas Chromatography-Differential Mobility Spectrometry for Airborne Vapor Monitoring.

Volatile Organic Compounds in Headspace over Electrical Components at 75 to 200?C Part 2. Analytical Response with Gas Chromatography-Differential Mobility Spectrometry for Airborne Vapor Monitoring.

Rapid detection of synthetic biomarkers of Escherichia coli in water using microAnalyzer: a field dependence study

Mixtures of 12 known volatile metabolites of Escherichia coli (2,5-dimethyltetrahydrofuran, dimethyl disulfide, 2-heptanone, 2,5-dimethylpyrazine, benzaldehyde, dimethyl trisulfide, 2-nonanone, nonanal, decanal, 2-undecanone, indole, and 2-tridecanone) were prepared from internal standards in water. The headspace gases were then analyzed using microAnalyzer™, a miniaturized Gas Chromatography-Differential Mobility Spectrometry (GC-DMS) system. Differential mobility spectra showed monomer peaks of the analytes with the observed retention times below 3 min. Dispersion voltages, determining the dependence of signal intensities and compensation voltages of each analyte on the applied radio frequency (RF) voltages, were recorded for positive and negative modes. It was found that at increasing RF voltages the monomer peaks of compounds of lower molecular masses tend to shift to more negative compensation voltages; whereas, those of higher molecular masses tend to shift to more positive compensation voltages. The shift results in increase separation among each peak. The signal intensities for all peaks, however, tend to decrease at higher RF voltages. The RF voltage which gave optimum peaks separation and signal resolution was 1,200 V (corresponds to 24 kV/cm). This study demonstrates the potential of microAnalyzer™ as an integrated gas chromatograph detector for a preliminary screening system in the on-site detection of E. coli in water. The operational principle of the instrument is also presented in this study.

Automated peak detection and matching algorithm for gas chromatography-differential mobility spectrometry.

A gas chromatography-differential mobility spectrometer (GC-DMS) involves a portable and selective mass analyzer that may be applied to chemical detection in the field. Existing approaches examine whole profiles and do not attempt to resolve peaks. A new approach for peak detection in the 2D GC-DMS chromatograms is reported. This method is demonstrated on three case studies: a simulated case study; a case study of headspace gas analysis of Mycobacterium tuberculosis (MTb) cultures consisting of three matching GC-DMS and GC-MS chromatograms; a case study consisting of 41 GC-DMS chromatograms of headspace gas analysis of MTb culture and media.

Fast gas chromatography-differential mobility spectrometry of explosives from TATP to Tetryl without gas atmosphere modifiers

Explosives in solution were determined as mixtures containing highly volatile improvised explosives such as peroxides and conventional military grade explosives such as PETN, RDX, and Tetryl using a high speed gas chromatograph with differential mobility detector in a single measurement. Instrument parameters were evaluated and adjusted to permit detection of nanogram amounts of explosives with this broad range of vapor pressures in times under 3 min for HMTD to TNT or under 16 min for HMTD to Tetryl. As in prior studies of response to explosives with mobility spectrometers, pre-separation of sample by gas chromatography improved response in the differential mobility detector; however, unlike prior configurations, the supporting gas atmosphere did not contain modifiers to adjust selectivity in mobility and selectivity was provided only by characteristic stability of product ions in negative and positive polarities. Field dependence of product ions in purified air was determined for each explosive and patterns were sufficiently distinct to suggest the addition of selectivity through the use of several differential mobility detectors operated in parallel or series with characteristic separation voltages.

Non-invasive metabolomic analysis of breath using differential mobility spectrometry in patients with chronic obstructive pulmonary disease and healthy smokers

The rapid, accurate and non-invasive diagnosis of respiratory disease represents a challenge to clinicians, and the development of new treatments can be confounded by insufficient knowledge of lung disease phenotypes. Exhaled breath contains a complex mixture of volatile organic compounds (VOCs), some of which could potentially represent biomarkers for lung diseases. We have developed an adaptive sampling methodology for collecting concentrated samples of exhaled air from participants with impaired respiratory function, against which we employed two-stage thermal desorption gas chromatography-differential mobility spectrometry (GC-DMS) analysis, and showed that it was possible to discriminate between participants with and without chronic obstructive pulmonary disease (COPD). A 2.5 dm(3) volume of end tidal breath was collected onto adsorbent traps (Tenax TA/Carbotrap), from participants with severe COPD and healthy volunteers. Samples were thermally desorbed and analysed by GC-DMS, and the chromatograms analysed by univariate and multivariate analyses. Kruskal-Wallis ANOVA indicated several discriminatory (p < 0.01) signals, with good classification performance (receiver operator characteristic area up to 0.82). Partial least squares discriminant analysis using the full DMS chromatograms also gave excellent discrimination between groups (alpha = 19% and beta = 12.4%).

Determination of volatile organic compounds by solid-phase microextraction—gas chromatography-differential mobility spectrometry

Up to now the analysis of aqueous samples by ion mobility spectrometry is a challenge due to the lack of suitable sample introduction systems. But just the introduction of asymmetric field strengths leading to a higher selectivity and sensitivity, this technique is of growing interest for on-site analysis of contaminated water. In this work, first results for the preparation of a portable GC-DMS system are presented. A differential mobility spectrometer (DMS) with a 63Ni ionization source has been used as a detector for the gaschromatographic separation of benzene, toluene, ethylbenzene m-, o-, and p-xylene (BTEX) in water. Separation from the matrix and pre-concentration of the analytes was achieved by solid-phase microextraction (SPME). The influence of the different chromatographic parameters, extraction conditions and differential mobility spectrometry settings on the sensitivity and signal shape were investigated. Results of the optimization of the analytical method as well as analytical validation parameters such as method detection limit, limit of quantification and the repeatability are discussed. Additionally, the applicability of the method was demonstrated by the analysis of a real surface water sample.

Limits of separation of a multi-capillary column with mixtures of volatile organic compounds for a flame ionization detector and a differential mobility detector

The resolving power of a multi-capillary column (MCC) was evaluated using 14 mixtures of volatile organic compounds with known composition and complexity which was incremented stepwise up to 129 constituents. The number of constituents in these mixtures versus the number of components separated and detected with a flame ionization detector showed a proportional rise, with a decreasing slope, to 76 peaks after which a plateau was reached. This was improved 23.7% to 94 constituents, or 73% of all compounds in the mixture, after simplex optimization of carrier gas linear velocity, initial temperature and program rate. When the detection method was differential mobility spectrometry (DMS), additional selectivity was introduced through ion formation and separation. Fifty nine compounds were detected by DMS and 46 were separated by retention time; 13 were co-eluted and 7 of these were resolved by differential ion mobility (90% of all components ionized). A correlation of −0.412 between retention time for gas chromatography (GC) and differential mobility for DMS suggested a significant level of orthogonal character and the method of GC–DMS should not be seen as sequential only.

Design-of-experiment optimization of exhaled breath condensate analysis using a miniature differential mobility spectrometer (DMS)

Analytical instruments that can measure small amounts of chemicals in complicated biological samples are often useful as diagnostic tools. However, it can be challenging to optimize these sensors using actual clinical samples, given the heterogeneous background and composition of the test materials. Here we use gas chromatography–differential mobility spectrometry (GC/DMS) to analyze the chemical content of human exhaled breath condensate (EBC). Ultimately, this system can be used for non-invasive disease diagnostics. Many parameters can be adjusted within this instrument system, and we implemented a factorial design-of-experiments to systematically test several combinations of parameter settings while concurrently analyzing effects and interactions. We examined four parameters that affect sensitivity and detection for our instrument, requiring a 2 4 factorial design. We optimized sensor function using EBC samples spiked with acetone, a known clinical biomarker in breath. Two outputs were recorded for each experiment combination: number of chemicals detected, and the amplitude of acetone signal. Our goal is to find the best parameter combination that yields the highest acetone peak while also preserving the largest number of other chemical peaks in the spectra. By optimizing the system, we can conduct further clinical experiments with our sensor more efficiently and accurately.