Automated chemical identification and library building using dispersion plots for differential mobility spectrometry.
ABSTRACT: Differential mobility spectrometry (DMS) based detectors require rapid data analysis capabilities, embedded into the devices to achieve the optimum detection capabiites as portable trace chemical detectors. Automated algorithm-based DMS dispersion plot data analysis method was applied for the first time to pre-process and separate 3-dimentional (3-D) DMS dispersion data. We previously demonstrated our AnalyzeIMS (AIMS) software was capable of analyzing complex gas chromatography differential mobility spectrometry (GC-DMS) data sets. In our present work, the AIMS software was able to easliy separate DMS dispersion data sets of five chemicals that are important in detection of volatile organic compounds (VOCs): 2-butanone, 2-propanone, ethyl acetate, methanol and ethanol. Identification of chemicals from mixtures, separation of chemicals from a mixture and prediction capability of the software were all tested. These automated algorithms may have potential applications in separation of chemicals (or ion peaks) from other 3-D data obtained by hybrid analytical devices such as mass spectrometry (MS). New algorithm developments are included as future considerations to improve the current numerical approaches to fingerprint chemicals (ions) from a significantly complicated dispersion plot. Comprehensive peak identifcation by DMS-MS, variations of the DMS data due to chemical concentration, gas phase ion chemistry, temperature and pressure of the drift gas are considered in future algorithm improvements.
Project description: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.
Project description:This work presents a simulation framework developed under the widely used Robot Operating System (ROS) to enable the validation of robotics systems and gas sensing algorithms under realistic environments. The framework is rooted in the principles of computational fluid dynamics and filament dispersion theory, modeling wind flow and gas dispersion in 3D real-world scenarios (i.e., accounting for walls, furniture, etc.). Moreover, it integrates the simulation of different environmental sensors, such as metal oxide gas sensors, photo ionization detectors, or anemometers. We illustrate the potential and applicability of the proposed tool by presenting a simulation case in a complex and realistic office-like environment where gas leaks of different chemicals occur simultaneously. Furthermore, we accomplish quantitative and qualitative validation by comparing our simulated results against real-world data recorded inside a wind tunnel where methane was released under different wind flow profiles. Based on these results, we conclude that our simulation framework can provide a good approximation to real world measurements when advective airflows are present in the environment.
Project description:The concept of a fractional base unit for the Kendrick mass defect (KMD) analysis of polymer ions is introduced for the first time. A fraction of the ethylene oxide (EO) repeat unit (namely EO/8) has been used for the KMD analysis of a poly(ethylene oxide) and found to amplify the variations of KMD between monoisotopic and 13C isotopes, producing an isotopically resolved KMD plot at full scale when the KMD plot computed with EO is fuzzy. The expansion of the KMD dimension using a fractional base unit has then been successfully used to unequivocally discriminate all the distributions from a blend of poly(ethylene oxide)s in a high resolution KMD plot calculated with EO/3 as base unit. Extending the concept of fractional base units to other repeat units, the visualization of the co-oligomers from a poly(ethylene oxide-b-propylene oxide-b-ethylene oxide) triblock copolymer has been dramatically improved using a fraction of the propylene oxide repeat unit (namely PO/3) in an oligomer and isotope resolved plot. High resolution KMD plots were eventually calculated from tandem mass spectra of poly(dimethylsiloxane) ions using a fraction of the dimethylsiloxane (DMS) unit (namely DMS/6) with clearer point alignments and a discrimination of all the product ion series, out of reach of the KMD analysis using DMS. Versatile and producing high resolution KMD plots, the introduction of fractional base units is believed to be a major step towards the implementation of the KMD analysis as a routine data mining tool for mass spectrometry in polymer chemistry.
Project description: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.
Project description:Designing mobile devices for the analysis of complex sample mixtures containing a variety of analytes at different concentrations across a large dynamic range remains a challenging task in many analytical scenarios. To meet this challenge, a compact hybrid analytical platform has been developed combining Fourier transform infrared spectroscopy based on substrate-integrated hollow waveguides (iHWG-FTIR) with gas chromatography coupled differential mobility spectrometry (GC-DMS). Due to the complementarity of these techniques regarding analyte type and concentration, their combination provides a promising tool for the detection of complex samples containing a broad range of molecules at different concentrations. To date, the combination of infrared spectroscopy and ion mobility techniques remains expensive and bound to a laboratory utilizing e.g. IMS as prefilter or IR as ionization source. In the present study, a cost-efficient and portable solution has been developed and characterized representing the first truly hyphenated IR-DMS system. As a model analyte mixture, 5 ppm isopropylmercaptan (IPM) in methane (CH4) were diluted, and the concentration-dependent DMS signal of IPM along with the concentration-dependent IR signal of CH4 were recorded for all three hybrid IR-DMS systems. While guiding the sample through the iHWG-FTIR or the GC-DMS first did not affect the obtained signals, optimizing the IR data acquisition parameters did benefit the analytical results.
Project description:Thousands of organohalogen compounds, including hazardous chemicals such as polychlorinated biphenyls (PCBs) and other persistent organic pollutants (POPs), were selectively and simultaneously detected and identified with simple, or no, purification from environmental sample extracts by using several advanced methods. The methods used were software extraction from two-dimensional gas chromatography-high-resolution time-of-flight mass spectrometry (GC?×?GC-HRTofMS) data, measurement by negative chemical ionization with HRTofMS, and neutral loss scanning (NLS) with GC?×?GC-MS/MS. Global and selective detection of organochlorines and bromines in environmental samples such as sediments and fly ash was achieved by NLS using GC?×?GC-MS/MS (QQQ), with the expected losses of 35Cl and 79Br. We confirmed that negative chemical ionization was effective for sensitive and selective ionization of organohalogens, even using GC?×?GC-HRTofMS. The 2D total ion chromatograms obtained by using negative chemical ionization and selective extraction of organohalogens using original software from data measured by electron impact ionization were very similar; the software thus functioned well to extract organohalogens. Combining measurements made by using these different methods will help to detect organohalogens selectively and globally. However, to compare the data obtained by individual measurements, the retention times of the peaks on the 2D chromatograms need to match.
Project description: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.
Project description:In an effort to reduce the experimental tests required to characterize the mixed-gas solubility and solubility-selectivity of materials for membrane separation processes, there is a need for reliable models which involve a minimum number of adjustable parameters. In this work, the ability of the Dual Mode Sorption (DMS) model to represent the sorption of CO?/CH? mixtures in three high free volume glassy polymers, poly(trimethylsilyl propyne) (PTMSP), the first reported polymer of intrinsic microporosity (PIM-1) and tetrazole-modified PIM-1 (TZ-PIM), was tested. The sorption of gas mixtures in these materials suitable for CO? separation has been characterized experimentally in previous works, which showed that these systems exhibit rather marked deviations from the ideal pure-gas behavior, especially due to competitive effects. The accuracy of the DMS model in representing the non-idealities that arise during mixed-gas sorption was assessed in a wide range of temperatures, pressures and compositions, by comparing with the experimental results available. Using the parameters obtained from the best fit of pure-gas sorption isotherms, the agreement between the mixed-gas calculations and the experimental data varied greatly in the different cases inspected, especially in the case of CH? absorbed in mixed-gas conditions. A sensitivity analysis revealed that pure-gas data can be represented with the same accuracy by several different parameter sets, which, however, yield markedly different mixed-gas predictions, that, in some cases, agree with the experimental data only qualitatively. However, the multicomponent calculations with the DMS model yield more reliable results than the use of pure-gas data in the estimation of the solubility-selectivity of the material.
Project description:Technology to enable rapid screening for radiation exposure has been identified as an important need, and, as a part of a NIH / NIAD effort in this direction, metabolomic biomarkers for radiation exposure have been identified in a recent series of papers. To reduce the time necessary to detect and measure these biomarkers, differential mobility spectrometry - mass spectrometry (DMS-MS) systems have been developed and tested. Differential mobility ion filters preselect specific ions and also suppress chemical noise created in typical atmospheric-pressure ionization sources (ESI, MALDI, and others). Differential-mobility-based ion selection is based on the field dependence of ion mobility, which, in turn, depends on ion characteristics that include conformation, charge distribution, molecular polarizability, and other properties, and on the transport gas composition which can be modified to enhance resolution. DMS-MS is able to resolve small-molecule biomarkers from nearly-isobaric interferences, and suppresses chemical noise generated in the ion source and in the mass spectrometer, improving selectivity and quantitative accuracy. Our planar DMS design is rapid, operating in a few milliseconds, and analyzes ions before fragmentation. Depending on MS inlet conditions, DMS-selected ions can be dissociated in the MS inlet expansion, before mass analysis, providing a capability similar to MS/MS with simpler instrumentation. This report presents selected DMS-MS experimental results, including resolution of complex test mixtures of isobaric compounds, separation of charge states, separation of isobaric biomarkers (citrate and isocitrate), and separation of nearly-isobaric biomarker anions in direct analysis of a bio-fluid sample from the radiation-treated group of a mouse-model study. These uses of DMS combined with moderate resolution MS instrumentation indicate the feasibility of field-deployable instrumentation for biomarker evaluation.
Project description:Comprehensive two-dimensional gas chromatography mass spectrometry (GC × GC-MS) brings much increased separation capacity, chemical selectivity and sensitivity for metabolomics and provides more accurate information about metabolite retention times and mass spectra. However, there is always a shift of retention times in the two columns that makes it difficult to compare metabolic profiles obtained from multiple samples exposed to different experimental conditions.The existing peak alignment algorithms for GC × GC-MS data use the peak distance and the spectra similarity sequentially and require predefined either distance-based window and/or spectral similarity-based window. To overcome the limitations of the current alignment methods, we developed an optimal peak alignment using a novel mixture similarity by employing the peak distance and the spectral similarity measures simultaneously without any variation windows. In addition, we examined the effect of the four different distance measures such as Euclidean, Maximum, Manhattan and Canberra distances on the peak alignment. The performance of our proposed peak alignment algorithm was compared with the existing alignment methods on the two sets of GC × GC-MS data. Our analysis showed that Canberra distance performed better than other distances and the proposed mixture similarity peak alignment algorithm prevailed against all literature reported methods.The data and software mSPA are available at http://stage.louisville.edu/faculty/x0zhan17/software/software-development.