Waleed M. Maswadeh

Field detection of bacillus spore aerosols with stand‐alone pyrolysis–gas chromatography–ion mobility spectrometry

A commercially available, hand-held chemical vapor detector was modified to detect gram-positive Bacillus subtilis var. globigii spores (BG) in outdoor field scenarios. An airborne vapor monitor (AVM) ion mobility spectrometry (IMS) vapor detector was interfaced to a biological sample processing and transfer introduction system. The biological sample processing was accomplished by quartz tube pyrolysis (Py), and the resultant vapor was transferred by gas chromatography (GC) to the IMS detector. The Py-GC/IMS system can be described as a hyphenated device where two analytical dimensions, in series, allow the separation and isolation of individual components from the pyrolytic decomposition of biological analytes. Gram-positive spores such as BG contain 5–15% by weight of dipicolinic acid (DPA), and picolinic acid is a pyrolysis product of DPA. Picolinic acid has a high proton affinity, and it is detected in a sensitive fashion by the atmospheric pressure-based IMS device. Picolinic acid occupies a unique region in the GC/IMS data domain with respect to other bacterial pyrolysis products. A 1000 to 1, air-to-air aerosol concentrator was interfaced to the Py-GC/IMS instrument, and the system was placed in an open-air, western United States desert environment. The system was tested with BG spore aerosol releases, and the instrument was remotely operated during a trial. A Met-One aerosol particle counter was placed next to the Py-GC/IMS so as to obtain a real-time record of the ambient and bacterial aerosol challenges. The presence/absence of an aerosol event, determined by an aerosol particle counter and a slit-sampler–agar-plate system, was compared to the presence/absence of a picolinic acid response in a GC/IMS data window at selected times in a trial with respect to a BG challenge. In the 21 BG trials, the Py-GC/IMS instrument experienced two true negatives and no false positives, and developed a software failure in one trial. The remaining 18 trials were true positive determinations for the presence of BG aerosol, and a limit of detection for the Py-GC/IMS instrument was estimated at approximately 3300 BG spore-containing particles.

Detection of gram-negative Erwinia herbicola outdoor aerosols with pyrolysis - gas chromatography/ion-mobility spectrometry.

Aerosol particulate species of the gram-negative bacterium Erwinia herbicola (EH) were detected by stand-alone, analytical instrumentation in an outdoor western United States desert test site. The device consisted of an aerosol collector interfaced to a quartz-tube pyrolysis–gas chromatography/ion-mobility spectrometer (Py-GC/IMS). The detector is about the size of a shoebox, that is, 12 × 9 × 6 in. Bacterial aerosols and background particulates in the 2 to 10 μm-diameter range were collected by a 1000-l/min aerosol concentrator and deposited onto a filter in a quartz tube. Rapid heating to 350 °C in 5 s effected vaporization, and a portion of the pyrolyzate was directed into a GC column. The eluate was detected by the atmospheric-pressure–based IMS. A distinct peak in the GC/IMS data window was used to signal the presence of the EH bacterial aerosol. The sensitivity of this method was relatively good in that values down to five EH-containing aerosol particles per liter of air could be detected in approximately 2.5 min.

Field detection of bacillus spore aerosols with stand-alone pyrolysis-gas chromatography and ion mobility spectrometry

A commercially available, hand-held chemical vapor detector was modified to detect Gram-positive Bacillus subtilis var. globigii spores (BG) in outdoor field scenarios. An Airborne Vapor Monitor (AVM) ion mobility spectrometry (IMS) vapor detector was interfaced to a biological sample processing and transfer introduction system. The biological sample processing was accomplished by quartz tube pyrolysis (Py), and the resultant vapor was transferred by gas chromatography (GC) to the IMS detector. The Py-GC/IMS system can be described as a hyphenated device where two analytical dimensions, in series, allow the separation and isolation of individual components from the pyrolytic decomposition of biological analytes. Gram positive spores such as BG contain 5 - 15% by weight of dipicolinic acid (DPA), and picolinic acid is a pyrolysis product of DPA. Picolinic acid has a high proton affinity, and it is detected in a sensitive fashion by the atmospheric pressure-based IMS device. Picolinic acid occupies a unique region in the GC/IMS data domain with respect to other bacterial pyrolysis products. A 1000 to 1, air-to-air, aerosol concentrator was interfaced to the Py-GC/IMS instrument, and the system was placed in an open-air, Western United States desert environment. The system was tested with BG spore aerosol releases, and the instrument was remotely operated during a trial. A Met-One aerosol particle counter was placed next to the Py-GC/IMS so as to obtain a real-time record of the ambient and bacterial aerosol challenges. The presence/absence of an aerosol event, determined by an aerosol particle counter and a slit sampler-agar plate system, was compared to the presence/absence of a picolinic acid response in a GC/IMS data window at selected times in a trial with respect to a BG challenge. In the 21 BG trials, the Py-GC/IMS instrument experienced two true negatives, no false positives, and the instrument developed a software failure in one trial. The remaining 18 trials were true positive determinations for the presence of BG aerosol, and a limit of detection for the Py-GC/IMS instrument was estimated at approximately 3300 BG spore-containing particles.

Multivariate statistical analysis characterization of application-based ion mobility spectra

Abstract Ion mobility spectral datasets were investigated for the potential to discriminate between classes of compounds using multivariate statistical analysis techniques. Entire ion mobility spectra, including the reactant ion peak (RIP), were obtained using a hand-held gas chromatography-ion mobility spectrometer (GC-IMS) to ensure vapor quality through Chromatographic prefractionation. The chosen datasets were application-based and consisted of (1) 15 compounds representative of illegal drug synthesis precursors/purification solvents, (2) 18 compounds that are airborne contaminants in the NASA space shuttle, (3) benzene, toluene, xylenes and six polyaromatic hydrocarbons among 41 alkane, alkene and alkylaromatic compounds typical of petroleum-based environmental contaminants. Principal component and discriminant rotation analyses of these datasets satisfactorily separated the various classes of compounds from each other. All spectra displayed an RIP that was between 20–75% of its maximum, and either the monomer or monomer and dimer peaks were present for every compound in the datasets. Despite these relatively wide ranges in the ion mobility response characteristics, it appears that there is potential for multivariate statistical analysis techniques to discriminate between the ion mobility spectra of a diverse set of compounds.

Chemical Agent Detection Using GC-IMS: A Comparative Study

Low-cost and portable gas chromatography-ion mobility spectrometry (GC-IMS) has been used to identify chemicals. To accomplish this, two parameters are used. The first parameter relates to the GC retention time (RT), which is the residence time of an analyte as it passes through the column. Different chemicals have different RTs. The second parameter is the drift time of ionized species derived for a specific chemical in the IMS. Due to molecular cross section, mass, and chemical properties, different chemicals produce ionized species with different drift times. Combining these two parameters, GC-IMS has been shown to distinguish between different chemicals. Chemical detection and identification are not that easy in practice. First, the concentration of chemicals may be very low, and it may be difficult to determine the chromatographic RT and IMS drift time for chemicals under these conditions. Second, the specific ionized species produced in the IMS are concentration dependent and the IMS spectra obtained at different analyte concentrations are not easily predictable. For example, at low concentrations, chemicals seldom form dimers following atmospheric pressure ionization. The possible presence of either monomers or dimers in the IMS drift tube may confuse the chemical classification process. Third, it is important to estimate the concentration of chemicals, as this information will provide toxicity, and the linear dynamic range of typical IMS systems is relatively low In this study, an image processing approach to enhancing the GC-IMS signal quality is introduced. The key idea in this approach is to treat GC-IMS data as an image and then apply an anomaly detector to detect and enhance abnormal regions in the image. The results of a study that compares a conventional approach to chemical detection and the introduced image enhancement approach are presented. Receiver operating characteristics curves were used to compare the detection performances of the two approaches.

Measurement and modeling of individual carbonaceous particle temperature profiles during fast CO2 laser heating: Part 1. Model char

In part 1, we discussed the temperature profiles of laser heated spherical carbonaceous particles along with the temperature modeling. In this paper, we perform the same set of experiments with selected coal particles. Coals of three different ranks (i.e. degree of coalification) represented by three different particle sizes (80, 100, and 120 μm), with and without aerodynamic size classification, were analyzed under the same set of experimental conditions. The temperature profiles were modeled using a heat transport model modified to include chemical kinetics (based on the FG-DVC model). Non-aerodynamically size-classified particles displayed better model prediction when laser heated in nitrogen atmosphere compared with heating in air. Best model predictions, however, were obtained in the case of aerodynamically size-classified particles heated under nitrogen atmosphere.

Variable ranking based on the estimated degree of separation for two distributions of data by the length of the receiver operating characteristic curve

Variable responses are fundamental for all experiments, and they can consist of information-rich, redundant, and low signal intensities. A dataset can consist of a collection of variable responses over multiple classes or groups. Usually some of the variables are removed in a dataset that contain very little information. Sometimes all the variables are used in the data analysis phase. It is common practice to discriminate between two distributions of data; however, there is no formal algorithm to arrive at a degree of separation (DS) between two distributions of data. The DS is defined herein as the average of the sum of the areas from the probability density functions (PDFs) of A and B that contain a≥percentage of A and/or B. Thus, DS90 is the average of the sum of the PDF areas of A and B that contain ≥90% of A and/or B. To arrive at a DS value, two synthesized PDFs or very large experimental datasets are required. Experimentally it is common practice to generate relatively small datasets. Therefore, the challenge was to find a statistical parameter that can be used on small datasets to estimate and highly correlate with the DS90 parameter. Established statistical methods include the overlap area of the two data distribution profiles, Welchs t-test, Kolmogorov-Smirnov (K-S) test, Mann-Whitney-Wilcoxon test, and the area under the receiver operating characteristics (ROC) curve (AUC). The area between the ROC curve and diagonal (ACD) and the length of the ROC curve (LROC) are introduced. The established, ACD, and LROC methods were correlated to the DS90 when applied on many pairs of synthesized PDFs. The LROC method provided the best linear correlation with, and estimation of, the DS90. The estimated DS90 from the LROC (DS90-LROC) is applied to a database, as an example, of three Italian wines consisting of thirteen variable responses for variable ranking consideration. An important highlight of the DS90-LROC method is utilizing the LROC curve methodology to test all variables one-at-a-time with all pairs of classes in a dataset.

Field detection and identification of a bioaerosol suite by pyrolysis-gas chromatography-ion mobility spectrometry

Improvements were made to a pyrolysis-gas chromatography-ion mobility spectrometry stand-alone biodetector to provide more pyrolyzate compound information to the IMS detector module. Air carrier gas flowing continuously through the pyrolysis tube, the rate of air flow, and pyrolysis rate were found to improve the relative quality and quantity of pyrolyzate compounds detected by the IMS detector compare to earlier work. These improvements allowed a greater degree of confidence in the correlation of biological aerosols obtain in outdoor testing scenarios to a standard GC-IMS biological aerosol dataset. The airflow improvement allowed more biomarker compounds to be observed in the GC-IMS data domain for aerosols of Gram-negative Erwinia herbicola (EH) and ovalbumin protein as compared to previous studies. Minimal differences were observed for Gram-positive spores of Bacillus subtilis var. globigii (BG) from that of earlier work. Prior outdoor aerosol challenges dealt with the detection of one organism, either EH or BG. Biological aerosols were disseminated in a Western Canadian prairie and the Py-GC-IMS was tested for its ability to detect the biological aerosols. The current series of outdoor trials consisted of three different biological aerosol challenges. Forty-two trials were conducted and a simple area calculation of the GC-IMS data domain biomarker peaks correlated with the correct bioaerosol challenge in 30 trials. In another 7 trials, the status of an aerosol was determined to be biological in origin. Two additional trials had no discernible, unambiguous GC-IMS biological response, because they were black water sprays. Reproducible limits of detection were at a concentration of less than 0.5 bacterial analyte-containing particles per liter of air (ACPLA). In order to realize this low concentration, an aerosol concentrator was used to concentrate 2000 liters of air in 2.2 minutes. Previous outdoor aerosol trials have shown the Py-GC-IMS device to be a credible detector with response to determining the presence of a biological aerosol. The current series of outdoor trials has provided a platform to show that the Py-PC-IMS can provide information more specific than a biological or non-biological analysis to an aerosol when the time of dissemination is unknown to the operator. The Py-GC-IMS is shown to be able to discriminate between aerosols of a Gram-positive spore, a Gram-negative bacterium and a protein.