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Deng Y, Kan H, Li Y, Liu Y, Qiu X. Analysis of Volatile Components in Rosa roxburghii Tratt. and Rosa sterilis Using Headspace-Solid-Phase Microextraction-Gas Chromatography-Mass Spectrometry. Molecules 2023; 28:7879. [PMID: 38067608 PMCID: PMC10708075 DOI: 10.3390/molecules28237879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/08/2023] [Accepted: 11/27/2023] [Indexed: 12/18/2023] Open
Abstract
Volatile organic compounds (VOCs) and flavor characteristics of Rosa roxburghii Tratt. (RR) and Rosa sterilis (RS) were analyzed using headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS). The flavor network was constructed by combining relative odor activity values (ROAVs), and the signature differential flavor components were screened using orthogonal partial least squares discriminant analysis (OPLS-DA) and random forest (RF). The results showed that 61 VOCs were detected in both RR and RS: 48 in RR, and 26 in RS. There were six key flavor components (ROAVs ≥ 1) in RR, namely nonanal, ethyl butanoate, ethyl hexanoate, (3Z)-3-hexen-1-yl acetate, ethyl caprylate, and styrene, among which ethyl butanoate had the highest contribution, whereas there were eight key flavor components (ROAVs ≥ 1) in RS, namely 2-nonanol, (E)-2-hexenal, nonanal, methyl salicylate, β-ocimene, caryophyllene, α-ionone, and styrene, among which nonanal contributed the most to RS. The flavor of RR is primarily fruity, sweet, green banana, and waxy, while the flavor of RS is primarily sweet and floral. In addition, OPLS-DA and RF suggested that (E)-2-hexenal, ethyl caprylate, β-ocimene, and ethyl butanoate could be the signature differential flavor components for distinguishing between RR and RS. In this study, the differences in VOCs between RR and RS were analyzed to provide a basis for further development and utilization.
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Affiliation(s)
- Yuhang Deng
- Key Laboratory of Forest Resources Conservation and Utilization in the Southwest Mountains of China Ministry of Education, Southwest Forestry University, Kunming 650224, China
- Forest Resources Exploitation and Utilization Engineering Research Center for Grand Health of Yunnan Provincial Universities, Kunming 650224, China
| | - Huan Kan
- Key Laboratory of Forest Resources Conservation and Utilization in the Southwest Mountains of China Ministry of Education, Southwest Forestry University, Kunming 650224, China
- Forest Resources Exploitation and Utilization Engineering Research Center for Grand Health of Yunnan Provincial Universities, Kunming 650224, China
| | - Yonghe Li
- College of Plant Protection, Yunnan Agricultural University, Kunming 650201, China
| | - Yun Liu
- Key Laboratory of Forest Resources Conservation and Utilization in the Southwest Mountains of China Ministry of Education, Southwest Forestry University, Kunming 650224, China
| | - Xu Qiu
- Forest Resources Exploitation and Utilization Engineering Research Center for Grand Health of Yunnan Provincial Universities, Kunming 650224, China
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2
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Gaida M, Stefanuto PH, Focant JF. Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography-A review. J Chromatogr A 2023; 1711:464467. [PMID: 37871505 DOI: 10.1016/j.chroma.2023.464467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 10/25/2023]
Abstract
In recent years, comprehensive two-dimensional gas chromatography (GC × GC) has been gradually gaining prominence as a preferred method for the analysis of complex samples due to its higher peak capacity and resolution power compared to conventional gas chromatography (GC). Nonetheless, to fully benefit from the capabilities of GC × GC, a holistic approach to method development and data processing is essential for a successful and informative analysis. Method development enables the fine-tuning of the chromatographic separation, resulting in high-quality data. While generating such data is pivotal, it does not necessarily guarantee that meaningful information will be extracted from it. To this end, the first part of this manuscript reviews the importance of theoretical modeling in achieving good optimization of the separation conditions, ultimately improving the quality of the chromatographic separation. Multiple theoretical modeling approaches are discussed, with a special focus on thermodynamic-based modeling. The second part of this review highlights the importance of establishing robust data processing workflows, with a special emphasis on the use of advanced data processing tools such as, Machine Learning (ML) algorithms. Three widely used ML algorithms are discussed: Random Forest (RF), Support Vector Machine (SVM), and Partial Least Square-Discriminate Analysis (PLS-DA), highlighting their role in discovery-based analysis.
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Affiliation(s)
- Meriem Gaida
- Organic and Biological Analytical Chemistry Group (OBiAChem), MolSys Research Unit, Liège University, Belgium
| | - Pierre-Hugues Stefanuto
- Organic and Biological Analytical Chemistry Group (OBiAChem), MolSys Research Unit, Liège University, Belgium
| | - Jean-François Focant
- Organic and Biological Analytical Chemistry Group (OBiAChem), MolSys Research Unit, Liège University, Belgium
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3
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Höjer Holmgren K, Hakulinen H, Norlin R, de Bruin-Hoegée M, Spiandore M, Qi Shu See S, Webster R, Jacques KL, Mauravaara L, Hwi Ang L, Evans CP, Ovenden S, Noort D, Delaporte G, Dahlén J, Fraga CG, Vanninen P, Åstot C. Interlaboratory comparison study of a chemical profiling method for methylphosphonic dichloride, a nerve agent precursor. Forensic Chem 2023. [DOI: 10.1016/j.forc.2023.100473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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4
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Webster RL, Ovenden SPB, McDowall LJ, Dennison GH, Laws MJ, McGill NW, Williams J, Zanatta SD. Chemical forensic profiling and attribution signature determination of sarin nerve agent using GC-MS, LC-MS and NMR. Anal Bioanal Chem 2022; 414:3863-3873. [PMID: 35396608 DOI: 10.1007/s00216-022-04027-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/14/2022] [Accepted: 03/16/2022] [Indexed: 11/30/2022]
Abstract
Sarin is a highly toxic nerve agent classified by the Chemical Weapon Convention as a Schedule 1 chemical with no use other than to kill or injure. Moreover, in recent times, chemical warfare agents have been deployed against both military and civilian populations. Chemical warfare agents always contain minor impurities that can provide important chemical attribution signatures (CAS) that can aid in forensic investigations. In order to understand the trace molecular composition of sarin, various analytical approaches including GC-MS, LC-MS and NMR were used to determine the chemical markers of a set of sarin samples. Precursor materials were studied and the full characterisation of a synthetic process was undertaken in order to provide new insights into potential chemical attribution signatures for this agent. Several compounds that were identified in the precursor were also found in the sarin samples linking it to its method of preparation. The identification of these CAS contributes critical information about a synthetic route to sarin, and has potential for translation to related nerve agents.
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Affiliation(s)
- Renée L Webster
- Defence Science and Technology Group, 506 Lorimer St, Fishermans Bend, VIC, 3207, Australia.
| | - Simon P B Ovenden
- Defence Science and Technology Group, 506 Lorimer St, Fishermans Bend, VIC, 3207, Australia
| | - Lyndal J McDowall
- Defence Science and Technology Group, 506 Lorimer St, Fishermans Bend, VIC, 3207, Australia
| | - Genevieve H Dennison
- Defence Science and Technology Group, 506 Lorimer St, Fishermans Bend, VIC, 3207, Australia
| | - Melissa J Laws
- Defence Science and Technology Group, 506 Lorimer St, Fishermans Bend, VIC, 3207, Australia
| | - Nathan W McGill
- Defence Science and Technology Group, 506 Lorimer St, Fishermans Bend, VIC, 3207, Australia
| | - Jilliarne Williams
- Defence Science and Technology Group, 506 Lorimer St, Fishermans Bend, VIC, 3207, Australia
| | - Shannon D Zanatta
- Defence Science and Technology Group, 506 Lorimer St, Fishermans Bend, VIC, 3207, Australia
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5
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Bendik J, Kalia R, Sukumaran J, Richardot WH, Hoh E, Kelley ST. Automated high confidence compound identification of electron ionization mass spectra for nontargeted analysis. J Chromatogr A 2021; 1660:462656. [PMID: 34798444 DOI: 10.1016/j.chroma.2021.462656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 10/19/2022]
Abstract
Nontargeted analysis based on mass spectrometry is a rising practice in environmental monitoring for identifying contaminants of emerging concern. Nontargeted analysis performed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC/TOF-MS) generates large numbers of possible analytes. Moreover, the default spectral library similarity score-based search algorithm used by LECO® ChromaTOF® does not ensure that high similarity scores result in correct library matches. Therefore, an additional manual screening is necessary, but leads to human errors especially when dealing with large amounts of data. To improve the speed and accuracy of the chemical identification, we developed CINeMA.py (Classification Is Never Manual Again). This programming suite automates GC×GC/TOF-MS data interpretation by determining the confidence of a match between the observed analyte mass spectrum and the LECO® ChromaTOF® software generated library hit from the NIST Electron Ionization Mass Spectral (NIST EI-MS) library. Our script allows the user to evaluate the confidence of the match using an algorithmic method that mimics the manual curation process and two different machine learning approaches (neural networks and random forest). The script allows the user to adjust various parameters (e.g., similarity threshold) and study their effects on prediction accuracy. To test CINeMA.py, we used data from two different environmental contaminant studies: an EPA study on household dust and a study on stormwater runoff. Using a reference set based on the analysis performed by highly trained users of the ChromaTOF and GC×GC/TOF-MS systems, the random forest model had the highest prediction accuracies of 86% and 83% on the EPA and Stormwater data sets, respectively. The algorithmic approach had the second-best prediction accuracy (82% and 79%), while the neural network accuracy had the lowest (63% and 67%). All the approaches required less than 1 min to classify 986 observed analytes, whereas manual data analysis required hours or days to complete. Our methods were also able to detect high confidence matches missed during the manual review. Overall, CINeMA.py provides users with a powerful suite of tools that should significantly speed-up data analysis while reducing the possibilities of manual errors and discrepancies among users, and can be applicable to other GC/EI-MS instrument based nontargeted analysis.
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Affiliation(s)
- Joseph Bendik
- Department of Biology, San Diego State University, San Diego, CA, USA
| | - Richa Kalia
- Department of Biology, San Diego State University, San Diego, CA, USA
| | - Jeet Sukumaran
- Department of Biology, San Diego State University, 5500 Campanile Drive, San Diego, CA 92104, USA
| | - William H Richardot
- San Diego State University Research Foundation, San Diego, CA, USA; School of Public Health, San Diego State University, San Diego, CA, USA
| | - Eunha Hoh
- School of Public Health, San Diego State University, San Diego, CA, USA
| | - Scott T Kelley
- Department of Biology, San Diego State University, San Diego, CA, USA; Department of Biology, San Diego State University, 5500 Campanile Drive, San Diego, CA 92104, USA.
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6
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Guelfo JL, Korzeniowski S, Mills MA, Anderson J, Anderson RH, Arblaster JA, Conder JM, Cousins IT, Dasu K, Henry BJ, Lee LS, Liu J, McKenzie ER, Willey J. Environmental Sources, Chemistry, Fate, and Transport of Per- and Polyfluoroalkyl Substances: State of the Science, Key Knowledge Gaps, and Recommendations Presented at the August 2019 SETAC Focus Topic Meeting. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2021; 40:3234-3260. [PMID: 34325493 PMCID: PMC8745034 DOI: 10.1002/etc.5182] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/24/2021] [Accepted: 07/27/2021] [Indexed: 05/19/2023]
Abstract
A Society of Environmental Toxicology and Chemistry (SETAC) Focused Topic Meeting (FTM) on the environmental management of per- and polyfluoroalkyl substances (PFAS) convened during August 2019 in Durham, North Carolina (USA). Experts from around the globe were brought together to critically evaluate new and emerging information on PFAS including chemistry, fate, transport, exposure, and toxicity. After plenary presentations, breakout groups were established and tasked to identify and adjudicate via panel discussions overarching conclusions and relevant data gaps. The present review is one in a series and summarizes outcomes of presentations and breakout discussions related to (1) primary sources and pathways in the environment, (2) sorption and transport in porous media, (3) precursor transformation, (4) practical approaches to the assessment of source zones, (5) standard and novel analytical methods with implications for environmental forensics and site management, and (6) classification and grouping from multiple perspectives. Outcomes illustrate that PFAS classification will continue to be a challenge, and additional pressing needs include increased availability of analytical standards and methods for assessment of PFAS and fate and transport, including precursor transformation. Although the state of the science is sufficient to support a degree of site-specific and flexible risk management, effective source prioritization tools, predictive fate and transport models, and improved and standardized analytical methods are needed to guide broader policies and best management practices. Environ Toxicol Chem 2021;40:3234-3260. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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Affiliation(s)
- Jennifer L. Guelfo
- Department of Civil, Environmental, & Construction EngineeringTexas Tech UniversityLubbockTexasUSA
| | - Stephen Korzeniowski
- American Chemistry CouncilWashingtonDCUSA
- Associated General Contractors of AmericaExtonPennsylvaniaUSA
| | - Marc A. Mills
- Office of Research and DevelopmentUS Environmental Protection Agency, CincinnatiOhioUSA
| | | | | | | | | | - Ian T. Cousins
- Department of Environmental Science and Analytical ChemistryStockholm UniversityStockholmSweden
| | | | | | - Linda S. Lee
- Department of AgronomyPurdue University, West LafayetteIndianaUSA
| | - Jinxia Liu
- Department of Civil EngineeringMcGill UniversityMontrealQuebecCanada
| | - Erica R. McKenzie
- Department of Civil and Environmental EngineeringTemple UniversityPhiladelphiaPennsylvaniaUSA
| | - Janice Willey
- Naval Sea Systems Command, Laboratory Quality and Accreditation Office, Goose CreekSouth CarolinaUSA
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Lu X, Zhang Z, Gao R, Wang H, Xiao J. Recent progress in the chemical attribution of chemical warfare agents and highly toxic organophosphorus pesticides. Forensic Toxicol 2021. [DOI: 10.1007/s11419-021-00578-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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8
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Taking the leap between analytical chemistry and artificial intelligence: A tutorial review. Anal Chim Acta 2021; 1161:338403. [DOI: 10.1016/j.aca.2021.338403] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 01/01/2023]
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9
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Höjer Holmgren K, Mörén L, Ahlinder L, Larsson A, Wiktelius D, Norlin R, Åstot C. Route Determination of Sulfur Mustard Using Nontargeted Chemical Attribution Signature Screening. Anal Chem 2021; 93:4850-4858. [PMID: 33709707 PMCID: PMC8041246 DOI: 10.1021/acs.analchem.0c04555] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
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Route determination
of sulfur mustard was accomplished through
comprehensive nontargeted screening of chemical attribution signatures.
Sulfur mustard samples prepared via 11 different synthetic routes
were analyzed using gas chromatography/high-resolution mass spectrometry.
A large number of compounds were detected, and multivariate data analysis
of the mass spectrometric results enabled the discovery of route-specific
signature profiles. The performance of two supervised machine learning
algorithms for retrospective synthetic route attribution, orthogonal
partial least squares discriminant analysis (OPLS-DA) and random forest
(RF), were compared using external test sets. Complete classification
accuracy was achieved for test set samples (2/2 and 9/9) by using
classification models to resolve the one-step routes starting from
ethylene and the thiodiglycol chlorination methods used in the two-step
routes. Retrospective determination of initial thiodiglycol synthesis
methods in sulfur mustard samples, following chlorination, was more
difficult. Nevertheless, the large number of markers detected using
the nontargeted methodology enabled correct assignment of 5/9 test
set samples using OPLS-DA and 8/9 using RF. RF was also used to construct
an 11-class model with a total classification accuracy of 10/11. The
developed methods were further evaluated by classifying sulfur mustard
spiked into soil and textile matrix samples. Due to matrix effects
and the low spiking level (0.05% w/w), route determination was more
challenging in these cases. Nevertheless, acceptable classification
performance was achieved during external test set validation: chlorination
methods were correctly classified for 12/18 and 11/15 in spiked soil
and textile samples, respectively.
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Affiliation(s)
- Karin Höjer Holmgren
- Department of CBRN Defence & Security, The Swedish Defence Research Agency (FOI), Cementvägen 20, Umeå SE-901 82, Sweden
| | - Lina Mörén
- Department of CBRN Defence & Security, The Swedish Defence Research Agency (FOI), Cementvägen 20, Umeå SE-901 82, Sweden
| | - Linnea Ahlinder
- Department of CBRN Defence & Security, The Swedish Defence Research Agency (FOI), Cementvägen 20, Umeå SE-901 82, Sweden
| | - Andreas Larsson
- Department of CBRN Defence & Security, The Swedish Defence Research Agency (FOI), Cementvägen 20, Umeå SE-901 82, Sweden
| | - Daniel Wiktelius
- Department of CBRN Defence & Security, The Swedish Defence Research Agency (FOI), Cementvägen 20, Umeå SE-901 82, Sweden
| | - Rikard Norlin
- Department of CBRN Defence & Security, The Swedish Defence Research Agency (FOI), Cementvägen 20, Umeå SE-901 82, Sweden
| | - Crister Åstot
- Department of CBRN Defence & Security, The Swedish Defence Research Agency (FOI), Cementvägen 20, Umeå SE-901 82, Sweden
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Stilo F, Bicchi C, Jimenez-Carvelo AM, Cuadros-Rodriguez L, Reichenbach SE, Cordero C. Chromatographic fingerprinting by comprehensive two-dimensional chromatography: Fundamentals and tools. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2020.116133] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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11
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Reichenbach SE, Zini CA, Nicolli KP, Welke JE, Cordero C, Tao Q. Benchmarking machine learning methods for comprehensive chemical fingerprinting and pattern recognition. J Chromatogr A 2019; 1595:158-167. [DOI: 10.1016/j.chroma.2019.02.027] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 02/04/2019] [Accepted: 02/11/2019] [Indexed: 11/29/2022]
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Holmgren KH, Valdez CA, Magnusson R, Vu AK, Lindberg S, Williams AM, Alcaraz A, Åstot C, Hok S, Norlin R. Part 1: Tracing Russian VX to its synthetic routes by multivariate statistics of chemical attribution signatures. Talanta 2018; 186:586-596. [PMID: 29784407 DOI: 10.1016/j.talanta.2018.02.104] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 02/07/2018] [Accepted: 02/26/2018] [Indexed: 11/25/2022]
Abstract
Chemical attribution signatures (CAS) associated with different synthetic routes used for the production of Russian VX (VR) were identified. The goal of the study was to retrospectively determine the production method employed for an unknown VR sample. Six different production methods were evaluated, carefully chosen to include established synthetic routes used in the past for large scale production of the agent, routes involving general phosphorus-sulfur chemistry pathways leading to the agent, and routes whose main characteristic is their innate simplicity in execution. Two laboratories worked in parallel and synthesized a total of 37 batches of VR via the six synthetic routes following predefined synthesis protocols. The chemical composition of impurities and byproducts in each route was analyzed by GC/MS-EI and 49 potential CAS were recognized as important markers in distinguishing these routes using Principal Component Analysis (PCA). The 49 potential CAS included expected species based on knowledge of reaction conditions and pathways but also several novel compounds that were fully identified and characterized by a combined analysis that included MS-CI, MS-EI and HR-MS. The CAS profiles of the calibration set were then analyzed using partial least squares discriminant analysis (PLS-DA) and a cross validated model was constructed. The model allowed the correct classification of an external test set without any misclassifications, demonstrating the utility of this methodology for attributing VR samples to a particular production method. This work is part one of a three-part series in this Forensic VSI issue of a Sweden-United States collaborative effort towards the understanding of the CAS of VR in diverse batches and matrices. This part focuses on the CAS in synthesized batches of crude VR and in the following two parts of the series the influence of food matrices on the CAS profiles are investigated.
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Affiliation(s)
- Karin Höjer Holmgren
- The Swedish Defence Research Agency, FOI CBRN Defence and Security, SE-901 82 Umeå, Sweden
| | - Carlos A Valdez
- Forensic Science Center, Lawrence Livermore National Laboratory, 7000 East Avenue L-091, Livermore, California 94550, United States
| | - Roger Magnusson
- The Swedish Defence Research Agency, FOI CBRN Defence and Security, SE-901 82 Umeå, Sweden
| | - Alexander K Vu
- Forensic Science Center, Lawrence Livermore National Laboratory, 7000 East Avenue L-091, Livermore, California 94550, United States
| | - Sandra Lindberg
- The Swedish Defence Research Agency, FOI CBRN Defence and Security, SE-901 82 Umeå, Sweden
| | - Audrey M Williams
- Forensic Science Center, Lawrence Livermore National Laboratory, 7000 East Avenue L-091, Livermore, California 94550, United States
| | - Armando Alcaraz
- Forensic Science Center, Lawrence Livermore National Laboratory, 7000 East Avenue L-091, Livermore, California 94550, United States
| | - Crister Åstot
- The Swedish Defence Research Agency, FOI CBRN Defence and Security, SE-901 82 Umeå, Sweden
| | - Saphon Hok
- Forensic Science Center, Lawrence Livermore National Laboratory, 7000 East Avenue L-091, Livermore, California 94550, United States.
| | - Rikard Norlin
- The Swedish Defence Research Agency, FOI CBRN Defence and Security, SE-901 82 Umeå, Sweden.
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Synthesis route attribution of sulfur mustard by multivariate data analysis of chemical signatures. Talanta 2018; 186:615-621. [PMID: 29784411 DOI: 10.1016/j.talanta.2018.02.100] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Revised: 02/15/2018] [Accepted: 02/26/2018] [Indexed: 11/20/2022]
Abstract
A multivariate model was developed to attribute samples to a synthetic method used in the production of sulfur mustard (HD). Eleven synthetic methods were used to produce 66 samples for model construction. Three chemists working in both participating laboratories took part in the production, with the aim to introduce variability while reducing the influence of laboratory or chemist specific impurities in multivariate analysis. A gas chromatographic/mass spectrometric data set of peak areas for 103 compounds was subjected to orthogonal partial least squares - discriminant analysis to extract chemical attribution signature profiles and to construct multivariate models for classification of samples. For one- and two-step routes, model quality allowed the classification of an external test set (16/16 samples) according to synthesis conditions in the reaction yielding sulfur mustard. Classification of samples according to first-step methodology was considerably more difficult, given the high purity and uniform quality of the intermediate thiodiglycol produced in the study. Model performance in classification of aged samples was also investigated.
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Zushi Y, Hashimoto S. Direct Classification of GC × GC-Analyzed Complex Mixtures Using Non-Negative Matrix Factorization-Based Feature Extraction. Anal Chem 2018; 90:3819-3825. [DOI: 10.1021/acs.analchem.7b04313] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Yasuyuki Zushi
- Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology, 16-1 Onogawa, Tsukuba, Ibaraki 305-8569, Japan
- Center for Environmental Measurement and Analysis, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
| | - Shunji Hashimoto
- Center for Environmental Measurement and Analysis, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
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Prebihalo SE, Berrier KL, Freye CE, Bahaghighat HD, Moore NR, Pinkerton DK, Synovec RE. Multidimensional Gas Chromatography: Advances in Instrumentation, Chemometrics, and Applications. Anal Chem 2017; 90:505-532. [DOI: 10.1021/acs.analchem.7b04226] [Citation(s) in RCA: 115] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Sarah E. Prebihalo
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - Kelsey L. Berrier
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - Chris E. Freye
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - H. Daniel Bahaghighat
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
- Department of Chemistry and Life Science, United States Military Academy, West Point, New York 10996, United States
| | - Nicholas R. Moore
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - David K. Pinkerton
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - Robert E. Synovec
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
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