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29 results for “Gas Chromatography”
Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) and Gas Chromatography - Mass Spectrometry (GC-MS) Reference Libraries from Global Natural Products Social Molecular Networking (GNPS) and National Institute of Standards and Technology (NIST) WebBook Processed for Spectral Library Matching
<div>In order to obtain a high-quality LC-MS/MS reference database for spectral library matching, we selected 22 high-quality GNPS tandem mass spectrometry databases generated under the positive ion mode. Further preprocessing similar to Huber et al involving mass-to-charge (m/z) and intensity filtering yields the database found in the file LCMS_GNPS_reference_library.csv which contains 14,705 electrospray ionization (ESI) mass spectra, each of which corresponds to a unique compound. The NIST WebBook database was used to construct GC-MS database contained in the file GCMS_NIST_WebBook.csv. This database contains 23,721 electron ionization (EI) mass spectra, each of which corresponds to a unique non-hyphenated Chemical Abstract Service (CAS) Registry Number.</div> <div> </div> <div>Both LC-MS/MS and GC-MS databases are organized into three columns: one for the identifier, one for the m/z values, and one for the intensity values. For example, if spectrum A has 20 ion fragments, then there will be 20 rows corresponding to spectrum A in the corresponding database with the identifier A repeated 20 times with the corresponding m/z and intensity values.</div>
Data from: Normalizing gas-chromatography–mass spectrometry data: method choice can alter biological inference
<p>Gas-Chromatography Mass Spectrometry data from European badger (<em>Meles meles</em>) sub-caudal gland secretion used in:</p> <p>Noonan, M.J., Tinnesand, H.V.,<sup> </sup>and Buesching, C.D. (2018). Normalizing gas-chromatography–mass spectrometry data: method choice can alter biological inference. BioEssays, 40(6): 0-0. DOI: 10.1002/bies.201700210.</p>
A Gas Chromatography – Ion Mobility Spectrometry dataset for colorectal cancer diagnostic of 56 urine samples corresponding to 29 subjects.
<p><strong>Contents of the dataset</strong></p> <p>The dataset includes the set of urine samples in .mea format, which can be<br> read using the GCIMS R package.</p> <p>It also contains analytical standards in the same format, used for quality<br> control of the equipment and as a retention time alignment reference.</p> <p>If you want to preview the data, you do not need to download the full Urines.zip<br> and AnalyticalStandards.zip files, but rather use the smaller UrinesDemo.zip and<br> AnalyticalStandardsDemo.zip, with a subset of just three samples of the whole<br> dataset.</p> <p>Besides the actual measurements, you will find the annotations.csv and<br> reference_peaks.csv files, with sample annotations and some reference peaks<br> identified in the samples.</p> <p>See further details below.</p> <p><br> <strong>Sample collection</strong></p> <p>Urine samples from 29 subjects were collected at Hospital de Reus. 15 subjects<br> were diagnosed with colorectal cancer, 14 subjects were controls. The study<br> protocol was approved by the Ethics Committee of Hospital de Reus (study<br> approval no. 074/2018).</p> <p>Samples were aliquoted and frozen at -80ºC for storage.</p> <p><strong>Sample preparation</strong><br> </p> <p>Sample preparation improves urine preservation by blocking bacterial growth in<br> the urine, and favours volatile extraction. It also adds an internal standard<br> for verification of instrument variability.</p> <p><em>Stock solution preparation</em></p> <p>Dissolve 11.69 g of NaCl in about 35 mL deionized water and add 6.5 mg sodium<br> azide (NaN3). Once dissolved, add 5.50 mL 5M HCl and mark up to volume with<br> deionized water until the final volume is 50mL. The HCl 5M is used to obtain<br> an acid pH. The pH is controlled with a pH test paper. The final pH level must<br> be 2 or below. The NaCl favors the volatile extraction, and the NaN3 omits<br> the bacterial growth in the urine.</p> <p><em>Internal standard solution preparation</em><br> </p> <p>The 4-flurobenzaldehyde is located in retention time around 200 seconds and<br> can be used as an internal standard.</p> <p>Prepare a methanol stock solution using 100 ml of methanol grade for<br> preparative chromatography and 200 ml of distilled water.</p> <p>Mix 5 mL of 4-fluorobenzaldehyde with 100 mL of the methanol stock solution.</p> <p>Dilute the previous mixture in 400 mL of mili-Q water.</p> <p><br> <em>Sample preparation</em><br> </p> <p>Aliquotes were thawed before analysis. Once thawed, 300uL of the stock solution<br> were added to the urine sample, and 1.5 ml of the acidified urine sample were<br> transferred into a 20ml vial, ensuring only the supernatant of the sample<br> is transferred.</p> <p>Finally, 20 mL of the internal standard solution is added to the sample.</p> <p><strong>GC-IMS Analysis</strong></p> <p>Samples were analyzed with a GC-IMS FlavourSpec® instrument from<br> G.A.S. Dortmund (Dortmund, Germany). Samples were incubated for 15 minutes<br> at 60ºC, the flow rate of the drift gas was set at 200 ml/min, and the carrier<br> gas was set 11 ml/min. Both the drift and carrier gas were Nitrogen 5.0. The GC<br> and IMS temperature were set at 60ºC and the measurement time lasted 33 minutes.</p> <p>Besides the urines, a set of measurements of a ketone mixture was also analyzed<br> at least once per day as an analytical standard control of the equipment. The mixture<br> included 6 ketones (2-butanone, 2-pentanone, 2-hexanone, 2-heptanone,<br> 2-ocatanone and 2-nonanone). This mixture is measured in the same conditions as<br> the urine samples.</p> <p>Samples are provided in the native instrument format (.mea format), that can be<br> read with the GCIMS R package or with the instrument software.</p> <p><strong>Sample annotations</strong></p> <p>The dataset includes a CSV file with sample annotations.</p> <p>The annotations include the following information:</p> <ul> <li>Diagnostic: Either ColorectalCancer or Control</li> <li>Sex: Either Male or Female</li> <li>Sample volume (in ml)</li> <li>Fasting: Whether the sample was collected with the patient in fasting conditions</li> <li>Age in years</li> <li>Weight_kg</li> <li>Height_cm</li> <li>BMI</li> <li>Smoker: TRUE/FALSE, whether the patient smoked</li> <li>Diseases: Whether the patient suffered from ArterialHypertension, CardiacFailure, Cholesterol, Dyslipidemia, Fibromyalgia or Tuberculosis</li> <li>AnalysisDateTime: Date and time of the GC-IMS analysis of the sample</li> </ul> <p><br> <strong>Reference peaks</strong></p> <p>Some peaks were manually annotated to ease the alignment of the samples and explore<br> alignment solutions. While manual peak labelling is not generally required, we<br> attach those reference peaks as well and their locations, in case they are of<br> interest.</p> <p>These reference peaks are found at reference_peaks.csv.</p> <p> </p>
Raw data for evaluation of Floral Volatile-Patterns in the Genus Narcissus using Gas Chromatography coupled Ion Mobility Spectrometry
<p>We used a commercial gaschromatography coupled ion mobility spectrometer, equipped with an integrated in-line enrichment system for fast, sensitive and automated analysis of floral volatile patterns in the genus <em>Narcissus</em>. The raw data of individual measurements are stored together with corresponding telemetry data as data matrices. The determined retention times and ion mobilities (series and columns) can be used for the identification of substances. The measured values (intensities) are used for a (semi)-quantoitative determination of individual substances.Based on these raw data, heatmaps can be generated in this way, which allow a comparison and potentially identification of floral volatiles. </p> <p>This data set is part of a proof of concept study for the use of GC-IMS in the investigation of flower volatiles.</p>
Data for: Development of a method for the measurement of human scent samples using comprehensive two-dimensional gas chromatography with mass detection
<p>This dataset was used for development of a method for the measurement of human scent samples using comprehensive two-dimensional gas chromatography with mass detection [<a href="https://doi.org/10.1016/j.forsciint.2016.09.011">https://doi.org/10.1016/j.forsciint.2016.09.011</a>].</p> <p>The dataset contains chromatograms of a model mixture of human scent and chromatograms of the human scent of one volunteer measured on different column setups. Each sample was processed in ChromaToF(version 4.72.0.0) by LECO corp. The processing step was executed at the signal-to-noise (SN) ratio levels 100, 300, and 500 (human scent samples chromatograms).</p>
Dataset for the optimization and validation of a gas chromatography-mass spectrometry method to analyze acetate, propionate and butyrate in the systemic circulation.
<p>This dataset contains data about the optimization and validation of a gas chromatography method to analyze acetate, propionate and butyrate in blood. Validation parameters include linearity, precision, accuracy and recovery. The method's applicability was demonstrated with the analysis of the short-chain fatty acids in human blood samples that were collected in a dietary intervention study.</p>
Novel Insights into the Bioactive Metabolites of Macrocybe gigantea (Massee) Pegler & Lodge, a Wild Edible Macrofungi Using Gas Chromatography Mass Spectrometry (GC-MS) Combined with Chemoinformatics Approaches
<p><em>Macrocybe gigantea </em>(MG) is an edible mushroom and has multiple pharmacological activities such as antibacterial, antioxidant, and antitumor activities. However, only a few reports were available on the bioactive compounds and bioactivity of this mushroom. In this concern, the present study was aimed to explore the unique chemical diversity from the fruiting body of MG<em>. </em>The species identification was done accurately with morphological and molecular methods followed by mycochemical extraction in different solvent systems. The ethanolic extract of the fruiting body gave maximum yield and its Gas Chromatography-Mass Spectrometry (GC-MS) analysis was performed along with antibacterial activity and cell viability by MTT assay. The GC-MS analysis revealed 50 metabolites and further chemoinformatics analysis of these metabolites revealed their possible biological activities. In addition, the mushrooms' physico-chemical and mineral element analysis revealed the quality and authenticity of the species. Altogether, the current investigation gives a comprehensive overview of the bioactive metabolites of MG.</p>
Data for: Development of a method for the measurement of human scent samples using comprehensive two-dimensional gas chromatography with mass detection
Open the record for dataset details and reuse information.
Comparison of Volatile Flavor Compounds in Plant-based and Real Pork Mince by Headspace-Gas Chromatography-Ion Mo-bility Spectrometry (HS-GC-IMS)
<p>Table S1: The peak intensity of VFCs that identified in six raw pork minces by GC-IMS;</p> <p>Table S2: The peak intensity of VFCs that identified in six steamed pork minces by GC-IMS;</p> <p>Table S3: The peak intensity of VFCs that identified in six stir-fried pork minces by GC-IMS.</p>
Data and code for "Large volume injection and assessment of reference standards for n-alkane δD and δ13C analysis via gas chromatography isotope ratio mass spectrometry"
<p>This file includes data and code related ot the publication, "<span>Large volume injection and assessment of reference standards for <em>n</em>-alkane δD and δ<sup>13</sup>C analysis via gas chromatography isotope ratio mass spectrometry" in Rapid Communications in Mass Spectrometry (in review). Included are datasets of <em>n</em>-alkane δD and δ<sup>13</sup>C measurements with a recently developed large-volume injeciton method. Measuremtns of reference standards and lake sediment samples from Eifel maar lakes of Germany are included. Additionally, code to implement the correction schemes and reporduce the figures and analysis described in the paper are included. </span></p>
Fig. 2 in Global metabolome analysis of Dunaliella tertiolecta, Phaeobacter italicus R11 Co-cultures using thermal desorption - Comprehensive two-dimensional gas chromatography - Time-of-flight mass spectrometry (TD-GC×GC-TOFMS)
Fig. 2. Workflow for sample preparation and injection. Culture samples were filtered and dried (A–B). Dried filter papers were placed in clean vials (C) and then resuspended in methanol (D) before being extracted with Chloroform (E). Water was added (F) and subsequently, the chloroform layer was aliquotted into GC vials (G) for further sample preparation. Extracts were dried (H) and then derivatized using a two-step methoximation/silylation process to yield derivatized extracts (I). 9-μL aliquots of derivatized extracts were automatically transferred to microvial inserts in thermal desorption tubes for injection (J) using an initial solvent vent step to remove excess solvent and derivatisation reagents (K), followed by thermal desorption to a cooled PTV inlet and subsequent splitless injection to the GC × GC-TOFMS system. Non-volatile residues from the extracts remained in the microvial insert for subsequent disposal (L). See text for details.
Fig. 4 in Global metabolome analysis of Dunaliella tertiolecta, Phaeobacter italicus R11 Co-cultures using thermal desorption - Comprehensive two-dimensional gas chromatography - Time-of-flight mass spectrometry (TD-GC×GC-TOFMS)
Fig. 4. From left to right: results of principal component analysis of the raw data (autoscaled), similarly scaled data normalised to class-specific TUPA, and the normalised, scaled data using the selected features from the FS-CR routine. Quality control samples were not included in the feature selection routine, and are displayed as filled icons connected to their corresponding replicate with a straight line, following projection into the optimised principal component space. Confidence ellipses were drawn about each sample class for a confidence interval of 0.95. Note the convention: DUN refers to D. tertiolecta samples, CO refers to co-culture samples, and BAC refers to P. italicus R11 samples.
Data from: Two-step pyrolysis-gas chromatography method with mass spectrometric detection for identification of tattoo ink ingredients and counterfeit products
Tattoo inks are complex mixtures of ingredients. Each of them possesses different chemical properties which have to be addressed upon chemical analysis. In this method for two-step pyrolysis online coupled to gas chromatography mass spectrometry (py-GC-MS) volatile compounds are analyzed during a first desorption run. In the second run, the same dried sample is pyrolyzed for analysis of non-volatile compounds such as pigments and polymers. These can be identified by their specific decomposition patterns. Additionally, this method can be used to differentiate original from counterfeit inks. Easy screening methods for data evaluation using the average mass spectra and self-made pyrolysis libraries are applied to speed up substance identification. Using specialized evaluation software for pyrolysis GS-MS data, a fast and reliable comparison of the full chromatogram can be achieved. Since GC-MS is used as separation technique, the method is limited to volatile substances upon desorption and after pyrolysis of the sample. The method can be applied for quick substance screening in market control surveys since it requires no sample preparation steps.
Application of ATLD-MCR to gas chromatography-mass spectrometric data for the quantification of PAHs in aerosols
<p>In the work, for the first time, alternating trilinear decomposition-assisted multivariate curve resolution (ATLD-MCR) was applied to analyse complex gas chromatography–mass spectrometric (GC-MS) data with severe baseline drifts, serious co-elution peaks and slight retention time shifts for the simultaneous identification and quantification of polycyclic aromatic hydrocarbons (PAHs) in aerosols. It was also compared with the classic multivariate curve resolution-alternating least-squares (MCR-ALS) and the GC-MS-based external standard method. In validation samples, average recoveries of five PAHs were within the range from (96.2 ± 6.8)% to (106.5 ± 4.1)% for ATLD-MCR, near to the results of MCR-ALS ((98.0 ± 1.5)% to (106.7 ± 4.3)%). In aerosol samples, the concentrations of pyrene provided by ATLD-MCR were not significantly different from those of MCR-ALS. The other four PAHs including chrysene, benzo[a]anthracene, fluoranthene and benzo[b]fluoranthene were not detected by ATLD-MCR and the GC-MS-based external standard method. The results of figures of merit further demonstrated that ATLD-MCR achieved high sensitivities (8.9 × 104 to 1.7 × 106 mAU ml µg−1) and low limits of detection (0.003 to 0.087 µg ml−1), which were better than or similar to MCR-ALS, presenting a great choice to deal with complex GC-MS data for the simultaneous determination of targeted PAHs in aerosols.</p>
Fig. 3 in Global metabolome analysis of Dunaliella tertiolecta, Phaeobacter italicus R11 Co-cultures using thermal desorption - Comprehensive two-dimensional gas chromatography - Time-of-flight mass spectrometry (TD-GC×GC-TOFMS)
Fig. 3. Example Total Ion Current (TIC) chromatograms from each sample class.
Figure 4 in Analysis of amino acids, fatty acids and neurotoxins using gas chromatography-mass spectrometry in four scorpions species inhabiting New Valley Governorate, Egypt
Figure 4. GC-MS chromatogram of Buthacus leptochelys.
Data from: Two-step pyrolysis-gas chromatography method with mass spectrometric detection for identification of tattoo ink ingredients and counterfeit products
Open the record for dataset details and reuse information.
Application of ATLD-MCR to gas chromatography-mass spectrometric data for the quantification of PAHs in aerosols
Open the record for dataset details and reuse information.
Figure 6 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Method development and validation for the determination of residual solvents in quinabut API by using gas chromatography. Message 2. Pharmacia 68(1): 53-59. https://doi.org/10.3897/pharmacia.68.e52119
Figure 6 Linearity of IPA solutions.
Figure 5 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Method development and validation for the determination of residual solvents in quinabut API by using gas chromatography. Message 2. Pharmacia 68(1): 53-59. https://doi.org/10.3897/pharmacia.68.e52119
Figure 5 Linearity of acetone solutions.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.