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174 results for “volatile compounds”
Data from: Effect of altitude on volatile organic and phenolic compounds of artemisia brevifolia wall ex Dc. from the Western Himalayas
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Data from: Bacterial phylogeny predicts volatile organic compound composition and olfactory response of an aphid parasitoid
There is increasing evidence that microorganisms emit a wide range of volatile compounds (mVOCs, microbial volatile organic compounds) that act as insect semiochemicals, and therefore play an important role in insect behaviour. Although it is generally believed that phylogenetically closely related microbes tend to have similar phenotypic characteristics and therefore may elicit similar responses in insects, currently little is known about whether the evolutionary history and phylogenetic relationships among microorganisms have an impact on insect-microbe interactions. In this study, we tested the hypothesis that phylogenetic relationships among 40 Bacillus strains isolated from diverse environmental sources predicted mVOC composition and the olfactory response of the generalist aphid parasitoid Aphidius colemani. Results revealed that phylogenetically closely related Bacillus strains emitted similar blends of mVOCs and elicited a comparable olfactory response of A. colemani in Y-tube olfactometer bioassays, varying between attraction and repellence. Analysis of the chemical composition of the mVOC blends showed that all Bacillus strains produced a highly similar set of volatiles, but often in different concentrations and ratios. Benzaldehyde was produced in relatively high concentrations by strains that repel A. colemani, while attractive mVOC blends contained relatively higher amounts of acetoin, 2,3-butanediol, 2,3-butanedione, eucalyptol and isoamylamine. Overall, these results indicate that bacterial phylogeny had a strong impact on mVOC compositions and as a result on the olfactory responses of insects.
Accompanying Data - Nitrogenous Compound Utilization and Production of Volatile Organic Compounds among Commercial Wine Yeasts Highlight Strain-Specific Metabolic Diversity
<p>This repository contains all the data and some of the performed statistical analysis from all the measurements in the following paper: Scott Jr WT, Van Mastrigt O, Block DE, Notebaart RA, Smid EJ. Nitrogenous compound utilization and production of volatile organic compounds among commercial wine yeasts highlight strain-specific metabolic diversity. Microbiology spectrum. 2021 Aug 31;9(1):10-128.</p><p>Please cite: Scott Jr WT, Van Mastrigt O, Block DE, Notebaart RA, Smid EJ. Nitrogenous compound utilization and production of volatile organic compounds among commercial wine yeasts highlight strain-specific metabolic diversity. Microbiology spectrum. 2021 Aug 31;9(1):10-128.</p><p>Contact: Dr. William T. Scott (william.scott@wur.nl) or Dr. Eddy J. Smid (eddy.smid@wur.nl) for more information</p>
Raw data for "A-Proof-of -Concept: In vivo Volatile Organic Compounds Secretion Monitoring as a Tool for Prediction of Natural Pest Control Ability"
<p>This upload refers to work entitled "A-Proof-of -Concept: In vivo Volatile Organic Compounds Secretion Monitoring as a Tool for Prediction of Natural Pest Control Ability" and contains the unprocessed data obtained during experiments perform. </p>
Pre-Analysis Bioinformatics Files for The Microbiome and Volatile Organic Compounds Reflect the State of Decomposition in an Indoor Environment
<p>Data statistics before and after trimming, FastQC reports before and after trimming, MultiQC reports before and after trimming, commands for the Kraken2-Bracken analysis, and classification reports. Read the read.me file for file names and descriptions. </p>
Volatile organic compounds (VOIs) in seawater and sediment pore water collected in Funka Bay and Bering and Chukchi Seas
<p>The data of volatile organic compounds (VOIs) in seawater and sediment pore water collected in the Funka Bay, Hokkaido, Japan, and the Bering and Chukchi Seas. Funka Bay's samples were collected in 2018-2019. Bering and Chukchi Seas' samples were collected in July of 2017 and 2018. The article entitled "Marine sediment as a likely source of methyl and ethyl iodides in subpolar and polar seas" by Ooki et al. published by Communications Earth & Environment used these datasets. The datasets are also provided as supplementary data1-4 in the article. Details of datasets are described in the article. Please cite this article when you use these datasets.</p>
Insect Odorant Binding Protein Dataset of Binding Affinities against Volatile Organic Compounds
<p>This is an archival version of the initial iOBPdb dataset of insect odorant binding protein binding affinities against volatile organic compounds. It contains 215 functional studies containing 381 unique OBPs from 91 insect species.</p>
Data and code for theoretical analyses for the evolution of biogenic volatile organic compounds (BVOCs) emission strategy
<p>These are Python code for analysis and JSON data obtained by simulation with the lattice model.</p> <p>To visualize figures better, we have slightly modified the content of "LatticeModelVisualization.ipynb".</p>
Quantification of volatile organic compound emissions from unconventional oil and gas development
<p>Oil and gas (O&G) development in the U.S. has accelerated in the past two decades, aided by unconventional extraction techniques including hydraulic fracturing and horizontal drilling. Potential environmental and health impacts of volatile organic compounds (VOCs) originating from O&G activities in populated regions have raised concerns. In Broomfield, Colorado, six new O&G well pads were approved for development in 2017 and an air monitoring program was established in October 2018 to collect weekly and plume-triggered air samples. This study addresses the limited existing knowledge of activity-specific VOC emission rates from unconventional O&G development (UOGD), utilizing these observations and dispersion model simulations through emission inversion methods. Emissions are characterized from well drilling, hydraulic fracturing, coiled tubing/millout, flowback, and production operations.</p> <p>Substantial variations in average VOC emission rates, determined using weekly canister observations, are observed across different UOGD phases. Drilling and coiled tubing/millout operations exhibit the highest VOC emission rates, attributed to hydrocarbon release from shale formations and drilling mud. In contrast, hydraulic fracturing gives lower emission rates, consistent with injection of fluids into the well, minimizing the probability of subsurface hydrocarbon emissions. Diesel-powered engines are identified as the primary ethyne sources during hydraulic fracturing. Production was characterized by lower VOC emission rates than pre-production phases but remains an important emission category due to its long duration (decades). Internal variations of emission rates within each phase highlight the complexity of factors and activities influencing emission rates, including, for example, vertical vs. horizontal drilling and periodic maintenance activities. VOC emission rates associated with drilling mud volatilization and hydraulic fracturing suggest that previously published emission estimates (EPA (2022), and Hecobian et al. (2019)) underestimate VOC emission rates during these activities. Significantly lower emission rates during flowback compared to previous work (Hecobian et al., 2019) reveal how improved management practices, including tankless, closed-loop fluid handling systems have effectively reduced what used to be a dominant source of pre-production VOC emissions. Plume-triggered samples, capturing transient high-concentration plumes, reveal short-term VOC emission rates approximately ten times higher for drilling and flowback than determined from weekly samples. In the case of flowback, short-term emission pulses have been linked to periodic emptying of sand canisters used to trap fracking sand emerging from previously fracked wells.</p>
volatile organic compounds that were measred at the Dead Sea - Israel (both terrestrial and coastal; Anthropogenic and biogenic)
<p>Multiple VOCs (biogenic and anthropogenic) were sampled by our research group at various terrestrial and coastal sites around the Dead Sea (Israel). Analysis was conducted by Prof. Donald Blake, and the data were included in a publication available at <a href="https://acp.copernicus.org/articles/19/7667/2019/" target="_new" rel="noreferrer">https://acp.copernicus.org/articles/19/7667/2019/</a>.</p>
Dataset on the characterization of the flavor of two red wine varieties using sensory descriptive analysis, volatile organic compounds quantitative analysis by GC-MS and odorant composition by GC-MS-O
<p>The dataset contains data that were collected on 2 sets of 8 French red wines from two grape varieties, Pinot Noir (PN) and Cabernet Franc (CF). It provides, for the 16 wines, (i) sensory descriptive data obtained with a trained panel, (ii) volatile organic compounds (VOC) quantification data obtained by Gas Chromatography–Mass Spectrometry (GC-MS) and (iii) odorant composition obtained by Gas Chromatography–Mass Spectrometry–Olfactometry (GC-MS-O).</p> <p> </p> <p>The dataset is a Microsoft Excel Worksheet containing 8 sheets.</p> <p>- Sheet 1: Information</p> <p>Gives information about the sheets contained in this .xlsx file</p> <p>- Sheet 2: Experimental_factors</p> <p>Each row represents a wine</p> <p>Each column corresponds to an experimental factors of the wines (Grape variety, Vintage and Protected Designation of Origin)</p> <p>- Sheet 3: List_sensory_descriptors</p> <p>Lists the 33 sensory descriptors used for the sensory descriptive analysis of the wines</p> <p>- Sheet 4: Sensory_descriptive_analysis</p> <p>Each row represents a wine</p> <p>Each column corresponds to a condition (2640 columns)</p> <p>Senso_(ortho or retro)_(Panelist1 to Panelist 16)_(1 to 33 Sensory descriptors)_(1 to 3 repetitions for ortho and 1 to 2 repetitions for retro)</p> <p>For the ortho (orthonasal) measurements, there is 16 panelists, 33 sensory descriptors and 3 repetitions = 1584 columns</p> <p>For the retro (retronasal) measurements, there is 16 panelists, 33 sensory descriptors and 2 repetitions = 1056 columns</p> <p>Each cell contains a sensory measurement for the corresponding condition in the corresponding wine</p> <p>- Sheet 5: List_VOC</p> <p>Lists the 45 VOC quantified in the wines with their corresponding CAS number</p> <p>VOC: Volatil Organic Compounds</p> <p>- Sheet 6: VOC_quantification</p> <p>Each row represents a wine</p> <p>Each column corresponds to a VOC (45 columns)</p> <p>Each cell contains the quantification of the corresponding VOC in the corresponding wine</p> <p>- Sheet 7: List_GC-MS-O</p> <p>Lists the 49 odor-active compounds identified with their corresponding CAS number and the 34 compounds identified by their apex indice</p> <p>- Sheet 8: GC-MS-O</p> <p>Each row represents a wine</p> <p>Each column corresponds to an odor-active compound identified by its CAS number or by its Apex indice if the compound was not identify (81 odor-active compounds) + the number of judges who smelled the compound and its description (by 8 judges) = 9 columns per odor-active compound for a total of 729 columns</p>
Technical note: Interferences of volatile organic compounds (VOC) on methane concentration measurements - Raw Data
<p>Technical note: Interferences of volatile organic compounds (VOC) on methane concentration measurements - Raw Data</p>
GCMS chromatogram: volatile compounds
<p>Supplementary material for the article </p> <p><strong>Authenticity of wines produced from ‘Frankovka’ grape variety originating in the Modré hory region (Czech Republic)</strong></p>
Measurement report: Unexpected high volatile organic compounds emission from vehicles on the Tibetan Plateau Dataset
<p>This dataset includes various emission profiles and related data, specifically:</p> <ol> <li> <p><strong>Source Profile Data at Different Altitudes</strong>.</p> </li> <li> <p><strong>Emission Factor Data</strong>.</p> </li> <li> <p><strong>Emission Ratio Data</strong>.</p> </li> <li><strong>Source Profile Data from PMF Source Apportionment</strong>: Data obtained through Positive Matrix Factorization (PMF), revealing the composition of emission sources.</li> <li> <p><strong>Average Profiles of Gasoline Vapors</strong>: Derived from sealed housing evaporative determination (SHED) tests, with references 1-7.</p> </li> <li> <p><strong>Average Profiles of Gasoline Vehicle Exhaust</strong>: Based on dynamometer tests, with references 2, 8-13.</p> </li> <li> <p><strong>Average Profiles of Vehicular Emissions</strong>: Collected from low-altitude tunnel measurements, reflecting emissions in real-world driving scenarios, with references 14-24.</p> </li> </ol>
Multiply improved positive matrix factorization for source apportionment of volatile organic compounds during the COVID-19 shutdown in Tianjin, China
<p>This dataset contains the input and output data of the multiple PMF for volatile organic compounds (VOCs) source apportionment in the suburbs of Tianjin, China, during the outbreak of COVID-19 period (November 2019 to March 2020).</p>
Volatile compounds of five mangrove species and parts
<p><span>Mangrove plants contain a variety of secondary metabolites, which are important for their survival and adaptation to the coastal environment, as well as for producing bioactive compounds. To reveal differences in mangrove volatile</span><span>s</span><span>, </span><span>a </span><span>GC-MS </span><span>method</span><span> was </span><span>built</span> <span>up</span> <span>and</span><span> used to analyze and identify five mangrove species' leaf, root, and stem. </span><span>The relative content and type of volatile compounds were counted and compared, and their pathway enrichment analysis was p</span><span>erformed. The results showed that 5</span><span>32 compounds were detected in the l</span><span>eaf, root, and stem parts of five mangrove species, which </span><span>were</span><span> grouped into 18 classes including alcohols, aldehydes, alkaloids, alkanes, etc. The number of compounds found was from 41 to 86 in each part of five mangrove species, <em>A</em>. <em>corniculatum</em> leaves and <em>A</em>. <em>marina</em> roots contained a maximum of 86 compounds. 247 compounds were found in <em>A</em>. <em>corniculatum</em>, 245 in <em>K</em>. <em>candel</em>, and 240 in <em>A</em>. <em>marina</em>. Roots, stems, and leaves each had 399, 342, and 339 compounds. There were 40 unique compounds in <em>A</em>. <em>corniculatum</em> leaves, and 39 in <em>A</em>. <em>corniculatum</em> stems.</span> <span>71 common compounds occurring in more than two species or organ parts were analyzed by PLS-DA model. Compared w</span><span>it</span><span>h the contents and compositions of their compounds, <em>A</em>. <em>ilicifolius</em> and <em>B</em>. <em>gymnorrhiza</em> differed significantly from the other species</span><span>, </span><span>while the leaves differed significantly from the other parts. </span><span>Unique compounds and common compounds were found to have a significant difference in composition and concentration between species and parts based on the results of one-way analysis of variance, principal component analysis, and hierarchical clustering analysis. </span><span>VIP screening and pathway enrichment analysis were performed </span><span>on</span><span> 17 common compounds closely related to mangrove tree species or parts</span><span>, and </span><span>these compounds </span><span>were involved in metabolic pathways of C10 isoprenoids, C15 isoprenoids, fatty alcohols, etc. These findings might help in the development of genetic varieties and medicinal utilization of mangrove plants.</span></p>
Insect Odorant Binding Protein Dataset of Binding Affinities against Volatile Organic Compounds
<p>This is an archival version of the initial iOBPdb dataset of insect odorant binding protein binding affinities against volatile organic compounds. It contains 181 functional studies containing 382 unique OBPs from 91 insect species for 622 individual VOC targets.</p>
Long-term variations of ambient volatile organic compounds (VOCs) from 2016 to 2020 in Beijing, China
<p>Ambient volatile organic compounds (VOCs) are crucial precursors for the formation of secondary organic aerosol (SOA) and ozone (O<sub>3</sub>). We have conducted in-situ observations and compiled a comprehensive dataset of ambient volatile organic compound (VOC) compositions and concentrations in Beijing, China, spanning the period from 2016 to 2020. The dataset covers a wide range of VOC species including 29 alkanes, 11 alkenes, 1 alkyne, 16 aromatics, 28 halohydrocarbons, 13 oxygenated VOCs (OVOCs), and 1 nitrogenous VOC (acetonitrile). The presentation and analysis of this dataset is available in a paper submitted to Earth System Science Data (Simon et al, in prep). The findings and analysis of this dataset have been documented in a paper that has been submitted to Earth System Science Data (Liu et al., in prep).</p> <p>If you use the dataset for related scientific research, please cite the corresponding reference:</p> <p> Liu et al (in prep); Long-term variations of ambient volatile organic compounds (VOCs) from 2016 to 2020 in Beijing, China; Earth System Science Data.</p>
Data from: Bacterial phylogeny predicts volatile organic compound composition and olfactory response of an aphid parasitoid
Open the record for dataset details and reuse information.
Volatile compounds of five mangrove species and parts
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