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29 results for “qToF”
UPLC-QTOF data from a river sample extracted with various solid phase extraction phases - mz5 files with scans in centroid spectrum format
<p>This dataset has been acquired from a river sample (Marne River, France) collected as part of the Screenatm'eau project (Observatoire des Sciences de l’Univers - Enveloppes FLUides de la Ville à l’Exobiologie - OSU-EFLUVE, Université Paris-Est Créteil). The sample was processed by solid-phase extraction using multiple cartridges and phases, in triplicates (see the list of samples in the samplemetadata.csv file).</p> <p>Data was acquired with a SYNAPT HDMS QTOF (Waters) coupled with a Nano ACQUITY UPLC System (Waters), in ESI positive mode. Raw data was converted using Proteowizard MSConvert version 3.0.21288, using zlib compression, with the CWT peakpicking algorithm (snr=0, peakSpace=0) to transform profile to centroid data, and the zeroSamples option (removeExtra 1-). The chosen output formats were .mzML and .mz5.</p> <p>A list of markers was obtained after peak picking and alignment across all samples using the PatRoon R package with the OpenMS algorithm, and exported as a markertable.csv file. Each detected marker has m/z and retention time values (see the variablemetadata.csv file), and intensity values in all samples (markertable.csv).</p> <p>For file naming explanation and details about each SPE phase, see the readme.txt file.</p>
Full-scale MBR coupled with PAC for the removal of 232 OMPs: operating conditions, conventional pollutants, concentration of OMPs, UHPLC-QTOF-MS analysis
<p>The spreadsheet contains data regarding the operation and performance of a full-scale MBR coupled with powdered activated carbon (PAC) added inside the reactor. This hybrid system is chosen to evaluate the removal of 232 organic micropollutants (OMPs) and the potential enhancement in the removal efficiencies with the addition of PAC at a concentration of 0.1 g/L and 0.2 g/L</p><ul><li>First worksheet contains minimum, maximum, and average concentrations of conventional pollutants (COD, BOD, SST, SSV, DOC, UV254, nitrogen compounds, phosphorous, <i>E. coli</i>) in the influent and effluent of the WWTP. Methodologies adopted are also reported.</li><li>Second worksheet reports the operating conditions of the full-scale MBR as well as information about the tubular UF membranes installed. Characteristics of the PAC purchased to perform the experiments regarding the removal of OMPs are also described.</li><li>Third worksheet reports minimum, maximum and average concentration of 232 OMPs in the influent and effluent during:<ol><li>The monitoring period considering only the MBR</li><li>The experimental periods where PAC is added and maintained at a concentration of 0.1g/L and 0.2g/L inside the MBR</li></ol></li><li>Fourth worksheet contains metadata regarding the UHPLC–QTOF–MS analysis performed to evaluate the occurrence of OMPs in the influent and effluent of the WWTP. Sampling, storage and sample preparation is described, followed to LC-ESI-tandem MS analysis. LOD and LOQ are reported.</li></ul>
IDSL.UFA comparison for Orbitrap and QTOF instruments
<p>Molecular formula annotation results were compared for LC-HRMS data from orbitrap and qtof instruments for a biorec plasma sample. </p>
Fig. 3 in Metabolic fingerprinting of Ganoderma spp. using UHPLC-ESI-QTOF-MS and its chemometric analysis
Fig. 3. Structures of five isomeric compounds with the molecular formula C30H42O7 and molecular mass 514.2931 found in the G44 sample.
Fig. 2 in Metabolic fingerprinting of Ganoderma spp. using UHPLC-ESI-QTOF-MS and its chemometric analysis
Fig. 2. General chemical structure of a typical triterpene showing the position of various side chains and list of side chains typically found in Ganoderma.
Fig. 1 in Metabolic fingerprinting of Ganoderma spp. using UHPLC-ESI-QTOF-MS and its chemometric analysis
Fig. 1. (a) Total ion chromatogram of G44 mushroom extract in ESI negative mode (b) Extracted ion chromatogram (EIC) of G44 mushroom extract in ESI negative mode.
Fig. 5 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 5. The hierarchical clustering of the differentially abundant metabolites based on GC-TOF-MS in the healthy flowers and petal blight flowers of R. agastum. The data were log2 transformed, and similarity assessment for clustering was based on the Euclidean distance coefficient and complete clustering algorithm. Columns and rows represent individual metabolites and different samples, respectively.
Fig. 2 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 2. Total ion current (TIC) chromatogram of healthy flowers (HF) and petal blight flowers (PBF) of R. agastum using GC-TOF-MS.
Fig. 3 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 3. The data analysis of the metabolites based on GC-TOF-MS in the healthy flowers and petal blight flowers of R. agastum. (A) Principal component analysis (PCA). (B) Orthogonal projections to latent structures discriminant analysis (OPLS-DA). The green and red circles display 95% confidence regions of petal blight and healthy flowers. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 1. The petal blight of R. agastum. (A) R. agastum grown in field habitat. (B) The healthy flower of R. agastum. (C) The petal blight flower of R. agastum (white arrow).
Fig. 4 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 4. The data analysis of the metabolites based on UHPLC-QTOF-MS/MS in the healthy flowers and petal blight flowers of R. agastum. (A) Principal component analysis (PCA) analysis in positive ion modes. (B) Principal component analysis (PCA) analysis in negative ion modes. (C) Orthogonal projections to latent structures discriminant (OPLS-DA) analysis in positive ion modes. (D) Orthogonal projections to latent structures discriminant (OPLS-DA) analysis in negative ion modes. The green and red circles display 95% confidence regions of petal blight and healthy flowers. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 6 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 6. The hierarchical clustering of the differentially abundant metabolites in the positive ion modes (A) and in the negative ion modes (B) based on UHPLC-QTOFMS/MS in the healthy flowers and petal blight flowers of R. agastum. The data were log2 transformed, and similarity assessment for clustering was based on the Euclidean distance coefficient and complete clustering algorithm. Columns and rows represent individual metabolites and different samples, respectively.
UPLC-IMS-QTOF data from road runoff samples - mz5 files with scans in centroid spectrum format
<p>This dataset has been acquired from road runoff samples collected as part of the Roulepur project (Office Français de la Biodiversité, Agence de l’eau de Seine-Normandie).</p> <p>All samples were extracted by SPE with HLB cartridges at pH 2. Data was acquired with a UPLC-IMS-QTOF (Waters Vion) running Unifi, in HDMSe mode. Samples were injected in a random sequence (see order in samplemetadata.csv file). A list of markers was obtained from Unifi after alignment across all samples, and exported as a markertable.csv file. Each detected marker has m/z, retention time, drift time (ion mobility) values, and intensity values in all samples. Raw data was converted using Proteowizard MSConvert version 3.0.19014-f9d5b8a3b, using zlib compression, combining ion mobility scans, with the CWT peakpicking algorithm (snr=0, peakSpace=0) to transform profile to centroid data, and the zeroSamples option (removeExtra 1-). The chosen output format was .mz5.</p> <p>For file naming explanation, see the readme.txt file.</p>
Yurok Tribe Sediment Data GC-EI-QTOF-HRMS
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Yurok Tribe Sediment Data HPLC-ESI(+)-QTOF-HRMS
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Klamath River Data HPLC-ESI(-)-QTOF-HRMS
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Yurok Tribe Sediment Data HPLC-ESI(-)-QTOF-HRMS
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Klamath River Data HPLC-ESI(+)-QTOF-HRMS
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Yurok Tribe Sediment Data GC-NCI-QTOF-HRMS
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Klamath River Data GC-EI-QTOF-HRMS
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