Inter-laboratory reproducibility of fast gas chromatography–electron impact–time of flight mass spectrometry (GC–EI–TOF/MS) based plant metabolomics View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2009-07-24

AUTHORS

J. William Allwood, Alexander Erban, Sjaak de Koning, Warwick B. Dunn, Alexander Luedemann, Arjen Lommen, Lorraine Kay, Ralf Löscher, Joachim Kopka, Royston Goodacre

ABSTRACT

The application of gas chromatography–mass spectrometry (GC–MS) to the ‘global’ analysis of metabolites in complex samples (i.e. metabolomics) has now become routine. The generation of these data-rich profiles demands new strategies in data mining and standardisation of experimental and reporting aspects across laboratories. As part of the META-PHOR project’s (METAbolomics for Plants Health and OutReach: http://www.meta-phor.eu/) priorities towards robust technology development, a GC–MS ring experiment based upon three complex matrices (melon, broccoli and rice) was launched. All sample preparation, data processing, multivariate analyses and comparisons of major metabolite features followed standardised protocols, identical models of GC (Agilent 6890N) and TOF/MS (Leco Pegasus III) were also employed. In addition comprehensive GC×GC–TOF/MS was compared with 1 dimensional GC–TOF/MS. Comparisons of the paired data from the various laboratories were made with a single data processing and analysis method providing an unbiased assessment of analytical method variants and inter-laboratory reproducibility. A range of processing and statistical methods were also assessed with a single exemplary dataset revealing near equal performance between them. Further investigations of long-term reproducibility are required, though the future generation of global and valid metabolomics databases offers much promise. More... »

PAGES

479-496

References to SciGraph publications

  • 2007-08-17. The metabolomics standards initiative (MSI) in METABOLOMICS
  • 2007-08-25. Proposed minimum reporting standards for data analysis in metabolomics in METABOLOMICS
  • 2004-09. Metabolite profiling: from diagnostics to systems biology in NATURE REVIEWS MOLECULAR CELL BIOLOGY
  • 2007-09-06. A roadmap for the establishment of standard data exchange structures for metabolomics in METABOLOMICS
  • 2008-08-27. Metabolomic analysis of the interaction between plants and herbivores in METABOLOMICS
  • 2007. Nonsupervised Construction and Application of Mass Spectral and Retention Time Index Libraries From Time-of-Flight Gas Chromatography-Mass Spectrometry Metabolite Profiles in METABOLOMICS
  • 1986. Principal Component Analysis in NONE
  • 2007-04-24. A comparative study of amino acid measurement in leaf extracts by gas chromatography-time of flight-mass spectrometry and high performance liquid chromatography with fluorescence detection in METABOLOMICS
  • 2000-11. Metabolite profiling for plant functional genomics in NATURE BIOTECHNOLOGY
  • 2006-06-27. Gas chromatography mass spectrometry–based metabolite profiling in plants in NATURE PROTOCOLS
  • 2004-12-06. A proposed framework for the description of plant metabolomics experiments and their results in NATURE BIOTECHNOLOGY
  • 1999-02. Variation in sugar levels and invertase activity in mature fruit representing a broad spectrum of Cucumis melo genotypes in GENETIC RESOURCES AND CROP EVOLUTION
  • 2007-08-23. Minimum reporting standards for plant biology context information in metabolomic studies in METABOLOMICS
  • 2006-01-01. Chemometrics in Metabolomics — An Introduction in PLANT METABOLOMICS
  • 2007-04-05. Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry in NATURE PROTOCOLS
  • 2007-09-12. Proposed minimum reporting standards for chemical analysis in METABOLOMICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11306-009-0169-z

    DOI

    http://dx.doi.org/10.1007/s11306-009-0169-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1004994369

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/20376177


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/03", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Chemical Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0301", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Analytical Chemistry", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK", 
              "id": "http://www.grid.ac/institutes/grid.5379.8", 
              "name": [
                "School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Allwood", 
            "givenName": "J. William", 
            "id": "sg:person.01235401314.61", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01235401314.61"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Max-Planck-Institut f\u00fcr Molekulare Pflanzenphysiologie, Am M\u00fchlenberg 1, 14476, Golm, Germany", 
              "id": "http://www.grid.ac/institutes/grid.418390.7", 
              "name": [
                "Max-Planck-Institut f\u00fcr Molekulare Pflanzenphysiologie, Am M\u00fchlenberg 1, 14476, Golm, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Erban", 
            "givenName": "Alexander", 
            "id": "sg:person.01353252733.38", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01353252733.38"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "LECO Instruments, Marie-Bernays-Ring 31, 41199, M\u00f6nchengladbach, Germany", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "LECO Instruments, Marie-Bernays-Ring 31, 41199, M\u00f6nchengladbach, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "de Koning", 
            "givenName": "Sjaak", 
            "id": "sg:person.01141643573.75", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01141643573.75"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Manchester Centre for Integrative Systems Biology (MCISB), Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK", 
              "id": "http://www.grid.ac/institutes/grid.5379.8", 
              "name": [
                "School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK", 
                "Manchester Centre for Integrative Systems Biology (MCISB), Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Dunn", 
            "givenName": "Warwick B.", 
            "id": "sg:person.01042634342.49", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01042634342.49"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Max-Planck-Institut f\u00fcr Molekulare Pflanzenphysiologie, Am M\u00fchlenberg 1, 14476, Golm, Germany", 
              "id": "http://www.grid.ac/institutes/grid.418390.7", 
              "name": [
                "Max-Planck-Institut f\u00fcr Molekulare Pflanzenphysiologie, Am M\u00fchlenberg 1, 14476, Golm, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Luedemann", 
            "givenName": "Alexander", 
            "id": "sg:person.0577356370.34", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0577356370.34"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "RIKILT, Institute for Food Safety, Wageningen-UR, Wageningen, The Netherlands", 
              "id": "http://www.grid.ac/institutes/grid.4818.5", 
              "name": [
                "RIKILT, Institute for Food Safety, Wageningen-UR, Wageningen, The Netherlands"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lommen", 
            "givenName": "Arjen", 
            "id": "sg:person.0713030357.17", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0713030357.17"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "LECO Instruments UK, Hazel Grove, SK7 5DA, Manchester, UK", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "LECO Instruments UK, Hazel Grove, SK7 5DA, Manchester, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kay", 
            "givenName": "Lorraine", 
            "id": "sg:person.0675340343.02", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0675340343.02"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "GERSTEL GmbH & Co. KG, Eberhard-Gerstel-Platz 1, 45473, M\u00fclheim an der Ruhr, Germany", 
              "id": "http://www.grid.ac/institutes/grid.434919.2", 
              "name": [
                "GERSTEL GmbH & Co. KG, Eberhard-Gerstel-Platz 1, 45473, M\u00fclheim an der Ruhr, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "L\u00f6scher", 
            "givenName": "Ralf", 
            "id": "sg:person.01122150420.68", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01122150420.68"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Max-Planck-Institut f\u00fcr Molekulare Pflanzenphysiologie, Am M\u00fchlenberg 1, 14476, Golm, Germany", 
              "id": "http://www.grid.ac/institutes/grid.418390.7", 
              "name": [
                "Max-Planck-Institut f\u00fcr Molekulare Pflanzenphysiologie, Am M\u00fchlenberg 1, 14476, Golm, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kopka", 
            "givenName": "Joachim", 
            "id": "sg:person.0633132075.84", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0633132075.84"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Manchester Centre for Integrative Systems Biology (MCISB), Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK", 
              "id": "http://www.grid.ac/institutes/grid.5379.8", 
              "name": [
                "School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK", 
                "Manchester Centre for Integrative Systems Biology (MCISB), Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Goodacre", 
            "givenName": "Royston", 
            "id": "sg:person.01007611374.40", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01007611374.40"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s11306-007-0082-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023480149", 
              "https://doi.org/10.1007/s11306-007-0082-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-59745-244-1_2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044052761", 
              "https://doi.org/10.1007/978-1-59745-244-1_2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrm1451", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051079961", 
              "https://doi.org/10.1038/nrm1451"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11306-007-0081-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039142034", 
              "https://doi.org/10.1007/s11306-007-0081-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11306-007-0068-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027022742", 
              "https://doi.org/10.1007/s11306-007-0068-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11306-008-0124-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040763523", 
              "https://doi.org/10.1007/s11306-008-0124-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nprot.2006.59", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036460337", 
              "https://doi.org/10.1038/nprot.2006.59"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nprot.2007.95", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029308877", 
              "https://doi.org/10.1038/nprot.2007.95"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/81137", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045090002", 
              "https://doi.org/10.1038/81137"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11306-007-0071-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033743065", 
              "https://doi.org/10.1007/s11306-007-0071-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt1041", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023320837", 
              "https://doi.org/10.1038/nbt1041"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11306-007-0070-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028319004", 
              "https://doi.org/10.1007/s11306-007-0070-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4757-1904-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031639131", 
              "https://doi.org/10.1007/978-1-4757-1904-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1008636732481", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001161877", 
              "https://doi.org/10.1023/a:1008636732481"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11306-007-0057-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009948441", 
              "https://doi.org/10.1007/s11306-007-0057-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-29782-0_9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027795653", 
              "https://doi.org/10.1007/3-540-29782-0_9"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2009-07-24", 
        "datePublishedReg": "2009-07-24", 
        "description": "The application of gas chromatography\u2013mass spectrometry (GC\u2013MS) to the \u2018global\u2019 analysis of metabolites in complex samples (i.e. metabolomics) has now become routine. The generation of these data-rich profiles demands new strategies in data mining and standardisation of experimental and reporting aspects across laboratories. As part of the META-PHOR project\u2019s (METAbolomics for Plants Health and OutReach: http://www.meta-phor.eu/) priorities towards robust technology development, a GC\u2013MS ring experiment based upon three complex matrices (melon, broccoli and rice) was launched. All sample preparation, data processing, multivariate analyses and comparisons of major metabolite features followed standardised protocols, identical models of GC (Agilent 6890N) and TOF/MS (Leco Pegasus III) were also employed. In addition comprehensive GC\u00d7GC\u2013TOF/MS was compared with 1 dimensional GC\u2013TOF/MS. Comparisons of the paired data from the various laboratories were made with a single data processing and analysis method providing an unbiased assessment of analytical method variants and inter-laboratory reproducibility. A range of processing and statistical methods were also assessed with a single exemplary dataset revealing near equal performance between them. Further investigations of long-term reproducibility are required, though the future generation of global and valid metabolomics databases offers much promise.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s11306-009-0169-z", 
        "inLanguage": "en", 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1036887", 
            "issn": [
              "1573-3882", 
              "1573-3890"
            ], 
            "name": "Metabolomics", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "4", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "5"
          }
        ], 
        "keywords": [
          "gas chromatography-mass spectrometry", 
          "GC\u00d7GC-TOF/MS", 
          "chromatography-mass spectrometry", 
          "flight mass spectrometry", 
          "TOF/MS", 
          "complex samples", 
          "analysis of metabolites", 
          "mass spectrometry", 
          "GC-TOF/MS", 
          "complex matrices", 
          "sample preparation", 
          "metabolomics databases", 
          "plant metabolomics", 
          "spectrometry", 
          "fast gas", 
          "MS", 
          "new strategy", 
          "metabolite features", 
          "reproducibility", 
          "long-term reproducibility", 
          "preparation", 
          "range of processing", 
          "inter-laboratory reproducibility", 
          "GC", 
          "metabolomics", 
          "gas", 
          "metabolites", 
          "matrix", 
          "method", 
          "applications", 
          "method variants", 
          "range", 
          "samples", 
          "promise", 
          "generation", 
          "investigation", 
          "analysis", 
          "laboratory", 
          "processing", 
          "comparison", 
          "performance", 
          "experiments", 
          "technology development", 
          "analysis method", 
          "further investigation", 
          "profile", 
          "strategies", 
          "protocol", 
          "equal performance", 
          "unbiased assessment", 
          "development", 
          "features", 
          "data processing", 
          "ring experiments", 
          "aspects", 
          "future generations", 
          "data", 
          "part", 
          "model", 
          "exemplary dataset", 
          "standardisation", 
          "variants", 
          "mining", 
          "identical models", 
          "assessment", 
          "statistical methods", 
          "standardised protocol", 
          "database", 
          "dataset", 
          "priority", 
          "data mining", 
          "multivariate analysis"
        ], 
        "name": "Inter-laboratory reproducibility of fast gas chromatography\u2013electron impact\u2013time of flight mass spectrometry (GC\u2013EI\u2013TOF/MS) based plant metabolomics", 
        "pagination": "479-496", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1004994369"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11306-009-0169-z"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "20376177"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11306-009-0169-z", 
          "https://app.dimensions.ai/details/publication/pub.1004994369"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-05-20T07:25", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_490.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s11306-009-0169-z"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11306-009-0169-z'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11306-009-0169-z'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11306-009-0169-z'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11306-009-0169-z'


     

    This table displays all metadata directly associated to this object as RDF triples.

    277 TRIPLES      22 PREDICATES      114 URIs      90 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11306-009-0169-z schema:about anzsrc-for:03
    2 anzsrc-for:0301
    3 schema:author N2a16afee11aa4fc39d56f705c63b4ff7
    4 schema:citation sg:pub.10.1007/3-540-29782-0_9
    5 sg:pub.10.1007/978-1-4757-1904-8
    6 sg:pub.10.1007/978-1-59745-244-1_2
    7 sg:pub.10.1007/s11306-007-0057-3
    8 sg:pub.10.1007/s11306-007-0068-0
    9 sg:pub.10.1007/s11306-007-0070-6
    10 sg:pub.10.1007/s11306-007-0071-5
    11 sg:pub.10.1007/s11306-007-0081-3
    12 sg:pub.10.1007/s11306-007-0082-2
    13 sg:pub.10.1007/s11306-008-0124-4
    14 sg:pub.10.1023/a:1008636732481
    15 sg:pub.10.1038/81137
    16 sg:pub.10.1038/nbt1041
    17 sg:pub.10.1038/nprot.2006.59
    18 sg:pub.10.1038/nprot.2007.95
    19 sg:pub.10.1038/nrm1451
    20 schema:datePublished 2009-07-24
    21 schema:datePublishedReg 2009-07-24
    22 schema:description The application of gas chromatography–mass spectrometry (GC–MS) to the ‘global’ analysis of metabolites in complex samples (i.e. metabolomics) has now become routine. The generation of these data-rich profiles demands new strategies in data mining and standardisation of experimental and reporting aspects across laboratories. As part of the META-PHOR project’s (METAbolomics for Plants Health and OutReach: http://www.meta-phor.eu/) priorities towards robust technology development, a GC–MS ring experiment based upon three complex matrices (melon, broccoli and rice) was launched. All sample preparation, data processing, multivariate analyses and comparisons of major metabolite features followed standardised protocols, identical models of GC (Agilent 6890N) and TOF/MS (Leco Pegasus III) were also employed. In addition comprehensive GC×GC–TOF/MS was compared with 1 dimensional GC–TOF/MS. Comparisons of the paired data from the various laboratories were made with a single data processing and analysis method providing an unbiased assessment of analytical method variants and inter-laboratory reproducibility. A range of processing and statistical methods were also assessed with a single exemplary dataset revealing near equal performance between them. Further investigations of long-term reproducibility are required, though the future generation of global and valid metabolomics databases offers much promise.
    23 schema:genre article
    24 schema:inLanguage en
    25 schema:isAccessibleForFree true
    26 schema:isPartOf N51affda8e2ab437ebc79a8304d8d7968
    27 N7cdde5d87d044e7b9fdd09dcd05ed4a1
    28 sg:journal.1036887
    29 schema:keywords GC
    30 GC-TOF/MS
    31 GC×GC-TOF/MS
    32 MS
    33 TOF/MS
    34 analysis
    35 analysis method
    36 analysis of metabolites
    37 applications
    38 aspects
    39 assessment
    40 chromatography-mass spectrometry
    41 comparison
    42 complex matrices
    43 complex samples
    44 data
    45 data mining
    46 data processing
    47 database
    48 dataset
    49 development
    50 equal performance
    51 exemplary dataset
    52 experiments
    53 fast gas
    54 features
    55 flight mass spectrometry
    56 further investigation
    57 future generations
    58 gas
    59 gas chromatography-mass spectrometry
    60 generation
    61 identical models
    62 inter-laboratory reproducibility
    63 investigation
    64 laboratory
    65 long-term reproducibility
    66 mass spectrometry
    67 matrix
    68 metabolite features
    69 metabolites
    70 metabolomics
    71 metabolomics databases
    72 method
    73 method variants
    74 mining
    75 model
    76 multivariate analysis
    77 new strategy
    78 part
    79 performance
    80 plant metabolomics
    81 preparation
    82 priority
    83 processing
    84 profile
    85 promise
    86 protocol
    87 range
    88 range of processing
    89 reproducibility
    90 ring experiments
    91 sample preparation
    92 samples
    93 spectrometry
    94 standardisation
    95 standardised protocol
    96 statistical methods
    97 strategies
    98 technology development
    99 unbiased assessment
    100 variants
    101 schema:name Inter-laboratory reproducibility of fast gas chromatography–electron impact–time of flight mass spectrometry (GC–EI–TOF/MS) based plant metabolomics
    102 schema:pagination 479-496
    103 schema:productId N2f8d6d3e8fad472094e2987d6e1a1aea
    104 N90ab06f308e94bbf957a556b21131115
    105 Na2cf95815c564dd5848dfa72a098b7ba
    106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004994369
    107 https://doi.org/10.1007/s11306-009-0169-z
    108 schema:sdDatePublished 2022-05-20T07:25
    109 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    110 schema:sdPublisher N83a3eebe0497411e90394523a155e67b
    111 schema:url https://doi.org/10.1007/s11306-009-0169-z
    112 sgo:license sg:explorer/license/
    113 sgo:sdDataset articles
    114 rdf:type schema:ScholarlyArticle
    115 N2a16afee11aa4fc39d56f705c63b4ff7 rdf:first sg:person.01235401314.61
    116 rdf:rest Na90bb94c19dc4769b1614d82276d58ca
    117 N2f8d6d3e8fad472094e2987d6e1a1aea schema:name dimensions_id
    118 schema:value pub.1004994369
    119 rdf:type schema:PropertyValue
    120 N3ed877803f77485093e2a3d79900f96c rdf:first sg:person.01141643573.75
    121 rdf:rest N8bc3b90526f440e4bc0c19eb5448cae0
    122 N51affda8e2ab437ebc79a8304d8d7968 schema:volumeNumber 5
    123 rdf:type schema:PublicationVolume
    124 N5427f7e80ded485ab320fdbaf6a9c4db rdf:first sg:person.0713030357.17
    125 rdf:rest N5822834553b84269858dbc410ccf15c0
    126 N5822834553b84269858dbc410ccf15c0 rdf:first sg:person.0675340343.02
    127 rdf:rest N9faceebe45f842159349aa02ac1145ed
    128 N7c91b020c5364915b2bdb43d2aa4e286 rdf:first sg:person.0577356370.34
    129 rdf:rest N5427f7e80ded485ab320fdbaf6a9c4db
    130 N7cdde5d87d044e7b9fdd09dcd05ed4a1 schema:issueNumber 4
    131 rdf:type schema:PublicationIssue
    132 N83a3eebe0497411e90394523a155e67b schema:name Springer Nature - SN SciGraph project
    133 rdf:type schema:Organization
    134 N8bc3b90526f440e4bc0c19eb5448cae0 rdf:first sg:person.01042634342.49
    135 rdf:rest N7c91b020c5364915b2bdb43d2aa4e286
    136 N90ab06f308e94bbf957a556b21131115 schema:name doi
    137 schema:value 10.1007/s11306-009-0169-z
    138 rdf:type schema:PropertyValue
    139 N9faceebe45f842159349aa02ac1145ed rdf:first sg:person.01122150420.68
    140 rdf:rest Ne2143e56919e46b2911227085f13cb4e
    141 Na2cf95815c564dd5848dfa72a098b7ba schema:name pubmed_id
    142 schema:value 20376177
    143 rdf:type schema:PropertyValue
    144 Na90bb94c19dc4769b1614d82276d58ca rdf:first sg:person.01353252733.38
    145 rdf:rest N3ed877803f77485093e2a3d79900f96c
    146 Nbf11151cda6d4fb3b7c1376eb6837e12 rdf:first sg:person.01007611374.40
    147 rdf:rest rdf:nil
    148 Ne2143e56919e46b2911227085f13cb4e rdf:first sg:person.0633132075.84
    149 rdf:rest Nbf11151cda6d4fb3b7c1376eb6837e12
    150 anzsrc-for:03 schema:inDefinedTermSet anzsrc-for:
    151 schema:name Chemical Sciences
    152 rdf:type schema:DefinedTerm
    153 anzsrc-for:0301 schema:inDefinedTermSet anzsrc-for:
    154 schema:name Analytical Chemistry
    155 rdf:type schema:DefinedTerm
    156 sg:journal.1036887 schema:issn 1573-3882
    157 1573-3890
    158 schema:name Metabolomics
    159 schema:publisher Springer Nature
    160 rdf:type schema:Periodical
    161 sg:person.01007611374.40 schema:affiliation grid-institutes:grid.5379.8
    162 schema:familyName Goodacre
    163 schema:givenName Royston
    164 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01007611374.40
    165 rdf:type schema:Person
    166 sg:person.01042634342.49 schema:affiliation grid-institutes:grid.5379.8
    167 schema:familyName Dunn
    168 schema:givenName Warwick B.
    169 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01042634342.49
    170 rdf:type schema:Person
    171 sg:person.01122150420.68 schema:affiliation grid-institutes:grid.434919.2
    172 schema:familyName Löscher
    173 schema:givenName Ralf
    174 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01122150420.68
    175 rdf:type schema:Person
    176 sg:person.01141643573.75 schema:affiliation grid-institutes:None
    177 schema:familyName de Koning
    178 schema:givenName Sjaak
    179 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01141643573.75
    180 rdf:type schema:Person
    181 sg:person.01235401314.61 schema:affiliation grid-institutes:grid.5379.8
    182 schema:familyName Allwood
    183 schema:givenName J. William
    184 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01235401314.61
    185 rdf:type schema:Person
    186 sg:person.01353252733.38 schema:affiliation grid-institutes:grid.418390.7
    187 schema:familyName Erban
    188 schema:givenName Alexander
    189 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01353252733.38
    190 rdf:type schema:Person
    191 sg:person.0577356370.34 schema:affiliation grid-institutes:grid.418390.7
    192 schema:familyName Luedemann
    193 schema:givenName Alexander
    194 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0577356370.34
    195 rdf:type schema:Person
    196 sg:person.0633132075.84 schema:affiliation grid-institutes:grid.418390.7
    197 schema:familyName Kopka
    198 schema:givenName Joachim
    199 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0633132075.84
    200 rdf:type schema:Person
    201 sg:person.0675340343.02 schema:affiliation grid-institutes:None
    202 schema:familyName Kay
    203 schema:givenName Lorraine
    204 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0675340343.02
    205 rdf:type schema:Person
    206 sg:person.0713030357.17 schema:affiliation grid-institutes:grid.4818.5
    207 schema:familyName Lommen
    208 schema:givenName Arjen
    209 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0713030357.17
    210 rdf:type schema:Person
    211 sg:pub.10.1007/3-540-29782-0_9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027795653
    212 https://doi.org/10.1007/3-540-29782-0_9
    213 rdf:type schema:CreativeWork
    214 sg:pub.10.1007/978-1-4757-1904-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031639131
    215 https://doi.org/10.1007/978-1-4757-1904-8
    216 rdf:type schema:CreativeWork
    217 sg:pub.10.1007/978-1-59745-244-1_2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044052761
    218 https://doi.org/10.1007/978-1-59745-244-1_2
    219 rdf:type schema:CreativeWork
    220 sg:pub.10.1007/s11306-007-0057-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009948441
    221 https://doi.org/10.1007/s11306-007-0057-3
    222 rdf:type schema:CreativeWork
    223 sg:pub.10.1007/s11306-007-0068-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027022742
    224 https://doi.org/10.1007/s11306-007-0068-0
    225 rdf:type schema:CreativeWork
    226 sg:pub.10.1007/s11306-007-0070-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028319004
    227 https://doi.org/10.1007/s11306-007-0070-6
    228 rdf:type schema:CreativeWork
    229 sg:pub.10.1007/s11306-007-0071-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033743065
    230 https://doi.org/10.1007/s11306-007-0071-5
    231 rdf:type schema:CreativeWork
    232 sg:pub.10.1007/s11306-007-0081-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039142034
    233 https://doi.org/10.1007/s11306-007-0081-3
    234 rdf:type schema:CreativeWork
    235 sg:pub.10.1007/s11306-007-0082-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023480149
    236 https://doi.org/10.1007/s11306-007-0082-2
    237 rdf:type schema:CreativeWork
    238 sg:pub.10.1007/s11306-008-0124-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040763523
    239 https://doi.org/10.1007/s11306-008-0124-4
    240 rdf:type schema:CreativeWork
    241 sg:pub.10.1023/a:1008636732481 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001161877
    242 https://doi.org/10.1023/a:1008636732481
    243 rdf:type schema:CreativeWork
    244 sg:pub.10.1038/81137 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045090002
    245 https://doi.org/10.1038/81137
    246 rdf:type schema:CreativeWork
    247 sg:pub.10.1038/nbt1041 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023320837
    248 https://doi.org/10.1038/nbt1041
    249 rdf:type schema:CreativeWork
    250 sg:pub.10.1038/nprot.2006.59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036460337
    251 https://doi.org/10.1038/nprot.2006.59
    252 rdf:type schema:CreativeWork
    253 sg:pub.10.1038/nprot.2007.95 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029308877
    254 https://doi.org/10.1038/nprot.2007.95
    255 rdf:type schema:CreativeWork
    256 sg:pub.10.1038/nrm1451 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051079961
    257 https://doi.org/10.1038/nrm1451
    258 rdf:type schema:CreativeWork
    259 grid-institutes:None schema:alternateName LECO Instruments UK, Hazel Grove, SK7 5DA, Manchester, UK
    260 LECO Instruments, Marie-Bernays-Ring 31, 41199, Mönchengladbach, Germany
    261 schema:name LECO Instruments UK, Hazel Grove, SK7 5DA, Manchester, UK
    262 LECO Instruments, Marie-Bernays-Ring 31, 41199, Mönchengladbach, Germany
    263 rdf:type schema:Organization
    264 grid-institutes:grid.418390.7 schema:alternateName Max-Planck-Institut für Molekulare Pflanzenphysiologie, Am Mühlenberg 1, 14476, Golm, Germany
    265 schema:name Max-Planck-Institut für Molekulare Pflanzenphysiologie, Am Mühlenberg 1, 14476, Golm, Germany
    266 rdf:type schema:Organization
    267 grid-institutes:grid.434919.2 schema:alternateName GERSTEL GmbH & Co. KG, Eberhard-Gerstel-Platz 1, 45473, Mülheim an der Ruhr, Germany
    268 schema:name GERSTEL GmbH & Co. KG, Eberhard-Gerstel-Platz 1, 45473, Mülheim an der Ruhr, Germany
    269 rdf:type schema:Organization
    270 grid-institutes:grid.4818.5 schema:alternateName RIKILT, Institute for Food Safety, Wageningen-UR, Wageningen, The Netherlands
    271 schema:name RIKILT, Institute for Food Safety, Wageningen-UR, Wageningen, The Netherlands
    272 rdf:type schema:Organization
    273 grid-institutes:grid.5379.8 schema:alternateName Manchester Centre for Integrative Systems Biology (MCISB), Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK
    274 School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK
    275 schema:name Manchester Centre for Integrative Systems Biology (MCISB), Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK
    276 School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, M1 7DN, Manchester, UK
    277 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...