Uncertainty contribution of derivatization in gas chromatography/mass spectrometric analysis
Rationale The purpose of the current work is to realistically assess the uncertainty contribution in gas chromatography/mass spectrometry (GC/MS) analysis originating from less‐than‐ideal derivatization efficiency. Methods As the exemplary analytical method a two‐step derivatization method with KOH...
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description | Rationale
The purpose of the current work is to realistically assess the uncertainty contribution in gas chromatography/mass spectrometry (GC/MS) analysis originating from less‐than‐ideal derivatization efficiency.
Methods
As the exemplary analytical method a two‐step derivatization method with KOH and BSTFA (N,O‐bis(trimethylsilyl)trifluoroacetamide), applied for the analysis of fatty acid triglycerides (using real measurement data), was selected. The derivatization efficiencies were in the range 0.89–1.04. In this study, two approaches for bottom‐up uncertainty evaluation were compared: the traditional GUM approach and the Monte Carlo method (MCM). Both were used with and without taking correlation between input quantities into account.
Results
The most reliable uncertainty estimates were in the range 0.07–0.08 (expanded uncertainties at 95% coverage probability). A strong negative correlation was found between the slope and intercept of the calibration graph (r = −0.71) and it markedly influenced the uncertainty estimate of derivatization efficiency. The MCM was found to give somewhat higher uncertainty estimates, which are considered more realistic.
Conclusions
Derivatization directly affects the analysis result. Thus, in the case of this exemplary analysis, just derivatization alone (i.e. if all other uncertainty sources are neglected) causes relative expanded uncertainty around 8%, being thus an important and in some cases the dominant uncertainty contributor. |
doi_str_mv | 10.1002/rcm.8704 |
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The purpose of the current work is to realistically assess the uncertainty contribution in gas chromatography/mass spectrometry (GC/MS) analysis originating from less‐than‐ideal derivatization efficiency.
Methods
As the exemplary analytical method a two‐step derivatization method with KOH and BSTFA (N,O‐bis(trimethylsilyl)trifluoroacetamide), applied for the analysis of fatty acid triglycerides (using real measurement data), was selected. The derivatization efficiencies were in the range 0.89–1.04. In this study, two approaches for bottom‐up uncertainty evaluation were compared: the traditional GUM approach and the Monte Carlo method (MCM). Both were used with and without taking correlation between input quantities into account.
Results
The most reliable uncertainty estimates were in the range 0.07–0.08 (expanded uncertainties at 95% coverage probability). A strong negative correlation was found between the slope and intercept of the calibration graph (r = −0.71) and it markedly influenced the uncertainty estimate of derivatization efficiency. The MCM was found to give somewhat higher uncertainty estimates, which are considered more realistic.
Conclusions
Derivatization directly affects the analysis result. Thus, in the case of this exemplary analysis, just derivatization alone (i.e. if all other uncertainty sources are neglected) causes relative expanded uncertainty around 8%, being thus an important and in some cases the dominant uncertainty contributor.</description><identifier>ISSN: 0951-4198</identifier><identifier>EISSN: 1097-0231</identifier><identifier>DOI: 10.1002/rcm.8704</identifier><identifier>PMID: 31845399</identifier><language>eng</language><publisher>England: Wiley Subscription Services, Inc</publisher><subject>Chromatography ; Fatty acids ; Gas chromatography ; Mass spectrometry ; Monte Carlo simulation ; Triglycerides ; Uncertainty</subject><ispartof>Rapid communications in mass spectrometry, 2020-08, Vol.34 (16), p.e8704-n/a</ispartof><rights>2019 John Wiley & Sons, Ltd.</rights><rights>2020 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3494-9b51734087811c1c6089106a31d86f6515477d582a55d1bdae0d84da09e325043</citedby><cites>FETCH-LOGICAL-c3494-9b51734087811c1c6089106a31d86f6515477d582a55d1bdae0d84da09e325043</cites><orcidid>0000-0002-3000-4964 ; 0000-0002-4158-7130</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Frcm.8704$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Frcm.8704$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,777,781,1412,27905,27906,45555,45556</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31845399$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Vilbaste, Martin</creatorcontrib><creatorcontrib>Tammekivi, Eliise</creatorcontrib><creatorcontrib>Leito, Ivo</creatorcontrib><title>Uncertainty contribution of derivatization in gas chromatography/mass spectrometric analysis</title><title>Rapid communications in mass spectrometry</title><addtitle>Rapid Commun Mass Spectrom</addtitle><description>Rationale
The purpose of the current work is to realistically assess the uncertainty contribution in gas chromatography/mass spectrometry (GC/MS) analysis originating from less‐than‐ideal derivatization efficiency.
Methods
As the exemplary analytical method a two‐step derivatization method with KOH and BSTFA (N,O‐bis(trimethylsilyl)trifluoroacetamide), applied for the analysis of fatty acid triglycerides (using real measurement data), was selected. The derivatization efficiencies were in the range 0.89–1.04. In this study, two approaches for bottom‐up uncertainty evaluation were compared: the traditional GUM approach and the Monte Carlo method (MCM). Both were used with and without taking correlation between input quantities into account.
Results
The most reliable uncertainty estimates were in the range 0.07–0.08 (expanded uncertainties at 95% coverage probability). A strong negative correlation was found between the slope and intercept of the calibration graph (r = −0.71) and it markedly influenced the uncertainty estimate of derivatization efficiency. The MCM was found to give somewhat higher uncertainty estimates, which are considered more realistic.
Conclusions
Derivatization directly affects the analysis result. Thus, in the case of this exemplary analysis, just derivatization alone (i.e. if all other uncertainty sources are neglected) causes relative expanded uncertainty around 8%, being thus an important and in some cases the dominant uncertainty contributor.</description><subject>Chromatography</subject><subject>Fatty acids</subject><subject>Gas chromatography</subject><subject>Mass spectrometry</subject><subject>Monte Carlo simulation</subject><subject>Triglycerides</subject><subject>Uncertainty</subject><issn>0951-4198</issn><issn>1097-0231</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp1kNtKxDAQQIMo7noBv0AKvvhSd6ZJmuRRFm-wIoi-CSWbZjXSNmvSKvXr7bpeQPBpYDhzGA4hBwgnCJBNgqlPpAC2QcYISqSQUdwkY1AcU4ZKjshOjM8AiDyDbTKiKBmnSo3Jw31jbGi1a9o-Mb5pg5t3rfNN4hdJaYN71a17158b1ySPOibmKfhat_4x6OVTP6l1jElcWtMOazvcm0Q3uuqji3tka6GraPe_5i65Pz-7m16ms5uLq-npLDWUKZaqOUdBGUghEQ2aHKRCyDXFUuaLnCNnQpRcZprzEueltlBKVmpQlmYcGN0lx2vvMviXzsa2qF00tqp0Y30Xi4xmQjFkdIUe_UGffReGfweKZVzkuRLqV2iCjzHYRbEMrtahLxCKVfFiKF6sig_o4Zewm9e2_AG_Ew9AugbeXGX7f0XF7fT6U_gBW7iJ3g</recordid><startdate>20200830</startdate><enddate>20200830</enddate><creator>Vilbaste, Martin</creator><creator>Tammekivi, Eliise</creator><creator>Leito, Ivo</creator><general>Wiley Subscription Services, Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-3000-4964</orcidid><orcidid>https://orcid.org/0000-0002-4158-7130</orcidid></search><sort><creationdate>20200830</creationdate><title>Uncertainty contribution of derivatization in gas chromatography/mass spectrometric analysis</title><author>Vilbaste, Martin ; Tammekivi, Eliise ; Leito, Ivo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3494-9b51734087811c1c6089106a31d86f6515477d582a55d1bdae0d84da09e325043</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Chromatography</topic><topic>Fatty acids</topic><topic>Gas chromatography</topic><topic>Mass spectrometry</topic><topic>Monte Carlo simulation</topic><topic>Triglycerides</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vilbaste, Martin</creatorcontrib><creatorcontrib>Tammekivi, Eliise</creatorcontrib><creatorcontrib>Leito, Ivo</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>MEDLINE - Academic</collection><jtitle>Rapid communications in mass spectrometry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vilbaste, Martin</au><au>Tammekivi, Eliise</au><au>Leito, Ivo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Uncertainty contribution of derivatization in gas chromatography/mass spectrometric analysis</atitle><jtitle>Rapid communications in mass spectrometry</jtitle><addtitle>Rapid Commun Mass Spectrom</addtitle><date>2020-08-30</date><risdate>2020</risdate><volume>34</volume><issue>16</issue><spage>e8704</spage><epage>n/a</epage><pages>e8704-n/a</pages><issn>0951-4198</issn><eissn>1097-0231</eissn><abstract>Rationale
The purpose of the current work is to realistically assess the uncertainty contribution in gas chromatography/mass spectrometry (GC/MS) analysis originating from less‐than‐ideal derivatization efficiency.
Methods
As the exemplary analytical method a two‐step derivatization method with KOH and BSTFA (N,O‐bis(trimethylsilyl)trifluoroacetamide), applied for the analysis of fatty acid triglycerides (using real measurement data), was selected. The derivatization efficiencies were in the range 0.89–1.04. In this study, two approaches for bottom‐up uncertainty evaluation were compared: the traditional GUM approach and the Monte Carlo method (MCM). Both were used with and without taking correlation between input quantities into account.
Results
The most reliable uncertainty estimates were in the range 0.07–0.08 (expanded uncertainties at 95% coverage probability). A strong negative correlation was found between the slope and intercept of the calibration graph (r = −0.71) and it markedly influenced the uncertainty estimate of derivatization efficiency. The MCM was found to give somewhat higher uncertainty estimates, which are considered more realistic.
Conclusions
Derivatization directly affects the analysis result. Thus, in the case of this exemplary analysis, just derivatization alone (i.e. if all other uncertainty sources are neglected) causes relative expanded uncertainty around 8%, being thus an important and in some cases the dominant uncertainty contributor.</abstract><cop>England</cop><pub>Wiley Subscription Services, Inc</pub><pmid>31845399</pmid><doi>10.1002/rcm.8704</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-3000-4964</orcidid><orcidid>https://orcid.org/0000-0002-4158-7130</orcidid></addata></record> |
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subjects | Chromatography Fatty acids Gas chromatography Mass spectrometry Monte Carlo simulation Triglycerides Uncertainty |
title | Uncertainty contribution of derivatization in gas chromatography/mass spectrometric analysis |
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