The reversed-axis method to estimate precision in standard additions analysis
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.microc.2015.08.006 http://hdl.handle.net/11449/167989 |
Resumo: | The standard additions (SA) method is one of the most important calibration strategies in quantitative chemical analysis. It is a powerful tool to minimize matrix effects and enable precise and accurate determinations. On the other hand, the SA method is time-consuming and cumbersome because it requires the preparation of a calibration curve for each individual sample. Considering the statistical treatment required, the estimation of precision in SA determinations can be as laborious and cumbersome as the experimental procedure itself. In this work, we describe a simple method to quickly estimate standard deviations in SA analyses using the determination of Na and K in biodiesel by flame atomic emission spectrometry (FAES) as a model. By taking analyte concentration as the dependent variable (y-axis), and instrument response as the independent variable (x-axis), the standard deviation of the analyte concentration in the sample is equal to the error in the y-axis intercept of the SA calibration curve. This value can be easily calculated using a simple equation or the regression feature in MS Excel. Standard deviation values calculated using MS Excel and this reversed-axis method are compared to results from the traditional statistical methods of extrapolation and error propagation. In all determinations, the results from the extrapolation, error propagation with covariance, and reversed-axis methods are identical, which demonstrates their equivalence. |
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Repositório Institucional da UNESP |
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The reversed-axis method to estimate precision in standard additions analysisBiodieselCalibrationError propagationMS ExcelStandard additionsStandard deviationThe standard additions (SA) method is one of the most important calibration strategies in quantitative chemical analysis. It is a powerful tool to minimize matrix effects and enable precise and accurate determinations. On the other hand, the SA method is time-consuming and cumbersome because it requires the preparation of a calibration curve for each individual sample. Considering the statistical treatment required, the estimation of precision in SA determinations can be as laborious and cumbersome as the experimental procedure itself. In this work, we describe a simple method to quickly estimate standard deviations in SA analyses using the determination of Na and K in biodiesel by flame atomic emission spectrometry (FAES) as a model. By taking analyte concentration as the dependent variable (y-axis), and instrument response as the independent variable (x-axis), the standard deviation of the analyte concentration in the sample is equal to the error in the y-axis intercept of the SA calibration curve. This value can be easily calculated using a simple equation or the regression feature in MS Excel. Standard deviation values calculated using MS Excel and this reversed-axis method are compared to results from the traditional statistical methods of extrapolation and error propagation. In all determinations, the results from the extrapolation, error propagation with covariance, and reversed-axis methods are identical, which demonstrates their equivalence.Department of Physics and Chemistry UNESP-Univ Estadual PaulistaDepartment of Chemistry Wake Forest University, Salem Hall Box 7486Department of Physics and Chemistry UNESP-Univ Estadual PaulistaUniversidade Estadual Paulista (Unesp)Wake Forest UniversityGoncalves, Daniel A. [UNESP]Jones, Bradley T.Donati, George L.2018-12-11T16:39:09Z2018-12-11T16:39:09Z2016-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article155-158application/pdfhttp://dx.doi.org/10.1016/j.microc.2015.08.006Microchemical Journal, v. 124, p. 155-158.0026-265Xhttp://hdl.handle.net/11449/16798910.1016/j.microc.2015.08.0062-s2.0-849408512612-s2.0-84940851261.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengMicrochemical Journalinfo:eu-repo/semantics/openAccess2023-10-13T06:05:44Zoai:repositorio.unesp.br:11449/167989Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:46:33.216282Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
The reversed-axis method to estimate precision in standard additions analysis |
title |
The reversed-axis method to estimate precision in standard additions analysis |
spellingShingle |
The reversed-axis method to estimate precision in standard additions analysis Goncalves, Daniel A. [UNESP] Biodiesel Calibration Error propagation MS Excel Standard additions Standard deviation |
title_short |
The reversed-axis method to estimate precision in standard additions analysis |
title_full |
The reversed-axis method to estimate precision in standard additions analysis |
title_fullStr |
The reversed-axis method to estimate precision in standard additions analysis |
title_full_unstemmed |
The reversed-axis method to estimate precision in standard additions analysis |
title_sort |
The reversed-axis method to estimate precision in standard additions analysis |
author |
Goncalves, Daniel A. [UNESP] |
author_facet |
Goncalves, Daniel A. [UNESP] Jones, Bradley T. Donati, George L. |
author_role |
author |
author2 |
Jones, Bradley T. Donati, George L. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Wake Forest University |
dc.contributor.author.fl_str_mv |
Goncalves, Daniel A. [UNESP] Jones, Bradley T. Donati, George L. |
dc.subject.por.fl_str_mv |
Biodiesel Calibration Error propagation MS Excel Standard additions Standard deviation |
topic |
Biodiesel Calibration Error propagation MS Excel Standard additions Standard deviation |
description |
The standard additions (SA) method is one of the most important calibration strategies in quantitative chemical analysis. It is a powerful tool to minimize matrix effects and enable precise and accurate determinations. On the other hand, the SA method is time-consuming and cumbersome because it requires the preparation of a calibration curve for each individual sample. Considering the statistical treatment required, the estimation of precision in SA determinations can be as laborious and cumbersome as the experimental procedure itself. In this work, we describe a simple method to quickly estimate standard deviations in SA analyses using the determination of Na and K in biodiesel by flame atomic emission spectrometry (FAES) as a model. By taking analyte concentration as the dependent variable (y-axis), and instrument response as the independent variable (x-axis), the standard deviation of the analyte concentration in the sample is equal to the error in the y-axis intercept of the SA calibration curve. This value can be easily calculated using a simple equation or the regression feature in MS Excel. Standard deviation values calculated using MS Excel and this reversed-axis method are compared to results from the traditional statistical methods of extrapolation and error propagation. In all determinations, the results from the extrapolation, error propagation with covariance, and reversed-axis methods are identical, which demonstrates their equivalence. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-01-01 2018-12-11T16:39:09Z 2018-12-11T16:39:09Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.microc.2015.08.006 Microchemical Journal, v. 124, p. 155-158. 0026-265X http://hdl.handle.net/11449/167989 10.1016/j.microc.2015.08.006 2-s2.0-84940851261 2-s2.0-84940851261.pdf |
url |
http://dx.doi.org/10.1016/j.microc.2015.08.006 http://hdl.handle.net/11449/167989 |
identifier_str_mv |
Microchemical Journal, v. 124, p. 155-158. 0026-265X 10.1016/j.microc.2015.08.006 2-s2.0-84940851261 2-s2.0-84940851261.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Microchemical Journal |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
155-158 application/pdf |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128415681740800 |