Sources of uncertainty in gas chromatography and high-performance liquid chromatography

Barwick, Vicki

doi:10.1016/s0021-9673(99)00537-3

Cited by 83 publications

(44 citation statements)

References 37 publications

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“…These certificates report each substance concentration, along with their expanded uncertainty (U) and the corresponding coverage factors (k), and these concentrations are the input variables of each measurement model. Uncertainty in chromatographic measurement may come from multiple sources including material injection to the machine, temperature control of the gas chromatograph, material adsorption in the lines upstream to the columns, and the detection process by sensors [17]. The effect of all these sources is manifested every time when the chromatograph is operated; for that reason, several replicas were done for each RM and for each sample, and the multiple values obtained of the areas were used to estimate the uncertainties corresponding to the input quantities y 0 and y i (y 1 , y 2 , and y 3 ).…”

Section: Identification and Estimation Of The Sources Of Uncertaintymentioning

confidence: 99%

Comparison of different methods to estimate the uncertainty in composition measurement by chromatography

Ariza¹,

Blanco²,

Sánchez³

et al. 2015

J. Sep. Science

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Natural gas is a mixture that contains hydrocarbons and other compounds, such as CO2 and N2. Natural gas composition is commonly measured by gas chromatography, and this measurement is important for the calculation of some thermodynamic properties that determine its commercial value. The estimation of uncertainty in chromatographic measurement is essential for an adequate presentation of the results and a necessary tool for supporting decision making. Various approaches have been proposed for the uncertainty estimation in chromatographic measurement. The present work is an evaluation of three approaches of uncertainty estimation, where two of them (guide to the expression of uncertainty in measurement method and prediction method) were compared with the Monte Carlo method, which has a wider scope of application. The aforementioned methods for uncertainty estimation were applied to gas chromatography assays of three different samples of natural gas. The results indicated that the prediction method and the guide to the expression of uncertainty in measurement method (in the simple version used) are not adequate to calculate the uncertainty in chromatography measurement, because uncertainty estimations obtained by those approaches are in general lower than those given by the Monte Carlo method.

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Section: Identification and Estimation Of The Sources Of Uncertaintymentioning

confidence: 99%

Comparison of different methods to estimate the uncertainty in composition measurement by chromatography

Ariza¹,

Blanco²,

Sánchez³

et al. 2015

J. Sep. Science

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“…26,27 As modificações apresentadas foram escolhidas na faixa que pode ser usualmente esperada quando há transferência do método entre laboratórios diferentes. O teste de robustez foi executado na mesma amostra do plano interlaboratorial, em 3 dias consecutivos: 1º dia, 2º dia e 3º dia.…”

Section: Teste De Robustezunclassified

Comparação de diferentes abordagens para avaliação da incerteza na cromatografia gasosa do gás natural

Oliveira

Aguiar

2009

Quím. Nova

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Recebido em 8/10/08; aceito em 12/2/09; publicado na web em 3/7/09 COMPARISON OF DIFFERENT APPROACHES TO EVALUATE THE UNCERTAINTY OF GAS CHROMATOGRAPHY FOR NATURAL GAS. The evaluation of uncertainty associated with an analytic result is an essential part of the measurement process. Recently, several approaches to evaluate the uncertainty in measurement have been developed. Here, the gas chromatography assay uncertainty for natural gas is compared by some of these approaches: the guide to the expression of uncertainty in measurement (GUM) approach, top-down approach (reproducibility estimate from an inter-laboratory study), Barwick & Ellison (data from validation), study of variability and fuzzy approach. The comparison shows that GUM, Barwick & Ellison and fuzzy approaches lead to comparable uncertainty evaluations, which does not happen with the top-down approach and study of variability by the absence of data normality.Keywords: measurement uncertainty; GUM approach; top-down approach. INTRODUÇÃOA incerteza de uma medição é definida como "um parâmetro, associado ao resultado de uma medição, que caracteriza a dispersão dos valores que podem ser fundamentalmente atribuídos a um mensurando".1 O resultado de uma medição é considerado como a melhor estimativa do valor do mensurando acompanhado de todas as fontes da incerteza que contribuem à propagação do mesmo.2,3 Consequentemente, um resultado de medição não pode corretamente ser interpretado sem conhecimento sobre a incerteza deste resultado. 2Vários conceitos foram desenvolvidos para a avaliação de incerteza relacionada a um resultado de medição. Uma das primeiras abordagens para avaliar a incerteza em Química Analítica foi publicada nos anos 80. Wernimont usou estimativas de precisão a partir de estudos interlaboratoriais em desempenho de métodos para avaliar a incerteza.4 Outra abordagem para a expressão da incerteza de medição é proposta pelo GUM.3 Esta abordagem combina as diversas fontes de incerteza, através da expansão da série de Taylor. No início dos anos 1990, o EURACHEM adotou o GUM como referência de uso em Química Analítica.5 Entretanto, como o número de situações no dia a dia do laboratório não é trivial, a identificação e quantificação de todas as fontes de incerteza fizeram com que os químicos analíticos relutassem em adotar esta abordagem. Em contra partida, o Comitê de Métodos Analíticos (AMC) da Sociedade Real de Química retomou a proposta de Wernimont, introduzindo a abordagem top-down baseada em estudos interlaboratoriais.2 Logo após, o Comitê Nórdico para Análises de Alimentos (NMKL) sugeriu uma abordagem similar, baseada somente em dados provenientes de estudos intralaboratoriais. 6 Os dados necessários para a abordagem topdown, ou ao menos uma parte deles, já estão geralmente disponíveis na validação do método.A discussão na avaliação da incerteza de medição influenciou também o ISO/IEC 17025 e os laboratórios que requisitam a acreditação.7 A seção desta norma sobre incerteza cita que "a estimativa razoável deve estar baseada no conheci...

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“…In an ideal case, peaks are narrow and follow a Gaussian distribution. However, several factors are contributing to peak broadening like detector, column, mobile phase, injection, column temperature and flow rate [2]. To get an optimal performance of the LC, GC or SFC system the theoretical plate height (HETP) should be minimized.…”

Section: Introductionmentioning

confidence: 99%

Flow Rates in Liquid Chromatography, Gas Chromatography and Supercritical Fluid Chromatography: A Tool for Optimization

Meurs

2016

JORS

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This paper aimed to develop a standalone application for optimizing flow rates in liquid chromatography (LC), gas chromatography (GC) and supercritical fluid chromatography (SFC). To do so, Van Deemter's equation, Knox' equation and Golay's equation were implemented in a MATLAB script and subsequently a graphical user interface (GUI) was created. The application will show the optimal flow rate or linear velocity and the corresponding plate height for the set input parameters. Furthermore, a plot will be shown in which the plate height is plotted against the linear flow velocity. Hence, this application will give optimized flow rates for any set conditions with minimal effort.

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Sources of uncertainty in gas chromatography and high-performance liquid chromatography

Cited by 83 publications

References 37 publications

Comparison of different methods to estimate the uncertainty in composition measurement by chromatography

Comparison of different methods to estimate the uncertainty in composition measurement by chromatography

Comparação de diferentes abordagens para avaliação da incerteza na cromatografia gasosa do gás natural

Flow Rates in Liquid Chromatography, Gas Chromatography and Supercritical Fluid Chromatography: A Tool for Optimization

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