Evaluation of Measurement Uncertainty Based on the Propagation of Distributions Using Monte Carlo Simulation

Cox, M G; Harris, P M; Siebert, Bernd

doi:10.1023/b:mete.0000008439.82231.ad

Cited by 83 publications

(55 citation statements)

References 4 publications

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“…Finally, the API mass fractions (w API ) were calculated using the mass balance approach (equation 6) w API = 100 -Sw org,i -Sw inorg,i -Sw vol,i (6) where w API is the API mass fraction of each CRM expressed in g per 100 g, and Sw org,i , Sw inorg,i , Sw vol,i are the sums of mass fractions of organic, inorganic, and volatiles, respectively, also expressed in g per 100 g. The API mass fraction determined according to the mass balance approach was cross-checked by differential scanning calorimetry (DSC).…”

Section: Reference Materials Certificationmentioning

confidence: 99%

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Monte Carlo simulation for the evaluation of measurement uncertainty of pharmaceutical certified reference materials

Rocha¹,

Nogueira²

2012

J. Braz. Chem. Soc.

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O suplemento do Guia para a Expressão da Incerteza de Medição (GUM) publicado em 2008, o qual trata da propagação de distribuições, encoraja o uso do método de Monte Carlo (MC) para a estimativa de incerteza de medição. Este artigo descreve a aplicação deste método para estimativa da incerteza de medição do teor de ingredientes ativos farmacêuticos em dois novos materiais de referência certificados (MRC): metronidazol e captopril. Os resultados obtidos pelo método de Monte Carlo e pelo método tradicional (GUM) mostraram concordância considerando um valor crítico δ de 0,005 para metronidazol e 0,05 para captopril. Deste modo, o método de Monte Carlo validou os resultados obtidos pelo método tradicional (GUM) para a expressão do teor dos fármacos com no mínimo duas casas decimais.The supplemental Guide to the Expression of Uncertainty Measurement (2008), which deals with the propagation of distributions, encourages the use of the Monte Carlo simulation (MCS) for estimating the uncertainty of measurands. This paper describes the application of this method to estimate the measurement uncertainty of active pharmaceutical ingredient (API) mass fractions of two certified reference materials (CRMs): metronidazole and captopril. The Monte Carlo results complied with the GUM results in terms of the critical value δ of 0.005 for metronidazole and 0.05 for captopril. Therefore, the Monte Carlo method validated the GUM through the expression of the API mass fraction with at least two decimal places.

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Section: Reference Materials Certificationmentioning

confidence: 99%

“…It also recognizes the Monte Carlo method as the most efficient numerical implementation for the propagation of distributions. [6][7][8] Validation of the law of propagation of uncertainty using the Monte Carlo simulation…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo simulation for the evaluation of measurement uncertainty of pharmaceutical certified reference materials

Rocha¹,

Nogueira²

2012

J. Braz. Chem. Soc.

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“…In some cases the PDF of the values attributable to the measurand can be obtained from the PDFs of the input variables in the measurement model equation, either analytically of by Monte Carlo Simulation [6][7][8]. This enables the probability P required to assess compliance to be calculated directly.…”

Section: Use Of Decision Rulesmentioning

confidence: 99%

Principles of the EURACHEM/CITAC guide “Use of uncertainty information in compliance assessment”

Williams¹

2008

Accred Qual Assur

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Many, possibly most, analytical measurements are carried out to assess compliance with a specification or a regulation, for example in the control contaminants in food or the detection of performance enhancing substances in sport. When making an assessment of compliance the presence of unavoidable measurement uncertainty introduces the risk of making incorrect decisions, that is of accepting a batch of material which is outside the specification or rejecting one that is within. This often leads to controversy over whether or not the compliance decision is correct. How to make reliable assessment decisions is described in the EURACHEM/CITAC Guide ''Use of uncertainty information in compliance assessment''. The key is the use of decision rules that lead to an unambiguous interpretation of the measurement result and its uncertainty. These decision rules need to be designed to ensure that requirements of the specification or regulation are met and that the risk of making an incorrect decision is acceptable. Ideally they should form part of the specification or regulation.

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“…For these complicated measurement problems, GUM S1 recommends theoretically sampling from the joint PDF of input quantities; still it is very difficult to implement in practice. So a few researchers focused mainly on considering correlation of input quantities with Gaussian distribution [3,[12][13][14], and there is limited information available in literature about MCM-based uncertainty evaluation methods with correlated but nonGaussian distributed input quantities. In fact, uncertainty sources deriving from instrument or reference are more often treated as Uniform or Triangular distribution but not Gaussian distribution [15].…”

Section: Introductionmentioning

confidence: 99%

Measurement Uncertainty Evaluation Method Considering Correlation and its Application to Precision Centrifuge

Ling¹,

Li²,

Li³

2014

Measurement Science Review

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Measurement uncertainty evaluation based on the Monte Carlo method (MCM) with the assumption that all uncertainty sources are independent is common. For some measure problems, however, the correlation between input quantities is of great importance and even essential. The purpose of this paper is to provide an uncertainty evaluation method based on MCM that can handle correlated cases, especially for measurement in which uncertainty sources are correlated and submit to non-Gaussian distribution. In this method, a linear-nonlinear transformation technique was developed to generate correlated random variables sampling sequences with target prescribed marginal probability distribution and correlation coefficients. Measurement of the arm stretch of a precision centrifuge of 10 -6 order was implemented by a high precision approach and associated uncertainty evaluation was carried out using the mentioned method and the method proposed in the Guide to the Expression of Uncertainty in Measurement (GUM). The obtained results were compared and discussed at last.

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Evaluation of Measurement Uncertainty Based on the Propagation of Distributions Using Monte Carlo Simulation

Cited by 83 publications

References 4 publications

Monte Carlo simulation for the evaluation of measurement uncertainty of pharmaceutical certified reference materials

Monte Carlo simulation for the evaluation of measurement uncertainty of pharmaceutical certified reference materials

Principles of the EURACHEM/CITAC guide “Use of uncertainty information in compliance assessment”

Measurement Uncertainty Evaluation Method Considering Correlation and its Application to Precision Centrifuge

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