Measurement uncertainty from physical sample preparation: estimation including systematic error

The Analyst ◽  
2003 ◽  
Vol 128 (11) ◽  
pp. 1391 ◽  
Author(s):  
Jennifer A. Lyn ◽  
Michael H. Ramsey ◽  
Richard J. Fussell ◽  
Roger Wood
Materials ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 2677 ◽  
Author(s):  
Nadia Waegeneers ◽  
Sandra De Vos ◽  
Eveline Verleysen ◽  
Ann Ruttens ◽  
Jan Mast

E174 (silver) is a food additive that may contain silver nanoparticles (AgNP). Validated methods are needed to size and quantify these particles in a regulatory context. However, no validations have yet been performed with food additives or real samples containing food additives requiring a sample preparation step prior to analysis. A single-particle inductively coupled plasma mass spectrometry (spICP-MS) method was developed and validated for sizing and quantifying the fraction of AgNP in E174 and in products containing E174, and associated uncertainties related to sample preparation, analysis and data interpretation were unraveled. The expanded measurement uncertainty for AgNP sizing was calculated to be 16% in E174-containing food products and increased up to 23% in E174 itself. The E174 food additives showed a large silver background concentration combined with a relatively low number of nanoparticles, making data interpretation more challenging than in the products. The standard uncertainties related to sample preparation, analysis, and challenging data interpretation were respectively 4.7%, 6.5%, and 6.0% for triplicate performances. For a single replicate sample, the uncertainty related to sample preparation increased to 6.8%. The expanded measurement uncertainty related to the concentration determination was 25–45% in these complex samples, without a clear distinction between additives and products. Overall, the validation parameters obtained for spICP-MS seem to be fit for the purpose of characterizing AgNP in E174 or E174-containing products.


1995 ◽  
Vol 78 (5) ◽  
pp. 1233-1237
Author(s):  
Charles M Zapf ◽  
Mark Parrish

Abstract We established that the choice of the laboratory mill has little effect on steam-distillable volatile oil (VOSD) analysis of Cassia bark when reasonable care is used in preparing single samples. When multiple samples are milled, caution is advised to avoid temperature increase. Data from 11 laboratories are presented for 4 test samples. Participating laboratories milled 200 g samples, and the variation of the resulting VOSD in the ground spice could not be attributed to the different grind-size profiles measured. The estimated systematic error due to type of mill was 0.04 mL/100 g. This represents a relative standard deviation of 2.2% for the lowest average volatile oil levels.


Talanta ◽  
2020 ◽  
Vol 207 ◽  
pp. 120274 ◽  
Author(s):  
Rocío Mateos ◽  
Cristina M. Oliveira ◽  
Ana María Díez-Pascual ◽  
Soledad Vera-López ◽  
María Paz San Andrés ◽  
...  

Metrology ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 39-51
Author(s):  
Harsha Vardhana Jetti ◽  
Simona Salicone

A Kalman filter is a concept that has been in existence for decades now and it is widely used in numerous areas. It provides a prediction of the system states as well as the uncertainty associated to it. The original Kalman filter can not propagate uncertainty in a correct way when the variables are not distributed normally or when there is a correlation in the measurements or when there is a systematic error in the measurements. For these reasons, there have been numerous variations of the original Kalman filter, most of them mathematically based (like the original one) on the theory of probability. Some of the variations indeed introduce some improvements, but without being completely successful. To deal with these problems, more recently, Kalman filters have also been defined using random-fuzzy variables (RFVs). These filters are capable of also propagating distributions that are not normal and propagating systematic contributions to uncertainty, thus providing the overall measurement uncertainty associated to the state predictions. In this paper, the authors make another step forward, by defining a possibilistic Kalman filter using random-fuzzy variables which not only considers and propagates both random and systematic contributions to uncertainty, but also reduces the overall uncertainty associated to the state predictions by compensating for the unknown residual systematic contributions.


2009 ◽  
Author(s):  
Anna Paviotti ◽  
Simone Carmignato ◽  
Alessandro Voltan ◽  
Nicola Laurenti ◽  
Guido M. Cortelazzo

2001 ◽  
Vol 6 (8) ◽  
pp. 368-371
Author(s):  
John R. Cowles ◽  
Simon Daily ◽  
S. L. R. Ellison ◽  
William A. Hardcastle ◽  
Carole Williams

Author(s):  
R. E. Ferrell ◽  
G. G. Paulson ◽  
C. W. Walker

Selected area electron diffraction (SAD) has been used successfully to determine crystal structures, identify traces of minerals in rocks, and characterize the phases formed during thermal treatment of micron-sized particles. There is an increased interest in the method because it has the potential capability of identifying micron-sized pollutants in air and water samples. This paper is a short review of the theory behind SAD and a discussion of the sample preparation employed for the analysis of multiple component environmental samples.


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