Drawing Attention to the Measurement Uncertainty Arising from Sampling in Chemical and Physicochemical Analyses: An Overview

Medeiros, Khrissy Aracélly Reis; Brandão, Luciene Pires; Atilio, Natália Cambiaghi; Oliveira, Elcio Cruz de

doi:10.1080/10408347.2022.2036943

Cited by 2 publications

(1 citation statement)

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“…Interestingly, however, very little attention was devoted to possible uncertainty arising from sampling or subsampling of lignin. At the same time it is widely recognised that sampling (or subsampling) is among the most important uncertaintybsources in most of chemical analysis [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty due to sampling in the quantitative NMR analysis of lignin

Pawade,

Toom,

Herodes

et al. 2023

Preprint

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In recent years, lignin analysis utilizing quantitative nuclear magnetic resonance (qNMR) has drawn much interest and has been the subject of numerous articles. However, evaluating the measurement uncertainty of lignin qNMR results is still a challenge. Specifically, uncertainty originating from lignin sampling or subsampling has been overlooked in the large majority of the articles. Although lignin is a reasonably homogeneous substance, it is nevertheless a solid, and separate samples collected for the analysis from the same bulk may have somewhat different compositions depending on mixing and the amount of sample taken. The objective of this paper is to evaluate the influence of the sampling uncertainty on the qNMR analysis of lignin on the basis of a case study. In our experiments, we have demonstrated that sample-to-sample variations contribute to about half of the variability coming from the quantitative nuclear magnetic resonance (qNMR) measurements. The relative standard deviation (RSD) of sample-to-sample variability was 2.4%, while the other sources of variability related to qNMR, including measurement, baseline irregularities, and partial peak overlap caused RSD 4.4%. The total variability RSD was 5.0%. We present two calculation approaches for evaluating uncertainty due to sampling from replicate measurement data of different samples, which may be helpful for practitioners in the field.

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Section: Introductionmentioning

confidence: 99%