Evaluating Sampling Uncertainty in the Quantitative 1H Nuclear Magnetic Resonance Analysis of Lignin
In recent years, lignin analysis utilizing quantitative nuclear magnetic resonance (qNMR) has attracted considerable interest and has been the subject of numerous studies. However, evaluating the measurement uncertainty of qNMR results of lignin remains a challenge. Specifically, uncertainty origina...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
North Carolina State University
2025-01-01
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| Series: | BioResources |
| Subjects: | |
| Online Access: | https://ojs.bioresources.com/index.php/BRJ/article/view/24185 |
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| Summary: | In recent years, lignin analysis utilizing quantitative nuclear magnetic resonance (qNMR) has attracted considerable interest and has been the subject of numerous studies. However, evaluating the measurement uncertainty of qNMR results of lignin remains a challenge. Specifically, uncertainty originating from lignin sampling or subsampling has been overlooked in a large majority of articles. Although lignin is a reasonably homogeneous substance, it is nevertheless a solid, and individual samples collected from the same bulk may have somewhat different compositions depending on mixing and the amount of sample taken. The objective of this study was to evaluate the influence of sampling uncertainty on qNMR analysis of lignin-based analysis as a case study, with an exclusive focus on the relative quantification method. The results from this study demonstrate that sample-to-sample variations can contribute to approximately half of the variability in actual qNMR measurements. The relative standard deviation (RSD) of sample-to-sample variability was 2.4%. In contrast, the other sources of variability related to qNMR, including measurement, baseline irregularities, and partial peak overlap, caused an RSD of 4.4%. The total variability RSD was 5.0%. In this article, two calculation approaches were presented for evaluating the uncertainty due to sampling from replicate measurement data of different samples, which may be helpful for practitioners in the field. |
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| ISSN: | 1930-2126 |