Chris Mussell (Presenter)
LGC
Bio: Chris is a Science Leader in the Science & Innovation Division at LGC in Teddington, UK. He has been an analytical chemist at LGC for over 25 years, and has had the good fortune to have worked in a broad range of application areas; whether it be the ä13C isotope ratio measurements of methane gas in samples from Azerbaijan, the structural elucidation of impurities in counterfeit Viagra tablets, to the analysis of army surplus tents for elevated levels of organophosphate pesticides linked to a civilian poisoning. In the last decade much of Chris’ work has been focused on the development of high accuracy, low measurement uncertainty Reference Measurement Procedures used to assign reference mass fractions to Joint Committee for Traceability in Laboratory Medicine (JCTLM) listed Certified Reference Materials (CRM). Chris is a member of the Organic Analysis Working Group of the Consultative Committee for Amount of Substance (CCQM), which through the international bureau of weights and measures (BIPM) organise National Metrology Institute (NMI) laboratory intercomparisons in a quest to standardise global chemical and biochemical measurements.
Authorship: Chris Mussell, Simon Cowen
LGC, Science & Innovation Division, Teddington, Middlesex, UK.
| Short Abstract Measurement Uncertainty (MU) is useful for many reasons, whether it is determining if data from separate time points or different laboratories are genuinely different or statistically the same, or to determine which sources of method bias or imprecision may require attention. Laboratories working to the ISO 15189 standard are required to estimate MU for each assay. In this presentation the basic MU concepts will be discussed along with examples of top down and bottom up approaches to MU calculation using mass spectrometry generated data. Sources of imprecision and bias relevant to mass spectrometry assays will also be discussed. |
Long Abstract
For National Metrology Institutes the calculation and reporting of Measurement Uncertainty (MU) has been a fundamental component of any quantitative measurement for many years. MU is considered useful for many reasons, whether it is determining if data from separate time points or different laboratories are genuinely different or statistically the same, or to determine which sources of method bias or imprecision may require attention.
For routine testing laboratories wishing to comply with the ISO standards 17025 and 15189, the calculation of MU is now a requirement for each assay. Due to the confusion which may arise, reporting of MU to a customer unfamiliar with the concept is not a requirement. However within the scientific community, the reporting and discussion of approaches to MU should be becoming more widespread, yet it is the exception rather than norm to see MU data presented or published for routine applications.
In this presentation the basic concepts and approaches to MU estimation will be discussed along with examples of top down and bottom approaches to calculation of MU using mass spectrometry generated data. Sources of imprecision and bias relevant to mass spectrometry assays will also be discussed.
References & Acknowledgements:
| Description | Y/N | Source |
| Grants | no | |
| Salary | no | |
| Board Member | no | |
| Stock | no | |
| Expenses | no | Waters |
IP Royalty: no
| Planning to mention or discuss specific products or technology of the company(ies) listed above: | yes |