Estimation of Measurement Uncertainty for the Quantification of Protein by ID-LC-MS/MS
Ashley Beasley Green, N. Alan Heckert
The emergence of mass spectrometry (MS)-based methods to quantify proteins for clinical applications has led to the need for accurate and consistent measurements. To meet the clinical needs of MS-based protein results, it is important that the protein results are traceable to higher-order standards and methods and have defined uncertainty values. Therefore, we outline a comprehensive approach for the estimation of measurement uncertainty (MU) for a MS-based measurement procedure for the quantification of a clinical protein biomarker. Using the bottom-up approach outlined in the "Guide to the Expression of Uncertainty of Measurement" (GUM), we evaluate the individual uncertainty components of a MS-based measurement procedure for a clinical protein biomarker in a complex matrix. The cause-and-effect diagram of the measurement procedure is used to identify each uncertainty component and statistical models are applied to derive the overall combined MU of the procedure. Evaluation of the uncertainty components not only enables the calculation of the overall combined MU but can also be used to determine if the procedure needs improvement. To demonstrate the use of the bottom-up approach, the overall combined MU is evaluated for the NIST candidate reference measurement procedure for albumin in human urine. The results of the MU estimation are applied to the determination of uncertainty for the certified value for albumin in candidate NIST Standard Reference Material® (SRM) 3666. This study provides a framework for MU estimation of a MS-based protein measurement procedure by identifying the uncertainty components of the procedure to derive the overall combined MU.
and Heckert, N.
Estimation of Measurement Uncertainty for the Quantification of Protein by ID-LC-MS/MS, Analytical and Bioanalytical Chemistry, [online], https://doi.org/10.1007/s00216-023-04705-8, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936452
(Accessed December 3, 2023)