This project is focused on improving the accuracy, uncertainty, and efficiency of SAM by experimental design and MC simulation, and then implementing it in technical protocols for analysis of SRMs and other samples. Improvements will be disseminated through peer-reviewed publications. These studies include:
Additional Technical Details:
The term multiplex is used to describe a variation of single-point standard additions wherein multiple units of an SRM are spiked to build the calibration curve and multiple elements are spiked into the sample simultaneously, with the key advantage being a reduction in the number of analytical samples required for measurement. The calibration curve generated for the SRM is often used to predict mass fraction data for control materials of a similar matrix. Often this method is applied to SRM value assignment of several mono-isotopic elements simultaneously, using collision cell technology (CCT-) ICP-MS, but it can be used in any situation where an analyte and an internal standard can be mixed to yield a homogeneous sample.
In practice, it has been determined that the uncertainty due to extrapolation, uex, is one of the largest sources of uncertainty for this calibration scheme. MC analysis is typically employed to estimate the uncertainty about the X-intercept.
A custom LabView MC simulation program has been developed to estimate a relative standard uncertainty for the regression parameters. The program returns data in graphical and spreadsheet form as shown in Fig. 3 for arsenic in SRM 955c Caprine Blood. It first performs a linear regression analysis and outputs the best fit location estimate and additional data needed for the uncertainty calculations. An MC simulation is performed, and centroid means and standard deviations are calculated (see Fig 3. white boxes) for the XY data clusters, which essentially condenses the data into a two-point calibration curve, with each point possessing a two-dimensional cloud of uncertainty. The MC slope and intercept outcomes are generated, and these are constrained about the centroid points through repeat random sampling, assuming a normal distribution.
Both Type A uncertainty and Type B uncertainty approaches have been applied to resultant data, with the Type B approach yielding more reasonable estimates of uncertainty. The Type A or Type B extrapolation uncertainty is estimated by first performing 10,000 MC trials of possible slope/intercept combinations to obtain a location estimate reflecting the concentration of analyte in the SRM, and a corresponding relative standard deviation (RSD) due to the extrapolation (see Fig 3. histogram and associated data boxes).
Future work will involve applying and testing the AC design to multiplex standard additions (MSA).
Figure 1. Theoretical standard additions constructs and corresponding uncertainty histograms.
Figure 2. Log-log plot
Figure 3. Example Microsoft Excel 2003 macro that feeds data to and receives data from Labview, using concentration and signal data (columns A and B, respectively) for the value assignment of As in SRM 955c Toxic Elements in Caprine Blood, Level 2.
Start Date:October 1, 2006
Lead Organizational Unit:mml
Lydia R. Barker
Related Programs and Projects:
Fundamental Chemical Metrology
Yu, L. L., Wood, L. J., Kelly, W.R., and Turk, G.C., Determination of Be in Alumina by ICP-OES after Carius Tube Digestion, Journal of Analytical Atomic Spectrometry, 22:1427-1429 (2007).
Christopher, S.J., Day, R.D., Bryan, C.E., and Turk, G.C., Improved Calibration Strategy for Measurement of Trace Elements in Biological and Clinical Whole Blood Reference Materials via Collision-Cell Inductively Coupled Plasma Mass Spectrometry, Journal of Analytical Atomic Spectrometry, 20:1035-1043 (2005).
Kelly, W.R., MacDonald, B.S., and Guthrie, W.F., Gravimetric Approach to the Standard Addition Method in Instrumental Analysis. 1. Analytical Chemistry, 80:6154-6158 (2008).
Barker, L.R., Kelly, W.R., and Guthrie, W.F., Determination of Sulfur in Biodiesel and Petroleum Diesel by XRF using the Gravimetric Standard Addition Method – II. Energy & Fuels, 22:2488-2490 (2008)
Steven J. Christopher