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A2LA (The American Association for Laboratory Accreditation) in its policy document P103b – Annex: Policy on estimating measurement uncertainty for life sciences testing labs reckons that when a test laboratory uses chemical, environmental or biological test methods traceable to published regulatory or consensus methods (such as FDA, EPA, AOAC, ASTM, ISO, BS, EN, etc), measurement uncertainty (MU) shall be estimated using available data, published information, and/or some designed experiments. That means uncertainty can be estimated using laboratory control samples (LCS), method validation studies, or by appropriate model for the propagation of error components.

ASTM E 2554-13: Standard practice for estimating and monitoring the uncertainty of test results of a test method in a single laboratory using a Control Sample Program indicates that LCS may be used to estimate MU, provided the samples are an appropriate matrix, stable and homogeneous in concentration distribution.

The A2LA G108 states that:

1. When the LCS has been through all method steps, the laboratory can then use the pooled standard deviation (s_p) from the LCS intermediate reproducibility s_R’ (also called intermediate precision) data as an estimate of combined standard uncertainty. A relative standard deviation (RSD) or coefficient of variation (CV) may also be used.
2. When the LCS have not been run through all method steps, then the laboratory should incorporate any appropriate additional uncertainty components as expressed in their respective standard uncertainty in its MU estimation. The additional components should be combined with s_p using the root sum square (RSS) method:

where s_a, s_b, … are the standard deviations of the other uncertainty components

3.   When a method has a known consistent bias that is inherent to the method (e.g.                 low recovery on difficult analytes), the bias must not be added to the uncertainty               calculations. The bias shall, however, be clearly stated and recorded along with                   the uncertainty estimate.

This method involving the use of LCS with same target values is the simplest amongst the three methods discussed so far. The other two methods that we had discussed are using the intermediate reproducibility duplicates, and the recovery replicates. It can be shown that this method, however, tends to produce a much bigger uncertainty as only single analysis data point is taken in the evaluation as compared to the other approaches using duplicates results or matrix spiked recoveries which provide better confidence in the outcome.

Image 1 below shows the A2LA example on 20 LCS replicates analyzed over a period of time under the laboratory intermediate reproducibility conditions:

This example has also covered most sources of uncertainty in the analytical process. It is recommended that 20 or more individual LCS data points are to be obtained to estimate the pooled standard uncertainty in terms of pooled standard deviation, s_p. The evaluation of the combined uncertainty is then expanded by multiplying s_p with a coverage factor k =2 for 95% confidence.

In concluding this series of MU estimation on microbial count testing, we should say that the three procedures described in this article and the other two earlier ones are having their own merits and disadvantages, and no one process is favored over the other.  It is up to the laboratory concerned to exercise its professional judgment in deciding whether the MU estimate obtained from any of these approaches is reasonable and whether it meets the needs of its customers.