ConsultGLP

In the last article, we showed how intermediate reproducibility duplicates data of the laboratory control samples (LCS’s) could be used to estimate the measurement uncertainty of an aerobic count method. If it is safe to assume that analysis recovery is reasonably constant for a particular organism in a given sample matrix, the uncertainty in a microbial enumeration test can be evaluated by studying the recovery data over time.

But, what is analysis recovery?

Recovery is defined as “Proportion of the amount of analyte, present in or added to the analytical portion of the test material, which is extracted and presented for measurement”. In a method validation protocol, analytical recovery studies are an essential component which cannot be ignored.

Recovery data R obtained as the ratio of the concentration of analyte found to that stated to be present, can be used to determine bias present in a particular test method by studying if the difference between the actual test result is found to be significantly different from the assigned or known value.  Even if the bias does exist, it is still not current conventional practice to correct for bias in quantitative microbiology results.

Therefore to get a recovery data, one has to add a known amount of analyte to a matrix and the whole matrix is then subject to the whole process of the test method. The amount recovered minus the original amount present should indicate the recovery factor.

In a perfect situation, R would be exactly unity (1) but in reality, circumstances such as imperfect extraction or serial dilutions often give observations that differ from the ideal.

Hence, we must take note of the sources of uncertainty in recovery estimation. Some of them are:

1. repeatability of the recovery experiment
2. uncertainties in reference material values
3. uncertainties in added spike quantity (in terms of weight or volume)
4. poor representation of native (originally present) analyte by the added spike
5. poor or restricted match between experimental matrix and the full range of sample matrices encountered
6. effect of analyte / spike level on recovery and imperfect match of spike or reference material analyte level and analyte level in samples.

Consequently, recovery differences over time reflect the various uncertainty contributors, including but not limiting to the following:

• random error which is a common cause variability
• analyst differences
• different equipment
• different environmental conditions

The aim of this method therefore is to use recovery variations of laboratory control samples over time to determine how various sources of uncertainty affect the final test results.

A2LA calculation method

A recovery test is conducted by plating the same amount of inoculum in petri dishes with and without the matrix of interest and counting the colony forming units (CFU’s) after growth in sterile nutrient agar at specified temperature over incubation time required.  The difference between the counts from the plate without matrix and the plate with matrix samples is a measure of recovery of the organism, usually expressed as a percentage of the CFU count in the inoculated sample.

A2LA carried out 20 replicates of great different inoculated levels and found the recovery was reasonably constant. Hence, it is assumed that the matrix used for this study is representative of those samples where an estimate of uncertainty is needed.

Image 1 below shows the data of the recovery replicates obtained over time and the subsequent calculation involving transformation of data to logarithmic values.

For example, for a result of 1,000 CFU, we can report the uncertainty interval as 606 to 1,647 CFU.

It may be noted that the actual calculated interval was 607 and 1,646, but for maximum coverage of 95% confidence interval, the results by convention are always “rounded down” for the value at the lower end of the range and always “rounded up” for the value at the upper end.

Like the method using the intermediate reproducibility data, this current method has also adequately captured in the estimate various types of uncertainty sources, namely: random error, counting error, dilutions, environment, equipment and analyst.