ConsultGLP

The conventional aerobic plate counting method for examining the level of microorganism in a food product has often been referred to AOAC Official Methods of Analysis and APHA Standard Methods for the Examination of Dairy Products.  Basically it uses physical counting for determining the number of colonies in a test sample.

Generally speaking, in order to interpret the results properly after using any physical, chemical or biological procedure, we must carefully consider the diverse sources of actual or potential error associated with the results obtained. Generally, any test result is influenced by a complex of three major errors, namely:

1. Gross errors, mostly caused by negligence and carelessness of the laboratory concerned, which are not excusable; the results are to be discarded.
2. Inherent systematic errors which are closely associated with the analytical method itself. Such systematic errors which create bias in the test result must be minimized, if not total eliminated by carefully studying the method performance.

Particularly in microbiological testing, when a method has a known consistent bias which is inherent to the method, through studies of low recoveries of targeted microorganisms, such a bias shall not be corrected with a factor after adjusting for recovery on a sample that is spiked with a known amount of the analyte. This is due to the empirical nature of microbial enumerations. Instead, the number of colony-forming units (CFUs) per unit of sample is to be reported with a bias clearly noted              for reference. One cannot in practice determine a true value in microbiological count testing. Even when using certified reference materials or values derived from inter-laboratory studies, only part of the total bias can be assessed.

   3.   Random errors, which are uncontrollable experimental factors including but not limiting to, the sample matrix effect, the culture media, equipment electronic variation, environmental, etc.

A measure of random errors of a test method can be handled by any statistical parameters associated with the test result, namely repeatability and intermediate precision in terms of standard deviation and variance. Because of the existence of such uncontrollable errors, the analytical data must be expressed with an interval (i.e. X + U) under a certain degree of confidence, such as 95%.

As we know, all accredited testing laboratories are required to apply procedures for estimating uncertainty of measurement (ISO/IEC 17025:2005).

The ISO/TS 19036:2006 “Microbiology of food and animal feeding stuffs – Guidelines for the estimation of measurement uncertainty for quantitative determinations” reckons that the usual ISO GUM bottom up or component-by-component approach with consideration of uncertainty components of each step of the test method underestimates the uncertainty because the factors which influence the uncertainty of microbiological enumerations are still not well understood and one may miss some significant uncertainty contributions by the process.

The Eurachem document “Accreditation of Microbiological Laboratories” (2^nd edition, 2013) also states that: “Microbiological tests generally come into the category of those that preclude the rigorous metrologically and statistically valid calculation of measurement uncertainty as described in the ISO GUM. It is generally appropriate to base the estimate of measurement uncertainty on repeatability and intermediate precision (within laboratory reproducibility) data.”

A guidance document G108 prepared by the American Association for Laboratory Accreditation (A2LA), titled “Guidelines for Estimating Uncertainty for Microbiological Counting Methods” is a good source of reference for estimating uncertainty in microbiological analysis. It suggests four top down approaches to estimate measurement uncertainty, namely:

1. Reproducibility replicates for laboratory control samples
2. Recovery replicates for laboratory control samples
3. Use of laboratory control samples with same target values
4. Use of method validation data

Coming blog articles will discuss each of these ways in simpler language and it is hoped that our microbiologists and laboratory colleagues will find them easy to apply these concepts in evaluating uncertainty.