The atmosphere in which the test system is immersed can have a major effect. Anaerobic organisms cannot grow in the presence
of oxygen, and tissue cultures may require the presence of 5% CO2 to grow well. Certain facultative organisms will adjust their metabolic paths to cope with reduced levels of oxygen. This,
in turn, can affect their growth rates. When media for general purposes, such as sterility tests, are being considered, it
is normal to include one medium that provides anaerobic conditions. The detection of anaerobes is important as they include
toxin-producing and other pathogenic bacteria.
One of the problems with quantitative microbiological tests is that as microbe counts become smaller, straight-forward linear
behavior is less common than that which follows the Poisson distribution. This is because random distribution is not even
distribution. Most quantitative tests for microorganisms require the plating of dilute liquid samples, and it is normal to
prepare samples to ensure the dispersion of microbes and a random distribution of bacteria or viruses. When concentrations
are high, the lack of even distribution is not a problem; simple linear averaging methods can compensate for the uneven distribution.
Problems arise with smaller numbers of microbes.
Consider an example where there are exactly 100,000 organisms per mL. If 0.1 mL is taken and mixed with 0.9 mL of a diluent,
it is highly unlikely that the new suspension will contain exactly 10,000 organisms; it would not be surprising to have anywhere
from 9,800 – 10,200 organisms. Back-calculating the result produces a range from 98,000 – 102,000 organisms in the original
sample, and, if there were enough replicates, the results could be averaged to obtain a number indistinguishable from 100,000.
This is the result that would be expected based on linear thinking.
However, if there were only 10 organisms per mL, it is quite possible that a 0.1 mL aliquot would not contain any organisms
at all. In fact, in this situation about one third of the aliquots will not contain a single organism. This could lead to
the conclusion, on averaging, that the sample only contained 6.7 organisms per mL, which is a significant deviation from the
A transition occurred from a high density that produces a fairly smooth, homogeneous distribution of organisms to a low density
that results in organisms that are distributed with significant distances between them. Under these conditions, the suspension
behaves according to the Poisson distribution and assumptions related to a normal distribution no longer hold. The Poisson
distribution is an exponential function. The problem is that parameters such as the standard deviations may be logarithmic
in nature, and when attempts are made to make these numbers "real" by taking the antilogarithms, the results may actually
have no "real" meaning. This can cause great difficulties when attempting to validate quantitative microbial test procedures.
When it is necessary to deal with the Poisson distribution, it is wise to consult a statistician who is versed in the use
of this distribution. It appears that the transition to the Poisson distribution occurs when approximately 100 colonies or
plaques are counted. This is unfortunate because at this level many analysts will declare a colony or plaque count to be "too
numerous to count" (TNTC) to avoid the tedium of these measurements. Therefore, most colony or plaque counting procedures
actually operate under the Poisson distribution and calculations based on the normal distribution will be incorrect.
The frequency of revalidation is a contentious question. There are many tests, such as the growth promotion test on culture
media, that are essentially self-validating and are run frequently. It could be argued that if performance parameters (for
example, percent recovery of indicator organisms) are monitored via control charting and no significant changes are seen,
revalidation is unnecessary. However, control charting usually does not measure all the parameters included in validation
studies. Consequently, it is wise to revalidate tests after any major change in constituents or procedures; in fact, revalidation
may be needed to justify the changes. Changes in suppliers (especially of media components) and changes in the composition
of test samples have resulted in major changes in microbiological tests. Finally, it is probably wise to revalidate procedures
approximately every second year to protect against unseen or unreported changes. A media supplier may change its own suppliers
or change its processing procedures without notifying customers. The supplier may have no idea of the impact these changes
could have on the end use of their product. In addition, personnel changes in the laboratory and the maturing of analysts'
techniques can have an effect.