Interpretation of the significance ofhigh numbers of indicator organisms
will depend on the indicator and the foodinvolved. Enterobacteriaceae or
coliform counts can provide an indication of the adequacy of process
hygiene, though they are naturally present in substantial numbers on
several raw foods.Escherichia coliis indicative of faecal contamination
and the possible presence of enteric pathogens, although there is no direct
relationship and interpretation is not clear-cut. Because of these uncertain-
ties, indicator tests are ofonly moderate stringency.
When looking for known pathogens, more stringent sampling plans
are appropriate and these become more demanding as the severity of the
illness the pathogen causes increases.
The conditions under which the food is to be handled after sampling
must also be accommodated in any plan. For example, a sampling plan
forE. coliin raw meats can be quite lenient since the organism is not
uncommon in raw meats and the product will presumably be cooked
before consumption, thus reducing the hazard. A more stringent plan is
required for the same organism if the subsequent handling of a food will
produce no change in the hazard; for example, ice cream which is stored
frozen until consumption. The most stringent plan would be required for
products where subsequent handling could increase the hazard. This
would be the case with dried milk where the product could be reconsti-
tuted and held at temperatures which would allow microbial growth to
resume.
Plan stringency should also take account of whether the food is to be
consumed by particularly vulnerable groups of the population such as
infants, the very old, or the very sick.
11.2.4 Variables Acceptance Sampling
Very often we have no idea how micro-organisms are distributed within a
batch of food and have no alternative but to use an attributes sampling
scheme which makes no assumption on this question. In many cases
though, studies have found that micro-organisms are distributed log-
normally, that is to say the logarithms of the counts from different
samples conform to a normal distibution. For example, a survey of
nearly 1300 batches of frozen and dried foods found that, on average,
only 7.8% of batches did not conform to a log-normal distribution.
When this is the case it is possible to use a variables acceptance sampling
procedure which achieves better discrimination by making full use of the
numerical data obtained from testing rather than just assigning test
results into classes as is done in sampling by attributes.
The shape of a normal distribution curve is determined by the param-
etersm, the population mean which determines the maximum height of
the curve, and s, the standard deviation of the distribution which
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