distinct categories: probability and
non-probability. Probability sampling is easy
to interpret; errors can actually be calculated and bias is therefore minimized.
On the downside, it requires a more
elaborate sampling plan as well as more
samples. It adds time and, ultimately,
cost to the inspection process. Non-probability sampling is non-random; it
introduces bias (not in favor of our
clients), thus making it more difficult to
interpret the sampling results and, more
importantly, sampling errors cannot be
calculated. “Garbage in, garbage out”
takes on a whole new meaning when applied to non-probability sampling. On
the upside, it’s fast, cheap and requires
fewer samples. Both strategies—
probability and non-probability—have merit if
properly applied and documented.
Probability sampling should be favored by the regulatory community
whenever possible and practical. Unfortunately, in most cases, it isn’t, much to
the detriment of food safety. Probability
sampling presents an unbiased and objective view of the conditions or products sampled. There are two basic,
categorical strategies in probability sampling: systematic and random; both are
ideally suited to the art and science of
food safety.
Systematic sampling relies on the
sampler’s experience with the product
sampled and any available information
about the sample set. This may include
knowledge about the ingredients, assembly and preparation, cooking method,
display and service, among other parameters. Basically, systematic sampling is
used to find a gradient, for example,
contaminants introduced in a production line or temperature variations from
changes in cooking or chilling. Because it
relies on a gradient, there is a need to develop a consistent grid or pattern for
each set sampled. Using a very simplistic
example, to measure the temperature of
a hotel pan of food (such as lasagna) that
is offered at a buffet table, a thermometer is inserted into several points to a
given depth along the diagonal of the
pan. This process measures the temperature gradient; each point is recorded and
an average temperature is calculated
from the collected data. While some bias
is introduced and numerous samples are
“The basic objective of contamination control is
to keep the contaminants out…”
required, it follows that the more samples taken, the less bias is introduced
into the interpretation of the results. In
short, systematic sampling produces a
fair representation of the temperature of
the product sampled.
The ultimate sampling strategy, however, is random sampling. Random sampling depends on the theory of random
chance probabilities to choose the most
representative sample(s) from the lot in
question. It introduces the least bias into
the sampling strategy, but requires the
greatest number of samples, depending
on the level of confidence needed. From
what I do in my practice, random sampling has definite application in evaluating purported allergen contaminants in
an assemblage of product, such as
peanut parts in toasted sesame seeds.
While 100% sampling would be ideal, if
it were used, there would be no product
left over. However, through random
sampling, a probability can be established that is fair and equitable to all par-ties. To accomplish this, there are two
American National Standards to choose
from: ANSI/ASQ Z1.4 and Z1.9. All
that is needed in addition to these standards is a table of random numbers that
is easily downloaded from the Internet.
dures and tables for inspection by variables for percent nonconforming. Its definition is also best described by its
abstract: “…an acceptance sampling system to be used on a conforming stream
of lots for AQL specified. It (also) provides tightened, normal and reduced
plans to be used on measurements which
are normally distributed. Variation may
be measured by sample standard deviation, sample range or known standard
deviation. It is applicable only when the
normality of the measurements is assured.” This standard is therefore ideally
suited to sampling food on a production
line or used in conducting an audit
where the parameters of the things under
investigation are prescribed.
Please don’t be intimidated by the
wording. These two ANSI Standard sampling strategies are quite easy to learn,
apply and interpret. With a little practice, one can become quite proficient in
their use. The more they’re used, the
greater becomes your familiarity with
their benefits and limitations. I never
found anything better for field use than
these two standards, particularly when
the outcome supports the arguments in a
litigious scenario.
Sampling Standards
Z1.4 is a standard containing sampling procedures and tables for inspection by attributes (here is where the
variability comes in) and is best described by quoting its abstract: “...an acceptance sampling system to be used
with switching roles on a continuing
stream of lots for AQL (Acceptable
Quality Level) specified. It provides
tightened, normal and reduced plans to
be applied for attributes inspection for
percent nonconforming or nonconfor-mities per 100 units.” Z1.4 is an updated
version of the old MIL-STD-105E and
most closely represents the “traditional”
random sampling strategy we all learned
in college.
Its companion is ANSI/ASQ Z1.9.
This standard contains sampling proce-
Non-probability Sampling
Approaches
Non-probability sampling strategies
represent the bulk of regulatory inspection sampling protocols. Non-probability sampling is characterized by three
distinct approaches: convenience, judgmental and one affectionately called
“snowball sampling.” In all fairness, all
three must be justified and detailed on
the inspection report if employed as a
regulatory tool. I listed many of these
variables in my previous column.
Convenience sampling is the most
frequently used by the sanitarian. It’s the
most intuitive but also the one that has
the least repeatability. By its very definition, it is expeditious and exploitative. It
takes advantage of convenient locations
or situations and requires the fewest
samples. When used correctly, it pro-
