Range is directly related to linearity, and ties in accuracy and precision as well. It represents the lowest and highest quantities
of material of interest contained within the samples under analysis that provide data with acceptable accuracy, precision,
Detection limit represents the least amount of material of interest contained within the sample under analysis that produces
a signal exceeding the underlying noise. No assertions pertaining to accuracy, precision, and linearity are necessary at this
level of material of interest. For example, if a method is validated to have a detection limit of 3 ng of total protein using
Method 3 (1), then a sample containing 3 ng would elicit a signal discernible from underlying noise. It would not be possible
to state from such data alone whether there was in fact an exact quantity ng of protein in the sample, only that there were
at least 3 ng.
Quantitation-limit determination is more demanding in that currently it is necessary to establish the minimum quantity of
material of interest contained within the sample that produces a signal that lies within the linear range of data. That is
to say, the quantitation limit represents the lowest end of the range.
Intermediate precision (ruggedness in USP Chapter ‹1225› ) pertains to the establishment of "...the effects of random events on the precision of the analytical
procedure" (4). Referring to the previous discussion under accuracy pertaining to error components, intermediate precision
considers random error introduced by such factors as specific equipment, analysts, laboratories, days, and so forth. It is
not meant to include systematic error (bias).
Robustness is probably most directly related to the consideration of conditions under which a validated method is shown to
be suitable. This text is very useful in considering robustness:
"If measurements are susceptible to variations in analytical conditions, the analytical conditions should be suitably controlled
or a precautionary statement should be included in the procedure. One consequence of the evaluation of robustness should be
that a series of system suitability parameters (e.g., resolution test) is established to ensure that the validity of the analytical
procedure is maintained whenever used (4)."
General requirements for verification
One question that may be asked of the compendia is whether a method provided as official (in the compendia or supplements)
requires validation. USP Chapter ‹1225› states:
"...users of analytical methods described in the USP-NF are not required to validate accuracy and reliability of these
methods, but merely verify their suitability under actual conditions of use (1)."
This text is consistent with the proposal in this article that the term validation be reserved for the process whereby one determines if a given method is suitable for its intended purpose (which must be
clearly defined), and that the term verification be reserved for the demonstration that the conditions under which the method is to be performed will be appropriate for the
Another question may be given that verification involves demonstrating that the conditions to be evaluated are suitable for
use with the validated method, how does one go about assessing that? It should be evident that a subset of the determinations
performed during the validation would be appropriate. Important conditions to consider include equipment, possible matrix
effects (components included in the article to be tested that were not evaluated during the validation), and other conditions
for which there is no clear indication provided in the method as to their suitability. A proposed new General Chapter ‹1226›
"Verification of Compendial Procedures" (see reference 9 for a discussion of this chapter) provides some guidance as to how
the verification process may be executed, but ultimately the user is responsible for selecting which of the characteristics
(data elements) evaluated during the validation should be examined as part of the verification. The user should establish
which of those validation characteristics are critical to the successful use of the validated method.