A professionally run test laboratory must have a set of internal quality control or check (IQC) procedures in place. Regrettably I have noticed that many accredited chemical laboratories do not institute such IQC system in their routine works.
The purpose of IQC is to ensure as far as possible that the magnitude of errors affecting the analytical system is not changing during its routine use since method validation or verification process. By not having any IQC system in place, the analyst would not be able to state with confidence that the test results generated for that particular batch of samples are precise, accurate and fit for purpose.
During method validation, we have estimated the uncertainty of the method and showed that it is fit for purpose. Therefore, when the method is put in routine use, every run of analysis should be checked to show that the errors of measurement are probably no larger than they were at validation time. Even when a standardized method is used for analysis, we have to demonstrate that our laboratory’s precision is no worse than the stated repeatability of the method.
For this IQC purpose, we can employ the concept of statistical control, which means in general that some critical feature of the system is behaving like a normally distributed variable. How are we going to do it?
For chemical analysis, we can add one or more “control materials or samples” to the run of test methods. These control materials are treated throughout in exactly the same manner as the test materials, from the weighing of the test portion to the final measurement. Of course, the control materials ideally must be of the same type as the materials for which the analytical system was validated, in respect of matrix composition and analyte concentration.
By doing so, we treat the control materials as a surrogate and their behavior is a proper indicator of the performance of the system. We can plot the results obtained in successive runs on a control chart for visual inspection on its moving trend over time. The control lines are determined by run-to-run intermediate precision of the data collected. Intermediate precision, by definition, is the pooled standard deviation of a number of successive runs in the same laboratory with inevitable changing measurement conditions (such as different analysts, instruments, newly prepared reagents, environmental variations, etc.) over time.