We have noted that each field of chemistry brings a unique perspective to the broader discipline of chemistry. For analytical chemistry this perspective was identified as an approach to solving problems, which was presented as a five-step process: (1) Identify and define the problem; (2) Design the experimental procedure; (3) Conduct an experiment and gather data; (4) Analyze the experimental data; and Propose a solution to the problem. The analytical approach, as presented thus far, appears to be a straightforward process of moving from problem-to-solution. Unfortunately (or perhaps fortunately for those who consider themselves to be analytical chemists!), an analysis is seldom routine. Even a well-established procedure, carefully followed, can yield poor data of little use.
An important feature of the analytical approach, which we have neglected thus far, is the presence of a “feedback loop” involving steps 2, 3, and 4. As a result, the outcome of one step may lead to a reevaluation of the other two steps. For example, after standardizing a spectrophotometric method for the analysis of iron we may find that its sensitivity does not meet the original design criteria. Considering this information we might choose to select a different method, to change the original design criteria, or to improve the sensitivity.
The “feedback loop” in the analytical approach is maintained by a quality assurance program (Figure 15.1), whose objective is to control systematic and random sources of error.1–5 The underlying assumption of a quality assurance program is that results obtained when an analytical system is in statistical control are free of bias and are characterized by well-defined confidence intervals. When used properly, a quality assurance program identifies the practices necessary to bring a system into statistical control, allows us to determine if the system remains in statistical control, and suggests a course of corrective action when the system has fallen out of statistical control.
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