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15.1: The Analytical Perspective Revisited

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    220790
  • As we noted in Chapter 1, each area of chemistry brings a unique perspective to the broader discipline of chemistry. For analytical chemistry this perspective is as an approach to solving problems, one representation of which is shown in Figure \(\PageIndex{1}\).

    Figure 15.1.png
    Figure \(\PageIndex{1}\). Flow diagram showing one view of the analytical approach to solving problems. This diagram is modified after Atkinson, G. F. J. Chem. Educ. 1982, 59, 201–202.

    Figure \(\PageIndex{1}\) is the same as Figure 1.2.1. You may wish to review our earlier discussion of this figure and of the analytical approach to solving problem.

    If you examine an analytical method it often seems that its development was a straightforward process of moving from a problem to its solution. Unfortunately—or, perhaps, fortunately for those who consider themselves analytical chemists!—developing an analytical method seldom is routine. Even a well-established standard analytical method, carefully followed, can yield poor data.

    An important feature of the analytical approach in Figure \(\PageIndex{1}\) is the feedback loop that includes steps 2, 3, and 4, in which the outcome of one step may lead us to reevaluate the other steps. For example, after standardizing a spectrophotometric method for the analysis of iron (step 3), we may find that its sensitivity does not meet our original design criteria (step 2). In response, we might choose a different method, change the original design criteria, or work to improve the sensitivity.

    The feedback loop in Figure \(\PageIndex{1}\) is maintained by a quality assurance program, whose objective is to control systematic and random sources of error [see, for example (a) Taylor, J. K. Anal. Chem. 1981, 53, 1588A–1596A; (b) Taylor, J. K. Anal. Chem. 1983, 55, 600A–608A; (c) Taylor, J. K. Am. Lab October 1985, 53, 67–75; (d) Nadkarni, R. A. Anal. Chem. 1991, 63, 675A–682A; (e) Valcárcel, M.; Ríos, A. Trends Anal. Chem. 1994, 13, 17–23.] The underlying assumption of a quality assurance program is that results obtained when an analysis is under 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 if the system falls out of statistical control.

    An analysis is in a state of statistical control when it is reproducible and free from bias.

    The focus of this chapter is on the two principal components of a quality assurance program: quality control and quality assessment. In addition, we will give considerable attention to the use of control charts for monitoring the quality of analytical data.

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