In Chapter 1 we made a distinction between analytical chemistry and chemical analysis. Among the goals of analytical chemistry are improving established methods of analysis, extending existing methods of analysis to new types of samples, and developing new analytical methods. Once we develop a new method, its routine application is best described as chemical analysis. We recognize the status of these established methods by calling them standard methods.
Numerous examples of standard methods are presented and discussed in Chapters 8–13. What we have yet to consider is what constitutes a standard method. In this chapter we discuss how we develop a standard method, including optimizing the experimental procedure, verifying that the method produces acceptable precision and accuracy in the hands of a single analyst, and validating the method for general use.
- 14.1: Optimizing the Experimental Procedure
- One of the most effective ways to think about an optimization is to visualize how a system’s response changes when we increase or decrease the levels of one or more of its factors. We call a plot of the system’s response as a function of the factor levels a response surface.
- 14.2: Verifying the Method
- After developing and optimizing a method, the next step is to determine how well it works in the hands of a single analyst. Three steps make up this process: determining single-operator characteristics, completing a blind analysis of standards, and determining the method’s ruggedness.
- 14.3: Validating the Method as a Standard Method
- For an analytical method to be useful, an analyst must be able to achieve results of acceptable accuracy and precision. Verifying a method, as described in the previous section, establishes this goal for a single analyst. The process by which we approve a method for general use is known as validation and it involves a collaborative test of the method by analysts in several laboratories.
- 14.4: Using Excel and R for an Analysis of Variance
- Although the calculations for an analysis of variance are relatively straight- forward, they become tedious when working with large data sets. Both Excel and R include functions for completing an analysis of variance. In addition, R provides a function for identifying the source(s) of significant differences within the data set.
- 14.5: Problems
- End-of-chapter problems to test your understanding of topics in this chapter.
- 14.6: Additional Resources
- A compendium of resources to accompany topics in this chapter.
- 14.7: Chapter Summary and Key Terms
- Summary of chapter's main topics and a list of key terms introduced in the chapter.