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# 5: Standardizing Analytical Methods

The American Chemical Society’s Committee on Environmental Improvement defines standardization as the process of determining the relationship between the signal and the amount of analyte in a sample. In Chapter 3 we defined this relationship as

$S_{total} = k_A n_A + S_{reag} \text{ or } S_{total} = k_A C_A + S_{reag} \nonumber$

where Stotal is the signal, nA is the moles of analyte, CA is the analyte’s concentration, kA is the method’s sensitivity for the analyte, and Sreag is the contribution to Stotal from sources other than the sample. To standardize a method we must determine values for kA and Sreag. Strategies for accomplishing this are the subject of this chapter.

• 5.1: Analytical Signals
To standardize an analytical method we use standards that contain known amounts of analyte. The accuracy of a standardization, therefore, depends on the quality of the reagents and the glassware we use to prepare these standards.
• 5.2: Calibrating the Signal
The accuracy with which we can determine $$k_A$$ and $$S_{reag}$$ depends on how accurately we can measure the signal, $$S_{total}$$. We measure signals using equipment, such as glassware and balances, and instrumentation, such as spectrophotometers and pH meters. To minimize determinate errors that might affect the signal, we first calibrate our equipment and instrumentation.
• 5.3: Determining the Sensitivity
To standardize an analytical method we also must determine the value of $$k_A$$. In principle, it should be possible to derive the value of $$k_A$$  for any analytical method by considering the chemical and physical processes generating the signal. Unfortunately, such calculations are not feasible when we lack a sufficiently developed theoretical model of the physical processes, or are not useful because of non-ideal chemical behavior.
• 5.4: Linear Regression and Calibration Curves
How do we find the best estimate for the relationship between the signal and the concentration of analyte in a multiple-point standardization? The process of determining the best equation for the calibration curve is called linear regression, which is the focus of this section.
• 5.5: Compensating for the Reagent Blank
Thus far in our discussion of strategies for standardizing analytical methods, we have assumed that a suitable reagent blank is available to correct for signals arising from sources other than the analyte. We did not, however ask an important question: “What constitutes an appropriate reagent blank?” Surprisingly, the answer is not immediately obvious.
• 5.6: Using Excel and R for a Linear Regression
Although the calculations in this chapter are relatively straightforward— consisting, as they do, mostly of summations—it is tedious to work through problems using nothing more than a calculator. Both Excel and R include functions for completing a linear regression analysis and for visually evaluating the resulting model.
• 5.7: Problems
End-of-chapter problems to test your understanding of topics in this chapter.
• 5.8: Additional Resources
A compendium of resources to accompany topics in this chapter.
• 5.9: Chapter Summary and Key Terms
Summary of chapter's main topics and a list of key terms introduced in the chapter.

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