# 5: Standardizing Analytical Methods

• • Contributed by David Harvey
• Professor (Chemistry and Biochemistry) at DePauw University

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.1 In Chapter 3 we defined this relationship as

$S_{\large{\textrm{total}}}=k_{\large{\textrm A}}n_{\large{\textrm A}}+S_{\large{\textrm{reag}}}\hspace{5mm}\textrm{or}\hspace{5mm}S_{\large{\textrm{total}}}=k_{\large{\textrm A}}C_{\large{\textrm A}}+S_{\large{\textrm{reag}}}$

where $$S_{total}$$ is the signal, $$n_A$$ is the moles of analyte, $$C_A$$ is the analyte’s concentration, $$k_A$$ is the method’s sensitivity for the analyte, and $$S_{reag}$$ is the contribution to $$S_{total}$$ from sources other than the sample. To standardize a method we must determine values for $$k_A$$ and $$S_{reag}$$. Strategies for accomplishing this are the subject of this chapter.

Thumbnail: Illustration showing the evaluation of a linear regression in which we assume that all uncertainty is the result of indeterminate errors affecting y. The points in blue, yi, are the original data and the points in red, ŷi, are the predicted values from the regression equation, ŷ = b0 + b1x. The smaller the total residual error (equation 5.16), the better the fit of the straight-line to the data