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5: Standardizing Analytical Methods

  • Page ID
    225
  • [ "article:topic-guide", "Author tag:Harvey", "authorname:harveyd", "showtoc:no" ]

    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.

    • 5.1: Analytical Standards
      To standardize an analytical method we use standards containing known amounts of analyte. The accuracy of a standardization, therefore, depends on the quality of the reagents and glassware used to prepare these standards. We divide analytical standards into two categories: primary standards and secondary standards.
    • 5.2: Calibrating the Signal
      The accuracy of our determination of \(k_A\) 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 affecting 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 nonideal chemical behavior.
    • 5.4: Linear Regression and Calibration Curves
      In a single-point external standardization we determine the value of \(k_A\) by measuring the signal for a single standard containing a known concentration of analyte. Using this value of \(k_A\) and the signal for our sample, we then calculate the concentration of analyte in our sample. With only a single determination of \(k_A\), a quantitative analysis using a single-point external standardization is straightforward.
    • 5.5: Blank Corrections
      Thus far in our discussion of strategies for standardizing analytical methods, we have assumed the use of a suitable reagent blank 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 Regression Analysis
      Although the calculations in this chapter are relatively straightforward—consisting, as they do, mostly of summations—it can be quite 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.E: Standardizing Analytical Methods (Exercises)
      This is a summary to accompany "Chapter 5: Standardizing Analytical Methods" from Harvey's "Analytical Chemistry 2.0" Textmap.
    • 5.S: Standardizing Analytical Methods (Summary)
      This is a summary to accompany "Chapter 5: Standardizing Analytical Methods" from Harvey's "Analytical Chemistry 2.0" Textmap.

    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