Developing an Analytical Method

In the presence of hydrogen peroxide, H2O2, and sulfuric acid, H2SO4, a solution that contains vanadium ions forms a reddish-brown color. Although the exact chemistry of the reaction is uncertain, it serves as a simple qualitative "spot test" for vanadium: the formation of a reddish-brown color upon adding several drops of H2O2 and H2SO4 to a sample is a positive test for vanadium.

A spot test provides nothing more than a simple binary response: yes, the sample contains vanadium, or no, the sample does not contain vanadium (at least at a concentration we can detect). Suppose we wish to adapt this qualitative test into a more quantitative method of analysis, one that allows us to report the concentration of vanadium in a sample. How might we accomplish this?

Given the reddish-brown color of a positive test, we might choose the solution's absorbance at a wavelength of 450 nm as the analytical signal. In addition to the concentration of vanadium, the intensity of the solution's color—and thus its absorbance—also depends on the amounts of H2O2 and H2SO4 added; in particular, a large excess of hydrogen peroxide decreases absorbance as the solution’s color changes from red-brown to yellow. As well, we must ensure that the development of color is reproducible and that the method yields accurate and precise results. We also need to determine if the method is susceptible to interferences and determine the smallest concentration of vanadium we can report with confidence. Finally, we want a rugged method, so that different analysts obtain similar results when analyzing the same sample. We call this process of optimizing and verifying a procedure method development.

This case study introduces method development within the context of an analysis for several pharmacolgically important constituents in a medicinal plant using a combination of a microwave extraction to isolate the analytes from the plant's roots and HPLC with UV detection to separate the analytes and to determine their concentrations.

Interspersed within the case study's narrative are a series of investigations, each of which asks you to stop and consider one or more important issues. Some of these investigations include data for you to analyze in the form of interactive figures, created using, that allow you to manipulate the data and that provide access to the original data in the form of a spreadsheet. The image below

shows the tools for interacting with the data, which are available when the cursor enters the figure; from left-to-right, the tools are:

  1. zoom by clicking and dragging within the figure
  2. pan from side-to-side by clicking and dragging
  3. zoom in
  4. zoom out
  5. autocale (returns figure to original magnification; double-clicking within a figure also autoscales the data)
  6. show closest on hover (provides x-axis and y-axis values for one data set)
  7. compare data on hover (provides x-axis and y-axis values for all data sets)
  8. link to

Some figures include data for multiple analytes or data sets and, as a consequence, include a legend; clicking on an analyte's or a data set's name in the figure's legend toggles on and off the display of the corresponding data. Clicking on the text "Play with this data!" provides access, via, to the figure and its underlying data; if you have a (free) account with, you can fork and edit the figure and data.