Figure 2.9
'Best fit' plot of data from Table 2.7 obtained by 'least squares' regression
analysis. (Important note: This graph implies a straight line relationship down
to zero concentration. It is, however, unsafe to use the extrapolated portion as
there are no experimental data for this part of the curve).routines can be used. This is more complex than dealing with straight lines, and a number of pitfalls
may be encountered. A specialist text needs to be consulted in these circumstances.
Chemometrics
Advanced mathematical and statistical techniques used in analytical chemistry are often referred to
under the umbrella term of chemometrics. This is a loose definition, and chemometrics are not readily
distinguished from the more rudimentary techniques discussed in the earlier parts of this chapter, except
in terms of sophistication. The techniques are applied to the development and assessment of analytical
methods as well as to the assessment and interpretation of results. Once the province of the
mathematician, the computational powers of the personal computer now make such techniques
routinely accessible to analysts. Hence, although it would be inappropriate to consider the detail of the
methods in a book at this level, it is nevertheless important to introduce some of the salient features to
give an indication of their value. Two important applications in analytical chemistry are in method
optimization and pattern recognition of results.
Analytical methods often contain many different variables which need to be optimized to attain best
performance. The different variables are not always independent. For example, pH and polarity in a
solution may be interdependent. Optimization by changing one variable at a time, while