The quality of an analysis depends on the quality of the calibration. The
quality of a calibration depends on attention to detail.

The quality of an analysis depends on the quality of the calibration. The
quality of a calibration depends on attention to detail.

- Selection of calibration samples:

- number

- concentration ranges

- families - Preparation of calibration samples
- Choice of calibration function
- Precision during calibration
- Optimisation of calibration curve

In optical emission spectrometry a typical calibration function has the form:

where *C*_{i} is the content (or concentration) of element *i*; *a*_{i} to *c*_{i} and *d*_{j} are
fitting parameters; *I*_{i} is the signal from element *i*;
and *I*_{j} is the signal from an interfering element *j*,
where there may be up to *N* interfering elements. -*a*_{i} is
called the *BEC* (background equivalent concentration).

The calibration function given here is linear (in its dependence on the
fitting parameters) and second order (the highest power of *I*_{i}).
Sometimes higher order functions or non-linear functions are used. Several
simplifications are also possible. If *c*_{i} = 0 then
the calibration function is first order. If all *d*_{j} = 0
then there are no interference corrections.

To determine the best values for the fitting parameters it is usual to find
the minimum of a least-squares function:

where *w*_{i} are the weights given to each calibration point *i*, *C*_{i} is the known content and and *C*^{^}_{i} is the content estimated using the calibration function. By minimising the
least-squares function we are endeavouring to make the estimated contents as
close as possible to the known contents. A higher weight means that a point has
a bigger effect. Generally, it is necessary, in optical emission spectrometry,
to give different weights to different points, because the precision of
measurement may vary from point to point or the accuracy of the known
concentration may vary from sample to sample. The software will normally set
appropriate weights automatically, but occasionally it is necessary to intervene
manually.

Several parameters are available to determine the goodness of fit. Unweighted
equations are shown here for simplicity though, of course, weighted functions
should be used.