Abstract
There are an increasing number of instrumental methods
for obtaining data from biochemical processes, many of
which now provide information on many (indeed many
hundreds) of variables simultaneously. The wealth of
data that these methods provide, however, is useless
without the means to extract the required information.
As instruments advance, and the quantity of data
produced increases, the fields of bioinformatics and
chemometrics have consequently grown greatly in
importance. The chemometric methods nowadays available
are both powerful and dangerous, and there are many
issues to be considered when using statistical analyses
on data for which there are numerous measurements
(which often exceed the number of samples). It is not
difficult to carry out statistical analysis on
multivariate data in such a way that the results appear
much more impressive than they really are. The authors
present some of the methods that we have developed and
exploited in Aberystwyth for gathering highly
multivariate data from bioprocesses, and some
techniques of sound multivariate statistical analyses
(and of related methods based on neural and
evolutionary computing) which can ensure that the
results will stand up to the most rigorous scrutiny.
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