of this study can be described as follows. The work starts with an overview
of the multivariate data analysis. Several up-to-date concepts, methods and
algorithms are presented and the advantages and problems are discussed. Thereby
the focus is on two concepts, multivariate calibration and selection of variables.
In the next chapter, a multivariate data analysis is performed
using a data set recorded in our lab as an example for a data analysis, which
is accepted as the current state of research in literature. Starting with this
state of research, the studies and innovations of this work enhance several
concepts presented in this and the previous chapter. Additionally, the different
concepts of sorption of analytes into sensitive layers are presented and discussed
in this chapter. The next chapter briefly presents the
different sensor setups used for recording several data sets, which are presented
following chapter, the principle of time-resolved measurements
is introduced and explained. A systematic investigation of the time-resolved
measurements is performed with respect to the theoretical background of this
principle and with respect to the interaction principle between the sensitive
layers and analytes used in this study. Thereby different properties of the
sensitive layers, which are the basis for the time-resolved measurements, are
investigated and modified allowing the optimization of the measurements.
with chapter 6, all methods and concepts, which are developed,
are demonstrated using one single data set. This allows an easy comparison of
the methods. Thus, the improvements by the continually developed concepts can
be monitored easily. First, common methods of multivariate calibration are applied
resulting in rather poor calibrations. In the next chapter,
neural networks as the most promising method are further developed by the implementation
of genetic algorithms, neural networks and statistical procedures into a framework,
which is introduced in this work for the first time. The framework shows a superior
calibration compared to the widespread methods for the multivariate calibration
applied to the data in the previous chapter.
that, two similar frameworks are introduced for the implementation of a
new type of neural networks, which are called growing neural networks, resulting
in the best calibration of the data set. These frameworks are unique with respect
to finding automatically optimal neural network topologies with practically
no input needed by the analyst. In chapter 9, an overview
of the results is given for all data sets using commonly applied multivariate
data analysis methods and the superior new frameworks for data analysis introduced
in this work. Miscellaneous minor issues of the frameworks are discussed afterwards.
The work ends with a summary of the results and some suggestions
for further research.