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Ph. D. ThesisPh. D. Thesis 1. Introduction1. Introduction 1.1. Outline1.1. Outline
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Ph. D. Thesis
  Abstract
  Table of Contents
  1. Introduction
    1.1. Outline
  2. Theory – Fundamentals of the Multivariate Data Analysis
  3. Theory – Quantification of the Refrigerants R22 and R134a: Part I
  4. Experiments, Setups and Data Sets
  5. Results – Kinetic Measurements
  6. Results – Multivariate Calibrations
  7. Results – Genetic Algorithm Framework
  8. Results – Growing Neural Network Framework
  9. Results – All Data Sets
  10. Results – Various Aspects of the Frameworks and Measurements
  11. Summary and Outlook
  12. References
  13. Acknowledgements
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1.1.   Outline

The outline 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 afterwards.

In the 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.

Starting 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.

After 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.

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