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Ph. D. ThesisPh. D. Thesis 9. Results  All Data Sets9. Results All Data Sets
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Ph. D. Thesis
  Table of Contents
  1. Introduction
  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
    9.1. Methanol and Ethanol by SPR
    9.2. Methanol, Ethanol and 1-Propanol by SPR
    9.3. Methanol, Ethanol and 1-Propanol by the RIfS Array and the 4l Setup
    9.4. Quaternary Mixtures by the SPR Setup and the RIfS Array
    9.5. Quantification of the Refrigerants R22 and R134a in Mixtures: Part II
  10. Results Various Aspects of the Frameworks and Measurements
  11. Summary and Outlook
  12. References
  13. Acknowledgements
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9.   Results All Data Sets

In this chapter, the principle of the time-resolved measurements introduced in chapter 5 and the different new data analysis methods introduced in chapter 7 and chapter 8 are applied to several data sets. Thereby the methods are compared with the commonly used methods of data analysis and with static measurements to demonstrate the superiority of the new approaches. Different data sets of mixtures of the homologous series of the low alcohols were recorded by different setups. The application of the new approaches to these data sets demonstrates that the methods proposed in this study can be easily transferred to different setups and to different analytical task, such as a varying number of analytes in mixtures and varying types of analytes. In the last part of this chapter, the principle of the time-resolved measurements is applied to the data set introduced in chapter3 also resulting in an improved calibration.

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