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Ph. D. ThesisPh. D. Thesis 4. Experiments, Setups and Data Sets 4. Experiments, Setups and Data Sets 4.5. Data Sets 4.5. Data Sets 4.5.1. Refrigerants R22 and R134a4.5.1. Refrigerants R22 and R134a 4.5.1.2. R22 and R134a by the RIfS Array and the 4l-Setup4.5.1.2. R22 and R134a by the RIfS Array and the 4l-Setup
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Ph. D. Thesis
  Abstract
  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
    4.1. The Sensor Principle
    4.2. SPR Setup
    4.3. RIfS Sensor Array
    4.4. 4l Miniaturized RIfS Sensor
    4.5. Data Sets
      4.5.1. Refrigerants R22 and R134a
        4.5.1.1. R22 and R134a by the SPR Setup
        4.5.1.2. R22 and R134a by the RIfS Array and the 4l-Setup
      4.5.2. Homologous Series of the Low Alcohols
  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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4.5.1.2.   R22 and R134a by the RIfS Array and the 4l-Setup

This data set was already introduced and described in chapter 3 and will be further investigated in section 9.5 as the sensor response of the Makrolon layer was also recorded in a time-resolved mode. The signal of the Makrolon sensor could be extracted from the raw measurements at 18 time points during sorption and desorption. Thus, 5 independent variables of the static measurements of 5 polymers and additionally 18 independent variables of the time-resolved measurements were available.

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