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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 4.5.1.1. R22 and R134a by the SPR Setup4.5.1.1. R22 and R134a by the SPR 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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4.5.1.1.   R22 and R134a by the SPR Setup

The SPR setup, which is described in section 4.2 in detail, was used for measuring the two refrigerants in air. The polycarbonate Makrolon (Makrolon M2400, Bayer AG, Leverkusen Germany) was used as sensitive layer. Details of the preparation of the 60 nm thick sensitive layer can be found in [188].

In addition to single analyte measurements with the sensor responses shown in figure 20 and in figure 22, two multicomponent data sets were recorded [189], which are used for the systematic investigation and development of different multivariate data analysis methods. These data sets will be further referred to as "refrigerant data". The first data set is a 21-level full factorial design whereby the concentrations (relative saturation pressures) of the two analytes were varied between 0 and 0.1 pi/pi0 relative saturation pressure. This data set is used for the calibration and optimization of the different multivariate models and will be further referred to as calibration data set. The second data set is a 20-level full factorial design and will be referred to as validation data set. The concentrations of this independent external validation data set were varied between 0.0025 and 0.0975 pi/pi0 in 0.005 steps. Thus, all concentration levels of the two data sets are different and consequently the validation data set should give a realistic estimate of the network performance in a real world situation [7]. In total, 841 different mixtures of the refrigerants R22 and R134a were measured by SPR. All measure­ments were performed in random order. The sensitive layer was exposed to the analyte-air mixtures for 60 seconds and afterwards to synthetic air for 300 seconds for recovery. During the sorption and desorption process the signal was recorded with a resolution of 45 data points (see figure 22). The signals at each time point were used as independent variables whereby only the 40 time points until 125 seconds of measurement time were used for the multivariate data evaluation (see chapter 5 for details of the time-resolved measurement proceedings).

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