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Ph. D. ThesisPh. D. Thesis 9. Results  All Data Sets9. Results All Data Sets 9.5. Quantification of the Refrigerants R22 and R134a in Mixtures: Part II9.5. Quantification of the Refrigerants R22 and R134a in Mixtures: Part II
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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
  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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9.4.   Quaternary Mixtures by the SPR Setup and the RIfS Array

9.4.1.   Introduction

In this chapter, the quaternary mixtures of methanol, ethanol, 1-propanol and 1-butanol measured by the RIfS array and the SPR setup are investigated. This allows a comparison of the two setups. The RIfS array shows a significantly worse signal to noise ratio than the SPR setup using Makrolon as sensitive layer. Thus, more time of exposure to analyte was necessary for the RIfS array setup than for the SPR setup. Additionally, it is investigated if the smoothing of the data of the RIfS array can improve the calibration (similar to the last section). More details of the data sets can be found in section 4.5.2.4.

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