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Ph. D. ThesisPh. D. Thesis 9. Results  All Data Sets9. Results All Data Sets 9.4. Quaternary Mixtures by the SPR Setup and the RIfS Array 9.4. Quaternary Mixtures by the SPR Setup and the RIfS Array 9.4.3. Conclusions9.4.3. Conclusions
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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
      9.4.1. Introduction
      9.4.2. Results
      9.4.3. Conclusions
    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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9.4.3.   Conclusions

It was demonstrated that by the use of time-resolved measurements quaternary mixtures can be quantified, which were measured by sensor setups employing less sensors than analytes to be quantified. Thereby the 1-sensor SPR setup practically achieves the same results than the RIfS array using 3 sensors, whereas the evaluation of single RIfS sensors showed worse results than the SPR setup. The evaluation of single time points of the 3-sensor RIfS setup, which corresponds to the common static sensor evaluation, showed unacceptably bad results, as the data analysis was mathematically underdetermined (3 sensor responses for 4 analytes). This demonstrates how the principle of time-resolved measurements can help to reduce the number of sensors and thus the hardware costs. The principle allows the quantitative determination of systems, which would never have been quantified using static measurements. Similar to section 9.3, it was demonstrated that the influence of smoothing the time-resolved sensor responses is two-sided. Smoothing of very noisy sensor responses of thin sensitive layers improves the calibration whereas smoothing of sensor responses with a high signal to noise ratio adversely influences the calibration. Additionally, it was demonstrated once more that the frameworks introduced in this work help to improve the multivariate calibration of the sensor signals of all data sets investigated.

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