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Ph. D. ThesisPh. D. Thesis 9. Results  All Data Sets9. Results All Data Sets 9.1. Methanol and Ethanol by SPR9.1. Methanol and Ethanol by SPR 9.1.5. Conclusions9.1.5. Conclusions
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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
      9.1.1. Single Analytes
      9.1.2. Parallel Growing Neural Network Framework
      9.1.3. Sensitivity Analysis
      9.1.4. Brute Force Variable Selection
      9.1.5. Conclusions
    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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9.1.5.   Conclusions

It has been shown that mixtures of methanol and ethanol vapors can be quantified by the use of a single SPR sensor coated with Makrolon and a subsequent data analysis by neural networks. Compared with fully connected neural networks the variable selection and calibration by the parallel growing neural network framework again showed several benefits. First, the calibration was significantly better with impressive low prediction errors, which were only slightly higher than the standard deviations of the signals of reproduced measurements. Secondly, the small number of variables of the optimized networks allowed an insight into the calibration model using a visual interpretation, whereby each variable could be assigned a chemical sense. Finally, the comparison with a brute force variable selection demonstrated that the parallel growing neural network framework selected the probably most predictive combination of 3 variables.

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