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Ph. D. ThesisPh. D. Thesis 9. Results  All Data Sets9. Results All Data Sets 9.2. Methanol, Ethanol and 1-Propanol by SPR9.2. Methanol, Ethanol and 1-Propanol by SPR
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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
      9.2.1. Single Analytes
      9.2.2. Multivariate Calibrations of the Mixtures
      9.2.3. Genetic Algorithm Framework
      9.2.4. Parallel Growing Neural Network Framework
      9.2.5. PCA-NN
      9.2.6. Conclusions
    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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9.2.   Methanol, Ethanol and 1-Propanol by SPR

Although the measurements of methanol, ethanol and 1-propanol by a single sensor SPR device can be seen as an extension of the previous sections with 1 more analyte to be quantified, this data set was recorded as the first data set of the series. Thereby the measurement parameters like the thickness of the sensitive layers, the number of recorded time points and the measurement time had been less optimized. Details of the measurements and data sets are explained in section and in [194]. An interesting point with consequences for some data analysis methods is the fact that the validation data set in contrast to the calibration data set was measured by averaging 2 repeated measurements. Several common data analysis method, like PLS, INLR, NN, PCA-NN and the new methods like the genetic algorithm framework and the parallel growing neural network framework are applied to the data set. This allows a comparison of the performance of the different methods on the basis of a second data set in addition to the refrigerant data.

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