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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 9.2.6. Conclusions9.2.6. 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
      9.2.1. 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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9.2.6.   Conclusions

Similar to the previous chapters, it has been demonstrated that both, the genetic algorithm framework and the parallel growing neural network framework are superior to the common methods of multivariate calibration in terms of calibration quality. Both frameworks allow a reproducible variable selection resulting in smaller optimized neural network models with a better generalization ability compared with common neural networks. Both frameworks show reproducible and robust results whereby the parallel growing neural network framework seems to be more robust and shows a slightly better generalization ability, which has also been observed for the binary mixtures of the refrigerants. The classical chemometric methods like the PLS and INLR once more have problems to model the nonlinear relationships of the data similar to chapter 6. It was also shown that the PCA-NN could not deal with changes of the noise in the data resulting in significant biases of the predictions.

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