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Ph. D. ThesisPh. D. Thesis 5. Results  Kinetic Measurements5. Results Kinetic Measurements 5.1. Static Sensor Measurements5.1. Static Sensor Measurements
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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
    5.1. Static Sensor Measurements
    5.2. Time-resolved Sensor Measurements
    5.3. Makrolon A Polymer for Time-resolved Measurements
    5.4. Conclusions
  6. Results Multivariate Calibrations
  7. Results Genetic Algorithm Framework
  8. Results Growing Neural Network Framework
  9. Results All Data Sets
  10. Results Various Aspects of the Frameworks and Measurements
  11. Summary and Outlook
  12. References
  13. Acknowledgements
Research Tutorials
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5.1.   Static Sensor Measurements

The quantification of multiple analytes in mixtures by sensors is faced by several problems. For the determination of the regression parameters of the MLR (expression (7)) and of all other multivariate calibration methods as many sensor signals as analytes to be quantified are needed since otherwise the set of equations would be statistically underdetermined [197]. In real world measurements, even more signals are needed as noise, drifts, interfering unknown analytes and other non-ideal influences "consume" additional degrees of freedom. Typically, the sensors used are not selective for a single analyte also known as crossreactivity. The resulting covariance of the sensor signals causes additional problems for the calibration resulting in the need of as many sensors as possible, which show different sensitivity patterns for the analytes of interest. Thus, the common approach to quantify several analytes simultaneously is the combination of many sensors on a sensor array with the sensors showing different sensitivity patterns for the different analytes and a subsequent multivariate data analysis [198]-[202]. An advantage of this unspecific multisensor approach is the possibility of using the same array setup for many different analytes without the need of finding specific sensor materials [203]. Usually only one single feature of the sensor response (value of the sensor signal) is extracted, such as the height of the sensor response at equilibrium or the slope of the sensor response resulting in the need of more sensors in the sensor array than the number of analytes to be quantified.


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