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Ph. D. ThesisPh. D. Thesis 4. Experiments, Setups and Data Sets 4. Experiments, Setups and Data Sets 4.1. The Sensor Principle4.1. The Sensor Principle
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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
    4.1. The Sensor Principle
    4.2. SPR Setup
    4.3. RIfS Sensor Array
    4.4. 4l Miniaturized RIfS Sensor
    4.5. 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
  10. Results – Various Aspects of the Frameworks and Measurements
  11. Summary and Outlook
  12. References
  13. Acknowledgements
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4.   Experiments, Setups and Data Sets

4.1.   The Sensor Principle

For this work data sets were used, which were measured by several different setups to demonstrate that the principles of data analysis introduced in this study can be generally applied to a broad spectrum of devices. All setups belong to the category of the sensor devices, which transform chemical information into physically measurable quantities. Sensors allow establishing durable and economical devices for fast measurements without the need of sample pretreatments rendering these devices ideal for monitoring environmental pollutions, for process monitoring and for all kinds of continuous on-line and in-line measurements. Depending on the sensitive layer, which is responsible for the recognition of the chemical properties, the sensors are often divided into two groups: The sensors belonging to the first group have polymers, metals or metal oxides as sensitive layers. These sensors are called chemosensors. The sensitive layers of the second group of sensors use biochemical interactions like antigen – antibody or DNA – DNA interactions resulting in the name biosensor. In this work, data sets are analyzed for the detection of gases and vapors of volatile organic compounds. These data sets were measured by colleagues using 3 different devices, which are all based on polymer chemosensors. The polymer-based chemo­sensors recognize the presence of analyte by changes of the thickness and changes of the refractive index of the sensitive layer when analyte sorbs into the sensitive polymer layer.  Two devices are based on the Reflectometric Interference Spectroscopy (RIfS) as detection principle and one device is based on the Surface Plasmon Resonance Spectroscopy (SPR).

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