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Ph. D. ThesisPh. D. Thesis 5. Results  Kinetic Measurements5. Results Kinetic Measurements 5.2. Time-resolved Sensor Measurements5.2. Time-resolved 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
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5.2.   Time-resolved Sensor Measurements

Recently a new approach has been proposed by several groups for reducing the number of required sensors [204]-[214]. This approach is based on the exploitation of the time specific information of sensor responses. If various analytes show different kinetics for the sorption into the sensitive layer, the resulting sensor response recorded versus time features a different shape for these analytes. This additional temporal information can render the parallel information of different sensors in an array redundant allowing reducing the number of the sensors. For that purpose the shape of the sensor responses has to be digitized by recording the sensor responses over time (further referred to as time points) and by performing a multivariate analysis of these time points. These time-resolved measurements were performed in various areas of sensor research. For example, Yan et al. [204] quantified binary mixtures of solvents in water by a single reflectometric interference spectroscopic sensor whereby the time shift of the highest signal after analyte exposure depended on the composition of the mixture similar to gas or liquid chromatography. The components of binary and ternary mixtures of organic analytes in water could also be determined by the use of a single amperometric sensor [207],[214]. Thereby, the consumption of oxygen by the metabolism of microorganisms with different time constants for the analytes was detected. In the gaseous phase, time-resolved measurements were used in combination with sensor arrays to obtain additional variables. Using time-resolved measurements with an array of quartz microbalances coated with three different polymer films the classification of six solvent vapors was improved compared with the classification using only the saturation mass [215]. Johnson et al. [210] classified 20 different analytes with only 4 fiber optic sensors and also classified these analytes semi-quantitatively into low, medium and high analyte concentrations using ten fiber optic sensors with 90 % of the test data being assigned to the correct concentration class. Podgorsek et al. [216] used a glassy polyimide for the detection of methanol and ethanol and showed the difference of the response times, which could be used for the discrimination of both analytes. However, a detection of both analytes in mixtures was not performed.

Yet, all these publications used the time-resolved measurements as phenomenological tool and no systematic research was performed concerning the optimization of the different mechanisms and components like the time delaying effects, the time-resolution of the recorded sensor response, the feature extraction, the data preparation and the multivariate data analysis, which was rather basic in most of the publications cited. For example in most of the approaches the sensor responses were recorded using a rather coarse time resolution [205],[206],[212] or even a coarse resolution combined with other more or less arbitrary features [210],[211],[217]. Although a coarse time-resolution grants an easily manageable quantity of information, the risk of losing important information can be high, especially if several analytes show similar or very fast sensor responses. Thus, a fine time grid should be generally preferred, which nevertheless needs a more sophisticated data analysis. Additionally, all approaches cited above were isolated applications and no transfer to other systems was performed.

In this work, an extensive and systematic research on time-resolved measurements is performed from the investigation of the effects causing the time delays to an optimization of the data analysis. The principle of time-resolved measurements is applied to several analytical problems and to several different sensor devices. In this work, the time-delaying effects are based on a microporous polymer. For the data analysis several methods are developed, which allow a highly efficient evaluation of a fine time grid of the sensor responses. These methods are embedded in frameworks, which allow a simultaneous variable selection and calibration of nonlinear data and which can be applied to any linear and nonlinear multivariate relationship.


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