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Ph. D. ThesisPh. D. Thesis 1. Introduction1. Introduction
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Ph. D. Thesis
  Table of Contents
  1. Introduction
    1.1. Outline
  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
  10. Results – Various Aspects of the Frameworks and Measurements
  11. Summary and Outlook
  12. References
  13. Acknowledgements
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1.   Introduction

During the last century, the instrumentation of analytical chemistry has dramatically changed. Advances in classical analytical setups, developments of new devices and applications of new measurement principles allow the acquisition of more information about an analytical problem in a shorter time. Faster working equipments and the parallelizing of devices enable measurements of more samples making in depth examinations of complex systems possible. State of the art devices allow the acquisition of more detailed information about samples by utilizing more wave­lengths or additional sensors. Finally yet importantly, new measurement principles such as time-resolved measurements render the access to new sources of information possible.

This constantly increasing flood of information puts a new challenge to the field of data analysis, which can be considered as the link between the raw information provided by the instrumentation and the questions to be answered for the analyst. Being so universal the data analysis has many facets in the different areas of analytical chemistry such as qualitative analysis, quantitative analysis, optimization problems, identification of significant factors and many more. The diversity of data analysis for analytically relevant questions is also reflected in a number of different names for the same discipline like chemometrics, chem(o)­informatics, bioinformatics, biometrics, environmetrics, and data mining.

This work covers a wide variety of aspects of data analysis for chemical sensor systems ranging from the introduction and optimization of new measurement procedures to the preprocessing of the raw sensor signals and from the calibration of the sensors to the identification of important factors. Being interconnected and thus influencing each other, all these aspects have to be considered when setting up a sensor system for a certain analytical task. However, the main objectives of this work can be subsumed into two focuses.

The first focus is the introduction and optimization of kinetic measurements in chemical sensing. Thereby the effect is exploited that different analytes show different kinetics of sorption into the sensor coatings. This allows access to a completely new domain of information compared with commonly used measurement procedures of chemical sensing. The new approach of time-resolved measurements uses the kinetic information of the sensor responses not for the investigation of the interaction kinetics of the analytes with the sensor coatings but for the quantitative determination of several analytes in mixtures. In contrast to some rare reports found in literature, which use the kinetic information as a given phenomenological effect to improve the multi-analyte quantification, a systematic investi­gation of the principles of time-resolved measurements is performed in this work. Thereby different aspects are investigated such as the interaction principles, the optimization of the measurement parameters, the relationships between the time-resolved sensor responses and the analytes, the transfer of the measurement principles to different setups and to different analytes and many more. This systematic investigation demonstrates that the principle of time-resolved measurements forms the basis for a simultaneous quantification of several analytes by single sensor systems. It is furthermore shown that sensor arrays also profit from this approach by the possibility of identifying and quantifying more analytes than before for a given sensor array setup. Consequently, this approach generally allows the reduction of the number of sensors resulting in smaller devices and less costs for the hardware. The systematic investigation also demonstrates that the principle is a very powerful and generic approach not limited to the setups, analytes, and interaction principles used in this study.

The large amount and the complexity of the data generated by time-resolved measure­ments necessitate the second focus of this work, which is the application and optimization of natural computing methods for the data analysis of sensors. The expression "natural computing" primarily refers to two concepts of computing copied from nature. The concept of neural networks has been inspired by the highly interconnected neural structures in the brain and the nervous system of mammals, whereas the concept of genetic algorithms has been inspired by the evolution in biology. For the data analysis in this study, the neural networks are used for the calibration of the data.  It is demonstrated in this work that only the neural networks out of many multivariate calibration methods are capable of calibrating the nonlinear relationship between the sensor responses and the concentrations of the different analytes. Genetic algorithms are applied for the identification and selection of significant factors respectively variables and thus for the optimization of the calibration. Yet, it is shown that an often-reported combination of both concepts is faced with several problems with respect to the limited number of measurements. Thus, several frameworks are developed, implemented and optimized in this work, which use data sets limited in size in a very efficient way. These frameworks contain neural networks for the calibration, genetic algorithms respectively growing neural networks for the selection of significant variables and additional procedures and approaches from statistics and chemometrics for significance test. These new frameworks are designed to fulfill the needs of analytical chemistry such as a high per­formance of data analysis, an easy application of the algorithms, a portability to a wide range of data sets and devices, an insight into the models built, an identification of important factors, a high robustness to noise in the data and the ability to cope with data sets limited in size. The frameworks are applied to several data sets, which were recorded by different devices in our laboratory. Two data sets have an environmental background based on the recycling of old refrigerants of air-conditioners and refrigerators. Additionally, the homolo­gous series of the lower alcohols was measured several times allowing a systematic investigation of the time-resolved measurements. For all data sets under investigation, the frameworks show excellent results for calibration and variable selection. The frameworks also demonstrate that there are several possibilities to tweak the time-resolved measurements with respect to measurement time, properties of the sensitive layers, carrier gas and much more. The frameworks developed in this work are not limited to the calibration and optimization of sensor data, but can be used for virtually any multivariate calibration.

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