Multianalyte quantifications by means of integration of artificial neural networks, genetic algorithms and chemometrics for time resolved analytical data [Elektronische Ressource] = Multi-Analyt-Quantifizierungen mit Hilfe der Integration von künstlichen neuronalen Netzen, genetischen Algorithmen und der Chemometrie für zeitaufgelöste analytische Daten / vorgelegt von Frank Jochen Dieterle

Frank Dieterle

Informations

• Publié par: eberhard_karls_universitat_tubingen
• Publié le: 01 janvier 2003
• Nombre de lectures: 29
• Langue: Deutsch
• Poids de l'ouvrage: 3 Mo

Extrait

Multianalyte Quantifications by Means of
Integration of Artificial Neural Networks,
Genetic Algorithms and Chemometrics for
Time-Resolved Analytical Data


Multi-Analyt Quantifizierungen mit Hilfe der
Integration von künstlichen neuronalen Netzen,
genetischen Algorithmen und der Chemometrie
für zeitaufgelöste analytische Daten




DISSERTATION



der Fakultät für Chemie und Pharmazie
der Eberhard-Karls-Universität Tübingen

zur Erlangung des Grades eines Doktors
der Naturwissenschaften



2003



vorgelegt von

Frank Jochen Dieterle











































Tag der mündlichen Prüfung: 25. Juli 2003

Dekan: Prof. Dr. H. Probst

1. Berichterstatter: Prof. Dr. G. Gauglitz

2. Berichterstatter: PD Dr. U. Weimar

3. Berichterstatter: Prof. Dr. J. GasteigerTable of Contents 3
Table of Contents

1. INTRODUCTION_______________________________________________ 6
2. THEORY – FUNDAMENTALS OF THE MULTIVARIATE DATA ANALYSIS10
2.1. Overview of the Multivariate Quantitative Data Analysis_________________________ 10
2.2. Experimental Design ______________________________________________________ 11
2.3. Data Preprocessing _______________________________________________________ 12
2.4. Data Splitting and Validation _______________________________________________ 13
2.5. Calibration of Linear Relationships __________________________________________ 16
2.6. Calibration of Nonlinear Relationships _______________________________________ 19
2.7. Neural Networks – Universal Calibration Tools ________________________________ 20
2.8. Too Much Information Deteriorates Calibration 24
2.9. Measures of Error and Validation ___________________________________________ 38
3. THEORY – QUANTIFICATION OF THE REFRIGERANTS R22 AND
R134A: PART I_______________________________________________ 39
3.1. Experimental ____________________________________________________________ 39
3.2. Single Analytes __________________________________________________________ 40
3.3. Sensitivities _____________________________________________________________ 44
3.4. Calibrations of the Mixtures ________________________________________________ 45
3.5. Variable Selection by Brute Force ___________________________________________ 48
3.6. Conclusions 48
4. EXPERIMENTS, SETUPS AND DATA SETS _______________________ 50
4.1. The Sensor Principle ______________________________________________________ 50
4.2. SPR Setup ______________________________________________________________ 51
4.3. RIfS Sensor Array ________________________________________________________ 52
4.4. 4λ Miniaturized RIfS Sensor ________________________________________________ 53
4.5. Data Sets _______________________________________________________________ 54
5. RESULTS – KINETIC MEASUREMENTS __________________________ 60
5.1. Static Sensor Measurements _______________________________________________ 60
5.2. Time-resolved Sensor Measurements ________________________________________ 61 4 Table of Contents

5.3. Makrolon – A Polymer for Time-resolved Measurements ________________________ 63
5.4. Conclusions _____________________________________________________________ 73
6. RESULTS – MULTIVARIATE CALIBRATIONS______________________ 74
6.1. PLS Calibration __________________________________________________________ 74
6.2. Box-Cox Transformation + PLS _____________________________________________ 80
6.3. INLR____________________________________________________________________ 82
6.4. QPLS ___________________________________________________________________ 83
6.5. CART 84
6.6. Model Trees _____________________________________________________________ 86
6.7. MARS 88
6.8. Neural Networks__________________________________________________________ 90
6.9. PCA-NN _________________________________________________________________ 91
6.10. Neural Networks and Pruning_______________________________________________ 92
6.11. Conclusions 94
7. RESULTS – GENETIC ALGORITHM FRAMEWORK _________________ 96
7.1. Single Run Genetic Algorithm ______________________________________________ 96
7.2. Genetic Algorithm Framework - Theory ______________________________________ 98
7.3. Genetic Algorithm Framework - Results _____________________________________ 102
7.4. Genetic Algorithm Framework – Conclusions ________________________________ 106
8. RESULTS – GROWING NEURAL NETWORK FRAMEWORK_________ 107
8.1. Modifications of the Growing Neural Network Algorithm _______________________ 108
8.2. Application of the Growing Neural Networks _________________________________ 109
8.3. Growing Neural Network Algorithm Frameworks______________________________ 112
8.4. Applications of the Growing Neural Network Frameworks ______________________ 115
8.5. Conclusions and Comparison of the Different Methods ________________________ 121
9. RESULTS – ALL DATA SETS __________________________________ 123
9.1. Methanol and Ethanol by SPR _____________________________________________ 123
9.2. Methanol, Ethanol and 1-Propanol by SPR ___________________________________ 129
9.3. Methanol, Ethanol and 1-Propanol by the RIfS Array and the 4λ Setup____________ 137 Table of Contents 5
9.4. Quaternary Mixtures by the SPR Setup and the RIfS Array _____________________ 144
9.5. Quantification of the Refrigerants R22 and R134a in Mixtures: Part II_____________ 148
10. RESULTS – VARIOUS ASPECTS OF THE FRAMEWORKS AND
MEASUREMENTS ___________________________________________ 149
10.1. Single or Multiple Analyte Rankings ________________________________________ 149
10.2. Stopping Criteria for the Parallel Frameworks ________________________________ 150
10.3. Optimization of the Measurements _________________________________________ 152
10.4. Robustness and Comparison with Martens' Uncertainty Test ___________________ 155
11. SUMMARY AND OUTLOOK ___________________________________ 156
12. REFERENCES ______________________________________________ 161
13. PUBLICATIONS_____________________________________________179
14. ACKNOWLEDGEMENTS______________________________________181
15. APPENDIX_________________________________________________183 6 1. Introduction

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 1. Introduction 7
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