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Real-Time Wavelet Compression and Self-Modeling Curve

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Real-Time Wavelet Compression and Self-Modeling Curve REAL-TIME WAVELET COMPRESSION AND SELF-MODELING CURVE RESOLUTION FOR ION MOBILITY SPECTROMETRY A dissertation presented to the faculty of the College of Arts and Sciences of Ohio University In partial fulfillment of the requirements for the degree Doctor of Philosophy Guoxiang Chen March 2003 This dissertation entitled REAL-TIME WAVELET COMPRESSION AND SELF-MODELING CURVE RESOLUTION FOR ION MOBILITY SPECTROMETRY BY GUOXIANG CHEN has been approved for the Department of Chemistry and Biochemistry and the College of Arts and Sciences by Peter de B. Harrington Associate Professor of Chemistry and Biochemistry Leslie A. Flemming Dean, College of Arts and Sciences CHEN, GUOXIANG. Ph.D. March 2003. Analytical Chemistry Real-Time Wavelet Compression and Self-Modeling Curve Resolution for Ion Mobility Spectrometry (203 pp.) Director of Dissertation: Peter de B. Harrington Chemometrics has proven useful for solving chemistry problems. Most of the chemometric methods are applied in post-run analyses, for which data are processed after being collected and archived. However, in many applications, real-time processing is required to obtain knowledge underlying complex chemical systems instantly. Moreover, real-time chemometrics can eliminate the storage burden for large amounts of raw data that occurs in post-run analyses. These attributes are important for the construction of portable intelligent instruments. Ion mobility spectrometry (IMS) furnishes inexpensive, sensitive, fast, and portable sensors that afford a wide variety of potential applications. SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) is a self-modeling curve resolution method that has been demonstrated as an effective tool for enhancing IMS measurements. However, all of the previously reported studies have applied SIMPLISMA as a post-run tool. A modified SIMPLISMA algorithm, referred to as RTSIMPLISMA, was developed for modeling IMS data in real-time. The real-time algorithm can determine the number of components in the IMS data automatically. Resolved concentration and spectral profiles are simultaneously displayed on a virtual instrument while the data is collected from an ion mobility spectrometer. The computational burden for real-time SIMPLISMA increases when the collected number of spectra grows in size. A spectrum will not be acquired when the data processing consumes too large a share of computer resources. To alleviate this problem, a two-dimensional wavelet compression (WC2) was applied prior to RTSIMPLISMA modeling. The optimal settings of WC2-RTSIMPLISMA for processing IMS data were obtained, by which satisfactory models could be resolved when the data was compressed to 1/256. A novel real-time WC2 has been developed to compress data as it is acquired from IMS sensors. RTSIMPLISMA was applied to the WC2 processed data in real-time, by which the real-time modeling could be significantly accelerated. An integrated software package was developed to implement the real-time WC2-RTSIMPLISMA algorithm and used for the rapid processing of the IMS data of drugs and explosives. The real-time algorithm was able to disclose the very small features in the IMS data and rapidly model the dynamic changes during an IMS measurement course. Approved: Peter de B. Harrington Associate Professor of Chemistry and Biochemistry 5 Acknowledgments I would like to thank my research advisor, Dr. Peter de B. Harrington, for his invaluable support and guidance during my stay at Ohio University. This dissertation could not have been written and the research could not have been accomplished without his help. I would also like to thank my dissertation committee members, Drs. Gary W. Small, Howard D. Dewald, Martin T. Tuck, Wen-jia R. Chen and Xiaozhuo Chen, for their great help in my academic progress and research pursuits. Paul Schmittauer is thanked for his assistances in electronic techniques. I would like to thank the Department of Chemistry and Biochemistry at Ohio University for offering me the opportunity to conduct my doctoral research. The Center for Intelligent Chemical Instrumentation at Ohio University is thanked for supporting the conference trips. Ohio University is thanked for the support of Donald R. Clippinger Fellowship. The US Army ERDEC, GeoCenters, and Ion Track Instruments are thanked for the partial support of this research. Metara Inc. is thanked for supporting me to write this dissertation while working. Dr. Willem Windig at Eigenvector Research Inc. is thanked for his permission for me to use the spectral data files and MATLAB scripts. I would also thank the members in Dr. Harrington’s research group for their helpful suggestions. Special thanks are given to Libo Cao for her consistent help over the years. Dr. Tricia L. Buxton Derringer is also thanked for the bacterial data set. I would like thank Zhuo Chen for her love, encouragement, and valuable support. I would like to thank my father and the other family members who are always caring and supportive in my life. 6 Table of Contents Page Abstract...............................................................................................................................3 Acknowledgments...............................................................................................................5 List of Tables ......................................................................................................................9 List of Figures...................................................................................................................10 List of Abbreviations ........................................................................................................18 Chapter 1 Introduction............................................................................................... 21 1.1 General Statement..................................................................................... 21 1.2 Ion Mobility Spectrometry........................................................................ 23 1.3 Self-Modeling Curve Resolution .............................................................. 26 1.4 Data Compression..................................................................................... 29 1.5 The Research Objectives........................................................................... 32 Chapter 2 SIMPLISMA and Wavelet Transform...................................................... 34 2.1 SIMPLISMA............................................................................................. 34 2.2 Wavelet Transform ................................................................................... 48 Chapter 3 Real-Time Self-Modeling Mixture Analysis ............................................ 59 3.1 Introduction............................................................................................... 59 7 3.2 Theory....................................................................................................... 61 3.3 Experimental Section................................................................................ 63 3.4 Results and Discussion............................................................................. 67 3.5 Conclusions............................................................................................... 86 Chapter 4 RTSIMPLISMA Applied to Two-Dimensional Wavelet Compressed Ion Mobility Data........................................................................................................ 87 4.1 Introduction............................................................................................... 87 4.2 Theory....................................................................................................... 91 4.3 Experimental Section................................................................................ 94 4.4 Results and Discussion............................................................................. 97 4.4.1 Conventional SIMPLISMA Models...................................................97 4.4.2 Optimization of WC2-RTSIMPLISMA 103 4.4.3 RTSIMPLISMA Applied to Windig Standard Data Sets 117 4.5 Conclusions............................................................................................. 137 Chapter 5 Real-Time Two-Dimensional Wavelet Compression and Its Application to Real-Time Self-Modeling of IMS data............................................................... 143 5.1 Introduction............................................................................................. 143 5.2 Theory..................................................................................................... 144 5.3 Experimental Section.............................................................................. 148 8 5.4 Results and Discussion........................................................................... 151 5.4.1 Time Performance of Real-Time WC2-RTSIMPLISMA .................151 5.4.2 Enhanced IMS Measurement by Real-Time WC2-RTSIMPLISMA156 5.4.3 Real-Time Self-Modeling of IMS Data of Explosives .....................164 5.4.4 Internal Reference Method for Real-Time WC2-RTSIMPLISMA...172 5.5 Conclusions............................................................................................. 186 Chapter 6 Summary and Future Work..................................................................... 187 References ................................................................................................................. 191 Appendix A: Publications.............................................................................................
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