University of Kentucky UKnowledge University of Kentucky Doctoral Dissertations Graduate School 2007 SIMULATIONS-GUIDED DESIGN OF PROCESS ANALYTICAL SENSOR USING MOLECULAR FACTOR COMPUTING Bin Dai University of Kentucky, [email protected] Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Recommended Citation Dai, Bin, "SIMULATIONS-GUIDED DESIGN OF PROCESS ANALYTICAL SENSOR USING MOLECULAR FACTOR COMPUTING" (2007). University of Kentucky Doctoral Dissertations. 483. https://uknowledge.uky.edu/gradschool_diss/483 This Dissertation is brought to you for free and open access by the Graduate School at UKnowledge. It has been accepted for inclusion in University of Kentucky Doctoral Dissertations by an authorized administrator of UKnowledge. For more information, please contact [email protected]. ABSTRACT OF DISSERTATION Bin Dai The Graduate School University of Kentucky 2007 SIMULATIONS-GUIDED DESIGN OF PROCESS ANALYTICAL SENSOR USING MOLECULAR FACTOR COMPUTING ABSTRACT OF DISSERTATION A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Chemistry at the University of Kentucky By Bin Dai Lexington, Kentucky Director: Dr. Robert A. Lodder, Professor of Chemistry Lexington, Kentucky 2007 Copyright © Bin Dai 2007 ABSTRACT OF DISSERTATION SIMULATIONS-GUIDED DESIGN OF PROCESS ANALYTICAL SENSOR USING MOLECULAR FACTOR COMPUTING Many areas of science now generate huge volumes of data that present visualization, modeling, and interpretation challenges. Methods for effectively representing the original data in a reduced coordinate space are therefore receiving much attention. The purpose of this research is to test the hypothesis that molecular computing of vectors for transformation matrices enables spectra to be represented in any arbitrary coordinate system. New coordinate systems are selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a spectrometer. A novel integrated sensing and processing system, termed “Molecular Factor Computing (MFC)” based near infrared (NIR) spectrometer, is proposed in this dissertation. In an MFC -based NIR spectrometer, spectral features are encoded by the transmission spectrum of MFC filters which effectively compute the calibration function or the discriminant functions by weighing the signals received from a broad wavelength band. Compared with the conventional spectrometers, the novel NIR analyzer proposed in this work is orders of magnitude faster and more rugged than traditional spectroscopy instruments without sacrificing the accuracy that makes it an ideal analytical tool for process analysis. Two different MFC filter-generating algorithms are developed and tested for searching a near-infrared spectral library to select molecular filters for MFC-based spectroscopy. One using genetic algorithms coupled with predictive modeling methods to select MFC filters from a spectral library for quantitative prediction is firstly described. The second filter-generating algorithm designed to select MFC filters for qualitative classification purpose is then presented. The concept of molecular factor computing (MFC)-based predictive spectroscopy is demonstrated with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument. KEYWORDS: chemometrics, process analytical technology (PAT), near-infrared (NIR), genetic algorithm (GA), integrated sensing and processing (ISP). Bin Dai ____________________________________________ March 5, 2006 ____________________________________________ SIMULATIONS-GUIDED DESIGN OF PROCESS ANALYTICAL SENSOR USING MOLECULAR FACTOR COMPUTING By Bin Dai Robert A. Lodder ____________________________________________ Director of Dissertation Robert B. Grossman ____________________________________________ Director of Graduate Studies March 5, 2007 ____________________________________________ RULES FOR THE USE OF DISSERTATIONS Unpublished dissertations submitted for the Doctor's degree and deposited in the University of Kentucky Library are as a rule open for inspection, but are to be used only with due regard to the rights of the authors. Bibliographical references may be noted, but quotations or summaries of parts may be published only with the permission of the author, and with the usual scholarly acknowledgments. Extensive copying or publication of the dissertation in whole or in part also requires the consent of the Dean of the Graduate School of the University of Kentucky. A library that borrows this dissertation for use by its patrons is expected to secure the signature of each user. Name Date DISSERTATION Bin Dai The Graduate School University of Kentucky 2007 SIMULATIONS-GUIDED DESIGN OF PROCESS ANALYTICAL SENSOR USING MOLECULAR FACTOR COMPUTING DISSERTATION A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Chemistry at the University of Kentucky By Bin Dai Lexington, Kentucky Director: Dr. Robert A. Lodder, Professor of Chemistry Lexington, Kentucky 2007 Copyright © Bin Dai 2007 For Xuebei (Kathy) Shi Acknowledgements I must first thank my advisor, Rob, for all his support and patient guidance over the course of this research. I also thank other members of my committee, Sylvia Daunert, Leonidas G. Bachas and Craig C. Douglas, for their help and time. I would like to thank all fellow members of the Analytical Spectroscopy Research Group (ASRG) at the University of Kentucky, whose support and cooperation were essential for this research work. Special thanks to my best friends and colleagues, Aaron Urbas and Joseph Medendorp, for their invaluable support and friendship during these years. To my family; I owe everything to them for making me what I am today. iii TABLE OF CONTENTS Acknowledgements........................................................................................................................ iii List of Table ................................................................................................................................... vi List of Figure................................................................................................................................. vii List of Abbreviations...................................................................................................................... ix List of Files .................................................................................................................................... xi Preface........................................................................................................................................... xii Chapter One: Near Infrared Spectroscopy and Chemometrics in Process Analytical Technology Background..........................................................................................................................1 Process Analytical Technology ............................................................................................2 Near Infrared Spectroscopy and Imaging in PAT ................................................................4 Chemometrics in PAT ..........................................................................................................8 Chapter Two: An Introduction to Molecular Factor Computing Based Spectroscopy and Integrated Computational Imaging Introduction........................................................................................................................21 Spectral Feature Encoding with Molecular Factor Computing .........................................22 Spectral Feature Encoding with Digital Mirror Array.......................................................31 The MFC Spectrometer Prototype.....................................................................................36 Near Field Scanning Microscopy using MFC...................................................................38 Conclusion .........................................................................................................................40 Chapter Two Figure...........................................................................................................43 Chapter Three: Genetic Algorithm Based Multivariate Linear Regression for Molecular Filter Selection in Molecular Factor Computing Introduction........................................................................................................................48 Theory................................................................................................................................52 Experimental Section.........................................................................................................56 Results and Discussion......................................................................................................59 Conclusion .........................................................................................................................64 Chapter Three Tables .........................................................................................................66 Chapter Three Figures........................................................................................................69 Chapter Four: Genetic Algorithm Based Linear Discriminant Analysis for Molecular Filter iv Selection in Molecular Factor Computing Introduction........................................................................................................................84