APPLICATIONS OF SOLID PHASE MICROEXTRACTION WITH ION AND DIFFERENTIAL MOBILITY SPECTROMETRY FOR THE STUDY OF JET FUELS AND ORGANOPHOSPHONATES

Transcription

1 APPLICATIONS OF SOLID PHASE MICROEXTRACTION WITH ION AND DIFFERENTIAL MOBILITY SPECTROMETRY FOR THE STUDY OF JET FUELS AND ORGANOPHOSPHONATES 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 Preshious R. A. Rearden March 2006

2 This dissertation entitled APPLICATIONS OF SOLID PHASE MICROEXTRACTION WITH ION AND DIFFERENTIAL MOBILITY SPECTROMETRY FOR THE STUDY OF JET FUELS AND ORGANOPHOSPHONATES by PRESHIOUS R. A. REARDEN has been approved for the Department of Chemistry and Biochemistry and the College of Arts and Sciences by Peter de B. Harrington Professor of Chemistry and Biochemistry Benjamin M. Ogles Interim Dean, College of Arts and Sciences

3 REARDEN, PRESHIOUS R. A. Ph.D. March Analytical Chemistry Application of Solid Phase Microextraction with Ion and Differential Mobility Spectrometry for the Study of Jet Fuels and Organophosphonates. (166 pp.) Director of Dissertation: Peter de B. Harrington Solid phase microextraction (SPME) with ion and differential mobility spectrometry (IMS and DMS) was investigated for forensics studies. SPME is a rapid extraction technique. IMS and DMS are ambient pressure separation techniques. IMS characterizes ions based on differences in gas-phase mobilities in weak electric fields and DMS in alternating strong and weak electric fields. Development of SPME/IMS and SPME/gas chromatography (GC)/DMS systems are addressed in this dissertation. SPME with IMS was explored for detection of chemical warfare agents in soil. The analytes used in this study were diisopropyl methylphosphonate (DIMP), diethyl methylphosphonate (DEMP), and dimethyl methylphosphonate (DMMP). A thermal desorption inlet was developed to interface SPME with a hand held ion mobility spectrometer. SPME-IMS offered good repeatability and detection of DIMP, DEMP, and DEMP in soil at concentrations as low as 10 µg/g. Fuel and volatile organic compounds (VOCs) studies were examined by GC-DMS. A micromachined differential mobility spectrometer with a 10.6 ev photoionization source was used as the GC detector. GC-DMS produces second-order data that is applicable to chemometric analysis. Savitzky- Golay filters were explored as a tool for data smoothing of jet fuel data

4 obtained with GC-DMS. Covariance maps were proposed for data visualization. Improved chromatographic resolution and signal-to-noise ratios were achieved with Savitzky-Golay filters. Fuels were also examined by GC-DMS. Fuzzy rule-building expert system (FuRES) was used as a pattern recognition method to classify gas chromatograms of fuels. Variations in day-to-day sample collection were evaluated with analysis of variance-principal component analysis (ANOVA- PCA). A classification rate of 95 ± 0.2% was obtained for the fuels using FuRES. Twelve samples collected one month later were classified correctly with the previously developed FuRES model. In addition to fuels, GC-DMS was used to characterize benzene, m- xylene, p-xylene, toluene, collectively referred to as BTX, and methyl tertbutyl ether (MTBE). Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and alternating least squares (ALS) were proposed to model and resolve overlapping chromatographic peaks. SIMPLISMA and ALS proved to be suitable tools to model GC-DMS data, modeling all components in a mixture of BTX and MTBE and resolving xylene isomers that co-eluted. Approved: Peter de B. Harrington Professor of Chemistry and Biochemistry

5 Acknowledgments I would first like to thank my research advisor, Dr. Peter B. Harrington for all the opportunities he has given me. His support and helpful comments guided me throughout my course of study. I would also like to thank my dissertation committee, Drs. Howard Dewald, Valerie Young, and Stephen Bergmeier. Your time and constructive criticisms were greatly appreciated. I want to give a special thanks to Dr. Bruce McCord for sharing your forensic knowledge and skills with me. The Center for Intelligent Chemical Instrumentation and Department of Chemistry and Biochemistry at Ohio University are acknowledged for their financial support. The USAF/WPAFB is also thanked for funding my research projects. Sionex is acknowledged for the use of the differential mobility spectrometer and Erkinjon Nazarov for his helpful suggestions. To all my group members, past and present, thank you for the friendships and helpful suggestions made while making time in the laboratory enjoyable. To all my friends and family thanks for your support throughout my education. Your encouraging words often gave me strength as I traveled along this academic journey. I am also grateful to Dr. David Young for his friendship and insightful advice. Olivier Collin and Allyson Kozak, I owe much gratitude to you both for going beyond the call of friendship. Finally, my deepest appreciation and love is given to my biggest supporters, my parents, Mary Ann and Arthur Rearden. Your love and support is dually noted. Thanks for your constant dedication and encouragement.

6 6 Table of Contents Page Abstract...3 Acknowledgments...5 List of Tables...10 List of Figures...11 List of Abbreviations...18 CHAPTER 1 INTRODUCTION INSTRUMENTAL PRINCIPLES AND PROCEDURES Solid Phase Microextraction Theory Instrumentation Operational Principles Extraction Modes Fibers Ion Mobility Spectrometry Operational Principles Ionization Ion Characterization Differential Mobility Spectrometry Operational Principles DATA ANALYSIS Savitzky-Golay Filters Multivariate Covariance Analysis of Variance-Principal Component Analysis Fuzzy Rule Building Expert System SIMPLISMA-ALS SIMPLISMA ALS...55

7 7 CHAPTER 2 RAPID SCREENING OF PRECURSOR AND DEGRADATION PRODUCTS OF CHEMICAL WARFARE AGENTS IN SOIL BY SOLID PHASE MICROEXTRACTION ION MOBILITY SPECTROMETRY (SPME-IMS) INTRODUCTION EXPERIMENTAL Reagents and Standards IMS SPME Material Thermal Desorption Unit Fiber Selection Extraction Time Profile Soil Analysis Calibration Procedures Analytes In Soil RESULTS AND DISCUSSION Reduced Mobilities Selection Of SPME fiber Extraction Time Calibration Curve Analysis Of Samples In Soil CONCLUSION...80 CHAPTER 3 MULTI-WAY PROCESSING OF GAS CHROMATOGRAPHY DIFFERENTIAL MOBILITY SPECTROMETRY DATA FOR CURVE RESOLUTION OF JET FUEL INTRODUCTION EXPERIMENTAL Chemicals GC-DMS System SPME Analysis...85

8 Data Analysis RESULTS AND DISCUSSION CONCLUSIONS CHAPTER 4 CLASSIFICATION OF FUELS USING SOLID PHASE MICROEXTRACTION GAS CHROMATOGRAPHY DIFFERENTIAL MOBILITY SPECTROMETRY INTRODUCTION EXPERIMENTAL Reagents Data Collection Data Processing Pattern Recognition Analysis RESULTS AND DISCUSSION GC-DMS Analysis ANOVA-PCA Analysis Classification Model CONCLUSIONS CHAPTER 5 MULTIVARIATE ANALYSIS OF VOLATILE ORGANIC COMPOUNDS BY GAS CHROMATOGRAPHY DIFFERENTIAL MOBILITY SPECTROMETRY INTRODUCTION EXPERIMENTAL Chemicals SPME Analysis GC-DMS Analysis Data Analysis RESULTS AND DISCUSSIONS Dispersion Voltage Study CONCLUSIONS

9 9 CHAPTER 6 SUMMARY AND FUTURE RESEARCH SUMMARY FUTURE RESEARCH

10 10 List of Tables Table Page 2 1. Calibration Curve Concentrations (ppm) for Chemical Warfare Precursor and Degradation Products CWA Precursor and Degradation Products Experimental Reduced Mobility Constants, (K 0 ) ab, Obtained for Each Method at 25 C Detection Limits for Chemical Warfare Precursor and Degradation Products Fuels Used in Training Set Confusion Matrix for Fourth Order Alignment of Fuels

11 11 List of Figures Figures Page 1 1. Schematic of the sampling assembly used for solid phase microextraction (SPME). Not drawn to scale. Composed of polymeric coated fiber, a plunger used to lower and retract the fiber, and an adjustable gauge to control the depth of the fiber in the sampling vial Schematic representation of headspace extraction. The extraction mode use for solid phase microextraction (SPME). Not drawn to scale. The fiber is exposed to the headspace of the analyte in the sampling vial for a predetermined amount of time. The fiber is then retracted and transferred to a desorption unit Adapted schematic (not drawn to scale) of the cross section of the Chemical Agent Monitor (CAM ). 33 The ion mobility spectrometer used for the detection of chemical warfare agents in soil. The analyte is drawn into the CAM where it is ionized by a 63 Ni source. The 63 Ni source emits β - particles that form both positive and negative reactant ions. The analyte undergoes charger transfer reactions with the reactant ions to form product ions. Separation of the product ions takes place in the drift region where ions are separated based on charge to size ratio Schematic of experimental setup (not drawn to scale) used for the detection of chemical warfare agents using solid phase microextraction (SPME) and ion mobility spectrometry (IMS). 34 The SPME fiber is thermally desorbed in the thermal desorption (TD) unit, where the analyte vapor is drawn into the ion mobility spectrometer (Chemical Agent Monitor (CAM )) and the data is collected using the data acquisition system (DAQ) Adapted conceptual graph of ion behavior in increasing electric fields. 42 The ratio of the mobility coefficients at high [K(E H )] and low [K(E L )] field deviates from unity at increasing electric field. The α values are the coefficients from Equation [1 16] An adapted cross-section schematic of a differential mobility spectrometer. 47 The sample enters the spectrometer where it is photoionized. The ions then pass between two electrodes where a dispersion voltage is applied by a radio-frequency (rf) generator and the compensation voltage, a direct current (dc), is superimposed on the asymmetric waveform. The ions that traverse the electrodes are detected by electrometers

12 1 7. Schematic of the experimental setup used for gas chromatography/differential mobility spectrometry (not drawn to scale). The differential mobility spectrometer (DMS) is interfaced to the gas chromatograph using stainless steel tubing and the data collected by a data acquisition system (DAQ). A picture of the actual GC-DMS interface is given above the experimental setup An adapted graphical representation of the analysis of varianceprincipal component analysis (ANOVA-PCA) model. 56 This model was applied to the gas chromatography differential mobility spectrometry (GC- DMS) dataset. In the model, the original data matrix is decomposed to the source of variances Graphical representation of the mathematical model used in Simpleto-use interactive self-modeling mixture analysis (SIMPLISMA) and alternating least squares (ALS) as it relates to gas chromatography differential mobility spectrometry (GC-DMS). The original data matrix D is decomposed to the product of the concentration profile matrix C (GC chromatograms) and component spectra matrix S (DMS spectra) Flow chart representing the steps involved when SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) and alternating least squares (ALS) is applied to data for curve resolution. Data matrix D is decomposed to the product of two matrices: a concentration profile matrix C and component spectra matrix S by an iterative calculation Structures of phosphonate containing precursors and degradation products, dimethyl methylphosphonate (DMMP), diisopropyl methylphosphonate (DIMP), and diethyl methylphosphonate (DEMP) used in the chemical warfare study The effect of SPME fiber type on extraction efficiency of diethyl methylphosphonate (DEMP), diisopropyl methylphosphonate (DIMP) and dimethyl methylphosphonate (DMMP) at concentrations of 3.06 mg/ml, 2.86 mg/ml, and 3.33 mg/ml, respectively. The fibers evaluated were polydimethylsiloxane/divinylbenzene (PDMS/DVB) and polydimethylsiloxane (PDMS). Peak heights are reported with 95% confidence intervals. No significance difference in extraction efficiency can be observed for each fiber

13 2 3. Extraction time profiles obtained for dimethyl methylphosphonate (DMMP), diisopropyl methylphosphonate (DIMP) and diethyl methylphosphonate (DEMP) at 288, 306, and 287 mg/ml, respectively. Peak intensities are reported with 95% confidence intervals. The peak intensities level off around 15 min indicating equilibrium has been reached between the fiber and the headspace of the analyte Calibration curves obtained for (A)dimethyl methylphosphonate (DMMP); (B) diisopropyl methylphosphonate (DIMP) and (C) diethyl methylphosphonate (DEMP) from the soil matrix. All concentrations obtained using 95% confidence intervals. Each analyte curve is nearly linear in the concentration range between 10 and 70 ppm SPME-IMS spectra obtained for extraction of: (A) DMMP, (B) DEMP and (C) DIMP in soil. The first peak observed in each spectrum is the water reactant ion followed by the monomer of each analyte and the dimer peak at shorter reduced mobilities SPME-IMS spectrum obtained for extraction of mixture of DMMP, DEMP and DIMP in soil. All reduced mobilities are obtained using 95% confidence intervals. All standard deviations are less than ± A monomer and dimer peak is observed for each analyte in addition to mixed dimers at peaks 5 and 7. The DIMP-DMMP dimer peak is not shown but would occur in the same position as the DMMP dimer peak Normalized total ion current chromatograms obtained for JP-TS jet fuel (A) raw and (B) processed data using Savitzky-Golay filters. Data was processed using the second derivative of a quartic and cubic polynomial for the DMS and GC orders, respectively, with a 5 point data window for the retention time order and a 25 point data window for compensation voltage. A decrease in noise is observed in the second derivative plots (B) Normalized total ion current chromatograms obtained for Jet A 3639 jet fuel (A) raw and (B) processed data using Savitzky-Golay filters. Data was processed using the second derivative of a quartic and cubic polynomial for the DMS and GC orders, respectively, with a 5 point data window for the retention time order and a 25 point data window for compensation voltage. A decrease in noise is observed in the second derivative plots (B)

14 3 3. Normalized total ion current chromatograms obtained for JP5 jet fuel (A) raw and (B) processed data using Savitzky-Golay filters. Data was processed using the second derivative of a quartic and cubic polynomial for the DMS and GC orders, respectively, with a 5 point data window for the retention time order and a 25 point data window for compensation voltage. A decrease in noise is observed in the second derivative plots (B) Normalized total ion current chromatogram windows obtained for JP-TS jet fuel (A) raw and (B) processed data using Savitzky-Golay filters. The retention time window corresponds to 11.6 min to 16.1 min. An increase in the sharpness and preservation of location of the maximum of each peak in the jet fuel datasets is observed Normalized total ion current chromatogram windows obtained for Jet A 3639 jet fuel (A) raw and (B) processed data using Savitzky-Golay filters. The retention time window corresponds to 11.6 min to 16.1 min. An increase in the sharpness and preservation of location of the maximum of each peak in the jet fuel datasets is observed Normalized total ion current chromatograms window obtained for JP5 jet fuel (A) raw and (B) processed data using Savitzky-Golay filters. The retention time window corresponds to 11.6 min to 16.1 min. An increase in the sharpness and preservation of location of the maximum of each peak in the jet fuel datasets is observed A comparison of the positive differential mobility spectra of JP-TS for the largest chromatographic peak. The spectra were normalized to unit vector length. Sharpening of the maximum spectral peak is observed after applying the Savitzky-Golay filter A comparison of the positive differential mobility spectra of Jet A 3639 for the largest chromatographic peak. The spectra were normalized to unit vector length. Sharpening of the maximum spectral peak is observed after applying the Savitzky-Golay filter A comparison of the positive differential mobility spectra of JP5 for the largest chromatographic peak. The spectra were normalized to unit vector length. Sharpening of the maximum spectral peak is observed after applying the Savitzky-Golay filter

15 3 10. Square root of the covariance for the raw (A) and processed (B) positive differential mobility spectrometry factor of JP-TS. The peaks in the diagonal correspond to the variance of the variables and the off diagonal peaks correspond to cross-variance variables. Narrower peaks are observed in the processed data after applying the Savitzky-Golay filter Square root of the covariance for the raw (A) and processed (B) positive differential mobility spectrometry factor of Jet A The peaks in the diagonal correspond to the variance of the variables and the off diagonal peaks correspond to cross-variance variables. Narrower peaks are observed in the processed data after applying the Savitzky-Golay filter Square root of the covariance for the raw (A) and processed (B) positive differential mobility spectrometry factor of JP5. The peaks in the diagonal correspond to the variance of the variables and the off diagonal peaks correspond to cross-variance variables. Narrower peaks are observed in the processed data after applying the Savitzky-Golay filter Square root of the covariance for the raw (A) and processed (B) chromatography factor of JP-TS. The peaks in the diagonal correspond to the variance of the variables and the off diagonal peaks correspond to cross-variance variables. Sharpening of the chromatographic data is observed in the processed data after applying the Savitzky-Golay filter Square root of the covariance for the raw (A) and processed (B) chromatography factor of Jet A The peaks in the diagonal correspond to the variance of the variables and the off diagonal peaks correspond to cross-variance variables. Sharpening of the chromatographic data is observed in the processed data after applying the Savitzky-Golay filter Square root of the covariance for the raw (A) and processed (B) chromatography factor of JP5. The peaks in the diagonal correspond to the variance of the variables and the off diagonal peaks correspond to cross-variance variables. Sharpening of the chromatographic data is observed in the processed data after applying the Savitzky-Golay filter

16 4 1. ANOVA-PCA score plot giving the date experimental factor. There is an obvious difference in data collected on Day 1 (101605). The first and second principal components account for 31% of the total variance. Percentage of principal components given in parenthesis with the absolute variance. A 95% confidence interval is drawn around the mean of the each day Representative gas chromatograms of fuels: A) commercial, B) jet propellant, C) rocket propellant, and D) diesel. These chromatograms are representative of all the chromatograms obtained with GC-DMS Principal component analysis scores for training dataset. Each letter represents a replicate of the fuel sample. This graph indicates no apparent separation of fuel type. The first and second principal components account for 45% of the total variance. Percentage of principal components given in parenthesis with the absolute variance. A 95% confidence interval is drawn around the mean of the each class The effect of polynomial order on alignment of spectral data for the classification of fuels using fuzzy rule-building expert systems (FuRES). The fourth order polynomial provided the best fit for the classification of the jet fuel data. Each result is reported with 95% confidence intervals FuRES classification tree for twelve fuels with a 95% classification rate, Nc= number of samples, H= entropy values. The numbers indicate the number of rules used to build the tree. There is no splitting of the fuels among the leaf (circle) node. All fuels were separated using this 11 rule tree Spectra for benzene at dispersion voltages (A) 1500 V, (B) 1300 V, (C) 1100 V, (D) 1200 V, and (E) 900 V. The compensation voltage with a 95% confidence interval for the monomer peak on the left in each spectrum is given above. Shifts in the monomer peak to more negative compensation voltages and the dimer peak to more positive compensation voltages at increasing dispersion voltages is observed Spectra for methyl tert-butyl ether (MTBE) at dispersion voltages (A) 1500 V, (B) 1300 V, (C) 1100 V, (D) 1200 V, and (E) 900 V. The compensation voltage with a 95% confidence interval for the monomer peak on the left in each spectrum is given above. Shifts in the monomer peak to more negative compensation voltages and the dimer peak to more positive compensation voltages at increasing dispersion voltages is observed

17 5 3. Spectra for toluene at dispersion voltages (A) 1500 V, (B) 1300 V, (C) 1100 V, (D) 1200 V, and (E) 900 V. The compensation voltage with a 95% confidence interval for the monomer peak on the left in each spectrum is given above. Shifts in the monomer peak to more negative compensation voltages and the dimer peak to more positive compensation voltages at increasing dispersion voltages is observed Spectra for p-xylene at dispersion voltages (A) 1500 V, (B) 1300 V, (C) 1100 V, (D) 1200 V, and (E) 900 V. The compensation voltage with a 95% confidence interval for the monomer peak on the left in each spectrum is given above. Shifts in the monomer peak to more negative compensation voltages and the dimer peak to more positive compensation voltages at increasing dispersion voltages is observed Spectra for m-xylene at dispersion voltages (A) 1500 V, (B) 1300 V, (C) 1100 V, (D) 1200 V, and (E) 900 V. The compensation voltage with a 95% confidence interval for the monomer peak on the left in each spectrum is given above. Shifts in the monomer peak to more negative compensation voltages and the dimer peak to more positive compensation voltages at increasing dispersion voltages is observed Spectra for (A) m-xylene, (B) p-xylene, (C) toluene, (D) methyl tert-butyl ether (MTBE) and (E) benzene at dispersion voltage 1200 V. The compensation voltage with a 95% confidence interval for the monomer peak on the left in each spectrum is given above Co-elution of m-xylene and p-xylene at 5 mg/ml depicted in a (A) chromatogram and (B) snapshot of contour plot from the instrumental graphical display. Only one peak can be seen in the chromatogram. The contour plot shows only two peaks instead of the four peaks (two monomers and dimers) that are expected from the mixing of two compounds in this system SIMPLISMA-ALS profiles for m-xylene and p-xylene mixture. Two peaks are observed in the chromatographic (A) and spectral profiles (B) after SIMPLISMA-ALS modeling SIMPLISMA-ALS spectral profile of a mixture of benzene, methyl tert-butyl ether (MTBE), m-xylene, and p-xylene

18 18 List of Abbreviations AChE... acetylcholinesterase ALS...alternating least squares ANOVA-PCA... analysis of variance-principal component analysis APCI...atmospheric pressure chemical ionization BTX... benzene, toluene, xylene CAM... chemical agent monitor CE... capillary electrophoresis CI...confidence interval C v...compensation voltage CWA... chemical warfare agent CWC... Chemical Weapons Convention DAD...diode array detector DAQ... data acquisition dc... direct current DEMP... diethyl methylphosphonate DIMP...diisopropyl methylphosphonate DMMP...dimethyl methylphosphonate DMS...differential mobility spectrometry DVB... divinylbenzene EPA... Environmental Protection Agency FAIMS...field asymmetric ion mobility spectrometry FIS...field ion spectrometry FuRES... fuzzy rule building expert systems GA...O-ethyl N,N-dimethyl phosphoramidocyanidate (tabun)

19 19 GB... O-isopropyl methylphosphonofluoridate (sarin) GC... gas chromatography GD... 1,2,2-trimethylpropyl methylphosphonofluoridate (soman) GF... cyclohexyl methylphosphonofluoridate HPLC... high performance liquid chromatography HS-SPME... headspace solid phase microextraction IMS... ion mobility spectrometry K...reduced mobility K 0... reduced mobility constant MCR... multivariate curve resolution MS... mass spectrometry MTBE... methyl tert-butyl ether NMR... nuclear magnetic resonance PA... polyacrylate PCT... principal component transform PDMS...polydimethylsiloxane rf... radio frequency RF-IMS... radio frequency ion mobility spectrometry RIP...reactant ion peak SIMPLISMA...Simple-to-use interactive self-modeling mixture analysis SNR...signal-to-noise-ratio SPME... solid phase microextraction std. dev... standard deviation TD...thermal desorption t r... retention time

20 20 USAF/WPAFB...United States Air Force/Wright-Patterson Air Force Base VOC...volatile organic compound VX...O-ethyl S-(2-diisopropylaminoethyl) methylphosphonothioate

21 21 Chapter 1 Introduction Sample preparation is an essential step in many analytical methods. According to a 1991 survey about chromatographic methods, sample preparation accounts for two-thirds of all analysis time. 1 Poor sample preparation can lead to unacceptable measurement uncertainty and additional costs. 2 The ideal sample preparation method would allow removal of interferents, pre-concentration of analytes, have the ability to convert analytes into a more suitable form for detection, and be robust and reproducible. Solid phase microextraction (SPME) is a sample preparation technique that can address all these pre-sampling needs. SPME has been used in combination with many analytical instruments including gas chromatography (GC) 3, high performance liquid chromatography (HPLC) 4, capillary electrophoresis (CE) 5-7, and ion mobility spectrometry (IMS) 8-10 for applications in many fields. The focus of this dissertation is the use of (SPME) with ion mobility spectrometry (IMS) and differential mobility spectrometry (DMS) for forensic analysis. In addition, this work focused on data preprocessing of complex forensic data sets. The forensic problems examined were fuel identification, and detection of chemical warfare agents (CWAs) and volatile organic compounds (VOCs). The research conducted for each forensic study is documented in this dissertation. This dissertation consists of six chapters. An introduction to the instrumental methods and data analysis techniques is presented in Chapter 1. Chapter 2 details the use of solid phase microextraction (SPME)

22 22 coupled to ion mobility spectrometry (IMS) to detect precursor and degradation products of chemical warfare agents (CWAs) in soil. The multi-way preprocessing of gas chromatography differential mobility spectrometry (GC-DMS) data is presented in Chapter 3. Identification of fuels by GC-DMS is given in Chapter 4. Multivariate curve resolution of complex datasets from VOCs obtained with GC-DMS is presented in Chapter 5 and the overall summary will be given in Chapter 6. The publications and presentations associated with this dissertation work are presented in the Appendix. 1.1 INSTRUMENTAL PRINCIPLES AND PROCEDURES Solid Phase Microextraction Solid phase microextraction (SPME), developed by Pawliszyn and coworkers at the University of Waterloo, Ontario, in the 1990s, is a fast sample preparation and introduction technique. 11 SPME is a solventless extraction technique that utilizes a fused silica fiber coated with a polymer to extract analytes from liquid, gas, and solid matrices. SPME was originally used in the field of environmental analysis 12, 13 to extract organic compounds from water and has since been successfully applied to many fields. Forensic analysis is one such field that has seen an increase in the use of SPME. 14 Recently, SPME has been used for explosives 15, 16, arson 17, and drugs analysis. In this dissertation, SPME was combined with ion mobility spectrometry (IMS) and differential mobility spectrometry (DMS) for forensic analysis. The use of SPME with IMS was investigated for

23 23 detection of chemical warfare agents (CWAs) in soil and with DMS for characterization of jet fuels and volatile organic compounds (VOCs) Theory Partitioning of analytes between the sample matrix and fiber coating is the basic principle of SPME. SPME is an equilibrium process with multiple phases. Direct extraction is a two phase system: the fiber coating and the sample matrix. Headspace extraction is a three phase system: the fiber coating, sample matrix, and headspace. According to Louch et al., 12 the mass balance equation that relates the analyte Concentration before and after extraction in a two phase system is given in Equations [1 2] and [1 2], n + C V = C V [1 1] s s 0 s n = C V [1 2] f f for which C s is the concentration of the analyte in the sample at equilibrium, C 0 is the initial concentration of the analyte, C f is the equilibrium concentration of analyte in the fiber coating, V s and V f are the volume of sample matrix and fiber coating, respectively; and n the amount of analyte being absorbed by the fiber. The partition coefficient (K fs ) of the analyte between the fiber coating and sample is defined by the equilibrium concentration of analyte in the fiber coating (C f ) and the equilibrium concentration of the analyte in sample (C s ). This relationship is given below in Equation [1 3], assuming

24 24 that the sample matrix is a single homogenous phase and no headspace exists in the sampling system. K fs C C [1 3] = f s The mass can further be expressed using the fiber/sample matrix partition coefficient (K fs ), initial concentration of the sample (C 0 ), and volume of the sample matrix (V s ) by substituting Equation [1 3] in Equation [1 1]. This relationship is given below in Equation [1 4]. n = KfsVf C0Vs K V + V [1 4] fs f s When the volume of the fiber coating is small compared to the volume of sample matrix, (V s >> V f ), then the amount of analyte extracted is independent of the sample volume and Equation [1 4] simplifies to: n K [1 5] = VC fs f 0 In a three phase system the headspace must be accounted for in the sampling system and the headspace/sample matrix partition coefficient (K hs ) and headspace volume (V h ) must be considered. Equation [1 4] is then rewritten as n = KfsKhsVf C0Vs K K V + K V + V [1 6] fs hs f hs h s Instrumentation SPME has many advantages as a sample preparation method such as low cost, simplicity, easy automation, improved sensitivity and on-site sampling ability. A schematic of the SPME sampling assembly used in this dissertation is given in Figure 1 1. The SPME device consists of a holder

25 25 containing an adjustable depth gauge, stainless steel plunger and a needle. The SPME fiber, typically polymer coated fused silica (1 cm in length), connects to the plunger. The needle, with the fiber retracted, is used to pierce the septum of the sampling vial or analytical instrument such as a GC. The height of the stainless steel needle can be adjusted by the gauge on the SPME holder. The fiber is exposed through the stainless steel needle to the sample matrix by depressing the plunger and removed from the sampling vial by retracting the plunger. The manual SPME holder and fibers used in this study were purchased from Supelco Inc. (Bellefonte, PA). Plunger Adjustable Gauge SPME Fiber Holder SPME Coated Fiber Figure 1 1. Schematic of the sampling assembly used for solid phase microextraction (SPME). Not drawn to scale. Composed of polymeric coated fiber, a plunger used to lower and retract the fiber, and an adjustable gauge to control the depth of the fiber in the sampling vial.

26 Operational Principles SPME sampling is a two step process involving extraction and desorption. The extraction process in SPME is based on an equilibrium process between the sample and the polymeric coated fiber. During extraction, the fiber is exposed to the sample matrix and the analytes are isolated via a partitioning process between the polymer-coated fiber and the sample matrix. Once equilibrium is reached the fiber is retracted into the needle and removed from the sampling vial. The needle is then introduced into an analytical instrument and the fiber exposed. Desorption of the analyte occurs and further analysis can be carried out. SPME combines sampling, extraction and preconcentration into a single step which reduces analysis time. This sample preparation technique requires little or no organic solvent thus eliminating waste. SPME is easily automated, and is ideally suited for use in the field Extraction Modes There are three basic extraction modes used in SPME that include direct, membrane, and headspace extraction. In direct extraction, the SPME fiber is inserted directly into a sample matrix. For headspace extraction, the fiber is placed in the headspace or gas phase of the sample. Membrane extraction is an indirect extraction method in which a membrane barrier separates the fiber from the sample while performing direct extraction. All SPME sampling in this dissertation was performed using static headspace extractions. 21 All extractions were performed in 4

27 27 ml amber vials with polytetrafluoroethylene lined silicone septa screw top caps (Supelco Inc., Bellefonte, PA). There are three phases involved in headspace SPME. These phases are the fiber coating, headspace, and sample matrix. The analytes first diffuse to the headspace from the sample matrix and then to the fiber coating. A depiction of headspace analysis is given in Figure 1 2. SPME Fiber Holder SPME Fiber Headspace Sampling Vial Figure 1 2. Schematic representation of headspace extraction. The extraction mode use for solid phase microextraction (SPME). Not drawn to scale. The fiber is exposed to the headspace of the analyte in the sampling vial for a predetermined amount of time. The fiber is then retracted and transferred to a desorption unit.

28 Fibers Fiber selection depends on the application. Several factors are considered when choosing a fiber such as coating type (polar or nonpolar), coating volume (influences sensitivity), and coating thickness (influences extraction time). The like prefers like rule is used in fiber selection. Polar fibers are more effective for extracting polar analytes and nonpolar fibers for nonpolar analytes. Several different fiber coatings are commercially available, such as polydimethylsiloxane (PDMS), polydimethylsiloxane/divinylbenzene (PDMS/DVB), polyacrylate (PA), and polyethylene glycol/divinylbenzene (carbowax/dvb), and carbon molecular sieves/polydimethylsiloxane, (carboxen/pdms). Analytes are either absorbed or adsorbed on the fiber depending on the fiber type. PDMS and PA fibers extract analytes via partitioning and the other coatings via adsorption. A PDMS, non-polar, non-porous, liquid coated fiber with 100 µm film thickness, was used for all studies in this dissertation due to the chemical nature of each of the analytes Ion Mobility Spectrometry Ion mobility spectrometry (IMS) is an ambient pressure ionseparation technique that characterizes chemical substances using gasphase mobilities of ions in weak electric fields. IMS was introduced as an analytical technique in the early 1970s. 22 It has found widespread application in both law enforcement and forensic fields. IMS has become a prevalent technique for the rapid and sensitive detection of trace

29 29 amounts of explosives 23-25, drugs of abuse 26-28, and chemical warfare agents Bench top and portable ion mobility spectrometers are available for use depending on the application. The Chemical Agent Monitor (CAM ), a handheld ion mobility spectrometer, was used for the IMS studies in this dissertation. The CAM is a military issued IMS. The CAM is often used by the military for chemical weapon detection such as during the Gulf War for nerve and blister agents. 32 An adapted schematic of the CAM 33, type N (Graseby Ionics, Watford, Herts, U.K.), is given in Figure 1 3. The CAM was coupled with SPME for detection of chemical warfare agents (CWAs) in soil. The experimental setup for the SPME-IMS study is given in Figure 1 4.

30 30 63 Ni Source Shutter Grid Aperture Grid Drift Region Membrane Faraday Plate Ioniz a tion Reaction Vent Pump Drift Flow Molecular Sieves Figure 1 3. Adapted schematic (not drawn to scale) of the cross section of the Chemical Agent Monitor (CAM ). 33 The ion mobility spectrometer used for the detection of chemical warfare agents in soil. The analyte is drawn into the CAM where it is ionized by a 63 Ni source. The 63 Ni source emits β - particles that form both positive and negative reactant ions. The analyte undergoes charger transfer reactions with the reactant ions to form product ions. Separation of the product ions takes place in the drift region where ions are separated based on charge to size ratio.

31 31 DAQ Figure 1 4. Schematic of experimental setup (not drawn to scale) used for the detection of chemical warfare agents using solid phase microextraction (SPME) and ion mobility spectrometry (IMS). 34 The SPME fiber is thermally desorbed in the thermal desorption (TD) unit, where the analyte vapor is drawn into the ion mobility spectrometer (Chemical Agent Monitor (CAM )) and the data is collected using the data acquisition system (DAQ).

32 Operational Principles The CAM, like most ion mobility spectrometers, consists of electronics, a source, drift tube, pumps, and a membrane. The drift tube is comprises two regions that include the ionization and drift region separated by a shutter grid. For sampling, the analyte vapor is drawn into the instrument by a pump at 500 ml/min where it passes over a semi-permeable polydimethylsiloxane membrane that impedes water from entering the IMS. The analyte vapor is then transported into the ionization region by a carrier gas where it is ionized. The carrier gas used in the CAM is air and a 10 mci 63 Ni metal foil is the ionization source. The 63 Ni source emits β - particles that form both positive and negative reactant ions, by atmospheric pressure chemical ionization (APCI) reactions. The analyte vapor then undergoes competitive charge transfer reactions with the reactant ions to form product ions. The movement of the product ions from the ionization region to the drift region is controlled by a shutter grid. The ion shutter grid is a gold screen placed between the ionization and drift region that is held at potentials strong enough to reverse the electric field direction of the ions. Every 25 ms the potential across the ion shutter is lowered to ground for 100 µs and the product ions are admitted into the drift region. The potential on the shutter grid is then reapplied to close the access of the drift region to the

33 33 ions forming in the source. A countercurrent flow of drift gas (air) prevents any neutral species from entering the drift region. Separation and detection of the ions take place in the drift region. The drift region is comprises focusing rings and a Faraday plate. The equally spaced gold rings create an electric-field gradient from a series of constant voltage steps. The product ions traverse the drift region under the influence of the electric field gradient of 100 V/cm. The ions are separated in the drift region based on their cross- sectional size to charge ratios so that smaller ions will have faster velocities and shorter arrival times at the detector. The Faraday plate, located at the end of the drift region, is used to measure the ion current. A bias aperture grid is placed before the Faraday plate to shield the detector from the charges of the approaching ions. The polarity of the electric field determines whether positive or negative ions are detected. The readout of the IMS is a waveform that corresponds to the ion current sampled at discrete times with respect to the opening of the ion shutter. The drift gas and carrier gas are recirculated through the CAM after being cleaned with a mixture of Type 5A (1/16-inch Pellets, Acros Organics, New Jersey, USA) and activated 8/12 mesh (Supelco, Bellefonte, PA) molecular sieves Ionization The ionization process can be initiated by β - particles emitted from the 63 Ni source. The carrier gas is ionized to form reactant ions by APCI. A

34 34 reagent gas, added to the carrier gas, is often used to increase selectivity by altering the reactant ions. No reagent gas was used in the CAM therefore all reaction chemistry was based on water. The CAM is operated in either negative or positive mode by controlling the polarity of the electric field. The charge affinities of the analytes are considered when selecting positive and negative modes. Proton transfer is the principle APCI reaction for formation of positively charged product ions in positive mode. The major reactant ions formed in the positive mode are NH + 4, NO + and H + (H 2 O) n where n denotes the number of oligomeric water molecules. 35 Monomer ions (MH + ) are formed from neutral molecules (M) by the proton transfer reaction: H (H O) + M ƒ MH + nh O [1 7] n 2 Dimer ions (M 2 H + ) observed at higher analyte concentrations can be formed by MH + M M H [1 8] In negative mode the major reactant ions formed are O - 2 and (H 2 O) n O Negatively charged product ions may be formed when the analyte captures an electron emitted from the 63 Ni source - - e + M M [1 9] or from a charge transfer (Equation [1 10]) reaction. (H O) O + M M + O + nh O [1 10] n Clustering of product ions are observed in both positive and negative mode when moisture is present in the drift region. 36 Polar molecules like

35 35 water tend to form clusters with the analytes that can lead to a loss in resolution and change in drift times. Equation [1 11] is an example of a cluster formation n 2 2 ( HO) O +Mƒ M O + nho [1 11] in which the superoxide anion is clustered with water and its drift time will vary based on the relative humidity. This dependence on relative humidity of the positive and negative reactant ions is the principal reason that the reactant ion peak cannot be used to standardize drift times or calculate reduced mobilities Ion Characterization The reduced mobility K (cm 2 s -1 V -1 ) of an ion is determined by the drift velocity, (v, cm 2 s -1 ), measured under the influence of an electric field (E. V cm -1 ) at atmospheric pressure, given by Equation [1 12]. v = KE [1 12] d The velocity of the ion (v d ) is dependent upon four factors: collisional cross section, mass, temperature, and electrostatic interactions. 36 The time it takes the ion to traverse the length of the drift tube, (d, cm), is referred to as its drift time (t d, seconds). This relationship is given in Equation [1 13]. d K= te [1 13] d In IMS, reduced mobility constants (K 0 ) are used for identification purposes. This metric provides a basis for comparison of results by correcting for varying environmental and instrumental experimental

36 36 conditions. The reduced mobility is normalized to a standard pressure (P, 760 Torr) and temperature (T, 273 K) and can be calculated using Equation [1 14]. K P = K T 760 [1 14] The drift times of a reference ion with known mobility can be used to determine the reduce mobility of an analyte under a given set of temperature, pressure, drift tube length, and electric field gradient. The reference ion can be used to calibrate the mobility scale using Equation [1 15]. K (unknown) = 0 K (standard) t (standard) 0 d t (unknown) [1 15] d The reference analyte used in the IMS study is 2,4-dimethylpyridine also referred to as 2,4-lutidine. The proton-bound dimer peak of lutidine has a reduced mobility of 1.41 cm 2 V -1 s -1 that is immutable with respect to changes in humidity at the temperature used in the chemical warfare agent study Differential Mobility Spectrometry Differential Mobility Spectrometry (DMS) is an ambient pressure ionseparation technique that characterizes chemical substances using the differences in gas-phase mobilities of ions in alternating strong and weak electric fields. These fields are generated using a high frequency asymmetric waveform. The basic concept of separating ions based on ion mobility using a high frequency asymmetric field was introduced in 1993, by Buryakov and

37 37 38, 39 coworkers. Over the past decade, techniques based on this concept have been referred to by many names, such as field ion spectrometry (FIS) 40, ion non-linearity drift spectrometer 41, field asymmetric ion mobility spectrometry (FAIMS) 42, radio frequency ion mobility spectrometry (RF-IMS) 43 and differential mobility spectrometry (DMS) 44. Similar to IMS, DMS operates at atmospheric pressure. However, with DMS and unlike IMS, the gaseous ion separation occurs as ions are conveyed by a drift gas that is orthogonal to the applied electric field, (E); and does not require an ion-shutter, drift rings, or aperture grids. Conventional IMS operates at lower electric fields (less than 1 kv/cm) where the coefficient of mobility of an ion is independent of the electric field strength and the velocity of the ion is proportional to the electric field strength. In IMS, ions are pulsed or gated into a drift tube with a constant electric field gradient and separation occurs with respect to the ion mobilities as they travel through the drift tube to the detector. DMS utilizes much stronger electric fields (greater than 10 kv/cm) and separates a continuous stream of ions as they are carried by a drift gas between closely spaced electrodes. At higher electric field strengths there is a nonlinear dependence of ion mobility on the electric field. Ions will cluster with other neutral species in the drift gas. The field dependence of the ion mobility arises from changes in the composition of these clusters with respect to the local temperature of the ion. 45 When the stronger electric field acts on the ion, the ion travels faster, and the local temperature will increase

38 from the increased number of collisions with the drift gas. As the ion heats up it will decluster and thereby increase in mobility. decluster (i.e., shed intermolecularly bonded gas molecules), decrease in size, and thereby increase its mobility. Under low-strength fields, the ion velocity is decreased when the ion cools, reforms clusters with the neutral gas molecules, and returns to a slower mobility that is characteristic of low-strength fields. Dimer ions exhibit the reverse behavior. The ionic charge is not as accessible to the neutral gas molecules as with the monomer ions. Now, under high field conditions and an increased local temperature, the ion mobility decreases compared to the low-field mobility. This decrease in mobility is caused by the increased number of collisions and expanded crosssections of the hotter dimer ions. The relationship for the electric field dependence on the mobility coefficients is given in Equation [1 16] 2 4 E E = L 1 + α 2 + α [1 16] N N ( ) K( E ) K E H for which, K(E H ) and K(E L ) are the mobility coefficients under high and low field conditions, respectively; N is gas density, and α 2 and α 4 are coefficients of the even power series expansion. A simplification of Equation [1 16] is given below 38 ( ) K( E ) 1 K E H E = L + α N [1 17]

39 39 for which α (E/N ) characterizes the electric field dependence and the alpha values are characteristic of the ions. A rearrangement of Equation [1 17] gives the field dependence of α as it relates to changes in ion mobility. E α( ) = N KE ( ) KE ( ) H KE ( ) L L [1 18] An adapted schematic of three possible examples of dependence of ion mobility on electric field strength 42 is given in Figure 1 5.

40 40 Mobility coefficient ratio K(E H )/K(E L ) α >0 α ~0 α <0 Increasing electric field strength (V/cm) Figure 1 5. Adapted conceptual graph of ion behavior in increasing electric fields. 42 The ratio of the mobility coefficients at high [K(E H )] and low [K(E L )] field deviates from unity at increasing electric field. The α values are the coefficients from Equation [1 16] Operational Principles Planar and cylindrical are the two main electrode geometries used for the drift tube or ion filters of instruments that utilized the concept of separating ions using high frequency asymmetric fields. The cylindrical geometry allows radial focusing of ions in the drift region. 46 This focusing effect decreases ion diffusion losses therefore increasing sensitivity; however, simultaneous focusing of both positive and negative ions is not possible.

41 41 There is no focusing effect with the planar design; however, it allows simultaneous detection of positive and negative ions and is amendable to miniaturization through micromachining technology unlike the cylindrical design. 42 Both the planar and cylindrical designs have the same basic operational principles. DMS is microfabricated analyzer with a planar drift design that allows simultaneous characterization of both positive and negative ions. An adapted schematic of a differential mobility spectrometer is given in Figure The ions are carried by a drift gas between two parallel-plate electrodes spaced from 1 to 5 mm apart for which a high frequency asymmetric electric field is applied to one plate and the other is held at ground. This applied field, referred to as the dispersion or separation voltage, causes ions to under go fast oscillations perpendicular to the gas flow. Some ions traverse the sensor while others undergo a gradual net displacement towards one of the electrodes. The ions that are experiencing a net displacement in the direction of the electrodes with each cycle of the asymmetric waveform will eventually collide with the walls of the electrodes where they are neutralized and are no longer detectable. Only ions with a net displacement or differential mobility of zero will traverse between the electrodes. The net migration of the ions can be corrected with a compensation voltage (C v ) that is a weaker dc voltage superimposed on the high field frequency asymmetric field. This dc voltage corrects the path of an ion so that it no longer has a net migration towards an electrode and the ion

42 42 traverses the detection electrodes. A differential mobility spectrum is obtained by scanning the compensation voltage over a range of voltages. A peak at a given compensation voltage is a measure of the difference in the mobility of an ion at high (K(E H )) to low (K(E L )) electric fields and can be used to characterize the ion. A differential mobility spectrometer was investigated as a detector for gas chromatography (GC). All GC-DMS experiments were performed on a system consisting of a HP 5890A (Agilent Technologies, Palo Alto, CA) gas chromatograph interfaced to a differential mobility spectrometer (Model SDP- 1, Sionex Corporation, Bedford, MA). The differential mobility spectrometer comprised two micromachined parallel plate electrodes with a 1 mm flow channel and an ultraviolet lamp (10.6 ev) photoionization source. SPME was used as sample introduction method for the GC-DMS studies. Gas chromatography was carried out on a (5% diphenyl, 95% dimethyl polysiloxane cross-linked (Rtx -5MS; Restek Corporation, Bellefonte, PA) wall-coated open tubular column (30 m 0.25 mm i.d., 0.25 µm film thickness). The GC and DMS were interfaced using stainless steel tubing and a Swagelok (Solon, OH) tee union. The total transfer line length was 14 cm. The GC column penetrated 2.5 cm into the DMS inlet. A schematic of the experimental setup used for the GC-DMS studies is given in Figure 1 7. All GC-DMS data was acquired using a LabView (National Instruments, Austin, TX) virtual instrument (VI) software program on a laptop computer (Dell Inspiron 2600, Round Rock, TX) with an 1.20 GHz,

43 43 Intel Celeron processor interfaced to the GC-DMS via a data acquisition board type DAQCard-6024E (National Instruments, Austin, TX). The chromatographic data was stored as formatted text and converted to a Microsoft Office Excel workbook in Excel (Microsoft Office Excel 2003, Redmond, WA).

44 44 Sample In Ionization Source Tunable Ion Filter Electrodes Top Electrode Electrometer (- Ions) Drift Gas (+) V t 1 Bottom Electrode Electrometer (+ Ions) t 2 dc 0 V (-) V rf Figure 1 6. An adapted cross-section schematic of a differential mobility spectrometer. 47 The sample enters the spectrometer where it is photoionized. The ions then pass between two electrodes where a dispersion voltage is applied by a radio-frequency (rf) generator and the compensation voltage, a direct current (dc), is superimposed on the asymmetric waveform. The ions that traverse the electrodes are detected by electrometers.

45 45 Gas Chromatograph Heated Transfer Line Makeup Drift Gas DMS GC Effluent DAQ Figure 1 7. Schematic of the experimental setup used for gas chromatography/differential mobility spectrometry (not drawn to scale). The differential mobility spectrometer (DMS) is interfaced to the gas chromatograph using stainless steel tubing and the data collected by a data acquisition system (DAQ). A picture of the actual GC-DMS interface is given above the experimental setup.

46 DATA ANALYSIS Several chemometric techniques were used to evaluate the data in the GC-DMS studies. Chemometrics is a branch of chemistry that extracts information from chemical measurements. The chemometric field encompasses the use of mathematics, statistics, and computer science. Multivariate chemometric techniques including analysis of variance-principal component analysis (ANOVA-PCA), fuzzy rule building expert systems (FuRES), Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA), alternating least squares (ALS) and Savitzky-Golay filters were used to evaluate the chromatographic data obtained in the GC-DMS studies in this dissertation. In the first study, two-way Savitzky-Golay filters were investigated as a smoothing method for processing chromatographic data obtained with GC-DMS. In the second GC-DMS study, the fuel data were evaluated using ANOVA-PCA and FuRES. The third study investigated the used of SIMPLISMA and ALS for curve resolution of chromatographic peaks obtained from VOCs. A brief description of the chemometric techniques used for data analysis will be given in this section Savitzky-Golay Filters Savitzky-Golay filters 48 are polynomial filters that have had broad application for processing chemical signals This polynomial filter is a low-pass filter that removes high frequency noise components that can

47 47 dominate when derivatives are calculated without major loss of intensity. Coefficients of the polynomials are found by a least squares method used to fit the polynomial over a window of data points. New data points are obtained from the derivatives of each fitted polynomial. Two-dimensional Savitzky-Golay filters were used to process the GC- DMS data. GC-DMS provides two-way data, so by deriving a two-way dimensional polynomial filter, spectra acquired in a sequence can be used to smooth and assist in the second derivative calculation. Second derivatives are useful for increasing the peak capacity for measurements such as DMS that furnish broad peaks. The original Savitzky-Golay paper contains numerical errors 53-55, therefore, our filters calculate the coefficients as needed instead of using the published values. The two dimensional polynomial is obtained as follows: f(x,y) = b + b x + b x + b x + b xyk + b y i, j= o 0 1,0 2,0 3,0 1,1 0,3 3 [1 19] for which the fitted polynomial f(x,y) is a function of retention time (e.g., x) and compensation voltage (e.g., y). The order of the polynomial is given by p and q. The polynomial is fitted to an area of data points to provide an estimate of the central point. It is the cross-terms (i.e., terms of x and y) that give the two-way approach the extra statistical power as opposed to processing each factor separately.

48 Multivariate Covariance Multivariate covariance was used to visualize the jet fuel data. A covariance matrix for the retention time and spectral order was calculated and plotted. For GC-DMS, the data are organized in a matrix so that each column is a compensation voltage measurement and each row is a retention time measurement. The covariance of the original data matrix D is calculated for compensation voltage (CV) given the average ( d number (m) of rows as C CV 證 V T ( D d ) ( D d CV = m 1 and for the retention time (RT), given the average( d CV ) RT CV ) and [1 20] ) and number (n) of columns as T ( D d ) ( D d ) RT RT C = RT 識 T [1 21] n 1 The covariance plots resemble two dimensional NMR data and measure the covariance between DMS peaks with respect to compensation voltage or GC peaks with respect to retention time Analysis of Variance-Principal Component Analysis Analysis of variance-principal component analysis (ANOVA-PCA) is a method used to study covariance among variables. ANOVA-PCA was used to evaluate the chromatographic dataset for differences among the data collected on different days in the GC-DMS jet fuel characterization study. This method was developed in Harrington s group in 2004 and has been

49 49 successfully applied to the optimization of proteomic assay for biomarkers 56 and profiling changes in protein expression 57. ANOVA is a statistical technique for determining the significance of the differences between two or more means. PCA is technique used to reduce the number of variables in a dataset while retaining as much information or variation from the original dataset. PCA uses a mathematical procedure that transforms the original dataset from a larger number of correlated variables into a smaller new set of uncorrelated variables referred to as principal components (PC). The uncorrelated variables are linear combinations of the original variables. The principal components are ordered in reducing variability such that the first principal component accounts for the maximum amount of variability. In ANOVA-PCA, the original data matrix is decomposed into additive matrices corresponding to the experimental factors and the residual error. The partitioning of the data, depicted in Figure 1 8, allows the means of any experimental factor to be compared against the residual error. PCA is applied to each combination of experimental factor and residual error after data decomposition. ANOVA-PCA score plots are scatter plots of the data objects projected onto the two principal components that account for the largest variance. The different levels of the target factors with the residual variance can be compared. A plot of 95% confidence intervals obtained from the Hotelling T 2 statistic around the mean of the factors is used to visualize statistical significance.

50 50 Data Total Matrix = + Hypothesis Mean + Time Hypothesis + Time + Residual Error Figure 1 8. An adapted graphical representation of the analysis of variance-principal component analysis (ANOVA-PCA) model. 56 This model was applied to the gas chromatography differential mobility spectrometry (GC-DMS) dataset. In the model, the original data matrix is decomposed to the source of variances.

51 Fuzzy Rule Building Expert System A fuzzy rule building expert system (FuRES) is a classification algorithm that uses fuzzy logic instead of classical logic to classify data. Fuzzy logic, introduce by Zadeh in 1965, allows partial membership of an element in a set. 58 Classical logic only allows discrete membership, either giving two values for an element in a set, full or non-membership. However, fuzzy logic may also assign an intermediate membership value to an element in a set. Fuzzy data analysis has been successfully applied to pattern recognition and signal processing FuRES was used to classify fuel data obtained from a GC-DMS. FuRES builds a collection of membership functions in the form of an inductive classification tree for which each branch is a multivariate fuzzy rule. Membership functions are mathematical concepts that assign a value of 1 to members and 0 to nonmembers of a subset of a total population. Membership functions in FuRES allow values between 0 and 1 and provide a measure of the degree of similarity of elements in the total population with the subsets. Rules in the FuRES system define conditions and the corresponding executing command. The rules are inferred and outputs crisp numbers for the data set. A FuRES classification tree, obtained by minimizing the entropy of classification, is constructed to visualize classification. A confusion matrix, a matrix that contains information about actual and predicted classifications, is constructed to evaluate the classifier s

52 52 performance. In the confusion matrix, information about the occurrence of each element in the predicted class is located in the columns and for the actual class in the rows SIMPLISMA-ALS Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) 67 and alternating least squares (ALS) are multivariate chemometric techniques used to model experimental data. SIMPLISMA and ALS have been demonstrated as effective tools for enhancing compressed ion mobility spectra of chemical warfare agent simulants 68, determining the presence of interferents in pharmaceuticals 69, and deconvoluting nearinfrared spectroscopy data from epoxy resins 70 and infrared data from the on-line polycondensation reaction of bis(hydroxyethylterephthalate) 71. The objective of SIMPLISMA and ALS is to resolve large datasets into pure component spectra and concentration profiles. A mathematical representation of SIMPLISMA and ALS is given below in Equation [1 22]. T D = CS + E [1 22] For the GC-DMS experiments, D (m n) is the original data matrix where the rows (m) represent the spectra recorded at different retention times and the columns (n) chromatographic profiles recorded at different compensation voltages. Data matrix D is decomposed to the product of two matrices: a concentration profile matrix C and component spectra matrix S. The transposition of matrix S is indicated by the superscript T. Matrix E is

53 53 the residual error. The two new matrices, concentration profiles and component spectra, provide quantitative and qualitative information, respectively. A schematic representation of the multivariate model used for GC-DMS data analysis is given in Figure 1 9. A brief description of SIMPLISMA and ALS is given below. A more detailed description of SIMPLISMA 67 and ALS 72 can be found elsewhere. D=CS T + E DMS Error Retention Time Chromatogram DMS = + Spectrum Figure 1 9. Graphical representation of the mathematical model used in Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and alternating least squares (ALS) as it relates to gas chromatography differential mobility spectrometry (GC-DMS). The original data matrix D is decomposed to the product of the concentration profile matrix C (GC chromatograms) and component spectra matrix S (DMS spectra) SIMPLISMA Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA), developed by Winding and Guilment 67, is a multivariate curve resolution

54 54 algorithm based on the selection of pure variables or components. A pure variable is defined as a variable in which the intensity or contributions is due to only one component in a mixture. SIMPLISMA estimates C from Equation [1 22] using pure variables. This results in the intensity being proportional to the spectral concentration. In SIMPLISMA a pure variable can be determined without prior knowledge of the pure component. SIMPLISMA was used to find the pure variables in order to estimate the initial concentration of the GC-DMS dataset for ALS. In order to find the pure variables, the first step in data analysis was to estimate the initial value of pure components. An initial number of components were chosen and entered into the SIMPLISMA algorithm. The pure variable was then found from the maximum purity (p ij ) given by σ j p = w ij ij [1 23] µ + α j for which indices i and j represent the components and original variables (compensation voltages), respectively. The mean and the standard deviation calculated for the pure variable j are indicated by µ j and σ j, respectively; and the α is a noise correction term. The w ij is a weighted term based on the determinant spectrum that characterizes the variable j with respect to predetermined variables. The variable with the highest purity will be the first pure variable. The spectral matrix S from Equation [1 22] can be extracted from the concentration profiles by a least square method as follows:

55 55 T 1 T T S = ( C C) C D [1 24] The spectra is normalized to unity and the least square method is use to calculate new concentration profiles by T -1 C=DS(S S) [1 25] Visual inspection of the new concentration profiles and spectra is performed to see if the correct number of pure components was used in the SIMPLISMA algorithm. Spurious artifacts and loss of spectral resolution occurs in the profiles if the number of components is incorrect. If the initial estimate or pure components is incorrect a new estimate is used and the procedure repeated until the correct number of pure component is determined ALS Alternating least squares (ALS) is a multivariate modeling method. ALS was used to model the GC-DMS data set after using the SIMPLISMA algorithm. An initial estimate of pure components in the data set is needed for ALS analysis. SIMPLISMA is used to for initial estimations of C and S T and the pure components. ALS is a iterative process that uses the alternating least square algorithm to optimize the fit of the data matrix D from the calculated matrices C and S T. During the iterative process, non-negativity constraints for spectral intensity and analyte concentration are applied. This constraint forces all spectral values to be positive and avoids chemically meaningless data such as negative peaks. The iterative process is stopped when the residual sum of squares between D and the reconstituted data

56 56 matrix drops below a threshold or the iterations reach a predetermined number. The SIMPLISMA-ALS method is depicted in Figure 1 10.

57 57 D Input Number of Components SIMPLISMA Pure Variables C, S T Estimates ALS Non-Negativity Constraints C S T E + Figure Flow chart representing the steps involved when SIMPLe-touse Interactive Self-modeling Mixture Analysis (SIMPLISMA) and alternating least squares (ALS) is applied to data for curve resolution. Data matrix D is decomposed to the product of two matrices: a concentration profile matrix C and component spectra matrix S by an iterative calculation.

58 58 Chapter 2 Rapid Screening of Precursor and Degradation Products of Chemical Warfare Agents in Soil by Solid Phase Microextraction Ion Mobility Spectrometry (SPME-IMS) 2.1 INTRODUCTION Soil contamination with chemical warfare agents (CWAs) can be a major concern for civilian and military populations. Exposure to CWAs can cause adverse health related illnesses or even deaths. CWAs were first used on a large scale in World War I and since then have been employed several times in conflicts around the world The chemical properties, lethality, and history of these agents are well documented CWAs are classified as nerve, vesicant, blood, or pulmonary agents. CWA classification is based on their physiologic effect on the human body. Nerve agents are the most lethal of the CWAs deriving their toxicity from their ability to inactivate the enzyme acetylcholinesterase (AChE) resulting in cholinesterase inhibition 81. All nerve agents are organophosphate compounds and are generally subdivided into two classes referred to as G and V agents. The most common G agents are O-ethyl dimethylamidophosphorylcyanide (GA, tabun), O-isopropyl methylphosphonofluoridate (GB, sarin), 1,2,2-trimethylpropyl methylphosphonofluoridate (GD, soman), and cyclohexyl methylphosphonofluoridate (GF). The primary V agent is O-ethyl S-(2- diisopropylaminoethyl) methylphosphonothioate (VX). 29

59 59 The contamination of soil by CWAs and their precursors and/or degradation products are often associated with military or terrorist operations either in the storage, production, disposal or usage of CWAs. 82 The Chemical Weapons Convention (CWC) treaty, ratified in 1997, banned the development, production, and stockpiling of chemical weapons. 83 Soil analysis is an important component of monitoring compliance with the CWC. Rapid and sensitive screening methods are needed to detect CWAs in soil to verify compliance with the CWC and alert to any soil contamination as it relates to health issues. In this work the feasibility of using solid-phase microextraction ion mobility spectrometry (SPME-IMS) to detect chemical warfare (CW) precursor and degradation products in soil using thermal desorption was investigated. Detection of CWAs by IMS is well documented. SPME has been used to sample CWAs in air, water and soil SPME is easily automated, and is ideally suited for use in the field. The concept of coupling solid-phase microextraction (SPME) to ion mobility spectrometry (IMS) combines rapid sampling with rapid detection while improving sensitivity and selectivity. The SPME technique requires a thermal source in order to desorb the analyte from the fiber. A thermal desorption (TD) unit was developed in our lab to thermally desorb the analyte from the SPME fiber. The instrumental response of the IMS was evaluated using dimethyl methylphosphonate (DMMP), diisopropyl methylphosphonate (DIMP), and diethyl methylphosphonate (DEMP). DMMP is a precursor of GB production. 21

60 DIMP is a byproduct or impurity of GB production and DEMP is a degradation 60 product of VX. 87 The molecular structures of DMMP, DIMP and DEMP are presented in Figure 2 1. The phosphonate group found in each of the compounds used in this study is a good target for ionization by the reactant ion peak (RIP) in IMS. Two SPME fibers with different stationary phases, 100 µm PDMS and 65 µm PDMS/DVB, were evaluated to determine the optimal fiber for extracting the CW precursor and degradation products. Extraction time for each of the analyte was an experimental factor that was optimized.

61 Figure 2 1. Structures of phosphonate containing precursors and degradation products, dimethyl methylphosphonate (DMMP), diisopropyl methylphosphonate (DIMP), and diethyl methylphosphonate (DEMP) used in the chemical warfare study. 61

62 EXPERIMENTAL Reagents and Standards DEMP (98%) and DIMP (98%) were purchased from Lancaster Synthesis (Morecambe, England) and DMMP (97%) from Lancaster Synthesis (Windham, NH). The soil used in this study was Certified Referenced sandy loam clean soil #3 purchased from Resource Technology Corporation (Laramie, WY). Sandy lam soil is found on the grounds of the U.S. Army Aberdeen Proving Ground, Edgewood MD and often used in analysis of 82, 88, 89 CWAs. This facility was established as a research and testing center for chemical warfare agents in Methanol was purchased from Fischer (Fair Lawn, NJ). All analytes were used without any further purification IMS The IMS used in this study was the Chemical Agent Monitor (CAM ). The instrumental principles of the CAM are presented in and a schematic in Figure 1 3 of Chapter 1. The CAM was interfaced to an Intel Pentium II processor, 200 MHz and 64MB RAM computer via a data acquisition board type AT-MIO-16X (National Instruments, Austin, TX). A lab constructed virtual instrument (VI) software program developed in LabView 6.1 was used to acquire and process the data. The data acquisition rate was set at 80 khz and each spectrum consisted of 1500 data points.

63 63 MATLAB (Mathworks, Natick, MA) was also used to process data. All graphs were generated using Axum 7 (MathSoft, Cambridge, MA). Standard spectra of each CW precursor and degradation product were collected using the CAM to determine reduce mobilities, by sampling the headspace of the bottle cap in which the analyte of interest was stored. The CAM was allowed to collect approximately 100 blank spectra before the bottle cap was placed approximately 1 cm before the nozzle of the CAM. The headspace of the bottle cap was sampled for less then 1 second. The data acquisition was stopped after 1000 scans were collected. All experiments were conducted in triplicate using a random block design SPME Material Two different fibers were evaluated to determine their extraction efficiencies for the precursor and degradation products used in this study. The following fiber coatings with film thickness indicated were used: polydimethylsiloxane/divinylbenzene (PDMS/DVB, 65 µm), and polydimethylsiloxane (PDMS, 100 µm). Prior to use, the fibers were conditioned as recommended by the manufacturer in a HP5890 GC injector port at 250 C for 1 hour.

64 Thermal Desorption Unit The thermal desorption unit (TD) used for SPME-IMS analysis was constructed in the lab from a quartz glass tube (9 cm length x 8 mm o.d., 5 mm, i.d.). Flexible electric heating tape (Thermolyne BriskHeat, Dubuque, IA) was coiled along the quartz tube. The TD unit was heated to 200 C using a variable autotransformer power supply unit (Staco Inc, Dayton, OH). The TD unit was used to introduce the precursor and degradation products into the inlet of the CAM. The heated sample was desorbed off the SPME fiber and was pulled into the CAM by its sampling pumps as given in Figure 1 3. A schematic of the SPME/TD unit is given in Figure Fiber Selection The SPME fiber selection was accomplished in a simple system (no soil) by obtaining three randomized triplicate samples from the 4 ml vial of the analyte solution. All SPME sampling was performed using headspace analysis. 21 The septum of the vial was pierced with the SPME device and the fiber was exposed to the headspace of each sample. An extraction time of 15 min was chosen to evaluate each fiber. After the extraction, the SPME fiber was retracted into the needle and the apparatus was quickly removed from the vial. The data acquisition software program was started on the CAM ( 100 blank spectra collected) and the SPME apparatus transferred to the TD unit and allowed to thermally desorb into the IMS. The outlet of the TD unit was then sampled with the CAM. The fiber was left inside the TD

65 65 unit in between runs for an additional 1 min to minimize carryover effects. Blank runs were also incorporated in the randomized triplicates to test for carryover effects. The most rugged and reliable fiber was selected for further sampling and analysis Extraction Time Profile An extraction time profile for each of the analytes was obtained with the 100 µm PDMS fiber. Stock solutions of 9.60, 9.56, and 10.2 mg/ml of DMMP, DIMP, and DEMP, respectively, were prepared in methanol. Sandy loam clean soil #3 (1 g) was placed in a 4 ml amber vial. Each vial of soil was spiked with 30.0 µl of the analyte stock solution. This procedure gave a concentration of 288, 287, and 306 ppm (m/m) for DMMP, DIMP, and DEMP, respectively. The vial was then shaken with a vortex mixer for 1 minute. The vial was allowed to stand for at least 1 hour before beginning the soil extraction. Extraction times of 1, 5, 10, 15, and 20 min were performed for each analyte. The headspace of the soil was sampled using the SPME-IMS setup. Each analyte and extraction time was evaluated using a random block design with three replicates. The concentration profile for each analyte was plotted for each extraction time. Peak intensity data points were selected after averaging three spectra in a range from which the IMS maximum concentration profile was stable. A graph of maximum peak intensity as a function of extraction time was plotted. From the graph, a suitable

66 66 extraction time for the headspace SPME analysis of the CW precursor and degradation products was established Soil Analysis Calibration Procedures Stock solutions of DMMP, DIMP, and DEMP were individually prepared by placing 10 µl of each compound in a 10.0 ml volumetric flask and diluting with methanol to give concentrations of 1.11, 0.95, and 1.03 mg/ml, respectively. Soil samples were prepared by spiking the soil with 10, 30, 50, and 70 µl of the analyte stock solutions. The concentrations obtained after spiking the soil with each analyte are listed in Table 2 1. Calibration curves and the linear range of the calibration method for each analyte were obtained from the spiked samples. The detection limit for the analyte in soil was estimated from the calibration line by two methods either using the slope 91 or the confidence intervals 92 of the calibration line. For the first method based on the slope of the calibration line the detection limit criterion was based on the concentration of the analyte in the sample that produces a peak with a signal-to-noise ratio (SNR) of 3:1. For the second method the limit of detection is determined from the confidence limits (bands) of the calibration curve where the lowest detectable concentration is represented by the concentration that corresponds to the point in the calibration curve where the lower confidence interval is equal to the upper confidence limits at the

67 67 ordinate intercept. The first method is a point base detection limit and the second method is based on the entire calibration line.

68 68 Table 2 1. Calibration Curve Concentrations (ppm) for Chemical Warfare Precursor and Degradation Products. DMMP DEMP DIMP Analytes In Soil Soil was screened for each of the precursor and degradation products using the SPME-IMS setup, the PDMS fiber and the 20 min extraction time. The same protocol used for the extraction time profile study was used to sample the analytes in soil. Each vial of soil (1 g) was spiked with 30.0 µl of the analyte stock solution to give a concentration of 288, 287, and 306 ppm (m/m) for DMMP, DIMP, and DEMP, respectively. The samples were then shaken with a vortex mixer for 1 min and the headspace of the soil sampled using the SPME-IMS setup. A mixture of CW precursor and degradation products in soil was sampled using the SPME-IMS setup. A vial of soil was spiked with 30.0 µl of each of the analyte stock solutions. A 15 min extraction time and 1 min vortex time was used in this part of the study. The reduced mobilities of

69 69 each of the peaks seen in the mixture were identified in spectra. Triplicate experiments were run for the precursors and degradation products soil mixture. 2.3 RESULTS AND DISCUSSION Reduced Mobilities The experimental reduced mobility constants (K 0 ) for each precursor and degradation product of CWAs, obtained for each method, are given in Table 2 2. All the analytes furnished both protonated monomer (MH + ) and dimer (M 2 H + ) peaks. The reduced mobility calculated for each analyte was reproducible in each method. Conventional IMS results for the precursor and degradation products were also compared to the SPME-IMS results and agreed well. The relative error between the reduced mobilities values calculated from the spectra collected with the IMS and SPME-IMS were negligible.

70 70 Table 2 2. CWA Precursor and Degradation Products Experimental Reduced Mobility Constants, (K 0 ) ab, Obtained for Each Method at 25 C. IMS SPME/IMS (no soil) Soil Analysis Precursor & Degradation Product MH + M 2 H + MH + M 2 H + MH + M 2 H + DIMP DEMP DMMP a Reduced mobilities are expressed in units of cm 2 V -1 s -1. b All reduced mobilities are obtained using 95% confidence intervals. All std. dev. are less than ± Selection Of SPME fiber The 100 µm PDMS 84 and 65 µm PDMS/DVB 85 fibers had been used in previous studies for detection of CWAs, therefore they were chosen to be evaluated in this study as fibers for extraction of the CW precursor and degradation products. The 10 min extraction time allowed sufficient equilibrium to be reached with the fiber and the headspace of the analyte solutions for all the CW precursor and degradation products. The standard deviations calculated from the reduced mobilities for both fibers for the CW precursor and degradation products were less than 0.03 cm 2 V -1 s -1.

71 71 Statistical evaluation of the 100 µm PDMS and 65 µm PDMS/DVB fibers evaluated in this study showed no statistical difference in their response to each analyte. However, when using the PDMS/DVB fiber in repeated experiments it had a shorter lifetime than the PDMS fiber. The PDMS fiber could be used repeatedly withstanding more extraction/adsorption cycles per fiber compared to the PDMS/DVB fiber. PDMS higher extraction cycle reflects its more ruggedness compared to the PDMS/DVB fiber and therefore was used for the soil analysis. The peak height of each analyte response as a function of each fiber is given in Figure 2 2.

72 72 Peak Height (mv) DEMP DIMP DMMP PDMS PDMS/DVB Figure 2 2. The effect of SPME fiber type on extraction efficiency of diethyl methylphosphonate (DEMP), diisopropyl methylphosphonate (DIMP) and dimethyl methylphosphonate (DMMP) at concentrations of 3.06 mg/ml, 2.86 mg/ml, and 3.33 mg/ml, respectively. The fibers evaluated were polydimethylsiloxane/divinylbenzene (PDMS/DVB) and polydimethylsiloxane (PDMS). Peak heights are reported with 95% confidence intervals. No significance difference in extraction efficiency can be observed for each fiber Extraction Time In order to consider the sample matrix influence, the extraction time profiles were obtained with the soil for each of the analyte. The extraction profiles are given in Figure 2 3. Each analyte approached a plateau region around 20 min indicating an equilibrium state between the precursor or degradation product on the SPME fiber and that of the precursor or

73 73 degradation product vapor in the headspace of the sample vials. Therefore an extraction time of 15 min allowed sufficient extraction of the CW precursor or degradation product from soil and was used for subsequent studies Peak Intensity (mv) DMMP DEMP DIMP Extraction Time (min) Figure 2 3. Extraction time profiles obtained for dimethyl methylphosphonate (DMMP), diisopropyl methylphosphonate (DIMP) and diethyl methylphosphonate (DEMP) at 288, 306, and 287 mg/ml, respectively. Peak intensities are reported with 95% confidence intervals. The peak intensities level off around 15 min indicating equilibrium has been reached between the fiber and the headspace of the analyte Calibration Curve The calibration curves created from the soil matrix for each of the CW precursor or degradation product are given in Figure 2 4. Each precursor

74 74 and degradation product curve is nearly linear in the concentration range between 10 and 70 ppm. The R 2 values for DMMP, DEMP, and DIMP calibration lines were , , and , respectively. Table 2 3 summarizes the detection limits (LODs) and regression data obtained for each analyte. Detection limits of 6.3 ± 0.2 ppm, 6.3 ± 0.4 ppm, and 7.6 ± 1.4 ppm were obtained for DMMP, DIMP, and DEMP, respectively, using Method 1 that is defined as the concentration of an analytical peak with a signal to noise ratio of 3:1. This method uses the slope of the calibration line. Detection limits of 9.2 ppm, 10.4 ppm, and 9.0 ppm were obtained for DMMP, DIMP, and DEMP, respectively from method 2. This method uses the 95% confidence intervals about the calibration line and provides the more conservative estimate. Both methods yielded LODs of the same magnitude.

75 a) (A) Intensity (V) Data Calibration line CI 95% Concentration (ppm) b) (B) Intensity (V) Data Calibration line CI 95% Concentration (ppm) c) (C) Intensity (V) Data Calibration line CI 95% Concentration (ppm) Figure 2 4. Calibration curves obtained for (A)dimethyl methylphosphonate (DMMP); (B) diisopropyl methylphosphonate (DIMP) and (C) diethyl methylphosphonate (DEMP) from the soil matrix. All concentrations obtained using 95% confidence intervals. Each analyte curve is nearly linear in the concentration range between 10 and 70 ppm.

76 76 Table 2 3. Detection Limits for Chemical Warfare Precursor and Degradation Products Analyte LOD (ppm) Method 1 a LOD (ppm) Method 2 b Slope (V/ppm) Intercept (V) R 2 DMMP 6.3 ± (4.9 ±0.2) ± DEMP 7.6 ± (4.7 ±0.9) ± DIMP 6.3 ± (6.3 ±0.4) ± a b LOD, limit of detection, criterion based on 3s bl /m, where s bl is the standard deviation of the instrumental signal for a blank and m is the slope. Precision on slope and intercept is based on 95% confidence intervals. LOD based on 95% confidence intervals of the entire calibration curve.

77 Analysis Of Samples In Soil The soil was screened for CW precursor and degradation products using the SPME-IMS setup using the optimized time obtained from the extraction profile study. DIMP, DEMP, and DMMP were all extracted from the spiked soil at 300 µg/g with the 100 µm PDMS using a 20 min extraction time with the SPME-IMS. The SPME-IMS spectra for DMMP, DIMP and DEMP in soil are presented in Figure 2 5. The top spectrum was acquired from DMMP in soil. The middle spectrum was obtained from the DEMP soil sample, and the bottom spectrum from the DIMP soil sample. The monomer and dimer peaks appear in all the spectra. Other small peaks are also observed in each spectrum. These small peaks are most likely degradation products of the analytes in soil. These small peaks are not seen in the neat solutions, soil (no analyte), or analyte stock solutions data. The reduced mobilities of the monomer and dimer peaks observed for the CW precursor and degradation products in the SPME-IMS spectra extracted from soil are reported in Table 2 2 with 95% confidence level from triplicate measurements. The spectrum generated from the mixture of DMMP, DIMP, and DEMP using the SPME-IMS setup and the PDMS fiber is presented in Figure 2 6. A complex spectrum of peaks was obtained for the mixture of the CW precursor and degradation products as excepted. Each of the three analytes should have furnished a monomer and dimer peak; however, eight peaks were found in the mixture spectrum. Reduced mobilities are a means

78 78 of identifying the peaks in the SPME-IMS spectra. Six of the peaks were identified through reduced mobility calculations as the monomer and dimer peaks of the analytes. The other peaks are consistent with the formation of mixed dimers from the three analytes. A mixed dimer peak is usually observed, in addition to other peaks still present in a mixture, as a new peak in an IMS spectrum that occurs at a reduced mobility that is midway between two proton-bound dimer peaks. The detection of mixed dimer peaks with the CAM has been previously reported 93. The two unidentified peaks in the SPME-IMS spectrum that appear at the reduced mobilities 1.35 cm 2 V -1 s -1 and 1.19 cm 2 V -1 s -1, have been putatively identified as the mixed dimers, DMMP-DEMP and DEMP-DIMP. However, mass spectroscopic identification would be needed to positively confirm the identities of these peaks. The DMMP-DIMP dimer would occur at a reduced mobility of 1.67 cm 2 V -1 s -1 and this peak would appear at the same drift time as the DMMP dimer. The relative errors between the reduced mobilities values calculated from the spectra collected for the individual analyte in soil and the analyte in the mixture were negligible. The screening time for the mixture analysis using the SPME-IMS was less than 30 min.

79 79 Reduced Mobilities (cm 2 V -1 s -1 ) (A) 0.20 Intensity (V) Drift Time (ms) Reduced Mobilities (cm 2 V -1 s -1 ) (B) 0.20 Intensity (V) Drift Time (ms) Reduced Mobilities (cm 2 V -1 s -1 ) (C) Intensity (V) Drift Time (ms) Figure 2 5. SPME-IMS spectra obtained for extraction of: (A) DMMP, (B) DEMP and (C) DIMP in soil. The first peak observed in each spectrum is the water reactant ion followed by the monomer of each analyte and the dimer peak at shorter reduced mobilities.

80 80 Reduced Mobilities (cm 2 V -1 s -1 ) Peaks (K o cm 2 V -1 s -1 ) DMMP monomer (1.83) 2. DEMP monomer (1.67) 3. DIMP monomer (1.54) Intensity (V) DMMP dimer (1.45) 5. DMMP-DEMP dimer (1.35) 6. DEMP dimer (1.26) 7. DEMP-DIMP dimer (1.19) 8. DIMP dimer (1.13) Drift Time (ms) Figure 2 6. SPME-IMS spectrum obtained for extraction of mixture of DMMP, DEMP and DIMP in soil. All reduced mobilities are obtained using 95% confidence intervals. All standard deviations are less than ± A monomer and dimer peak is observed for each analyte in addition to mixed dimers at peaks 5 and 7. The DIMP-DMMP dimer peak is not shown but would occur in the same position as the DMMP dimer peak. 2.4 CONCLUSION Precursor and degradation products of CWA were sampled by SPME- IMS in a simple system without soil and as contaminants of soil. Evaluation of SPME fibers in a system without soil, showed polydimethylsiloxane /divinylbenzene (PDMS/DVB) to have a greater extraction affinity for the CW

81 81 precursor and degradation product than the polydimethylsiloxane (PDMS) fiber coating. However, greater ruggedness was observed for the PDMS fiber. DIMP, DEMP, and DMMP were all detected as low as 10 µg/g in soil. The prototype SPME thermal desorption inlet system allows for preconcentration of analytes and field sampling. Sample analysis time for soil screening was less than 45 min for all the precursor and degradation products. SPME sampling with IMS analyses offered good repeatability and rapid screening of precursor and degradation products as soil contaminants.

82 82 Chapter 3 Multi-way Processing of Gas Chromatography Differential Mobility Spectrometry Data for Curve Resolution of Jet Fuel 3.1 INTRODUCTION Two-way data is obtained from the hyphenation of chromatographic and spectroscopic instrumentation such as gas chromatography/mass spectrometry (GC-MS), high performance liquid chromatography/diode array detection (HPLC-DAD), and capillary electrophoresis/nuclear magnetic resonance (CE-NMR). These hyphenated techniques produce data with additional dimensionality that may overwhelm the analyst. Recently differential mobility spectrometry (DMS) was coupled to GC GC-DMS provides two-way data in that ion current is collected as a function of retention time and compensation voltage. Preprocessing data by transforming to the first and second derivatives is used to remove baseline drift and to overcome overlapping spectral features. Derivative methods have proven advantageous for characterization 97, resolution enhancement 98, 99, and quantitative analysis 100, 101. Differentiation of chromatograms or spectra gives enhanced features by decreasing baseline fluctuations and sharpening peaks. As derivative degree is increased, spectral/chromatographic features are sharpened but the signal-to-noise-ratio is decreased. 3 Differentiation is often used in combination with a smoothing technique to recover the lost signal-to-noiseratio (SNR).

83 83 Savitzky-Golay filters can be used to simultaneously differentiate and smooth time dependent data like GC-DMS. The principle of Savitzky-Golay filters are described in Section in Chapter 1. Savitzky-Golay filters were used to process jet fuel data obtained using GC-DMS. Aviation jet fuel is a mixture of processed hydrocarbons and additives and analysis of such compounds often results in intricate datasets. Edwards introduce the use of Savitzky-Golay filters for two-dimensional data processing assuming that the 102, 103 data is at equally spaced intervals. The intrinsic nature of the jet fuel datasets makes analyzing the spectra complex; therefore, filtering the data removes noise and improves signal to noise ratios. Two-dimensional covariance matrices can also be used to visual intricate time-dependent dataset. 104 Covariance is a statistical measure of correlation of two variables. Covariance mapping has not previously been applied to the analysis of GC-DMS data sets. Covariance mapping compares the variations between variables and allows correlated variables to be visualized. This study examines the use of a Savitzky-Golay filter for smoothing of jet fuel data obtain with a GC-DMS. Solid phase microextraction (SPME) is used as a sampling technique for the jet fuels. Multivariate covariance plots are used to facilitate the comparison of raw and filtered jet fuel data. The use of derivative analysis shows promise as a preprocessing method for GC- DMS.

84 EXPERIMENTAL Chemicals Three types of aviation fuels are used in this study including military (JPTS), commercial (JetA3639) and rocket propellant (RP1). Samples of JP- TS, RP-1, and Jet A 3639 were provided by the United States Air Force (Wright-Patterson Air Force Base) USAF/WPAFB. Each sample was stored in a borosilicate glass vial with polytetrafluoroethylene lined caps at 20 C and used as received GC-DMS System The chromatographic analysis was carried out on a GC-DMS system. The experimental setup is given in Figure 7. The helium carrier gas was maintained at a constant flow rate of 3.0 ml/min. The injector was operated in the splitless mode with a purge delay of 2 min. The initial GC oven temperature of 50 C was held for 3 min, followed by a temperature ramp of 3 C/min to 200 C, held for 5 min. Dry air was used as a makeup drift gas for the DMS at a flow rate of 180 ml/min. The DMS was operated at a separation voltage or rf voltage of 1500 V. The DMS compensation voltages were scan at 1 V/s from -10 V to +6 V. Positive ion mode data is only reported because no peaks were observed in the negative ion mode.

85 SPME Analysis Neat jet fuel samples were sampled using headspace solid phase microextraction (HS-SPME). A 100 µl aliquot of neat jet fuel sample was placed in the vial and allowed to equilibrate for 30 min. The SPME fiber was then exposed to the headspace for 3 min. After extraction the SPME fiber was immediately transferred to the GC injector and chromatographic analysis by GC-DMS carried out. Desorption time was 5 min and desorption temperature 250 C. Five replicates of each of the fuels were collected using a random block design. The entire series of fuel samples was run once in random order in each of the five replicate blocks Data Analysis The chromatographic data was stored as formatted text and converted to a Microsoft Office Excel workbook in Excel (Microsoft Office Excel 2003, Redmond, WA). MATLAB 7.1 R14 SP3 (Mathworks, Natick, MA) was used to process data and generate graphs. The data was read by MATLAB and converted to a data matrix ( ) such that the rows corresponded to retention time (t r ) and the columns corresponded to the compensation voltage (C v ). The first 4 points in each spectrum were removed, because the baseline drifted to greater values for these points at the most negative compensation voltages. Each spectrum was baseline corrected by subtracting the mean of the spectrum that was calculated from the first 25 data points from the original

86 86 spectrum. These points corresponded to compensation voltages between V and V. The GC-DMS data were processed using two-way Savitzky-Golay filters with the second derivative obtained from a quartic polynomial of the DMS order and a smoothing cubic polynomial of the GC order. The equation used to obtain the second derivative is given in Equation 18. The retention time and compensation voltage windows consisted of 5 and 25 points, respectively. All the positive points in the processed data were set to zero and then the negative peaks were inverted so that the twoway processed peaks would resemble analytical peaks. Note, the second derivative of a Gaussian peak is negative at the maximum. Three plots were constructed from the raw and filtered dataset including a total ion chromatogram, plot of the maximum peak in the DMS spectrum and plot of peaks in a retention window from 11.5 min to 16.1 min. The covariance of the original and second derivative compensation voltage and retention time data was calculated. A correlation plot of the square root of the covariance for original and filtered data was also constructed. 3.3 RESULTS AND DISCUSSION Total ion chromatograms for the raw and filtered data were constructed from the summed data matrix. Representative total ion chromatograms obtained for JP-TS, JetA3688, and JP5 are given in Figure 3 1A, Figure 3 2A, and Figure 3 3A, respectively. The chromatographic run time was 58 min. The corresponding filtered data is presented in Figure 3

87 87 1B, Figure 3 2B, and Figure 3 3B. The data smoothing was obtained with a Savitzky-Golay filter using the second derivative of a quartic and cubic polynomial for the DMS and GC orders, respectively, with a 5 point data window for the retention time order and a 25 point data window for compensation voltage. The optimum polynomial degree and window sizes were determined by a grid search. The Savitzky-Golay filters allowed smoothing and differentiation in a single step. The effectiveness of the filtering process is observed by the decrease in the noise in the second derivative plots for each jet fuel. A retention time window plot for the raw and filtered dataset from the total ion chromatogram at spectra scans 500 to 700, corresponding to 11.6 min to 16.1 min, was constructed. The retention time window plots for the raw data (Figures Figure 3 4A, Figure 3 5A, Figure 3 6A) and filtered data (Figure 3 4B, Figure 3 5B, Figure 3 6B) for JP-TS, JetA3688, and JP5, respectively, allows better visualization of the filtering process. The Savitzky-Golay filters increased the sharpness and preserved the location of the maximum of each peak in the jet fuel datasets. The spectral peak with the highest intensity was located in the original and filtered DMS spectrum by finding the peak with the highest maxima and normalizing it to unit length. The maximum spectral peak was plotted for JPTS (Figure 3 7), JetA3639 (Figure 3 8), and JP5 (Figure 3 9). DMS peaks tend to be broad but sharpening of the maximum spectral peak in each jet fuel is observed after applying the Savitzky-Golay filter.

88 88 Normalized Total Ion Current Normalized Total Ion Current Retention Time (min) Retention Time (min) (A) (B) Figure 3 1. Normalized total ion current chromatograms obtained for JP-TS jet fuel (A) raw and (B) processed data using Savitzky-Golay filters. Data was processed using the second derivative of a quartic and cubic polynomial for the DMS and GC orders, respectively, with a 5 point data window for the retention time order and a 25 point data window for compensation voltage. A decrease in noise is observed in the second derivative plots (B).

89 89 Normalized Total Ion Current Normalized Total Ion Current Retention Time (min) Retention Time (min) (A) (B) Figure 3 2. Normalized total ion current chromatograms obtained for Jet A 3639 jet fuel (A) raw and (B) processed data using Savitzky-Golay filters. Data was processed using the second derivative of a quartic and cubic polynomial for the DMS and GC orders, respectively, with a 5 point data window for the retention time order and a 25 point data window for compensation voltage. A decrease in noise is observed in the second derivative plots (B).

90 90 Normalized Total Ion Current Normalized Total Ion Current Retention Time (min) Retention Time (min) (A) (B) Figure 3 3. Normalized total ion current chromatograms obtained for JP5 jet fuel (A) raw and (B) processed data using Savitzky-Golay filters. Data was processed using the second derivative of a quartic and cubic polynomial for the DMS and GC orders, respectively, with a 5 point data window for the retention time order and a 25 point data window for compensation voltage. A decrease in noise is observed in the second derivative plots (B).

91 91 Normalized Total Ion Current Normalized Total Ion Current Retention Time (min) Retention Time (min) (A) (B) Figure 3 4. Normalized total ion current chromatogram windows obtained for JP-TS jet fuel (A) raw and (B) processed data using Savitzky-Golay filters. The retention time window corresponds to 11.6 min to 16.1 min. An increase in the sharpness and preservation of location of the maximum of each peak in the jet fuel datasets is observed.

92 92 Normalized Total Ion Current Normalized Total Ion Current Retention Time (min) Retention Time (min) (A) (B) Figure 3 5. Normalized total ion current chromatogram windows obtained for Jet A 3639 jet fuel (A) raw and (B) processed data using Savitzky-Golay filters. The retention time window corresponds to 11.6 min to 16.1 min. An increase in the sharpness and preservation of location of the maximum of each peak in the jet fuel datasets is observed.

93 93 Normalized Total Ion Current Normalized Total Ion Current Retention Time (min) Retention Time (min) (A) (B) Figure 3 6. Normalized total ion current chromatograms window obtained for JP5 jet fuel (A) raw and (B) processed data using Savitzky-Golay filters. The retention time window corresponds to 11.6 min to 16.1 min. An increase in the sharpness and preservation of location of the maximum of each peak in the jet fuel datasets is observed.

94 Raw Preprocessed Normalized Intensity Compensation Voltage (V) Figure 3 7. A comparison of the positive differential mobility spectra of JP-TS for the largest chromatographic peak. The spectra were normalized to unit vector length. Sharpening of the maximum spectral peak is observed after applying the Savitzky-Golay filter.

95 Raw Preprocessed Normalized Intensity Compensation Voltage (V) Figure 3 8. A comparison of the positive differential mobility spectra of Jet A 3639 for the largest chromatographic peak. The spectra were normalized to unit vector length. Sharpening of the maximum spectral peak is observed after applying the Savitzky-Golay filter.

96 96 Normalized Intensity Raw Preprocessed Compensation Voltage (V) Figure 3 9. A comparison of the positive differential mobility spectra of JP5 for the largest chromatographic peak. The spectra were normalized to unit vector length. Sharpening of the maximum spectral peak is observed after applying the Savitzky-Golay filter.

97 97 Multivariate covariance plots were also used to visualize the jet fuel data. Multivariate covariance is useful to assess chromatographic and spectral information. To visualize the data a two-dimensional spectrum or chromatogram is obtained from plotting the square root of the covariance for the retention time and compensation voltage. The covariance of the original data for the DMS (Figure 3 10A, Figure 3 11A, and Figure 3 12A) and GC (Figures Figure 3 10B, Figure 3 11B, and Figure 3 12B) is presented as contour plots. Covariance contour plots of the filtered data for DMS (Figures Figure 3 13A, Figure 3 14A, and Figure 3 15A) and GC (Figures Figure 3 13B, Figure 3 14B, and Figure 3 15B) are also presented. The square root of the covariance was used to enhance features of the small peaks and convert to original data units. The peaks in the diagonal correspond to the variance of the variables and the off diagonal peaks correspond to crossvariance variables. For example, in the JPTS compensation voltage contour plot for the original dataset has high correlation among the DMS spectra at coordinates ( 3.0,-2.5). The points appear on the same DMS peak indicated by the high intensity of the color in the plot. DMS peaks are generally broad, this accounts for the size of the peaks in the original data contour plots. After processing the data with Savitzky-Golay filters the peaks are narrower as illustrated in the compensation voltage second derivative contour plots. This sharpening trend is true for compensation voltage and retention time datasets for all the jet fuels.

98 98 Compensation Voltage (V) Compensation Voltage (V) ( Compensation Voltage (V) ( Compensation Voltage (V) (A) x (B) Figure Square root of the covariance for the raw (A) and processed (B) positive differential mobility spectrometry factor of JP-TS. The peaks in the diagonal correspond to the variance of the variables and the off diagonal peaks correspond to cross-variance variables. Narrower peaks are observed in the processed data after applying the Savitzky-Golay filter.

99 99 Compensation Voltage (V) ( Compensation Voltage (V) 0 (A) Compensation Voltage (V) ( x Compensation Voltage (V) (B) Figure Square root of the covariance for the raw (A) and processed (B) positive differential mobility spectrometry factor of Jet A The peaks in the diagonal correspond to the variance of the variables and the off diagonal peaks correspond to cross-variance variables. Narrower peaks are observed in the processed data after applying the Savitzky-Golay filter.

100 100 Compensation Voltage (V) ( Compensation Voltage (V) 0 (A) Compensation Voltage (V) ( x Compensation Voltage (V) (B) Figure Square root of the covariance for the raw (A) and processed (B) positive differential mobility spectrometry factor of JP5. The peaks in the diagonal correspond to the variance of the variables and the off diagonal peaks correspond to cross-variance variables. Narrower peaks are observed in the processed data after applying the Savitzky-Golay filter.

101 Retention Time (min) Retention Time (min) (A) ( 16 x Retention Time (min) Retention Time (min) (B) 0 ( Figure Square root of the covariance for the raw (A) and processed (B) chromatography factor of JP-TS. The peaks in the diagonal correspond to the variance of the variables and the off diagonal peaks correspond to cross-variance variables. Sharpening of the chromatographic data is observed in the processed data after applying the Savitzky-Golay filter.

102 Retention Time (min) Retention Time (min) (A) ( x Retention Time (min) Retention Time (min) 0 (B) ( Figure Square root of the covariance for the raw (A) and processed (B) chromatography factor of Jet A The peaks in the diagonal correspond to the variance of the variables and the off diagonal peaks correspond to cross-variance variables. Sharpening of the chromatographic data is observed in the processed data after applying the Savitzky-Golay filter.

103 Retention Time (min) Retention Time (min) (A) 0.02 ( x Retention Time (min) Retention Time (min) (B) ( Figure Square root of the covariance for the raw (A) and processed (B) chromatography factor of JP5. The peaks in the diagonal correspond to the variance of the variables and the off diagonal peaks correspond to cross-variance variables. Sharpening of the chromatographic data is observed in the processed data after applying the Savitzky-Golay filter.

104 CONCLUSIONS Two-dimensional signal processing is a valuable tool for hyphenated chromatographic methods that collect spectra with respect to time. Multiway smoothing of data obtained with GC-DMS using Savitzky-Golay filters improves signal-to-noise ratios and chromatographic resolution. Second derivatives sharpen DMS peaks, which is important because the spectra will become more distinct with respect to retention time. Covariance mapping allows visualization of the GC-DMS two-way data. GC-DMS data can be enhanced using derivative analysis.

105 105 Chapter 4 Classification of Fuels using Solid Phase Microextraction Gas Chromatography Differential Mobility Spectrometry 4.1 INTRODUCTION Fuel characterization/identification is important for quality assurance , arson , and environmental analysis Gas chromatography (GC) is often used for analyzing fuels because the volatile nature of fuels and the high resolution achieved with GC capillary columns. Flame ionization (FID) and mass spectrometric detectors are the most commonly used GC detectors. FID allows analysis of hydrocarbons and oxygenates while mass spectrometry allows confirmation of peak identity. Recently the coupling of GC with differential mobility spectrometry (DMS) detector has offered a new hyphenated system for gas phase separation and 96, detection. DMS offers low cost, simple design, high sensitivity, and selectivity. In this study, a DMS with a 10.6 ev photoionization source was used as a detector for fuel identification. Fuel is a complex mixture of processed hydrocarbons and additives and can be characterized by the distribution of the chemical components of this mixture. Pattern recognition or chemical fingerprinting is often used to characterize these datasets. Several pattern recognition techniques have been enlisted for fuel analysis including genetic algorithms (GA) 117, 118, artificial neural networks (ANN) , and principal component analysis (PCA) 122, 123. A fuzzy rule-building expert system (FuRES) combined with the

106 106 principal component transform (PCT) was used in this study. Analysis of variance-principal component analysis (ANOVA-PCA) was used to evaluate the chromatographic dataset for differences among the data collected on different days with the GC-DMS. FuRES was used to classify the fuels. This study demonstrates the use of DMS as an alternative detector for fuel analysis. Neat fuel samples were sampled using solid phase microextraction (SPME). These fuels included rocket propellants (RP-1, RG- 1), diesel, and military jet propellant (JP-4, JP-5, JP-7, JP-TS,) and commercial grade jet propellant (Jet A-3639, Jet A-3688, Jet A-3690, Jet A- 3694, Jet A-generic) fuels. The PCT was used as a lossless compression method so that the number of variables equals the number of objects in the training set. These training set objects were then used for FURES modeling. The PCT prior to FURES reduces training time significantly. These studies will provide useful information from complex datasets obtained using GC-DMS and characterized with chemometric techniques. 4.2 EXPERIMENTAL Reagents Neat samples of RP-1, RG-1, JP-4, JP-TS, JP-7, JP-5, Jet A-3639, Jet A-3688, Jet A-3690, Jet A-3694, Jet A-generic and diesel fuels were provided by the Wright Patterson Air Force Base at Dayton, OH. The numbers behind each Jet A fuel indicates the location where each fuel was obtained. Each

107 107 sample was stored in a borosilicate glass vial with polytetrafluoroethylene lined caps at 20 C and used as received. All experiments were performed on a GC-DMS system. A schematic of the GC-DMS setup was given in Figure 1 7. The GC injector was operated in the splitless mode with a purge delay of 2 min. The temperature of the GC oven was program from 50 C (3 min hold) to 200 C (5 min hold) at 3 C/min. The GC carrier gas was helium at a constant flow rate of 3.0 ml/min. The DMS was operated at a dispersion or rf voltage of 1500 V. The DMS compensation voltages were scanned at 12 V/s from -13 V to +6 V. The makeup gas for the DMS was compressed air at a flow rate of 250 ml/min. DMS is capable of simultaneous detection of both positive and negative ions however only positive ion mode data was reported because no peaks were observed when the DMS it operated in negative polarity Data Collection All samples were sampled using headspace solid phase microextraction (HS-SPME) using a polydimethylsiloxane (PDMS, 100 µm) fiber. Prior to use, the fiber was conditioned as recommended by the manufacturer in the GC injector port at 250 o C for 1 h. A 100 µl aliquot of neat jet fuel sample was placed in the vial and allowed to equilibrate for 30 min. The SPME fiber was then exposed to the headspace for 3 min. After extraction the SPME fiber was immediately transferred to the GC injector and chromatographic analysis by GC-DMS performed. The desorption time was 5 min and the desorption

108 108 temperature was 250 C. The fiber was place in the GC injector for 20 min between runs to minimize carryover effects. Six replicates of each of the 12 fuels were collected with the exception of Jet A-3694 (4 replicates) and JP4 (6 replicates). All samples were collected using a random block design. The entire series of fuel samples were run once in random order in each of the replicate blocks. The 12 fuel samples were rerun as blind unknowns 1 month after the initial spectra were collected Data Processing The chromatographic data was stored as formatted text and converted to a Microsoft Office Excel workbook in Excel (Microsoft Office Excel 2003, Redmond, WA. MATLAB 7.1 R14 SP3 (Mathworks; Natick, MA) was used to process data and generate graphs. The data was read by MATLAB and converted to a data matrix such that the rows corresponded to retention times (t r ) and the columns corresponded to the compensation voltages (C v ) from the DMS. Due to the large size of the dataset from each sample ( ), a 2-level biorthogonal Villasenor 5 wavelet 124 compression was used. The data matrix was reduced from 250 to 63 columns of the Villasenor approximation wavelet coefficients. The data was compressed without loss of information because the DMS peaks are relatively broad. For the replicates, a data tensor of 60x2240x65 was collected for the sample, retention time, and compensation voltage ways, respectively. The class designations were

109 109 represented by a 60x12 binary encoded matrix. Each column designated one of the 12 classes. Each spectrum was baseline corrected by subtracting the mean of the DMS intensities from spectra that was calculated from the 5 outermost compressed points on each side of the spectrum. These points corresponded to compensation voltages of [-13, -11.8] V and [+4.4, +5.9] V. A linear interpolation was used to standardize the retention time data to 1 s interval from 2 s to 58 min, which yielded 1799 retention time measurements Pattern Recognition Analysis ANOVA-PCA of the factor of day-to-day variation revealed that the first day of sample collection was flawed compared to the other days (Figure 4 1). The first day was statistically different and this difference correlated with a change in the experimental protocol, so the first block of replicates (i.e., day 1 was eliminated from analysis). For pattern recognition, training and testing data was generated using Latin partitions. The Latin partition method randomly divides the data set into training and testing set pairs so that every spectrum in the data set is use only once in the prediction set. 125 Latin partitions allow constant class distribution among the training and testing sets. Five training/testing set pairs were created for each Latin partition. Each training dataset contained 48 training two-way objects and each testing data set contained 12 two-way objects. The results of the 5 Latin-partitions were pooled. Because the

110 110 sampling is random, these analyses were boot-strapped 5 times to yield confidence intervals on the prediction performance. For each study 25 models were evaluated. No testing two-way object was ever used for model building or optimization in any of the evaluations, including the PCT and alignment steps. Retention time drift due to variations in pressure, temperature, and flow rate occurring during the chromatographic run was adjusted for using polynomial interpolation. The fminsearch in MATLAB was used to maximize the correlation between the individual spectra and the two-way mean of the samples. Polynomial orders 0-7 were evaluated in order to find the best order to align the spectra. After retention time drift adjustment the data, was unfolded to yield vectors with a 113,400 dimension for each measurement. These spectra were compressed using the PCT and normalized to unit length.

111 111 PC #2 ( 9%, ) A B C D E F E E C CE D C D E DE C D C D D D B CD C D D B DB DF B B F FB F F B F B A F A A A A A A PC #1 (22%, ) Figure 4 1. ANOVA-PCA score plot giving the date experimental factor. There is an obvious difference in data collected on Day 1 (101605). The first and second principal components account for 31% of the total variance. Percentage of principal components given in parenthesis with the absolute variance. A 95% confidence interval is drawn around the mean of the each day. The bootstrap method 126 was used to evaluate the classification model five times. During each bootstrap, Latin partitions was used to create five training and testing pairs and polynomial orders 0-7 evaluated. A classification tree was built using FuRES. The classification tree was used to create the model and predict the unknowns in the test data. The same procedure was repeated for the 12 prediction fuel objects from each Latin partition. At no point during the Latin-partitions were the prediction data

112 112 used for model building, so only the training data were used for calculating the alignment average and the principal components used for the transform. The FURES model and parameters optimized by the Latin-partition study were validated by running the same 12 fuel samples on the GC-DMS one month after the initial fuel collection. 4.3 RESULTS AND DISCUSSION GC-DMS Analysis All 12 fuels were used for classification; however, the fuels can be subdivided into 4 groups: rocket propellants (RP-1, RG-1), diesel, and military jet propellant (JP-4, JP-5, JP-7, JP-TS) and commercial jet propellant (Jet A-3639, Jet A-3688, Jet A-3690, Jet A-3694, Jet A generic) fuel. Chromatograms of one fuel from each fuel type acquired with the GC-DMS are given in Figure 4 2. These chromatograms are representative of all the chromatograms obtained with GC-DMS.

113 A) B) 70 JetAgeneric 70 Total Ion Current Total Ion Current JP Retention Time (min) Retention Time (min) Total Ion Current C) D) RP1 60 Diesel Total Ion Current Retention Time (min) Retention Time (min) Figure 4 2. Representative gas chromatograms of fuels: A) commercial, B) jet propellant, C) rocket propellant, and D) diesel. These chromatograms are representative of all the chromatograms obtained with GC-DMS.

114 ANOVA-PCA Analysis Prior to initiating the pattern recognition study, ANOVA-PCA was applied and one factor studied was the date of the data collection. After the flaw in the first day of data collection was recognized an additional block of data was collected two weeks later. The PCA score plot of the first two principal components is given in Figure 4 1. PC 1 accounted for 22% of the variance and PC 2 accounted for 9%. Day 1 was determined to be a bad collection and this block of data was eliminated from further analysis. A difference in instrumental warm-up time is believed to be responsible for the differences of Day 1. After removing this data, sixty two-way data sets were used for evaluating the FuRES classifier. The fuels used for the training set are listed in Table 4 1. PCA was also used to evaluate the degree of class separation or clustering for the remaining two-way data. PCA was performed on the entire data set and the ANOVA-PCA score plot for the effect of jet fuel class is given in Figure 4 3. Confidence intervals (95%) obtained from the Hotelling T 2 statistics 127 are drawn around the mean of the class. The first two principal components account for 45% of the total variance. Considerable overlap among fuel classes was observed. The first two principal components do not indicate linear separability of the dataset. The absence of a hyper-planar boundary among the fuel clusters indicates the need for a non-linear classification model.

115 115 Table 4 1. Fuels Used in Training Set. Fuel Number of Samples Diesel 5 Jet A Jet A Jet A Jet A Jet A-generic 5 JP4 6 JP5 5 JP7 5 JPTS 5 RG1 5 RP-1 5 Total 60

116 116 PC #2 (12%, ) B BB B EE B EE JJ J J C AF CC AA A C H HH F A H H F I I E G GI G L L L LL K D DD KK D A - Diesel B - JP4 C - JP5 D - JP7 E - JPTS F - JetA3639 G - JetA3688 H - JetA3690 I - JetA3694 J - JetAgeneric K - RG1 L - RP PC #1 (33%, ) Figure 4 3. Principal component analysis scores for training dataset. Each letter represents a replicate of the fuel sample. This graph indicates no apparent separation of fuel type. The first and second principal components account for 45% of the total variance. Percentage of principal components given in parenthesis with the absolute variance. A 95% confidence interval is drawn around the mean of the each class Classification Model Retention time drift is an important source of variation for gas chromatography, especially when the chromatograms are used for pattern recognition. A polynomial retention time alignment method was applied to the two-way objects so that each two-way object would maximize its

117 117 correlation with the mean spectrum in the training set of data. If two high of a polynomial order is used the two-way data objects may become distorted when the algorithm is trapped in local minima. The effect of retention time alignment for each two-way object was evaluated by the FuRES prediction rates. Because Latin-partitions were used a precise statistical analysis was obtained for polynomial orders 0-7. No retention time alignment was included as a control. The percentages misclassified for the two-way data of the sixty fuels using no alignment and each polynomial order are displayed in Figure 4 4. A small increase in error rate is observed between no alignment and the zero order polynomial alignment. The error rate then decreases with increasing polynomial order until the quartic polynomial where an increase in error rate is observed for the quartic polynomial. The instability in error rate can be observed for the results obtained from distortion that may occur for the higher order polynomial fits. The quartic polynomial provided the best fit for retention time alignment based on the FuRES modeling and furnished a classification rate of 95.0 ± 0.03%. The FuRES models built from the quartic polynomial retention time alignment correctly identified 57 out of the 60 fuels. The prediction results of the FuRES are reported as a confusion matrix in Table 4 2. A confusion matrix is used to evaluate the performance criteria of the classification model providing information about the actual and predicted results. 128 Information about the occurrence of each element in the predicted class is located in the columns of the confusion matrix and for the

118 118 actual class in the rows. The number of correctly classified fuels for each class can be seen along the diagonal of the matrix and the number of misclassified fuels is located along the off diagonal. When classifying the replicates of each fuel, JP4 was misclassified 0.4 times in 2 out of 5 bootstraps as Jet A Jet A-3639 was misclassified 0.2 times in 1 out of 5 bootstraps as Jet A-Generic. JP-TS misclassified 0.4 times in 2 out of 5 bootstraps as Jet A-3694 and 0.6 times in 3 out of 5 bootstraps as JP5. One spectrum in Jet A-Generic was misclassified 5 out 5 bootstraps as JP-5. The consistent misclassification of one Jet A-Generic spectrum as JP-5 may be due to one bad spectrum in the five replicates; however a closer inspection of the five JP-5 spectrums did not show any obvious differences Error Rate (%) no alignment zero 1st 2nd 3rd 4th 5th 6th 7th Polynomial Order

119 Figure 4 4. The effect of polynomial order on alignment of spectral data for the classification of fuels using fuzzy rule-building expert systems (FuRES). The fourth order polynomial provided the best fit for the classification of the jet fuel data. Each result is reported with 95% confidence intervals.. 119

120 120 Table 4 2. Confusion Matrix for Fourth Order Alignment of Fuels. FUEL Diesel JP4 JP5 JP7 JPTS Jet A 3639 Jet A 3688 Jet A 3690 Jet A 3694 Jet A generic RG1 RP1 Diesel JP ± ± JP JP JPTS ± ± Jet A ± ± Jet A Jet A Jet A Jet A Generic RG RP

121 121 The entire data set was used to construct the FuRES classification tree for all 12 fuels in Figure 4 5. The FuRES algorithm used eleven rules to build the classification tree. The robustness and predictive ability of the FuRES model was tested using 12 blind fuel unknowns. The 12 blind fuel unknowns were run on the GC-DMS one month after the initial fuel samples. The blind fuel unknowns were subjected to the same data processing and pattern recognition analysis as the 60 fuel samples that yielded the optimum results from the Latin-partitions (i.e., a fourth order alignment). All 12 blind unknown fuel samples were accurately classified yielding a 100% classification rate.

122 122 # 1 H=2.04 # 2 H=1.77 # 10 H=0.60 # 3 H=1.42 # 9 H=0.16 # 11 H=0.16 RP1 Nc=5 # 4 H=0.85 # 7 H=0.59 JP4 Nc=6 JPTS Nc=5 JP7 Nc=5 RG1 Nc=5 # 5 H=0.16 # 6 H=0.16 # 8 H=0.16 JetA3690 Nc=5 Diesel Nc=5 JetA3639 Nc=5 JP5 Nc=5 JetAgeneric Nc=5 JetA3688 Nc=5 JetA3694 Nc=4 Figure 4 5. FuRES classification tree for twelve fuels with a 95% classification rate, Nc= number of samples, H= entropy values. The numbers indicate the number of rules used to build the tree. There is no splitting of the fuels among the leaf (circle) node. All fuels were separated using this 11 rule tree.

123 CONCLUSIONS The differential mobility spectrometer can be used as a GC detector for fuel analysis. The GC-DMS system with SPME yielded characteristic profiles for fuels. FuRES demonstrated to be a useful pattern recognition tool for classification of chromatographic data from GC-DMS. The classification rate for the model use in this study was 95 ± 0.2%. The robustness of the model was demonstrated by correctly classifying fuel samples after 1 month. GC- DMS with pattern recognition techniques such as FuRES can be used to characterize complex samples such as fuel.

124 124 Chapter 5 Multivariate Analysis of Volatile Organic Compounds by Gas Chromatography Differential Mobility Spectrometry 5.1 INTRODUCTION There is growing interest in the use of differential mobility spectrometry (DMS) for analysis of volatile organic compounds (VOCs). The combination of gas chromatography (GC) with a selective DMS detector for VOC detection allows analysis of samples in mixtures and complex matrices at trace levels with simultaneous detection of both positive and negative ions. The selectivity of the DMS method has resulted in its use in many VOC applications ranging from petroleum 129, BTX 47, 130 (benzene, toluene, xylene), and oxygen-containing VOCs. 131 Previous BTX studies only used DMS without GC. GC-DMS instrumental principles were detailed in Section in Chapter 1. GC is a well-established separation technique and has been interfaced with a variety of detection systems. These hyphenated chromatographic techniques are well documented. The recent coupling of GC with DMS has offered a new hyphenated system for gas phase separation and detection. DMS is becoming a popular choice as a detection system because of its low cost, simple design, high sensitivity, and selectivity. VOCs are carbon based compounds produced by vehicle emissions, chemical manufacturing, and petroleum products. VOCs are important health and environmental concerns because many are known carcinogens.

125 125 The VOCs of interest in this project are benzene, m-xylene, p-xylene, toluene, collectively referred to as BTX, and methyl tert-butyl ether (MTBE). These compounds are all major components of gasoline. BTX is considered primary sources of aquifer contamination and is classified as priority pollutants by the U.S. Environmental Protection Agency (EPA) due to its water solubility and toxicity. 132 The EPA recommends monitoring of MTBE and placed it on the Contaminant Candidate List for contaminants that may require future regulation. 133 The objective of this project is to characterize BTX and MTBE by GC- DMS. Separation of isomeric compounds such as m-xylene, p-xylene is often obtained using columns with special stationary phases designed for isomeric separations. Stationary phases not optimized for separation of these components results in coeluting peaks for m-xylene and p-xylene. Multivariate curve resolution (MCR) methods such as Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and alternating least squares (ALS) were proposed to model and resolve chromatographic peaks that co-elute in mixtures such as m-xylene and p-xylene. The principles of SIMPLISMA and ALS were presented in Section in Chapter 1. The GC- DMS system with the use of solid phase microextraction (SPME) allows for short analysis times for complex mixtures of volatile organic compounds in various matrices. The SPME/GC-DMS system was evaluated with respect to selectivity for each analyte and mixtures of analytes.

126 EXPERIMENTAL Chemicals The chemicals used in this study were benzene (99%, Spectrum, New Brunswick, NJ, Lot RH1803), methanol (99.9%, Fisher, Fair Lawn, NJ, Lot ), tert-butyl methyl ether (99.9%, Lancaster, Pelham, NH, Lot GBFA023327), toluene (99.5%, EM Science, Gibbstown, NJ, Lot ), p- xylene (99.6%, Fisher, Fair Lawn, NJ, Lot ), and m-xylene (99.7%, Fisher, Fair Lawn, NJ, Lot ). All reagents in this study were used without further purification SPME Analysis Standard solutions (50 mg/ml) of benzene, toluene, m-xylene, p- xylene and MTBE were prepared in methanol. Working solutions of mixtures of the analytes were prepared by diluting the standard solution to 5 mg/ml with ultra-pure (18.2 MΩ) water (Millipore, Bellerica, MA). All samples were sampled using headspace solid phase microextraction (HS-SPME). The SPME extraction times were 3 min for pure analytes and 15 min for mixtures. The SPME desorption time was 5 min at 250 C. Prior to use, the fiber was conditioned as recommended by the manufacturer in the GC injector port at 250 o C for 1 h. The fiber was placed in the GC injector port for 10 min allowing any remaining sample to thermally desorb from the fiber to prevent sample carryover between runs.

127 GC-DMS Analysis Chromatographic analysis was carried out on a GC-DMS system. The experimental setup is given in Figure 7. The GC carrier gas was helium at a constant flow rate of 3.0 ml/min. The DMS makeup gas was compressed air at 300 ml/min. The GC injector was operated at 250 C in splitless mode with a purge delay of 2 min. The GC oven temperature was 80 C. Three replicates of each analyte were collected at dispersion voltages 900 V, 1100 V, 1300 V and 1500 V using a random block design for a field dependence study. Mixtures of the analytes were also run in triplicate using a random block design at the optimum dispersion voltage. The GC-DMS was allowed to collect approximately 100 blank spectra before analytes were injected. The DMS compensation voltages were scan at 1 V/s from -20 V to +12 V Data Analysis MATLAB 7.1 R14 SP3 (Mathworks, Natick, MA) was used to process data and generate graphs. The data was read by MATLAB and converted to a data matrix such that the rows corresponded to retention time (t r ) and the columns corresponded to the compensation voltage (C v ). The spectrum was then baseline corrected by subtracting the mean of the intensities of the first 25 DMS spectra from the original spectra. The chromatogram and spectrum of each analyte were plotted from the maximum peak in each spectrum. SIMPLISMA and ALS were used to extract the concentration and spectral profiles of each pure analyte and pure components in the mixtures. The

128 128 number of pure components was entered into the SIMPLISMA-ALS program. The concentration and spectral profiles were then generated using SIMPLISMA and ALS. 5.3 RESULTS AND DISCUSSIONS Dispersion Voltage Study A field dependence study was performed to determine compensation voltage and optimum dispersion voltage for each analyte. The spectra for benzene, MTBE, toluene, m-xylene, and p-xylene at dispersion voltages of 900 V, 1100 V, 1200 V, 1300 V, and 1500 V are given in Figure 5 1, Figure 5 2, Figure 5 3, Figure 5 4, and Figure 5 5, respectively. Two peaks corresponding to monomer and dimer peaks were observed for each analyte. This identification is tentatively based on studies by Eiceman and 45, 134 coworkers. A peak at compensation voltage 1.2 V can be seen in each spectrum at every dispersion voltage. The second peak corresponds to a dimer formation. Only the compensation voltages recorded for the protonated monomer peaks were used for identification purposes. Similar dimer formation were observed for benzene, toluene, and m-xylene in water in a previous SPME-DMS study. 47 A third peak is observed for benzene between the monomer and dimer peak in the 1500 V dispersion voltage plot. This peak is seen in all three replicates of 1500 V. A third small peak is similarly observed for p-xylene at dispersion voltages 1300 V and 1500 V. The p-xylene used in this study has

129 129 a purity of 99.6%. This third peak in the p-xylene spectrum may be due to the m-xylene isomer present in the neat p-xylene standard; however, mass spectroscopic studies are needed for positive identification of the third peak in both the benzene and p-xylene spectra. The intensity of the MTBE monomer peak is much less than the BTX compounds. This poor instrumental response is due to the structure of MTBE. Photoionization is very sensitive to compounds with double bonds like benzene. MTBE contains no double bonds but the oxygen in its chemical structure permits this molecule to undergo photoionization. This difference in chemical structure accounts for the weaker response for MTBE with respect to BTX. Shifts in the monomer peak to more negative compensation voltages at increasing dispersion voltages is observed for all analytes; whereas, the dimer peak exhibits a small shift to more positive compensation voltages. The shift results in increase separation between the monomer and dimer peaks. A change in peak shape is also noted at higher dispersion voltages. The general trend is toward more broadening of the band at higher dispersion voltages. The field study data was analyzed to find the optimum dispersion voltage. At the lowest dispersion voltage (900 V) used in this study the monomer peak of the BTX analytes was not completely separated from the dimer peak. The peaks were better resolved at 1100 V but the compensation voltage for the monomer peaks of the m-xylene and p-xylene were similar.

130 130 The resolution between the monomer and dimer peak increased as the dispersion voltage increased but the broadness of the peak increased also. Small peaks along with the monomer and dimer peaks were also observed for benzene and p-xylene at higher dispersion voltage. A dispersion value of 1200 V was chosen as a tradeoff between peak shape and resolution. The spectrum for each analyte at dispersion voltage 1200 V is presented in Figure 5 6. This dispersion voltage was used for the analyte mixture study.

131 (A) V ± 0.3 Shifted Intensity (V) (B) (C) (D) (E) V ± V ± V ± V ± Compensation Voltages (V) Figure 5 1. Spectra for benzene at dispersion voltages (A) 1500 V, (B) 1300 V, (C) 1100 V, (D) 1200 V, and (E) 900 V. The compensation voltage with a 95% confidence interval for the monomer peak on the left in each spectrum is given above. Shifts in the monomer peak to more negative compensation voltages and the dimer peak to more positive compensation voltages at increasing dispersion voltages is observed.

132 (A) V ± Shifted Intensity (V) (B) (C) (D) (E) -9.9 V ± V ± V ± V ± Compensation Voltages (V) Figure 5 2. Spectra for methyl tert-butyl ether (MTBE) at dispersion voltages (A) 1500 V, (B) 1300 V, (C) 1100 V, (D) 1200 V, and (E) 900 V. The compensation voltage with a 95% confidence interval for the monomer peak on the left in each spectrum is given above. Shifts in the monomer peak to more negative compensation voltages and the dimer peak to more positive compensation voltages at increasing dispersion voltages is observed.

133 (A) -8.1 V ± Shifted Intensity (V) (B) (C) (D) (E) -7.4 V ± V ± V ± V ± Compensation Voltages (V) Figure 5 3. Spectra for toluene at dispersion voltages (A) 1500 V, (B) 1300 V, (C) 1100 V, (D) 1200 V, and (E) 900 V. The compensation voltage with a 95% confidence interval for the monomer peak on the left in each spectrum is given above. Shifts in the monomer peak to more negative compensation voltages and the dimer peak to more positive compensation voltages at increasing dispersion voltages is observed.

134 (A) -9.9 V ± (B) -8.1 V ± 0.3 Shifted Intensity (V) (C) (D) (E) -5.8 V ± V ± V ± Compensation Voltages (V) Figure 5 4. Spectra for p-xylene at dispersion voltages (A) 1500 V, (B) 1300 V, (C) 1100 V, (D) 1200 V, and (E) 900 V. The compensation voltage with a 95% confidence interval for the monomer peak on the left in each spectrum is given above. Shifts in the monomer peak to more negative compensation voltages and the dimer peak to more positive compensation voltages at increasing dispersion voltages is observed.

135 (A) -5.8 V ± Shifted Intensity (V) (B) (C) (D) (E) -5.1 V ± V ± V ± V ± Compensation Voltages (V) Figure 5 5. Spectra for m-xylene at dispersion voltages (A) 1500 V, (B) 1300 V, (C) 1100 V, (D) 1200 V, and (E) 900 V. The compensation voltage with a 95% confidence interval for the monomer peak on the left in each spectrum is given above. Shifts in the monomer peak to more negative compensation voltages and the dimer peak to more positive compensation voltages at increasing dispersion voltages is observed.

136 (A) -4.9 V ± 0.3 Shifted Intensity (V) (B) (C) (D) (E) -5.8 V ± V ± V ± V ± Compensation Voltages (V) Figure 5 6. Spectra for (A) m-xylene, (B) p-xylene, (C) toluene, (D) methyl tert-butyl ether (MTBE) and (E) benzene at dispersion voltage 1200 V. The compensation voltage with a 95% confidence interval for the monomer peak on the left in each spectrum is given above.

137 137 SIMPLISMA and ALS were used to model BTX and MTBE mixture data. A 5 mg/ml mixture of m-xylene, and p-xylene was analyzed using the SPME- GC-DMS system. Co-elution of the m-xylene, and p-xylene peak was observed in Figure 5 7. Only one peak can be seen in the chromatogram of the m-xylene and p-xylene mixture. The contour plot shows only two peaks instead of the four peaks (two monomers and dimers) that are expected from the mixing of two compounds in this system. This observation is to be expected because the stationary phase used in this study is not optimized for the separation of the xylene isomers. The mixture data was modeled with SIMPLISMA-ALS to test the power of the algorithms for modeling compounds that strongly co-elute. Two peaks are observed in the chromatographic (Figure 5 8A) and spectral profiles (Figure 5 8B) corresponding to the retention times and compensation voltages of each analyte after SIMPLISMA- ALS modeling. The p-xylene peak gives a very sharp band but the m-xylene peak is very broad causing the p-xylene peak to co-elute within the m-xylene peak. SIMPLISMA-ALS was then used to model a mixture of all the analytes. A 5 mg/ml mixture of benzene, methyl tert-butyl ether (MTBE), m-xylene, and p-xylene was ran and subjected to SIMPLISMA-ALS modeling. All five analytes were successfully modeled using SIMPLISMA-ALS. This modeling is depicted in the chromatographic profile of the mixture in Figure 5 9. All the DMS peaks are relatively the same width unlike the m-xylene, and p-xylene

138 138 mixture in which the m-xylene peak was much broader than the p-xylene peak. (A) (A) Intensity (V) Time (s) (B) (B) Figure 5 7. Co-elution of m-xylene and p-xylene at 5 mg/ml depicted in a (A) chromatogram and (B) snapshot of contour plot from the instrumental graphical display. Only one peak can be seen in the chromatogram. The contour plot shows only two peaks instead of the four peaks (two monomers and dimers) that are expected from the mixing of two compounds in this system.