EFFECTS OF EXTRACTION PROCEDURE AND GAS CHROMATOGRAPHY TEMPERATURE PROGRAM ON DISCRIMINATION OF MDMA EXHIBITS USING IMPURITY PROFILES

Transcription

1 EFFECTS OF EXTRACTION PROCEDURE AND GAS CHROMATOGRAPHY TEMPERATURE PROGRAM ON DISCRIMINATION OF MDMA EXHIBITS USING IMPURITY PROFILES By Karlie Marie McManaman A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Forensic Science 2012

2 ABSTRACT EFFECTS OF EXTRACTION PROCEDURE AND GAS CHROMATOGRAPHY TEMPERATURE PROGRAM ON DISCRIMINATION OF MDMA EXHIBITS USING IMPURITY PROFILES By Karlie Marie McManaman MDMA impurity profiling is used to determine the synthesis route of exhibits, and can be used to link separate exhibits to each other and to clandestine laboratories based on the impurities present. Profiles are typically generated using a liquid-liquid extraction (LLE) procedure and gas chromatography-mass spectrometry (GC-MS). However, headspace solid phase microextraction (HS-SPME) is used in some forensic laboratories as an alternative extraction procedure, and has shown potential benefits. In addition, GC temperature programs vary between forensic laboratories, making comparisons of chromatographic data difficult. The aim of this research was to compare the LLE and HS-SPME procedures, as well as four GC temperature programs found in the literature, to determine their effects on the ability to associate replicates and discriminate exhibits of MDMA. Principal components analysis (PCA) was used to evaluate the data, and it was determined that HS-SPME provided increased discrimination of exhibits over LLE. While LLE provided higher precision of peak areas and, therefore, closer clustering of replicates, HS-SPME allowed for the extraction of several trace impurities, which provided additional discriminatory power over LLE. In the temperature program investigation, one temperature program was more successful than the others at associating and discriminating samples. However, this temperature program was significantly longer than the rest, therefore further investigation is needed to determine the beneficial parameters of this program and create a shorter, more practical temperature program.

3 ACKNOWLEDGEMENTS I would like to first thank my advisor, Dr. Ruth Smith, for her guidance and motivation throughout this research project. Without her, none of this research would have been possible. He enthusiasm for forensic chemistry and her dedication to her students is truly inspiring. I would also like to thank my committee members, Mr. Frank Schehr and Dr. Jeremy Wilson, for their time and feedback on this thesis. I would like to thank the Michigan State Police for providing the MDMA exhibits used in this research. Additionally, I would like to thank, Elaine Dougherty, Kathy Boyer and analysts at the Bridgeport laboratory, who also provided training and guidance during my internship. I would like to thank Dr. Kathryn Severin and the staff at the Michigan State University Mass Spectrometry Facility for the assistance with instrumentation throughout this research. I would like to thank the MSU Forensic Science Program for funding to present this research at the MAFS 2011 Fall Meeting in Chicago, IL; the FSF Lucas Grant for funding for supplies and instrument time; and the NIJ/FSF Forensic Science Student Research Grant for funding for supplies, instrument time, and travel to present this research at the AAFS 2012 Annual Meeting in Atlanta, GA. I would like to thank the forensic chemistry students, especially John McIlroy, Christy Hay, Seth Hogg, Melissa Willard, Emily Riddell, Suzanne Towner, and Drew DeJarnette for their assistance, encouragement, and entertainment throughout my time at MSU. Lastly, I would like to thank my family and friends who have supported me without question throughout all of my endeavors. Their love and encouragement is what motivates me in everything I do. iii

4 TABLE OF CONTENTS 1. Introduction MDMA History and Use MDMA Synthesis Chemical Profiling Using Liquid-Liquid Extraction Chemical Profiling Using Headspace Solid Phase Microextraction Gas Chromatography Temperature Programs Research Objectives...13 References Theory Liquid-Liquid Extraction Headspace Solid Phase Microextraction Gas Chromatography Mass Spectrometry Data Pretreatment Retention Time Alignment Normalization Principal Components Analysis...36 References Materials and Methods MDMA Exhibits Liquid-Liquid Extraction (LLE) Procedure Headspace Solid Phase Microextraction (HS-SPME) Procedure Gas Chromatography-Mass Spectrometry (GC-MS) Analysis Data Analysis Total Ion Chromatograms (TICs) Selected Compounds...49 References Effect of Gas Chromatography Temperature Program on the Association and Discrimination of MDMA Exhibits Extracted using a Liquid-Liquid Extraction Procedure MDMA Exhibits Extracted by LLE and Analyzed using Temperature Program A MDMA Exhibits Extracted by LLE and Analyzed using All Temperature Programs Association and Discrimination of Exhibits Extracted by LLE based on PCA using Total Ion Chromatograms Association and Discrimination of Exhibits Extracted by LLE and Analyzed using Temperature Program D based on PCA using TICs Association and Discrimination of Exhibits Extracted by LLE and Analyzed using Temperature Program A based on PCA using TICs Association and Discrimination of Exhibits Extracted by LLE based on PCA using Selected Compounds...82 iv

5 Association and Discrimination of Exhibits Extracted by LLE and Analyzed using Temperature Program A based on PCA using Selected Compounds Comparison of Association and Discrimination of Exhibits Extracted by LLE and Analyzed using Temperature Program A based on TICs and Selected Compounds Further Investigation of Normalization Procedures on Exhibits Extracted by LLE and Analyzed using Temperature Program A Comparison of Association and Discrimination of Exhibits Extracted by LLE and Analyzed using All Temperature Programs based on Selected Compounds Summary of Findings using LLE...97 References Effect of Gas Chromatography Temperature Program on the Association and Discrimination of MDMA Exhibits Extracted using a Headspace Solid Phase Microextration Procedure MDMA Exhibits Extracted by HS-SPME and Analyzed using Temperature Program A MDMA Exhibits Extracted by HS-SPME and Analyzed using Temperature Program D Association and Discrimination of Exhibits Extracted by HS-SPME based on PCA using Selected Compounds Association and Discrimination of Exhibits Extracted by HS-SPME and Analyzed using Temperature Program D based on PCA using Selected Compounds Association and Discrimination of Exhibits Extracted by HS-SPME and Analyzed using Temperature Program A based on PCA using Selected Compounds Comparison of Association and Discrimination of Exhibits Extracted by LLE and HS-SPME based on PCA using Selected Compounds Comparison of Association and Discrimination of Exhibits Analyzed using Temperature Program D Comparison of Association and Discrimination of Exhibits Analyzed using Temperature Program A Summary of Findings using HS-SPME References Conclusions & Further Work Conclusions Effect of Temperature Program on MDMA Impurity Profiles Effect of Extraction Procedure on MDMA Impurity Profiles Future Work v

6 LIST OF TABLES Table 3.1: Physical characteristics of MDMA exhibits used in this study...43 Table 3.2: GC temperature programs investigated in this research...45 Table 4.1: Selected compounds for data analysis of MDMA exhibits extracted by LLE using Temperature Programs A-D...85 Table 5.1: Selected compounds for data analysis of MDMA exhibits extracted by HS-SPME using Temperature Programs A and D vi

7 LIST OF FIGURES Figure 1.1: Structure of MDMA with substitutions on the phenethylamine core circled in green...1 Figure 2.1: Headspace solid phase microextraction (HS-SPME) setup...21 Figure 2.2: Components of a gas chromatography (GC) system...23 Figure 2.3: Ideal peak shape (left), fronting peak (center), and tailing peak (right) with corresponding isotherms below...28 Figure 2.4: Components of a mass spectrometry (MS) system...30 Figure 2.5: Quadrupole mass analyzer...32 Figure 2.6: Electron multiplier...32 Figure 3.1: MDMA exhibits used in this study (a) T-17 (b) T-27 (c) T-29 (d) T-30 (e) MSU Figure 4.1: (a-e) TICs of exhibits after LLE and GC-MS analysis using Temperature Program A Figure 4.2: (a-b) Chemical structures of MDEA and MDDMA...60 Figure 4.3: (a-d) TICs of Exhibit T-17 after LLE and GC-MS using Temperature Programs A-D Figure 4.4: PCA scores plot for five MDMA exhibits after LLE and GC-MS analysis using Temperature Program D...68 Figure 4.5: (a-b) Loadings plots for PCs 1 and 2 for five MDMA exhibits after LLE and GC-MS analysis using Temperature Program D Figure 4.6: (a-f) Effect of mean-centering on compound contribution: TIC showing caffeine peak in Exhibit T-17, mean-centered caffeine peak in Exhibit T-17, loadings for caffeine on PC1 and PC2, negative loading for caffeine on PC1 and PC2 in Exhibit T Figure 4.7: PCA scores plot for five MDMA exhibits after LLE and GC-MS analysis using Temperature Program A...78 Figure 4.8: (a-b) Loadings plots for PCs 1 and 2 for five MDMA exhibits after LLE and GC-MS analysis using Temperature Program A vii

8 Figure 4.9: Misaligned caffeine peak in replicates of Exhibits T-27 and T-30 after LLE and GC-MS analysis using Temperature Program A. Alignment was performed using a correlation optimized warping algorithm with a warp of 2 and a segment size of 150. Inset: Scores plot obtained for five MDMA exhibits after LLE and GC-MS analysis using Temperature Program A...84 Figure 4.10: PCA scores plot based on selected compounds for five MDMA exhibits extracted using LLE and analyzed by GC-MS using Temperature Program A Figure 4.11: Loadings plot based on selected compounds for five MDMA exhibits extracted using LLE and analyzed by GC-MS using Temperature Program A...88 Figure 4.12: PCA scores plot based on selected compounds after logarithmic normalization for five MDMA exhibits extracted by LLE and analyzed by GC-MS using Temperature Program...92 Figure 4.13: PCA scores plot based on selected compounds for five MDMA exhibits extracted using LLE and analyzed by GC-MS using Temperature Program B...94 Figure 4.14: PCA scores plot based on selected compounds for five MDMA exhibits extracted using LLE and analyzed by GC-MS using Temperature Program C...95 Figure 4.15: PCA scores plot based on selected compounds for five MDMA exhibits extracted using LLE and analyzed by GC-MS using Temperature Program D...96 Figure 5.1: (a-e) TICs of exhibits after HS-SPME and GC-MS analysis using Temperature Program A Figure 5.2: TICs of Exhibit T-17 after HS-SPME (top) and LLE (bottom) and GC-MS analysis using Temperature Program A Figure 5.3: TIC of Exhibit T-17 after HS-SPME and GC-MS analysis using Temperature Program D Figure 5.4: PCA scores plot for five MDMA exhibits after HS-SPME and GC-MS analysis using Temperature Program D Figure 5.5: PCA loadings plot for five MDMA exhibits after HS-SPME and GC-MS analysis using Temperature Program D Figure 5.6: PCA scores plot for five MDMA exhibits after HS-SPME and GC-MS analysis using Temperature Program A Figure 5.7: PCA loadings plot for five MDMA exhibits after HS-SPME and GC-MS analysis using Temperature Program D viii

9 Figure 5.8: PCA scores plots based on selected compounds for five MDMA exhibits extracted using LLE (left) and HS-SPME (right) and analyzed by GC-MS using Temperature Program D Figure 5.9: PCA scores plots based on selected compounds for five MDMA exhibits extracted using LLE (left) and HS-SPME (right) and analyzed by GC-MS using Temperature Program A ix

10 KEY TO ABBREVIATIONS CAR CHAMP COW DC DVB EI GC GC-MS HCA HS-SPME LLE LR MA MDDMA MD-DMB MDEA MDMA MDP2P MDP2P-OH MS NIST NRIPS Carboxen Collaborative Harmonization of Methods for Profiling of Amphetamine Type Stimulants Correlation optimized warping Direct current Divinylbenzene Electron ionization Gas chromatography Gas chromatography-mass spectrometry Hierarchical cluster analysis Headspace solid phase microextraction Liquid-liquid extraction Likelihood ratio Methamphetamine 3,4-methylenedioxydimethylamphetamine 3,4-methylenedioxy-N,N-dimethylbenzylamine 3,4-methylenedioxy-N-ethylamphetamine 3,4-methylenedioxymethamphetamine 3,4-methylenedioxyphenyl-2-propanone 3,4-methylenedioxyphenyl-2-propanol Mass spectrometry National Institute of Standards and Technology Japan s method for profiling methamphetamine x

11 ONCB P2P PC1 PC2 PCA PDMS PDMS/DVB PPMC RF RSD SPME TIC Thailand s method for profiling methamphetamine phenyl-2-propanone First principal component Second principal component Principal components analysis Polydimethylsiloxane Polydimethylsiloxane/divinylbenzene Pearson product moment correlation Radio frequency Relative standard deviation Solid phase microextraction Total ion chromatogram xi

12 Chapter 1 Introduction 1.1 MDMA History and Use 3,4-methylenedioxymethamphetamine (MDMA) is a stimulant and hallucinogen that became federally regulated under the Controlled Substances Act in 1985, after gaining popularity as the active ingredient in ecstasy tablets. 1 MDMA (Figure 1.1) is a phenethylamine with a methylenedioxy substitution on the aromatic ring, which produces hallucinogenic effects; a methyl group substitution on the nitrogen atom, which doubles the potency of the drug; and a methyl group substitution on the α-carbon, which stimulates the central nervous system and suppresses appetite. 2 methylenedioxy α-carbon methyl group Figure 1.1: Structure of MDMA with substitutions on the phenethylamine core circled in green. For interpretation of the references to color in this and all other figures, the reader is referred to the electronic version of this thesis. MDMA is listed as a Schedule I hallucinogenic compound, which means it has a high potential for abuse and no current medical use. Although the drug was originally created for pharmaceutical development in Germany, 1 it became a substance of abuse in the 1970s after 1

13 psychiatrists began treating patients with this drug, even though it had not been tested by the US Food and Drug Administration. The increase in abuse of MDMA throughout the 1970s and 80s and lack of verified medical use prompted the government to schedule this drug in the Controlled Substances Act. Although MDMA is now regulated, use of illicit substances is still a growing trend in this country, especially among young adults. Surveys such as the Monitoring the Future survey provide statistics on current and past abuse of illegal substances, including MDMA. 3 Even though the majority of students who participated in the 2010 Monitoring the Future Survey admit to being aware of the harmfulness of MDMA, it continues to be abused by youths. An estimated 1.1 million people tried MDMA for the first time in 2009, most of them middle school through college age, which was a significant increase from the year before. This survey also revealed that there has been a significant increase in MDMA use for all age groups surveyed (8 th, 10 th and 12 th grades) since the survey five years prior, with 3.3% of 8 th grade, 6.4% of 10 th grade and 7.3% of 12 th grade students reporting taking the substance at least once in their lives. 1.2 MDMA Synthesis MDMA tablets are manufactured in clandestine laboratories throughout this country and in many European nations. Each lab produces tablets that are slightly different based on the method used to synthesize the MDMA and the processing techniques used to produce the final tablet. The synthesis method used depends on the ingredients available to the clandestine chemist. The most common method of MDMA production is through the reductive amination of 3,4-methylenedioxyphenyl-2-propanone (MDP2P), and three different variations of the reductive 2

14 amination method are used: the high-pressure, cold, or aluminum amalgam methods. 4 The highpressure method uses hydrogen under increased pressure with platinum as a catalyst. In the cold method, sodium borohydride is the reducing agent and the synthesis takes place at cooler temperatures by storing the reaction mixture in a freezer to prevent reduction of MDP2P. In the aluminum amalgam method, aluminum foil and mercury chloride are used as the reducing agent. 5 Since MDP2P itself is now a List 1 Chemical of Concern in the United States, this compound is also produced illegally. The most common methods of MDP2P production are oxidation of isosafrole in acid, and reductive amination of piperonal using substances such as MDP-2-nitropropene or methylamine. 4 With all of the variations in synthesis methods, the MDMA produced has obvious differences. The aim of chemical profiling MDMA is to identify impurities, byproducts, and additives in the tablets. This chemical profile will vary among tablets because of the differences in the ingredients available and the synthesis method used. Having these profiles allows authorities to identify the synthesis method, which can be used to link tablets back to a common clandestine laboratory and potentially to a common production batch Chemical Profiling Using Liquid-Liquid Extraction Researchers have investigated chemical profiling of illicit substances to determine synthetic route, aiming to identify links among exhibits, for many years. Typically, a liquidliquid extraction (LLE) procedure is used to extract the compounds and the resulting extract is analyzed by gas chromatography-mass spectrometry (GC-MS). The LLE procedure has been thoroughly investigated and optimized for numerous illicit compounds, including MDMA and 3

15 methamphetamine. Van Deursen et al. optimized a LLE procedure specifically for MDMA profiling in In this investigation, the authors compared extraction solvents, such as alkanes and toluene; phosphate buffers of different concentrations (0.066 M and 0.33 M) and ph (1.0, 5.0, 7.0 and 8.1); and filtering membranes such as regenerated cellulose and PTFE, with varying pore sizes. Based on the abundance of impurities and the repeatability of the extraction, it was ultimately determined that toluene should be used as the extraction solvent, with a 0.33 M phosphate buffer at ph 7.0, and a regenerated cellulose membrane with a pore size of 0.45 µm. This method was then used as the standard extraction procedure in the Collaborative Harmonization of Methods for Profiling of Amphetamine Type Stimulants (CHAMP) project in Europe. 7 The aim of the CHAMP project was to standardize MDMA profiling procedures to allow for better future comparisons of the resulting impurity profiles among laboratories. Various other studies have used LLE and GC-MS to generate chemical profiles of MDMA tablets with the aim of identifying the synthesis route used. Gimeno et al. related many of the impurities present to specific synthesis routes. 8 For example, the presence of 3,4- methylenedioxy-n,n-dimethylbenzylamine indicated synthesis of MDMA via the reductive amination of piperonal by dimethylamine, while 3,4-methylenedioxy-N-ethyl-Nmethylbenzylamine indicated reductive amination of piperonal by ethylmethylamine, and 3,4- methylenedioxy-n-methylbenzylamine indicated reductive amination of piperonal by methylamine. In another study investigating synthetic route determination, Swist et al. manufactured MDP2P, the most popular precursor of MDMA, using methods commonly used in clandestine laboratories. 4 Using LLE and GC-MS, different chemical markers for each method were 4

16 identified in the synthesized MDP2P. Then, the MDP2P was used to synthesize MDMA via reductive amination using sodium borohydride as the reducing agent. The MDMA was subjected to LLE and the resulting extract was analyzed by GC-MS to generate chemical profiles. These profiles were then used to successfully determine the likely method used to synthesize the MDP2P precursor. These studies were helpful in identifying the impurities that can be used to determine synthesis methods, but there was no attempt to link MDMA exhibits to common sources based on the impurities present. Cheng et al. used statistical procedures, along with recent knowledge of route specific impurities, to relate MDMA exhibits based on the impurities present. 9 In one study, 89 ecstasy tablets were analyzed using LLE and GC-MS and the profiles were compared using hierarchical cluster analysis (HCA), which clusters samples together based on chemical similarities. Using HCA, the tablets were separated into four groups at a similarity index of 0.5. The tablets were grouped based on differences in the synthesis route, which was verified by the identification of route specific impurities in the corresponding chemical profiles. To further investigate the use of HCA to group tablets based on chemical profiles, four test samples were prepared from one of the afore-mentioned tablets which was of high purity, containing nearly 100% MDMA. 9 Samples from the tablet were cut with two different cutting agents in different proportions, reducing the purity of the MDMA to 50% and 25%. The samples were extracted using LLE and analyzed by GC-MS to generate the profiles, which were then assessed using HCA. Test samples containing the same cutting agents were closely clustered, indicating that the additives present have a greater impact on clustering than the purity of the sample. All four test samples were clustered in the same group as the corresponding tablet, meaning that the original source of the samples was still identifiable. This study was a step 5

17 forward in MDMA chemical profiling, but it only linked exhibits by common synthesis methods and not necessarily common clandestine laboratories, which would be the next step in aiding police identify drug trafficking patterns. In order to link different exhibits by possible production laboratories, the statistical methods of analysis need to be investigated further, and a larger population of samples from around the world should be used. A collaborative effort of several European forensic laboratories sought to identify links among MDMA exhibits based on chemical profiles and physical characteristics of the tablets. Using the extraction procedure optimized by van Deursen et al., 6 laboratories in four different countries extracted the same 26 MDMA exhibits, as well as an additional 80 samples collected in each of their own countries, using LLE. All extracts were analyzed by GC-MS using a temperature program from The Netherlands Forensic Institute. 7 Thirty-two common compounds were identified in the 26 exhibits, and the eight compounds that were most variable in abundance were selected as the most discriminating compounds. Pearson product moment correlation (PPMC) coefficients and principal components analysis (PCA) were used to assess association and discrimination of tablets based on all 32 compounds, as well as the eight most discriminating compounds. The PCA scores plots were assessed visually, and PPMC coefficients, which were modified to fit a scale of 0-100, were given a threshold of 2.69 or less to indicate that samples were statistically similar with a false positive rate of 2% or less. Of the 26 exhibits, all exhibits were discriminated using all 32 compounds, while 99% of the exhibits were associated and discriminated correctly using only the eight selected compounds. The same statistical procedures were then applied to the 80 exhibits collected in the respective countries and links were discovered between separate exhibits based on consistencies in their profiles and modified Pearson values. 6

18 The above study continued by investigating additional statistical procedures to link exhibits based on physical characteristics (e.g. diameter, thickness and weight), as well as percent purity of MDMA in the tablets. 10 Using both PPMC coefficients and Euclidean distances, a likelihood ratio (LR) was calculated to determine the likelihood that two tablets originated from the same production batch. The ability to associate and discriminate tablets based on each physical characteristic individually, as well as all three characteristics, was assessed. Using all three physical characteristics to relate samples to each other provided higher LRs than each characteristic individually. While the three physical characteristics gave LRs that were determined to be acceptable, the discriminatory power was greatly improved when percent purity was added, increasing the LRs nearly 10-fold in some cases. These collaborative works are very useful to associate and discriminate MDMA exhibits seized in different countries. However, no similar collaborative works have been instigated in the U.S., which is necessary to better understand drug-trafficking patterns throughout this country. 1.4 Chemical Profiling Using Headspace Solid Phase Microextraction While LLE is the most common extraction method used in impurity profiling, there are some drawbacks to this procedure. The extraction is lengthy because of the number of steps involved, a large mass of sample is necessary, often equivalent to up to one entire tablet, and there are several potentially harmful chemicals used. 11 Due to these limitations, an alternative to this procedure is headspace solid phase microextraction (HS-SPME). Compared to the typical LLE method, HS-SPME is less time consuming in sample preparation, requires less sample (as little as ¼ tablet or less) per extraction, is a relatively non-destructive method, and is less 7

19 hazardous since no chemicals are used, yet it has been demonstrated to be just as effective for impurity extraction. Recent literature shows great potential for linking MDMA tablets when HS-SPME is used to extract the compounds. Bonadio et al. compared HS-SPME to LLE in terms of the identity and number of compounds extracted from the same MDMA tablets. 11 While the number of compounds extracted was relatively similar for both extraction procedures (31 compounds for HS-SPME and 32 for LLE), the extraction of other compounds, such as lubricants, only occurred in HS-SPME because these compounds are removed during the filtration step in the LLE procedure. While these other compounds do not give information on the synthesis route, they are used in the tablet production process and are important in linking tablets back to a specific clandestine laboratory. In the comparison study by Bonadio et al., the ability of the HS-SPME and LLE procedures to correctly associate and discriminate tablets was assessed. Four datasets were evaluated, composed of either all of the selected reproducible compounds (31 for HS-SPME and 32 for LLE), or the most discriminating reproducible compounds (10 for HS-SPME and 8 for LLE). The authors optimized and discussed normalization procedures, and the data were analyzed using PPMC coefficients, again modified to a scale of Each method was efficient in associating and discriminating tablets correctly, regardless of the number of compounds used. After observing the potential benefits of using HS-SPME for MDMA chemical profiling, Bonadio et al. developed and optimized the extraction procedure specifically for this use. 12 Four different types of HS-SPME fiber coatings were tested: polydimethylsiloxane (PDMS), carboxen (CAR)/PDMS, PDMS/divinylbenzene (DVB), and DVB/CAR/PDMS. Different masses (10, 40, 8

20 and 100 mg) of homogenized MDMA tablets were extracted at different temperatures (60, 80, 90 and 100 C) for different times (15, 30, 45, and 60 min). Of the four fibers, PDMS/DVB had the strongest affinity for the eight target compounds, which were selected based on repeatability and discriminating power. Ideally, the smallest mass of sample should be used, as long as it is enough to allow the less abundant compounds to be extracted. However, using only 10 mg of sample, some of the target compounds were only extracted in low abundance or were not extracted at all. Since there was no appreciable difference in the number or abundance of compounds extracted using 40 mg or 100 mg of sample, the lower mass was deemed optimal. At high temperatures (e.g. 100 C) and/or with longer extraction times (e.g. 60 min), the fiber was overloaded, causing many compounds to co-elute and hence, making identification more difficult. However, using low extraction temperatures (60 C or less), many less volatile compounds were not extracted at sufficiently high concentration to be detected. Based on these considerations, the optimal HS-SPME procedure used a PDMS/DVB fiber and 40 mg of sample, with an extraction temperature of 80 C for 15 min. The optimization of the HS-SPME extraction procedure is very beneficial for future work and can be used as a standard for extracting ecstasy tablets for comparison. Researchers have also compared HS-SPME and LLE procedures for chemical profiling of other controlled substances. Kuwayama et al. used both extraction procedures to profile 69 methamphetamine samples seized throughout Japan and 42 samples from Thailand. 13 The LLE extracts were analyzed by GC-MS while the HS-SPME extracts were analyzed by GC with a flame ionization detector. The resulting chromatograms were compared using HCA to statistically assess similarities among samples. The HS-SPME procedure was more successful than LLE in distinguishing high purity samples from one another. This is mainly because 9

21 compounds present at lower abundance are easily visible in the HS-SPME profiles, but are not observed in the profiles obtained with LLE because LLE is more efficient at extracting the controlled substance (in this case, methamphetamine) than impurities. Pre-concentration of impurities on the SPME fiber also allows for more impurities to be extracted at higher abundances than with LLE. The combination of LLE and HS-SPME profiles allowed for the successful discrimination of all exhibits using HCA. Lee et al. also compared the LLE and HS-SPME procedures for chemical profiling of methamphetamine using 48 exhibits seized in Korea. 14 In this study, the authors determined that less volatile compounds, such as a methamphetamine dimer, were only extracted using LLE and were not observed in exhibits extracted using HS-SPME. However, several volatile components, such as diphenylketone and many unknown compounds, were clearly separated in the HS-SPME chromatograms, while in the LLE chromatograms they were often masked by the broad amphetamine peak which resulted from a degradation of methamphetamine with LLE but not HS-SPME. HCA was performed on the chromatograms of the 48 exhibits obtained using LLE and again on the chromatograms obtained using HS-SPME. 14 The samples were clustered into five groups irrespective of extraction procedure; however, the exhibits within each group showed some variation depending on extraction procedure. In these cases, the exhibits were subclassified such that the identities matched between the two procedures. For example, one of the HS-SPME clusters contained 10 methamphetamine exhibits. When these were cross-matched, nine of the exhibits were in the same LLE cluster, while one exhibit was in a different LLE cluster. Similar to the findings reported by Kuwayama et al., it was determined that the combination of LLE and HS-SPME chemical profiles provides more accurate discrimination of 10

22 exhibits than either extraction procedure alone. 13,14 While this may be true for methamphetamine samples, similar studies comparing extraction procedures should be conducted for MDMA. Illicit MDMA tablets are typically not as high purity as methamphetamine, and the extra compounds, along with the less abundant controlled substance, in the tablets may allow for better discrimination using one extraction procedure over the other. 1.5 Gas Chromatography Temperature Programs All of the afore-mentioned studies were good resources to identify many of the compounds found in MDMA tablets; however, the profiles from studies like these are not readily comparable to each other because different instrument parameters and oven temperature programs were used to analyze the extracts. This makes comparisons difficult because the same compounds will elute at different retention times, depending on the temperature program. To make comparisons of impurity profiles among labs more valid, a standardized GC temperature program for MDMA impurity profiling should be investigated. While slower ramp rates (e.g. 5 C/min) and longer hold times (e.g. 15 minutes) may provide better separation of compounds with similar retention times, these conditions may also cause broadening of peaks due to the longer time the compounds spend in the column. Broadened peaks are not ideal in chromatography since they can mask lower abundance peaks eluting at similar retention times. Baerncopf et al. studied the effect of different GC temperature programs on the ability to associate and discriminate five different diesel samples. 15 In this study, six GC temperature programs, ranging from 15 to 113 minutes, with both one- and two-step ramps were adapted from the literature. The resulting total ion chromatograms were then subjected to analysis using PPMC coefficients and PCA. Association of replicates of the same diesel with discrimination of 11

23 the five different diesels was observed, irrespective of temperature program. While this study focused on diesel, similar data analysis procedures can be applied to the chemical profiles of MDMA tablets to investigate the effect of GC temperature program on association and discrimination of the tablets. Kuwayama et al. compared GC instrument parameters, as well as extraction procedures, for methamphetamine profiling. 13 Methamphetamine samples seized in Japan and Thailand were extracted using LLE procedures and then analyzed using two different GC methods commonly used in these countries. While both GC methods used very similar temperature programs, there were other notable differences in the methods. The method used in Japan (referred to as NRIPS) used an injection temperature that was 40 C lower than the method used in Thailand (referred to as ONCB). Additionally, the NRIPS method used a transfer line temperature that was 20 C higher and a final hold time that was 5 min shorter than the ONCB method. There were also differences in the flow rates and columns used for the analysis. In the NRIPS method, the carrier gas was maintained at a constant flow rate of 2 ml/min, while in the ONCB method, the carrier gas was maintained at constant pressure. While both methods used non-polar stationary phases, there were differences in the phase thickness (1.0 µm in the NRIPS method compared to 0.33 µm in the ONCB method). Phase thickness affects mass transfer of compounds between the carrier gas and stationary phase. Hence, this can lead to differences in retention time of compounds, and could allow for co-elution of compounds. After analyzing liquid-liquid extracts of the methamphetamine samples using each GC program, the NRIPS method used in Japan demonstrated improved chromatographic separation and greater success in detecting compounds, both of which are essential in chemical profiling. 12

24 However, along with different GC methods, the actual LLE procedures used were also different. 13 Although the buffer and solvent used in each case were similar, the sample concentration was nearly double in the ONCB method compared to the NRIPS method. The authors noted a large methamphetamine peak with the ONCB method that made it difficult to analyze minor impurities in high purity samples because of co-elution. This is likely a result of the increased concentration, not the GC parameters. Therefore, to truly evaluate the GC methods, the same extraction procedure should have been performed. A similar study comparing temperature programs and extraction procedures, with statistical analysis of the results, should be conducted for MDMA exhibits. A study such as this is essential in establishing a standard procedure for MDMA profiling. 1.6 Research Objectives The objectives of this research are as follows: (1) Use statistical procedures to investigate the effect of extraction procedures on association and discrimination of MDMA exhibits based on chemical profiles. (2) Use statistical procedures to investigate the effect of GC temperature programs on association and discrimination of MDMA exhibits based on chemical profiles. To address the first objective, impurities from five different MDMA exhibits will be extracted using LLE and HS-SPME procedures previously optimized in the literature. 6,12 The extracts will then be analyzed by GC-MS, using a temperature program available in the literature. Principal components analysis will be used to assess the effect of the extraction procedure on the ability to associate samples from the same exhibit and differentiate samples 13

25 from different exhibits. The second objective will be addressed by analyzing MDMA extracts, obtained using both LLE and HS-SPME, using different GC temperature programs, each taken from the literature. 4,6,8,12 Again, the effect of each program on the ability to associate samples from the same exhibit and discriminate exhibits will be investigated using PCA. Determining the effects of extraction procedure and temperature program on the chemical profiles is an essential step in creating a standard method for MDMA profiling. Currently, MDMA profiles are not comparable between forensic laboratories due to the different extraction and analysis procedures used. Establishing a standard extraction procedure and GC temperature program will allow for comparisons of MDMA profiles between forensic laboratories and will greatly benefit law enforcement in their ability to link exhibits to one another and to clandestine laboratories. 14

26 REFERENCES 15

27 REFERENCES 1. National Institute on Drug Abuse. Research Reports: MDMA (Ecstasy) Abuse. National Institutes of Health. Bethesda, MD. March, Available at: (Accessed March, 2011) 2. Smith, FP, Siegel JA, editors. Handbook of Forensic Drug Analysis. Burlington, MA: Elsevier Academic Press, National Institute on Drug Abuse DrugFacts: MDMA (Ecstasy). National Institutes of Health. Bethesda, MD. Revised December, Available at: (Accessed March, 2011) 4. Swist M, Wilamowski J, Zuba D, Kochana J, Parczewski A. Determination of synthesis route of 1-(3-4-methylenedioxyphenyl)-2-propanone (MDP2P) based on impurity profiles of MDMA. Forensic Sci Int 2005;149: Koper C, van den Boom C, Wiarda W, Schrader M, de Joode P, van der Peijl G, Bolck A. Elemental analysis of 3,4-methylenedioxymethamphetamine (MDMA): A tool to determine the synthesis method and trace links. Forensic Sci Int 2007;171: Van Deursen M, Poortman-van der Meer A. Organic impurity profiling of 3,4- methylenedioxymethamphetamine (MDMA) tablets seized in the Netherlands. Sci Justice 2006;46: Weyermann C, Marquis R, Delaporte C, Esseiva P, Lock E, Aalberg L, Bonzenko Jr. J, Dieckmann S, Dujourdy L, Zrcek F. Drug intelligence based on MDMA tablets data I. Organic impurities profiling. Forensic Sci Int 2008;177: Gimeno P, Besacier F, Chaudron-Thozet H, Girard J, Lamotte A. A contribution to the chemical profiling of 3,4-methylenedioxymethamphetamine (MDMA) tablets. Forensic Sci Int 2002;127: Cheng J, Chan M, Chan T, Hung M. Impurity profiling of ecstasy tablets seized in Hong Kong by gas chromatography-mass spectrometry. Forensic Sci Int 2006;162: Bolck A, Weyermann C, Dujourdy L, Esseiva P, van den Berg J. Different likelihood ratio approaches to evaluate the strength of evidence of MDMA tablet comparison. Forensic Sci Int 2009;191:

28 11. Bonadio F, Margot P, Delémont O, Esseiva P. Headspace solid-phase microextraction (HS-SPME) and liquid-liquid extraction (LLE): Comparison of the performance in classification of ecstasy tablets (Part 2). Forensic Sci Int 2008;182: Bonadio F, Margot P, Delémont O, Esseiva P. Optimization of HS-SPME/GC-MS analysis and its use in the profiling of illicit ecstasy tablets (Part 1). Forensic Sci Int 2009;187: Kuwayama K, Inoue H, Phorachata J, Kongpatnitiroj K, Puthaviriyakorn V, Tsujikawa K, Miyaguchi H, Kanamori T, Iwata Y, Kamo N, Kishi T. Comparison and classification of methamphetamine seized in Japan and Thailand using gas chromatography with liquid-liquid extraction and solid-phase microextraction. Forensic Sci Int 2008;175: Lee J, Park Y, Yang W, Chung H, Choi W, Inoue H, Kuwayama K, Park J. Crossexamination of liquid-liquid extraction (LLE) and solid-phase microextraction (SPME) methods for impurity profiling of methamphetamine. Forensic Sci Int 2012;215: Baerncopf J, McGuffin V, Waddell Smith R. Effect of gas chromatography temperature program on the association and discrimination of diesel samples. J Forensic Sci 2010;55:

29 Chapter 2 Theory 2.1 Liquid-Liquid Extraction Extraction procedures such as liquid-liquid extraction (LLE) are used to separate compounds in a complex sample based on their solubilities. 1 In LLE, a water-based solvent and an organic solvent are typically used because these two liquids are immiscible. The sample is first dissolved in an aqueous solvent, and then extracted into an organic solvent, such as toluene, which can be used for analysis by gas chromatography-mass spectrometry (GC-MS). Since the two liquids do not mix, two distinct layers are formed, allowing for a clean separation of the desired organic layer from the aqueous layer. Only substances which are soluble in the organic solvent are extracted into that layer. Therefore, solvents must be chosen for each sample based on the compounds desired to be extracted. Often, the aqueous solvent is a buffer, such as the phosphate buffer that was used in this research. 1 Buffers act to keep a constant ph by resisting change when either an acid or base is added. In this research, a buffer with a neutral ph of 7.0 was used to correct for the influence of any compounds that may have been either slightly basic or acidic. Weakly basic compounds are extracted slightly more efficiently at a higher ph, but a ph above 7 leads to an excess of MDMA being extracted. 2 Therefore, a neutral ph is preferable for the extraction of MDMA tablets. 2.2 Headspace Solid Phase Microextraction Solvent-free sampling methods are often beneficial due to their ease of use, low cost, and reduced safety hazards. 3 Solid phase microextraction (SPME) is a type of solvent-free extraction that was invented in the early 1990s by Janusz Pawliszyn. This sampling method is used to 18

30 minimize sample preparation and avoid destruction of the sample. In SPME, a fiber coated in a sorbent material adsorbs the compounds until an equilibrium is reached between the sample and the fiber coating. The coating can be solid, liquid, or a combination of the two. Equation 2.1 describes this equilibrium to give the number of moles, n, of compound that is extracted from the sample onto the fiber coating: 3 Equation 2.1 where K fs is the distribution constant for the fiber coating/sample matrix, which is the ratio of equilibrium concentration of compound on the fiber (C f ) to compound in the sample (C s ); V f is the volume of the fiber coating; V s is the volume of the sample; and C 0 is the initial concentration of compound in the sample. There are several types of fiber coatings available, varying in material and thickness, and each coating leads to a different K fs. 3 Therefore, both the composition and thickness of the coating must be chosen specifically for each application. For example, a thicker coating (e.g. 100 µm) means a greater coating volume and, therefore, increased extraction of compound, but it also leads to longer extraction and desorption times. Thin coatings are more effective at extracting low volatility compounds, while thicker coatings are used for extracting high volatility compounds. The most practical and common coating is a liquid polydimethylsiloxane (PDMS), which is popular because of its ability to withstand high temperatures (up to 300 C), as well as its ability to extract compounds with wide ranges of volatility and polarity. This is achieved by altering the thickness of the coating or combining it with another coating. Divinylbenzene (DVB) is often used with PDMS as a solid/liquid combination coating because of its uniform structure, 19

31 which allows for a wider range of molecular weights of compounds to be adsorbed. In this research, headspace SPME (HS-SPME) was used to extract impurities from the MDMA tablets. Using the headspace approach, the sample is often heated in a vial to increase vapor pressure and volatility of the compounds, therefore increasing the concentration of compounds in the headspace where they can be directly extracted onto the fiber coating. 3 Using this method, volatile compounds pass through the air before reaching the fiber, thus protecting the fiber from contact with the sample matrix and reducing the amount of cleaning of the fiber after desorption. Figure 2.1 depicts the HS-SPME extraction setup used in this research, with a sample in a closed vial and the fiber inserted through the cap of the vial. The compounds move from the sample into the headspace and are extracted from the headspace onto the fiber. When using HS-SPME, Equation 2.1 is modified to account for the compounds in the headspace, as shown in Equation Equation 2.2 K fs, which represented the distribution constant between the fiber and sample matrix in Equation 2.1, now requires two terms: K fh and K hs. K fh is the distribution constant for the fiber and headspace, and is defined as the ratio of equilibrium concentration of compound on the fiber (C f ) to compound in the headspace (C h ), and K hs is the distribution constant for the headspace and sample. V h refers to the volume of the headspace. 20

32 fiber holder fiber headspace, moving analytes sample vial sample Figure 2.1: Headspace solid phase microextraction (HS-SPME) setup. Alterations to the basic extraction procedure may be made to increase the number of moles of compound extracted onto the fiber. Dissolving the sample in a buffer can change the distribution constants between the sample and headspace or fiber, and the ph of the buffer can be altered to determine the optimal distribution ratios. 3 Oftentimes, altering the ph can increase the extraction of certain compounds. For example, increasing the ph to allow for more extraction of slightly basic compounds. Changing the temperature of the sample can also affect the distribution ratios, but without using any chemicals. Heating the sample vial in a water bath, for example, increases the volatility of the compounds, and therefore increases the concentration of compounds in the headspace, leading to more compounds being extracted onto the fiber. However, since increasing the temperature increases the concentration of compounds extracted, it can cause overloading of the GC column and can cause co-elution of compounds. While there are many advantages to HS-SPME, there are several potential disadvantages. Pre-concentrating the fiber allows for the extraction of trace compounds, but it also increases the concentration of high abundance compounds, which again can cause overloading of the GC 21

33 column. Therefore, the extraction time and temperature must be individually optimized for different sample types to determine the parameters that form a compromise between extracting trace compounds and overloading abundant compounds. Overall, HS-SPME is a less reproducible extraction procedure than LLE because of the variability in the concentration of compounds extracted onto the fiber. This can cause difficulties during data analysis, as peaks in the chromatograms do not always align, and the precision of peak areas between replicates is, in general, lower than those obtained using LLE. 2.3 Gas Chromatography Chromatography is a separation technique that uses two phases: a stationary phase which is bonded to a vessel, such as a column, and a mobile phase that flows over the stationary phase. 1 As the sample mixture moves through the column, individual compounds are separated over time based on the amount of interactions of each compound with the stationary phase. There are a variety of chromatography procedures, such as thin layer chromatography, liquid chromatography, and gas chromatography. In forensic laboratories, gas chromatography (GC) is the most common procedure used for a variety of applications, such as drug analysis and fire debris analysis. A schematic of a typical GC instrument is shown in Figure 2.2. A cylinder containing the carrier gas is connected to the GC, where the gas combines with the sample from the injector port and flows onto the column. 1 The sample and gas are carried through the column, which contains the stationary phase and is housed inside an oven. The compounds in the sample are separated in the column and reach the detector at different times. A chromatogram is shown on the monitor, which displays a peak for each compound as it elutes from the column. 22

34 injector port monitor detector column oven carrier gas tank Figure 2.2: Components of a gas chromatography (GC) system. For analysis, the sample mixture is usually dissolved in a liquid, and the solution is injected into the heated injector port of the GC. 1 Injection volumes are typically 1 µl, and, for very concentrated samples, much of this volume is discarded to prevent overloading the column. This is known as a split injection and a user-set split ratio determines how much of the sample goes onto the GC column and how much is discarded. For example, using a 50:1 split ratio, 50 parts of the sample/carrier gas mixture are discarded for every 1 part that is carried onto the GC column. In contrast, using a splitless injection, all the injected sample is transferred onto the column for analysis. Since the samples in this research contained several trace impurities, a splitless injection was used. 23

35 The temperature of the injector port is selected by the user, and needs to be sufficiently high (typically ~ 250 C) to completely volatilize the sample to ensure that all of the sample is in the gas phase and can enter the GC column. 1 In the injector port, the carrier gas (i.e., the mobile phase) carries the sample onto the GC column. The carrier gas must be of high or ultra-high purity so that no additional compounds from the gas are observed in the sample. Ultra-high purity helium, hydrogen, or nitrogen are common choices because, as well as being high purity, they are light and inert, and, therefore, will not react with the sample. Helium is the preferred gas when a mass spectrometer is used as the detector in GC because of its stability, its high viscosity and diffusivity that allow for efficient chromatographic separation, as well as its inertness and high purity as described above. The flow rate of the carrier gas is also set by the user, and is generally 1 ml/min when a mass spectrometer is used as the detector. GC columns are typically open tubular columns, which are fused silica with a polyimide coating. The inner walls are coated with the stationary phase, such as polyethylene glycol or dimethyl polysiloxane. 1 The length of a GC column used for drug analysis is typically around 30 m, the internal diameter around 0.25 mm, and the stationary phase thickness around 0.25 µm. Stationary phases range in polarity, and are chosen specifically for the samples of interest. Combination stationary phases are available to account for a range of polar compounds. For example, in this research, the stationary phase consisted of 5% diphenyl and 95% dimethyl polysiloxane. This is a low polarity stationary phase, but it is more polar than 100% dimethyl polysiloxane. This stationary phase is recommended for drugs of abuse because of its slight polarity, which makes it efficient at analyzing the controlled substances, which are slightly polar. This column is also an ultra-low bleed column, meaning there is limited deterioration of the column observed in the chromatograms at high temperatures. 24

36 The GC column in housed inside an oven, the temperature of which can be changed during the analysis. 1 A compound will elute from the column (and reach the detector) when the oven temperature is near the boiling point of the compound. The time taken for the compound to pass through the column and reach the detector is known as the retention time of the compound. The temperature program may be run isothermal, meaning that the temperature remains constant throughout the analysis. This allows for the better separation of compounds that have similar boiling points (other factors effecting retention time will be discussed later). However, isothermal analysis leads to longer retention times since separation of compounds is based purely on interactions with the stationary phase and is not aided by temperature. For sample mixtures containing compounds with a wide range of boiling points, the oven temperature can be varied during the analysis. Typically, the oven temperature is ramped from a low initial temperature to a high final temperature. Faster ramp rates (e.g. 20 C/min) result in a shorter overall analysis, but may not allow sufficient resolution between compounds that have similar retention times. If the sample contains some compounds that have similar boiling points and hence, would have similar retention times, then a slower ramp rate (e.g. 5 C/min) may be used or the oven may be held at a specific temperature during the temperature program. In addition to boiling point, the retention time of a compound is affected by its interactions with the stationary phase. 1 While the sample mixture is being carried through the column, compounds in the mobile phase interact with both the surface (via adsorption interactions) and the bulk (via absorption interactions) of the stationary phase. The extent of the interactions determines the retention time of the compound. Compounds that have extensive interactions will have longer retention times than those compounds that have less interaction with the stationary phase. 25

37 Ideally, in an efficient chromatography system, each compound in the sample mixture is resolved from the others, and has a Gaussian-shaped peak in the resulting chromatogram. However, due to the column interactions, some band broadening occurs, resulting in less efficient separations. The efficiency of a chromatography system is defined in the van Deemter equation (Equation 2.3), in which H is the theoretical plate height. 1 More efficient chromatography systems have a lower H value. Therefore, to improve efficiency, the three terms in the van Deemter equation should be minimized. Equation 2.3 The A term in Equation 2.3 describes the ability of the mobile phase to take multiple paths through the stationary phase. However, this term is not applicable in GC since the stationary phase is a liquid, rather than a solid. The B term in the equation is the molecular diffusion term. Diffusion occurs because of the compounds' tendency to move from an area of high concentration to low concentration. The sample enters the column as a narrow band, but as it travels through the column it spreads from its highly concentrated band into areas of the mobile phase that have a low concentration of the sample. B accounts for this phenomenon, and can be improved by increasing the flow rate of the mobile phase, u, allowing less time for the band to spread. However, increasing the flow rate also increases the C term, which accounts for mass transfer of the compounds. As the mobile phase moves across the stationary phase and interactions take place, some compounds prolong interactions with the column while others continue to move, resulting in band broadening. Re-establishing equilibrium between the mobile and stationary phases takes place as the mobile phase flows through the column, but requires time and is, therefore, more efficient with a slower flow rate. However, the C term can also be 26

38 minimized by decreasing the stationary phase thickness, which allows compounds to diffuse faster from the stationary phase back into the mobile phase. While the above factors can lead to broadening of peaks, there are other factors that can also distort the Gaussian shape of peaks. 1 Figure 2.3 displays the ideal symmetrical peak shape, as well as a fronting peak and a tailing peak. Isotherms, which display the concentration of sample in the stationary phase (C s ) versus the concentration in the mobile phase (C m ), can be seen below the corresponding peak shapes. Fronting peaks are caused by overloading the column, so that C s is greater than C m. If a sample is too concentrated, more interactions take place with the stationary phase so that the stationary phase may become overloaded with a compound. This leaves little compound in the mobile phase while a large concentration of the compound lags behind in the stationary phase. The compound then elutes gradually from the column, but ends abruptly. Tailing peaks, on the other hand, occur when C s is less than C m. These are caused by hydrogen bonding at active sites in the stationary phase to polar compounds in the sample. As the column degrades, OH groups are exposed in the stationary phase, which may bind to some of a polar compound, causing it to trail behind the rest. As the compound elutes from the column, the end of the peak is a more gradual decrease in concentration than is ideal since some of the compound sticks to the column longer. 27

39 Gaussian Fronting Tailing Cs Cs Cs Abundance Abundance Abundance Time Time Time C m C m C m Figure 2.3: Ideal peak shape (left), fronting peak (center), and tailing peak (right) with corresponding isotherms below. The results of GC analysis are displayed in the form of a chromatogram. 1 These graphs display retention time along the x-axis and abundance along the y-axis. Retention times for compounds are reproducible when the same type of column and same instrument parameters (including temperature program, injector port parameters, and flow rate) are used. Therefore, reference standards analyzed under the same conditions, ideally, using the same instrument and column, can be used to identify compounds by comparing retention times. However, multiple compounds can elute at very similar retention times, and it may not be possible to distinguish certain compounds based on retention time alone. Therefore, to definitively determine the unique identity of a compound, mass spectrometers are often used as the detector for the GC system. 28

40 2.4 Mass Spectrometry Mass spectrometry (MS) is a type of detector commonly used with gas chromatography, especially in forensic laboratories for the identification of controlled substances. Figure 2.4 depicts a schematic representation of a mass spectrometer. As compounds elute from the GC column, which is at atmospheric pressure, they enter the ion source of the mass spectrometer, which is under vacuum to prevent ions from being deflected by collisions with gaseous molecules. 1 The MS is typically pumped down in stages: first, a rough pump reduces the pressure to torr, then a turbomolecular or diffusion pump is used to reduce it even farther, the approximately 10-5 torr. Sample molecules enter the ion source from the GC. Here, the molecules are ionized and the ions are then transferred to the analyzer, where they are separated by their mass to charge ratio (m/z). Ions within a specified range of m/z then reach the detector, and a mass spectrum is generated based on the m/z present for each compound. 29

41 ion source mass analyzer ion detector vacuum GC data acquisition Figure 2.4: Components of a mass spectrometry (MS) system. In the ion source of the MS, compounds are ionized and fragmented. A common method of ionization, and the method used in this research, is electron ionization (EI). The EI source contains a filament which emits electrons with, typically, 70 ev energy, that travel in spiral pathways toward a collection plate. Electrons of 70 ev are commonly used in EI since this is in excess of the energy needed to break most organic bonds (typically 4-20 ev required). As the sample molecules enter the ion source, they interact with these electrons. The sample molecules absorb some of the energy, which causes them to lose an electron, thus becoming ionized. This process forms a molecular ion, designated M+. Since 70 ev electrons provide excess energy, following ionization, there is sufficient excess energy to cause fragmentation of the molecular ion. A positive repeller plate then pushes the ions toward focus plates, which apply a high voltage (~1,000 10,000 V) to focus the ions into a narrow beam and accelerate them into the analyzer. 30

42 While the molecular ion is important in determining the molecular mass of a compound, fragments are important in determining structural information, which can be useful in determining the identity of the original compound. Due to the excessive fragmentation that typically occurs in EI, this ionization mechanism is termed a hard ionization method and is commonly used where structural information is necessary. There are several types of mass analyzers available, but perhaps the most commonly used in GC-MS instruments is the quadrupole mass analyzer (Figure 2.5), which was also used in this research. 1 The quadrupole consists of four metal rods, arranged as two opposite pairs, to which an oscillating radio frequency (RF) and a direct current (DC) voltage are applied. Ions are accelerated in to one end of the quadrupole, where their path is altered by the ratio of the RF and DC voltages. For a given RF/DC ratio, only ions within a narrow m/z range have a stable trajectory and reach the detector at the opposite end. All other ions are neutralized by colliding with the rods, and then pumped away by the vacuum system. The RF and DC can be altered, while keeping the ratio constant, to allow ions of different m/z to reach the detector. During analysis, the RF/DC is scanned for a specific range of m/z (e.g m/z) depending on the molecular weights of the compounds of interest. 31

43 Figure 2.5: Quadrupole mass analyzer. Ions of stable trajectory pass through the quadrupole to the detector, which, in this research, was an electron multiplier. Figure 2.6 depicts an electron multiplier, which contains a series of dynodes, arranged in a horn-like funnel, that release electrons when struck by ions. The electrons released by the first impact then hit the opposite side of the funnel, where more electrons are released and hit the opposite side and so on. The original signal is multiplied by ~10 5 when it reaches the anode where the current is detected. secondary electrons signal out ion path Figure 2.6: Electron multiplier. 32

44 The results of analysis by MS are in the form of a mass spectrum, which displays m/z along the x-axis and abundance, as determined by the amount of current detected by the electron multiplier, along the y-axis. 1 For ions produced by EI, the charge (z) is typically equal to 1, therefore, in the resulting mass spectrum, the x-axis is equivalent to the ion mass. Mass spectra are used to identify compounds based on the molecular ion as well as fragments, from which structural elucidation is possible. Fragmentation of a compound occurs in a reproducible manner under the same ionization and analysis conditions. Therefore, the original structure of a compound can be determined based on the mass of the molecular ion, as well as the mass of the fragments formed. This also allows for definitive identification of compounds, since each mass spectrum is unique to a specific compound. In controlled substance identification, the mass spectrum of a questioned sample can be compared to the spectrum of a known reference standard collected using the same instrument parameters or to a database library containing reference spectra. The spectra are compared based on the presence and absence of each m/z, as well as the ratios of the ions that are present. When MS is used as the detector for GC, a full mass spectrum is collected for every separated compound in the sample mixture. Therefore, a questioned sample and reference standard can be compared based on both retention time and the mass spectra, allowing definitive identification of the substance in the questioned sample. 2.5 Data Pretreatment Due to variations in the GC-MS instrument during analysis, non-chemical sources of variance can be introduced into the chromatograms of the samples. Examples of such variations include flow rate fluctuation or column degradation leading to retention time drift, dissimilarities 33

45 in sample injection size imparting differences in abundance between replicates, and deterioration of the GC column at high temperatures causing high background signal. Prior to data analysis, such variance should be removed since this otherwise could be identified as differences among samples. Numerous data pretreatment procedures are available and are selected according to the source of variation to be minimized or eliminated. In this research, the major sources of variance were retention time drift and differences in the volume of sample injected for analysis by GC-MS. Retention time drift is a result of minor column deterioration over time as well as slight differences in operating temperatures and carrier gas flow rates. 4 Differences in the volume of sample injected results in variability in abundance of the same compound among replicates of a given sample. 5 In this research, an alignment algorithm was used to minimize the effects of retention time drift, while normalization was used to account for differences in injection volume Retention Time Alignment A correlation optimized warping (COW) algorithm was used in this research to retention time align chromatograms. Using the COW alignment, chromatograms are aligned to a target chromatogram. While there are numerous methods to select or generate a target chromatogram, an average chromatogram was used in this research. To generate the average target chromatogram, the abundances of all chromatograms in the dataset are averaged at each retention time. The average abundances are then plotted to create a chromatogram representative of all samples. The first step in the COW alignment is to split the target and sample chromatograms into segments. 4 The segment size refers to the number of datapoints within each segment. This is a 34

46 user-defined parameter and is selected based on the number of datapoints within the entire chromatogram and the approximate number of datapoints across each peak. The segment size should be greater than the number of points across any single peak. Each chromatogram is then individually aligned to the target chromatogram by shifting, or warping, a certain number of datapoints within each segment. 4 The warp is also a user-defined parameter and typical warp sizes are 1-10, depending on the size of the peaks. The warp refers to the number of datapoints that can be added or subtracted for each peak. The COW algorithm begins at the end of the chromatogram and aligns one segment at a time. 6 For each segment, correlation coefficients are calculated for each warp (i.e. for a warp of 10, any number of datapoints up to 10 may be added or subtracted at each segment) and the warp that results in the highest coefficient is considered the optimal alignment of that segment. However, coefficients for all warps are stored. Then, the algorithm moves to the next segment and repeats. Once each segment has been aligned, a global correlation coefficient is calculated to determine the optimal alignment of the chromatogram to the target. This entire process is repeated for all chromatograms in the dataset. The algorithm determines the alignment based on peak shape, not height or area. 6 This means that the apex of the peak could be aligned to the leading or tailing edge of a peak in the sample yet still produce a high correlation coefficient. Therefore, visual assessment of aligned chromatograms is recommended, and the optimal parameters are somewhat determined on a trial and error basis. This type of alignment is beneficial for datasets containing samples with varying abundances of the same compounds since the peaks are aligned even if the heights differ. For this reason, normalization is often a necessary step after alignment to account for differences in 35

47 abundance of the same compound, especially among sample replicates Normalization There are several methods of normalization reported in the literature, but the methods used in this research included total area and maximum peak normalization, as well as investigations of logarithmic, square root, and fourth root normalizations. This data pretreatment procedure is used to account for differences in abundance which are due to differences in injection volume, as well as differences in detector response. Normalization works by dividing each of the chromatograms in a dataset by a constant, such as the maximum abundance or the total area of a chromatogram, therefore putting them all on the same scale. 5 In maximum peak normalization, for example, the highest abundance in each chromatogram is determined and the average maximum across the dataset calculated. Then, the abundance at each retention time is divided by the maximum abundance of that chromatogram, and multiplied by the average maximum to bring it back up to same order of magnitude as the original data. Since, experimentally, these values are not constant across all chromatograms (i.e. maximum abundance not identical for each sample), some procedures may be more efficient at normalizing a specific dataset than others. 2.6 Principal Components Analysis Principal components analysis (PCA) is a statistical procedure used to evaluate multidimensional data, such as chromatograms which contain thousands of datapoints (i.e. variables). Variance in the dataset is accounted for by principal components (PCs), the number of which is equal to or less than the number of variables being investigated. 7 The magnitude of 36

48 each PC describes the percentage of variance that is accounted for by that particular PC, and is referred to as the eigenvalue. Typically, nearly all of the variance in a dataset can be accounted for using the first three to four PCs. PCA uses mean-centered data to determine which variables (i.e. compounds) contribute most to the variance, as well as how these variables interact with each other. 8 Mean-centering the data is a form of scaling, which removes any influence from the magnitude of the data, and ensures that first principal component accounts for the most variance. For chromatographic data, the mean-centered data are calculated by averaging the abundance at each retention time in the dataset. The average abundance is then subtracted from the abundance at the corresponding retention times in each individual sample. Mean-centered data are then used to calculate the covariance matrix for dataset. The covariance of a dataset (Equation 2.4) describes how the dimensions (i.e. datapoints or selected compounds) vary from the mean in similar manners. 8 The covariance can be either positive, meaning that both dimensions increase or decrease simultaneously, or negative, meaning that one dimension increases as the other decreases. The equation for covariance is shown below, with x and y representing variables in these dimensions and n representing the total number of dimensions in the dataset. 7,8 Equation 2.4 The covariance is calculated for all variables in the dataset and is displayed in the covariance matrix, which is n n in dimension. For example, for a dataset of three dimensions (x, y, and z), the covariance matrix would be 3 3, as seen below: 8 37

49 The diagonal of the matrix represents the covariance of each dimension with itself, or the variance, and the matrix is symmetrical around the diagonal; that is, the covariance between x and z is equivalent to the covariance between z and x. From the covariance matrix, eigenvectors and eigenvalues are then calculated. 7 Eigenvectors are vectors which can be multiplied by the covariance matrix to yield a vector that is a multiple of the original. Eigenvalues are the values by which the original vectors were multiplied. A dataset of n dimensions has n eigenvectors, all orthogonal to one another, and each eigenvector has a corresponding eigenvalue. The first eigenvector, also known as the first principal component (PC1), accounts for the most variance, and the amount of variance accounted for decreases with each succeeding PC. PCA can be used to provide a visual representation of the data in a graph of two PCs, referred to as a scores plot. 7,8 Any PCs can be plotted on a scores plot, but since the first two PCs account for the most variance, a scores plot typically displays PC1 and PC2 on the x-axis and y-axis, respectively. To generate the score on PC1 for a given sample, the loadings at each retention time are first calculated by multiplying the mean-centered data by the eigenvector for PC1. The score for the sample (i.e. one chromatogram) on PC1 is the sum of these loadings at all datapoints (i.e. retention times). Scores for the sample on other PCs are calculated in a similar manner, using the appropriate eigenvector. 7 On the scores plot, chemically similar samples cluster closely and chemically dissimilar samples are spread across the plot, thus allowing a visual distinction of the samples. 38

50 Loadings plots can also be generated, and used to explain positioning of the samples on the scores plot. 7,8 In this research, for example, the loadings at each retention time for PC1 and PC2 were individually plotted against retention time. Within the loadings plots some compounds load negatively due to mean-centering the data. Samples containing a high abundance of a compound that is weighted positively in the loadings plot for PC1, for example, will therefore be positioned positively on PC1 in the scores plot. Additionally, since the loading plots can be plotted against retention time, the resulting plots resemble chromatograms. As a result, the compounds in the loadings plots, which contribute most to the variance being described, can be identified based on retention time. 39

51 REFERENCES 40

52 REFERENCES 1. Harris D. Quantitative Chemical Analysis, Seventh Edition. New York, NY: W. H. Freeman and Company, van Deursen M, Poortman-van der Meer A. Organic impurity profiling of 3,4- methylenedioxymethamphetamine (MDMA) tablets seized in the Netherlands. Sci Justice 2006;46: Pawliszyn J. Solid Phase Microextraction Theory and Practice. New York, NY: Wiley- VCH, Inc., Tomasi G, van den Berg F, Andersson C. Correlation Optimized Warping and Dynamic Time Warping as Preprocessing Methods for Chromatographic Data. J Chemometrics 2004;18: Beebe K, Pell R, Seasholtz M. Chemometrics: A Practical Guide. New York, NY: John Wiley & Sons, Inc., Nielsen N, Carstensen J, Smedsgaard, J. Aligning of Single and Multiple Wavelength Chromatographic Profiles for Chemometric Data Analysis using Correlation Optimised Warping. J Chromatogr A 1998;805: Brereton R. Applied Chemometrics for Scientists. West Sussex, England: John Wiley & Sons Ltd., Smith L. A Tutorial on Principal Components Analysis Available at: (Accessed June, 2012). 41

53 Chapter 3 Materials & Methods 3.1 MDMA Exhibits Five MDMA exhibits were received from various Michigan State Police laboratories for this study. In this research, "exhibit" refers to batches of tablets which were seized together and thus assigned an identifier by the police. Figure 1 shows photographs of one representative tablet from each of the five exhibits, and the physical characteristics of each exhibit are outlined in Table 1, listed by their police-assigned identifiers. Seven tablets from each exhibit were homogenized with a mortar and pestle, and samples were taken from these homogenized batches for subsequent extraction and analysis. (a) (b) (c) (d) (e) Figure 3.1: MDMA exhibits used in this study (a) T-17 (b) T-27 (c) T-29 (d) T-30 (e) MSU