Differentiation of Ignitable Liquids in Fire Debris Using Solid-Phase

Differentiation of Ignitable Liquids in Fire Debris Using Solid-Phase

Microextraction Paired with Gas Chromatography-Mass Spectroscopy and

Chemometric Analysis

A Thesis Presented to The Honors Tutorial College

Ohio University

In partial Fulfillment of the Requirements for Graduation from the Honors Tutorial

College

With the degree of Bachelor of Science in Chemistry

By Amanda McKeon

May 2019

Abstract: A method for testing fire debris to identify common ignitable liquids present in fire debris samples is proposed. The sampling method used is solid-phase microextraction paired with gas chromatography-mass spectrometry. The data was analyzed using principal component analysis and linear discriminant analysis. This method was successfully implemented to classify burned carpet samples as belonging to one of three classes: carpet samples with no ignitable liquids present, carpet samples with gasoline present, and carpet samples with diesel fuel present. This thesis has been approved by

The Honors Tutorial College and the Department of Chemistry and Biochemistry at Ohio

University

______

Dr. Peter de B. Harrington

Professor, Chemistry

Thesis Advisor

______

Dr. Lauren McMills

Director of Studies, Chemistry

______

Cary Frith

Interim Dean, Honors Tutorial College Table of Contents

Introduction 1

Chapter 1: Challenges of Classification 4

Chapter 2: Methods of Extraction and Concentration 10

Chapter 3: Analytical Instruments 16

Chapter 4: Chemometric Analysis 22

Chapter 5: Research Project 28

Conclusion and Acknowledgements 48

Figure Description Example of Random Scission Showing the Breakdown of Polyethylene As Shown in Stauffer’s Paper 1 Concept of Pyrolysis for Fire Debris Analysis Example of Side Group Scission Showing the Breakdown of Polyvinyl Chloride As Shown in Stauffer’s 2 Paper Concept of Pyrolysis for Fire Debris Analysis Example of Monomer Reversion Showing the Breakdown of Polymethacrylate As Shown in Stauffer’s Paper 3 Concept of Pyrolysis for Fire Debris Analysis Diagram of a) A Commercial SPME Syringe Setup b) A Close-Up Cross-Sectional View of the Adjustable 4 Depth Gauge 5 Diagram of a Gas Chromatograph-Mass Spectrometer A Two-Dimensional Data Set, Including Its Two Principal Components, As Seen in Vidal et al.’s 2016 Book 6 Generalized Principal Component Analysis 7 A Score Plot Between Principal Components 1 and 2 From a Set of Data Obtained by Monfreda and Gregori 8 Total Ion Chromatograms of Diesel Samples 9 Total Ion Chromatograms of Gasoline Samples 10 Average Mass Spectra of Diesel Fuel Samples 11 Average Mass Spectra of Gasoline Samples 12 Total Ion Chromatograms of Class A 13 Total Ion Chromatograms of Class B 14 Total Ion Chromatograms of Class C 15 Total Ion Chromatograms of Class D 16 Total Ion Chromatograms of Class E 17 Total Ion Chromatograms of Class F 18 Total Ion Chromatograms of Class G 19 Total Ion Chromatograms of Class H 20 Total Ion Chromatograms of Class I 21 Total Ion Chromatograms of Class J 22 Total Ion Chromatograms of Class K 23 Total Ion Chromatograms of Class L 24 Total Ion Chromatograms of Class M 25 Total Ion Chromatograms of Class N 26 Total Ion Chromatograms of Class O 27 Total Ion Chromatograms of Class P 28 Total Ion Chromatograms of Class Q 29 Total Ion Chromatograms of Class R 30 Total Ion Chromatograms of Class S 31 Total Ion Chromatograms of Class T 32 Total Ion Chromatograms of Class U 33 Total Ion Chromatograms of Class V 34 Total Ion Chromatograms of Class W 35 Two-Way Representation of the Average Diesel Fuel Data 36 Two-Way Representation of the Average Gasoline Data 37 PCA Score Plot of the Three-Way Chromatographic and Spectral Data 38 PCA Score Plot of TIC Data 39 PCA Score Plot of Mass Spectral Data 40 Linear Discriminant Score Plot of the Combined TIC and Mass Spectral Data Table Description 1 Legend of Class Letters 1

Introduction

Since at least the nineteen-fifties, it has been accepted that arson is one of the most dangerous and destructive crimes to be committed. As Wakefield pointed out in his

1951 publication, “it is a weapon for murder, robbery, assault, fraud, revenge, spite, and, when completed, has destroyed in many cases the evidence that it was even committed.”1

The history of chemical fire debris analysis is a relatively short one. While there are several papers on arson investigation published in the 1950’s,1–5 the first paper that discussed the detection of trace amounts of fire accelerant was not published until 1960.6

As Burd explains in this paper, “While fire investigators can frequently detect that flammable liquids were used or could have been used at a fire scene, they frequently encounter difficulty in recovering suitable samples of debris from which the laboratory can separate identifiable amounts of the fluid employed.”6 This problem is of vital importance because, in terms of criminal justice, the case is not decided because the perpetrator has been identified. It is the job of investigators and forensic experts to provide sufficient evidence to convict the perpetrator in a court of law. Without this evidence, it is very likely that they will continue to commit crimes, thus disrupting society.

Stauffer et al. defined fire debris analysis as “the science related to the examination of fire debris samples performed to detect and identify ignitable liquid residues (ILR).”7 In other words, it is the application of analytical chemistry to evidence that has been collected from the scene of a suspected arson or explosion with the intent to determine if an ignitable liquid or accelerant was used to start or increase the rate of a fire. While many people outside of the field believe that the terms “ignitable liquid” and 2

“accelerant” are synonymous, their definitions vary slightly. The main difference is that, while an ignitable liquid can be used as an accelerant, it does not have to be used as an accelerant. For example, gasoline is an ignitable liquid, but in its day-to-day use, it is not an accelerant if used for its intended purpose. An accelerant is anything that serves to increase the rate of a conflagration and can be a liquid, solid, or gas. The publications reviewed in this thesis focus on ignitable liquids that are commonly used as accelerants.

Ignitable liquids can be broken down into seven main classes, as defined by the

American Society of Materials and Testing (ASTM). These classes are 1) aromatic products such as xylenes and some lamp oils, 2) gasoline, 3) petroleum distillates such as kerosene and diesel fuel, 4) isoparaffinic products such as some paint thinners and mineral spirits, 5) naphthenic-paraffinic products such as industrial solvents, 6) normal alkane products such as pentane and some toners, and 7) oxygenated solvents such as alcohols and some lacquer thinners. If a sample does not meet the criteria for any of the seven categories, it is classified as miscellaneous. Additionally, all categories except gasoline can be further broken down into light, medium, or heavy subclasses, based on the number of carbon atoms per molecule.8 Samples are typically categorized by matching a chromatogram of the sample to a chromatogram of a known standard, as specified in ASTM E1618-01.9 It is important to recognize that different categories of ignitable liquid will behave differently based on their chemical compositions. For example, Fettig et al. used only two different types of ignitable liquids: gasoline and diesel fuel. While the intended uses of these two fluids are very similar, they consist of

“different substances with a variety of components and different polarities.”10 Both liquids contain alkanes and alkylbenzenes, but gasoline also contains toluene, indane, and 3 xylene, while diesel fuel contains dodecane, tetradecane, and eicosane, among other compounds. Fettig et al. found that even between these two functionally similar liquids, there were differences in behavior. Because diesel fuel has components with a wider range of boiling points, a higher temperature was determined to be ideal for extraction based on data from several trials with differing extraction temperature, thus highlighting the different behaviors of the two liquids when heated.

This thesis proposes and validates a method of testing fire debris analysis in suspected arson cases for the presence of ignitable liquid residues. The background of the field of fire debris analysis was thoroughly researched in order to design a suitable thesting method. This project focuses on the use of solid-phase microextraction paired with gas chromatography-mass spectrometry to collect sample data. The data was then analyzed using principal component analysis and linear discriminant analysis. All methods used are described in detail in the following chapters. 4

Chapter 1: Challenges of Classification

One of the main challenges of analyzing fire debris is that, by the nature of fire itself, once the crime is complete, much of the evidence where it took place has been destroyed. In fact, one of the reasons people commit arson, as laid out by Wakefield, is to cover up evidence of another crime.1 There are several reasons that only residues from ignitable fluids would be left on fire debris, the first being the chemical makeup of the fluids. Most ignitable fluids are made up of several components, some of which are highly volatile, meaning they have very low boiling points, and therefore evaporate quickly in the heat of a fire, which is referred to as thermal weathering. Weathering is problematic because a fire debris chromatogram may have missing or attenuated peaks from the volatile compounds that are typically found in the earlier parts of the chromatogram. The fire debris chromatogram often may appear to be very different form the reference chromatogram of the pure ignitable fluid.11

Another problem that arises due to the heat of the conflagration is the phenomenon of pyrolysis. Stauffer defined pyrolysis as “the decomposition of a material into simpler compounds by the action of heat alone.” In other words, there is no oxidant or reductant acting on the material. He goes on to say that the rate of pyrolysis will increase with increasing temperature. “Eventually, if an oxidant…is present in the correct proportion, a flaming fire will result.”12 The challenge comes not only with the loss of some of the original compounds but with the addition of new compounds that have been synthesized by the scission of the matrix, or the supporting material of the fire debris.12 5

There are three main methods of scission by which a polymer will break down.

Random scission is the process by which the backbone of the polymer is broken down randomly. An example of random scission is given in Figure 1. The result of random scission is carbon chains of various lengths that are shorter than the original polymer chain length. These chains may have single or double carbon bonds. Polymers that undergo predominantly random scission are polyethylene and polypropylene.

Side group scission is the process in which the side groups of the polymer are stripped (this process causes the backbone to become polyunsaturated, meaning it has several double bonds, and it happens when the bonds to the side group are weaker than those between the components of the backbone). An example of side group scission is given in Figure 2 below. The products of side group scission are aromatic ring structures.

Polymers that undergo side group scission most commonly are polyvinyl acetate, polyvinyl chloride, and polyvinyl alcohol.

Monomer reversion is the last mechanism and refers to the process through which the individual units that make up the backbone break apart to re-form monomer units. An example of monomer reversion is given in Figure 3. Polymers that predominantly undergo monomer reversion are polytetrafluoroethylene and polyoxymethylene.

Polymers that undergo more than one type of scission with similar frequencies are polystyrene and nylon-6.12 6

Figure 1. Example of Random Scission Showing the Breakdown of Polyethylene

Adapted From Stauffer’s Paper Concept of Pyrolysis for Fire Debris Analysis12

Figure 2. Example of Side Group Scission Showing the Breakdown of Polyvinyl

Chloride Adapted From Stauffer’s Paper Concept of Pyrolysis for Fire Debris Analysis12 7

Figure 3. Example of Monomer Reversion Showing the Breakdown of Polymethacrylate

Adapted From Stauffer’s Paper Concept of Pyrolysis for Fire Debris Analysis12

Heat is not the only environmental factor that can pose challenges to analysis.

Another problem comes from the storage of materials. Because so many of the components of the ignitable fluids readily evaporate, even at low temperatures, it is common practice to keep fire debris samples in airtight containers such as paint cans, jars, or heat-sealed nylon or fire debris bags.13 This packaging prevents not only the ignitable fluids from dissipating, but also any water that may be present. Thus, fire debris evidence can become a prime breeding ground for colonies of bacteria and mold. Many laboratories have extensive backlogs in terms of testing samples, therefore samples can sit for weeks or months before being analyzed, increasing the chance of bacterial growth.13 Turner et al., upon analyzing samples of damp soil that had been spiked with ignitable fluids, found that the resulting chromatograms were missing peaks for certain compounds. Their data indicate that different species will target compounds with either 8 even numbers of carbon atoms or odd numbers of carbon atoms selectively. This behavior is in contrast to heat weathering (evaporation), which targets lower mass and therefore more volatile compounds of both odd and even carbon numbers equally.11

Another environmental factor that can introduce complications is the type of matrix or substrate containing the ignitable liquid. Li et al. wrote An Analysis of

Background Interference on Fire Debris to fill a gap in the research at the time (2013) regarding the interference introduced from the burning of carpet samples. They found that compounds such as toluene, ethylbenzene, styrene, and different derivatives of benzene, which are all compounds frequently detected in fresh gasoline, were also produced by the burning of certain carpets. The presence of these compounds alone does not necessarily indicate that an ignitable fluid was used.14 It is, therefore, necessary to collect reference samples from the scene that are not suspected of having ignitable liquid on them to identify the compounds that come from the matrix. A possible area of advancement for research regarding environmental matrix interference would be to study the compounds that are not native to the scene of the fire. An example would be the chemicals utilized by first responders to extinguish fires, such as water, carbon dioxide, or retardant powders.15

Environmental circumstances are not the only challenges fire debris analysts face.

For example, some novel compounds, especially the ones that have been engineered recently, can resist classification. In Baerncopf and Hutches’s paper titled A Review of

Modern Challenges in Fire Debris Analysis, they posit that “Every fire debris examiner has come across a unique liquid, matrix, or case complication that has given them pause.”16 They give the examples of vegetable oils, biodiesel, and lubricating oils. 9

Biodiesels are a possible problem because, unless there is more than five percent biodiesel in a sample, manufacturers are not required by law to disclose that it contains biodiesel at all. This omission can cause problems for classification, because, if we operate under the assumption that a diesel fuel has no biodiesel in it, it could cause confusion on from where the fuel originated. These novel samples are one of the primary reasons that the authors advocate for a free exchange of information between analysts.

Without this exchange, an analyst may be stuck for months on a sample of an ignitable liquid that another analyst has already identified.16

10

Chapter 2: Methods of Extraction and Concentration

There have been several methods developed for the purpose of extracting ignitable liquid residues from fire debris. Some of these methods include passive headspace sampling which is described above, steam distillation-solvent extraction, solid/liquid extraction, solid-phase microextraction, dynamic headspace concentration, and static headspace.10,17,18 Fettig et al. states that “The disadvantage of solvent extraction and headspace sampling is its high detection limit.”10 They go on to explain that if there are only trace amounts of ignitable liquids, it is better to use methods like dynamic headspace concentration or solid-phase microextraction, as both of these methods serve to enrich or concentrate the volatile samples. Furthermore, Steffen and Pawliszyn state that “There are limitations inherent in the current techniques”18 (though it should be kept in mind that this paper was published in 1996, so it must be understood that the “current techniques” they are referring to may not be the same current techniques in use today).

The problems they address are the fact that there is low discriminatory power (the ability to tell two components apart) of volatile components when using static headspace, and when adsorption methods are utilized, they often require hazardous solvents, which will be discussed later. Additionally, some of these methods require dedicated instruments, which can be cumbersome and expensive.18 Because many of these methods require multiple steps for sample extraction and concentration, the likelihood of losing valuable sample material is increased.

One of the most widely used methods of extraction of ILRs is passive headspace sampling using an activated carbon strip (ACS). This method, as described by Baerncopf and Hutches, involves suspending an activated carbon strip in the airtight container 11 housing the sample, and heating the sample for a specified amount of time (this time is variable based on the laboratory performing the analysis and the type of sample). The heat vaporizes the volatile ignitable liquid, which is adsorbed onto the strip. After the specified time period has passed, the can is cooled to room temperature. The strip is then removed and eluted with a solvent, commonly carbon disulfide or hexane. The solvent will displace the sample on the strip, and the sample will dissolve in the solvent, which is then injected into a gas chromatograph/mass spectrometer (GC/MS) for analysis.16 While this process is relatively simple, carbon disulfide is an extremely hazardous chemical.

According to the Material Safety Data Sheet for carbon disulfide, it is extremely dangerous if ingested, inhaled, or brought into contact with skin or eyes. It can cause inflammation of the eye, as well as scaling, reddening, or blistering to the skin.19 It would be advantageous to eliminate the use of such a chemical from the standard operating procedures (SOPs) used in crime labs to protect those performing fire debris analysis.

According to Pawlizyn’s 1997 book Solid Phase Microextraction; Theory and

Practice, he states that “sample preparation techniques that use little or no organic solvent have been available for some time. They can be classified… according to the extracting phases of gas, membrane, and sorbent.”20 Examples of methods in the category of gas phase extractions are static headspace sampling and the purge-and-trap method.

He goes on to explain that headspace extraction methods are widely used in the analysis of volatile compounds (such as ignitable liquids), “because the extracting phase (air, helium, or nitrogen) is compatible with most instruments.”20 However, as mentioned previously in this review, headspace methods tend to have low discriminatory power18, and, as Pawliszyn points out, static headspace does not concentrate the target compounds, 12 also known as analytes, so the method has a high limit of detection.20 Supercritical fluid extraction (SFE) also belongs to the gas extraction category. In this method, a gas such as carbon dioxide is compressed until it has liquid-like properties and is used as the extracting phase. The advantage of this method is that, in addition to acting as a purging mechanism, the supercritical fluid can also act as a solvent, removing more of the analytes. The drawback of this technique is that it requires specialized instrumentation that can handle the highly pressurized SF carbon dioxide, and these types of instruments are very expensive.20

Membrane analyses typically have two steps that take place simultaneously.

First, the volatile compounds are extracted onto the membrane from the headspace of the sample. Second, the compounds are desorbed from the membrane, typically using a gaseous phase. Because these steps happen simultaneously and are directly connected to a GC/MS, sample loss is kept to a minimum.20

The last category of extraction is sorbent extraction. This category involves extracting samples from aqueous solutions, air, or soil. Based on the chemical nature of the adsorbent material, some compounds may have a stronger affinity for the adsorbent material, which allows for concentration of the analytes. The most prominent method of sorbent extractions in use since the 1990’s is solid-phase microextraction (known colloquially as SPME, pronounced spee-mee). SPME is performed with a specialized syringe, first commercially produced by Supelco. The syringe consists of the adsorbent- coated fiber, which is housed within a needle. This needle allows the operator to pierce the septum of a vial containing the sample of interest without damaging the thin fiber.

Once the needle is inside the vial or another type of container, the spring-loaded plunger 13 on the syringe can be depressed, extending the fiber from the tip of the needle, thus exposing the fiber to the sample. Once the extraction is complete, the plunger is depressed again, retracting the fiber back inside the needle, protecting the fiber from breakage and any contaminants potentially in the air.20 A diagram of a commercial SPME syringe produced by Supelco is given in Figure 4. The needle is one made for manual sampling instead of use with an autosampler. The main difference between fiber assemblies for manual use and those intended for autosampler use is that those for manual use have a tensioning spring between the screw hub and the sealing septum while the autosampler assemblies have no spring and the needle is retracted by the autosampler instead of by tension.

Figure 4. Diagram of a) A Commercial SPME Syringe Setup b) A Close-Up Cross-

Sectional View of the Adjustable Depth Gauge21

14

There are two main steps in the method of SPME, the first of which exposes the adsorptive-coated fiber to the sample (this exposure can be the sample’s headspace, the sample itself if it is a liquid, or a combination of both) and letting it equilibrate. The second step inserts the fiber into the injection port of an analytical instrument (commonly a gas chromatograph/mass spectrometer, or GC/MS) and heating it to desorb the concentrated sample.20 Because there are relatively few steps in this process, sample loss is kept to a minimum. Additionally, because the sample is concentrated on the fiber, lower detection limits can be obtained.

Based on a review of the available literature, solid-phase microextraction is the superior method for detection of ignitable liquid residues, as well as explosive residues found at explosion sites. There are several advantages of SPME over other techniques, which are described below.

As stated previously, the affinity of analytes will change based on the chemical properties of the adsorbent phase of the fiber. Furton and Pawliszyn tested several different fiber types, including polydimethylsiloxane (PDMS), partially crosslinked polydimethylsiloxane-divinylbenzene (PDMS-DVB), partially crosslinked polyacrylate, partially crosslinked carboxen-PDMS, partially crosslinked carbowax-DVB, and partially crosslinked carbowax-template resin, to determine the most effective fiber type for a range of explosive residues. They concluded that, while the PDMS-DVB fiber worked best for most liquids, the carbowax-DVB and polyacrylate fibers work for more polar analytes, as these fibers are more polar themselves, meaning they have a higher binding affinity for polar compounds. Based on this information, it was concluded that “optimal fibers for specific applications will often need to be blended phases with multiple 15 chemistries on a single fiber.”22 Nearly all other studies in which SPME was tested as a method of extraction of ignitable liquid residues used only a PDMS fiber,14,18,23–25 with the exception of one study that used a mixed polymeric stationary phase fiber

(DVB/Carboxen/PDMS).10 Because there are so many single and mixed phase fibers to choose from, an analyst can choose the fiber that a target analyte will have the highest affinity to, making the concentration step very efficient.

Additional advantages of SPME are that it is solventless, it can be used in conjunction with analytical instruments such as GC/MS, thus eliminating the need for expensive and cumbersome dedicated instrumentation for fire debris analysis in crime labs, and there is a concentrating effect included in the partitioning of the analyte from the sample to the fiber, meaning analytical instruments with lower sensitivity can be used for analysis of SPME extractions. Almirall et al. summarized these findings well in the conclusion of their 1996 publication by stating “Compared to the established method of extraction of this type of sample (solvent extraction), the SPME technique described here increases the sensitivity, is less laborious, and does not require the use of any solvents.”23

16

Chapter 3: Analytical Instrumentation

There were several analytical instruments utilized in the publications reviewed in preparation for this thesis, the most common of which was gas chromatography/mass spectrometry (GC/MS).9–14,16,24–28 Of the papers that did not use GC/MS, two used flame ionization detectors (FIDs) coupled with GC and other analytical instruments,18,29 one used high-performance liquid chromatography,22 and one used Raman spectroscopy.30

The trend toward using chromatographic instruments is clear.

GC is a common technique utilized in chemistry labs because it allows complex mixtures to be analyzed by separating the components of the mixture. Gas chromatography is so named because the mobile phase in this technique is in the gaseous phase. Inside a narrow column, there is a stationary phase. The stationary phase is solid or liquid, which is either packed into the column or coated on the inner wall of the column. A sample of either liquid or gas is injected into a heated port where, if the sample is in liquid form, it is vaporized before being carried into the capillary column by the mobile phase, which is typically an inert gas such as helium, nitrogen, or hydrogen.

The column is housed in an oven that is controlled by a computer. The oven can be kept at a steady temperature or it can be programmed to change at defined intervals and rates throughout the length of the run. Based on the physical and chemical properties of the components in the sample mixture, they will interact with the stationary phase for differing amounts of time. When each component reaches the end of the column, it is recorded by a detector (commonly a mass spectrometer or flame-ionization detector) and the electric signal is processed by a computer.31 17

Liquid chromatography is like gas chromatography in that it is a technique used to separate the components of a mixture. The main difference between GC and LC is that in

LC, the mobile phase is liquid. Thus, the sample does not need to be volatile, as it does not need to be vaporized to be run but instead solvated into the mobile phase. The techniques included in this class are thin layer chromatography (TLC), paper chromatography, and column liquid chromatography. The technique utilized in the papers reviewed for this thesis is column liquid chromatography. In this technique, the stationary phase can be solid or liquid. If the stationary phase is liquid, the diffusion of the sample components between phases is referred to partitioning. If the stationary phase is solid, the retention of the sample components onto to stationary phase surface is referred to as adsorption and the reverse process is referred to as desorption. In column liquid chromatography, the sample is dissolved in a small amount of the selected mobile phase and this liquid is passed through the column with the help of gravity or a pump.

Like in GC, once the sample components reach the end of the column, it is measured by a detector and the signal is sent to a computer.32

High-performance liquid chromatography (HPLC) is a specific type of column liquid chromatography that takes place at high pressures by utilizing a pump. A computer system controls the pump and detector. HPLC instruments can also be paired with autoinjectors for increased convenience. In addition to the convenience of the computerized process, another benefit of using HPLC is that the higher-pressure pumps used in these instruments lead to faster separation. The computerized system also gives rise to less variability between runs and more reproduceable results.33 18

Mass spectrometry is a detector that is frequently used in conjunction with gas and liquid chromatography. Mass spectrometry provides structural information about the sample in the gas phase by first ionizing the sample and then passing the ionized sample through a magnetic or electric field. Based on the ratio of mass to charge (m/z) of an ion, the ion will move characteristically through the field. After passing through the field, the ion is detected. The sample can produce a multitude of ion fragments, and the number of fragments produced from a sample molecule depends on the ionization technique used.

Matrix Assisted Laser Desorption-Ionization (MALDI) and Electrospray Ionization (ESI) are examples of soft ionization methods, which means they produce relatively few fragment ions from sample molecules.34,35 Electron ionization (EI) is an example of a hard ionization method, in that this method tends to produce many fragment ions. Based on the intensities measured for each m/z, the structure of the sample molecule can be derived.35 A diagram of a generic gas chromatograph-mass spectrometer is given in

Figure 5.

Figure 5. Diagram of a Gas Chromatograph-Mass Spectrometer36 19

FIDs are another detector that is commonly paired with GC. In this type of detector, the sample is passed through a flame after leaving the GC. The flame ionizes the sample, and the difference in the ambient electrical current and the electrical current in the detector after the sample is ionized is measured. This instrument provides quantitative results, meaning the amount of sample can be determined, but does not provide structural information about the compound.37

Raman spectrometry is an analytical technique that utilizes non-elastic scattering of light by a sample. A monochromatic light source (such as a laser) is passed through the sample, and the light is scattered by the sample. While most of the scattered light is of the same wavelength and energy as the light source, some of the scattered light is of a different wavelength. If the scattered light has a lower frequency (larger wavelength) than the light source, it is referred to as Stokes scattering. Conversely, if the scattered light has a higher frequency (smaller wavelength) than the light source, it is referred to as anti-Stokes scattering. The scattered light is measured by a detector (traditionally photomultiplier tubes, but more now commonly charge-coupled devices or charge injection devices) that are placed at a 90° angle to the light source so as to not measure the light from the light source. The magnitude of the shift in the wavelength is dependent on the vibrations of molecular bonds in the sample. Based on the spectrum collected, structural information can be derived about the sample.38

González-Rodríguez et al. explain that a “more detailed description of the different components found in fire debris or pyrolysis samples can be obtained using” chromatographic techniques. They go on to state, however, that when it is necessary to reconstruct a fire scene, the information obtained by chromatographic techniques is 20 largely useless. To reconstruct a scene, “identification of the different materials involved in a fire is of paramount importance… In these cases, spectroscopic techniques can be very useful tools for the chemical profiling of pyrolysis products and fire debris.” They explained that spectroscopic methods that focus on near infrared or middle infrared ranges of radiation, though not commonly used, have been used to analyze the products of decomposed wood, plastics, and paper. Raman spectroscopy has been previously used to analyze plastics and other polymers. González-Rodríguez et al. examined the discriminatory power of Raman spectroscopy in distinguishing between different polymers after burning. They determined that, in conjunction with principal component analysis (PCA), which will be discussed later, it was possible to discriminate between the different polymers, even after their structures had been severely compromised by fire.30

One advancement that could prove beneficial to the advancement of fire debris analysis is the creation of portable analytical instruments. Several types of portable instruments have been introduced into the analytical chemistry field including a portable

GC/MS39 (which was the instrument that was initially going to be used for the experiment performed in this thesis), portable Raman spectrometers,40–42 and a portable FT-IR spectrometer.43 While some people may have concerns that portable instruments would have poorer resolution or limits of detection, direct detection onsite has many advantages.

Samples that are rich in ignitable fluids can be detected, isolated, and taken back to the laboratory and measurements made onsite avoid problems with sample decomposition or loss during transport to the laboratory.

According to Contreras et al., the resolution achieved by the mass spectrometer for the m/z range from below 100 to above 200 using the GUARDION-7 instrument was 21 comparable to benchtop mass spectrometers. The limits of detection for direct injection using a 0.5 μL syringe and headspace sampling using solid phase microextraction were in the range of hundreds of pg and 0.1 ppb-10 ppb, respectively.39 In Lu et al.’s experiment with a portable gas chromatograph with tunable retention, limits of detection ranged anywhere from lower than 10 ppb to 40 ppb, depending on the compound. Both of these groups deemed the instruments to be of great merit as portable analytical instruments.39,44

The limit of detection of a surface enhanced Raman scattering sensor was found to be on the same magnitude for the portable system as it was for the benchtop instrument, which was on the order of 10-6 M, which is equivalent to 200 ppb.41 Vitek et al. concluded that, while the resolution of portable Raman systems is not as high as for benchtop instruments, portable instruments have an advantage in terms of speed of analysis and field research.42 The GasID FT-IR system utilized by Levy and Diken had a positive classification rate of 91%, with only hexane producing false positives for other hazardous hydrocarbons.43

22

Chapter 4: Chemometric Analysis

Chemometrics is defined as the sub-discipline of analytical chemistry that maximizes the information gained from experimentation. There are several methods used in chemometric analysis to model data that makes the data easier to understand or can help predict values outside of the data set. For example, it is useful to be able to classify an unknown sample based on a set of known values. Several chemometric methods will be discussed here that have been applied to fire debris. Dr. Peter Harrington’s research group specializes in analyzing large amounts of data using chemometrics, which often includes the use of two-way data that includes chromatographic and spectroscopic data.45,46

A common principle in chemometrics is clustering. As Everitt et al. note in the introduction of the 2011 edition of their book Cluster Analysis, “one of the most basic abilities of living creatures involves the grouping of similar objects to produce a classification.”47 They go on to discuss the vitality of classification of everything from science to the developments of new languages. Noting the similarities and differences between different individuals or objects allows for the separation of these objects into categories. Everitt et al. described several reasons to classify entities. The first of which is that classifying allows for an easier understanding of large groups of data.47 For example, if a data set contains 100 items or variables, it can be difficult to comprehend each individual item. However, if the items are separated into categories based on size or color for example, it becomes much easier to comprehend the 100 items as 5 groups instead of 100 individual items. Everitt et al. go on to explain that in fields such as medicine, the purpose of classifying diseases or medications will be two-pronged. The 23 first purpose is that of prediction. Diseases that fit into the same class may react similarly to different medications. The second purpose is to determine the causes of different diseases. Again, diseases in the same class may be caused by similar genes or environmental factors. There are several different types of cluster analysis.47 The type that is most commonly applied to fire debris analysis in the publications encountered during research for this thesis is principle component analysis (PCA).11,24,30,48

PCA is a method of cluster analysis that focuses on the variance, or difference, between individual samples. According to Vidal et al., PCA “has become one of the most useful tools for data modeling, compression, and visualization.”49 PCA is a method for modeling “high-dimensional data,” which is data with a large number of attributes

(i.e., chemical measurements), in a low-dimensional space. For example, multivariate data (data with many variables) can become increasingly difficult to plot in an understandable way when the dimensions of the data grow beyond three. PCA determines two to three axes that account for the largest amount of variability among the data by using linear combinations of variables. Plotting the data projected onto the new optimized coordinate system reduces the dimensionality of the data into simpler principal component scores. The relationships among objects or spectra in a dataset can easily be visualized in the two-dimensional score plot. The principal components can be calculated quickly with a single MATLAB® command48 or XLSTAT.11 While the specifics of PCA can get relatively complicated (especially in high-dimensional data), the basics of it are shown below. There are two terms that must be explained to explain

PCA. The term eigenvector refers to “a vector whose direction remains unchanged when a linear transformation is applied to it.” In other words, the transformation is only a scalar 24 transformation. An eigenvalue is the scalar transformation that is applied to the eigenvector.50

One of the first steps involved in PCA is determining the principal are. These are the axes in the data that account for the greatest amount of statistical variation. The principal components will be the two or three axes at which the data distribution is the widest. Additionally, the principal component will be the eigenvector with the highest eigenvalue (the eigenvalue is simply a measure of the variance on each new dimension.

Figure 6 is an example set of two-dimensional data with the two principal components.

Figure 6. A Two-Dimensional Data Set, Including Its Two Principal Components, As

Seen in Vidal et al.’s 2016 Book Generalized Principal Component Analysis49

Once the principal components have been identified (there will be as many principal components as there are initial variables), the principal components that account for the most amount of variance in the data set are chosen. The projection of each spectrum onto each principal component is referred to as a score. The principal 25 components will maximize the variance of the scores and the scores are plotted in the simplified principal component score plot. Based on where the data points fall on these plots, they can be put into categories. An example plot is shown in Figure 7, which includes data found in Monfreda and Gregori’s Differentiation of Unevaporated Gasoline

Samples According to Their Brands, by SPME-GC-MS and Multivariate Statistical

Analysis. Based on the large separation between data points belonging to set E and points from the other four sets, it can be determined that most new points that are on the right of

1.5 on the PC1 axis would belong to data set E. If the separation between different sets is not enough to be able to discriminate between sets, further manipulations such as discriminant analysis can be used to increase the classification rate.

Figure 7. A Score Plot Between Principal Components 1 and 2 From a Set of

Data Obtained by Monfreda and Gregori24 26

Several studies have used PCA, either on its own or in conjunction with other chemometric techniques, to help classify ignitable liquids. Baerncopf et al. coupled PCA with Pearson product moment correlation (PPMC) coefficients to associate ignitable liquid residues with the corresponding neat (original, uncontaminated) sample. Even with increased weathering, these two techniques used in conjunction with each other were sufficient to match the residues with the neat samples, as well as distinguish the samples from background interference introduced by the burning of the matrix.48 In this study, PCA was applied to the entire set of data points, while PPMC was used to make pairwise comparisons to the ILR and the corresponding neat sample. PPMC is a technique which measures the amount of linear association between two values. The

PPMC coefficient can range from -1 to 1, with 1 being an exact fit for the data collected.51 Baerncopf et al. stated that “PPMC coefficients were useful in addition to

PCA because PPMC coefficients use all variables in the chromatogram whereas PCA uses only variables in the chromatograms that account for the greatest variance,”48 which explains why they felt both techniques were necessary.

In addition to PCA, linear discriminant analysis (LDA) was also utilized in the analysis of data collected for this thesis experiment. The first step in LDA is to define the groups for the samples. The groups often are for different classes of ignitable fluids but is determined by the problem to be solved. LDA is related to PCA except the principal components are rotated so as to maximize the separation among the scores of the different groups while minimizing the variation of the scores within each group. The linear discriminant function measures the weighted distance between the questioned score 27 and the average scores for each group. The questioned score is classified as belonging to the group to which it is closest. The distance is weighted by each group’s covariance, which provides more selectivity for discrimination. Because LDA is a powerful classification method, it is easy to overfit the model by increasing the number of rotated principal components.52 28

Chapter 5: Research Project

5.1 Evolution of the Project

The original plan for this experiment was to use a Tridion-9 Portable GC/MS from Torion Technologies with a CUSTODION SPME syringe, made by Torion

Technologies to be used with their portable GC/MS instruments. This instrument was used to test sample onsite for the presence of environmental volatile compounds, explosives, chemical warfare agents (CWAs), and other hazardous materials.53 This instrument has been sitting in Dr. Harrington’s laboratory for several years, and I was tasked with determining if the instrument was still functioning. Throughout months of working with it, I determined that while the instrument could run properly, it was not reliable in terms of running consistently (by which I mean it would work perfectly fine one day, but a few days later, it would stop detecting samples), so I made the decision to switch to our bench-top TRACE GC Ultra-PolarisQ mass spectrometer. This switch occurred in November.

The basic principles of the experimental procedure designed for this experiment were modelled after the procedure used in Forensic Application of Gas

Chromatography–Differential Mobility Spectrometry with Two-Way Classification of

Ignitable Liquids from Fire Debris by Yao Lu and Peter de B. Harrington.54 The original plan for collecting ignitable liquid standards was to place 2 cm × 2 cm squares of

Kimwipe in a 15 mL headspace vial with 5 μL of ignitable liquid. These were to be left overnight in the sealed vial before exposing the fiber to the headspace for 10 minutes and then to place the fiber in the injection port of the instrument for 2 minutes to desorb.

Because there was poor peak resolution of sample components using this method, the 29 equilibrium time was shortened to 2 minutes at first, and the desorption time was shortened to 30 s. When the peak resolution was still poor, the equilibrium time was also shortened to 30 s. Because the amount of ignitable liquid residue left after burning the carpet is small, all carpet samples (including unburned and burned carpet blanks as well as burned carpet samples with ignitable liquids) were sampled using an equilibrium time of 2 minutes.

When this project began, I had planned on testing 8 different liquids, including turpentine, lacquer thinner, odorless mineral spirits, caulk remover, all-purpose cleaner, diesel fuel, and gasoline. After running standards of all these liquids, I determined that the all-purpose cleaner I had intended on using was not volatile, and after looking into the product, it turns out it is actually “solvent-free” (that is the phrase they use, but the phrase is most likely false—they probably meant is that there are no volatile solvents in their formulation). Therefore, this liquid was removed from the experimental sample list. In

January, due to dwindling time, the ignitable liquids sampling list was cut down to just diesel and gasoline, because these two ignitable fluids are commonly used in arsons.

Originally, I had planned to test another variable in addition to those that I have already discussed, and that was going to be the time that each sample was burned for.

The original plan was to burn each carpet/ignitable liquid combination for three different times (30 s, 60 s, and 120 s). When time became a problem, I first planned to only burn samples for 120 s to reduce the number of samples that needed to be run. I later decided to change the burn time to 90 s, because the carpet samples that were being burned were almost completely gone after burning for 120 s, so there was not enough sample left to test. 30

5.2 Experimental Instrumentation, Methodology, and Parameters

The GC utilized for this experiment is a TRACE™ GC Ultra from Thermo

Electron Corporation equipped with a Thermo Polaris Q MS. The GC column used was a

Shimadzu SHRXI-5MS column with a 30 m length, an internal diameter of 0.25 mm and a film thickness of 0.25 μm. This column is suitable for temperatures to -60 °C to 330 °C or 350 °C. The instrument was controlled by a Dell® computer equipped with the

Xcalibur™ software provided by Thermo Fisher Scientific with the instrument.

The instrumental parameters used for this experiment are as follows: The initial oven temperature was 40 °C, which was held for 5.00 min. Afterward, the temperature was ramped up to 120 °C at a rate of 10.0 °C/min. Once this temperature was reached, the temperature was held constant for 1.00 min, and then the temperature was ramped up to 250 °C at a rate of 30.0 °C/min. Finally, this temperature was held constant for 1.00 min, after which the GC run was concluded. The total run time for the GC program was

19.33 min. The injection inlet was held constant at a temperature of 270 °C and was operated in splitless mode with a splitless time of 1.00 min and with a constant septum purge. The carrier gas used for this experiment was ultra-pure helium gas, and the carrier gas pressure was held constant at 100 kPa for the entirety of the GC run. The MS was run for the entire GC run time in positive ion mode and full scan (m/z 50-650) mode.

The scan event time was 0.58 sec. The number of microscans was set to three, and the max ion time was set to 25 ms. The ion source was set to 280 °C.

Carpet samples were obtained from local carpet retailers. Three types of carpet polymer were tested (polyester, nylon, and triexta). Gasoline and diesel fuel samples were obtained from Bascom French, the Clippinger shop supervisor. Carpet samples 31 were cut into 5 cm × 5 cm squares for sample analysis. The SPME fiber assembly used was a 100 μm Polydimethylsiloxane (PDMS) non-bonded assembly obtained from

SUPELCO® and was used with a manual SPME holder (SUPELCO® is Sigma-Aldrich’s analytical product line). The fiber was conditioned prior to use by placing in an injection port set to 250 °C for 30 min, as laid out in the product information provided by

SUPELCO®.

Fiber blanks were run every day that standards or samples were run in the GC/MS to ensure both the column and the fiber were clean before proceeding with each day’s runs. Ignitable liquid standards were prepared by placing a 2 cm × 2 cm square of

Kimwipe in a 15 mL headspace vial with 5 μL of ignitable liquid. The vials were left to equilibrate at least overnight. Three standards were prepared for each liquid, and three replicates of each of these standards were collected. Ignitable liquid standards were sampled using a fiber exposure time of 30 s and a 30 s desorption time in the injection port of the instrument.

Unburned carpet controls were collected by placing a 5 cm × 5 cm carpet square of each type of carpet into a separate 250 mL wide-mouth septum-top jar from Thermo

Scientific I-Chem® and the headspace equilibrated at least overnight. These standards were sampled using a fiber exposure time of 2 min and a desorption time of 30 s in the injection port of the instrument. Burned carpet standards were collected by burning a 5 cm × 5 cm carpet square for 120 s. Two different extinguishing methods were used. For the first, the burning carpet square was placed in a Pyrex evaporating dish and a watch glass was placed over the top, cutting off the oxygen supply to the flame and thus extinguishing the carpet. In this case, as much of the carpet sample as possible was 32 transferred into a 250 mL wide-mouth septum-top jar. For the second extinguishing technique, the burning carpet square was placed in a Pyrex evaporating dish and was doused with tap water. The pieces of the standard that were not stuck to the bottom of the dish were then transferred into the same type of jar as used for the samples extinguished using the first extinguishing method and for carpet blanks. In both cases, between burning each sample, the evaporating dish was scraped clean if there was any carpet sample stuck to the bottom, then thoroughly rinsed with tap water and dried.

Carpet samples to be burned with ignitable liquids were prepared by adding 200

μL of the ignitable liquid to be used to the 5 cm × 5 cm carpet square and allowing it to sit for 5 min to allow the ignitable liquid to soak into the fabric. After five min, the carpet sample was set on fire with a Bunsen burner and allowed to freely burn for 90 s before being extinguished using the appropriate method for the sample. Once the carpet sample was extinguished, it was transferred to a wide-mouth septum top jar like those used for the carpet blanks. The samples were left overnight to allow the volatile components in the sample to equilibrate. Once the sample had equilibrated, the headspace was sampled using SPME. The fiber was given 2 min to equilibrate in the headspace. After being withdrawn from the vial, the fiber was inserted into the heated injection port, the GC run was started, and the fiber desorbed for 30 s before being removed from the port. After each sample run, the fiber and column were cleaned by inserting the fiber into the injection port set at 250 °C and allowing it to remain for 10 min while the column was run at 250 °C. The fiber was then cooled for another 10 min before the next sample was run. Each sample was tested between five and six times each throughout the next week, never more than once per day. 33

The data from each run was saved as a .RAW file which could be viewed in the

Xcalibur software where the mass spectrum of certain peaks could be searched against a library database. The data files were converted to CDF files so they could be transferred to different computers and could be read into MATLAB® to be further analyzed. The version of MATLAB® used for analysis was R2016B. The program used to analyze the data collected was written by Dr. Peter Harrington. This program was changed multiple times to fit the needs of the experiment. The final version of the program has many functions. First, it sorts the data files into the groups based on their file names, which are based on which carpet type and ignitable liquid was used for each sample. Then, using the fiber blanks collected, it subtracts the background from the sample runs. The data is also normalized by the program so each sample had a weight of unity as described by its

2-norm. The program also carries out PCA and LDA on the data. These methods were discussed in Chapter 4 of this thesis.

5.3 Results

Each combination of carpet fiber, ignitable liquid, and extinguishing method were given a class letter. Standards for diesel fuel and gasoline, unburned carpet standards, and fiber blanks were all also given their own class set. Class designations are given in

Table 1 below.

Table 1. Legend of Class Letters (continued on next page) Class Ignitable Liquid Carpet Fiber Extinguishing A Diesel Nylon Water LetterB Diesel NylonType SuffocationMethod C Gasoline Nylon Water D Gasoline Nylon Suffocation E None Nylon Suffocation F None Nylon Water G None Nylon Unburned H Diesel Polyester Water 34

Class Ignitable Liquid Carpet Fiber Extinguishing I Diesel Polyester Suffocation LetterJ Gasoline PolyesterType MethodWater K Gasoline Polyester Suffocation L None Polyester Suffocation M None Polyester Water N None Polyester Unburned O Diesel Triexta Water P Diesel Triexta Suffocation Q Gasoline Triexta Water R Gasoline Triexta Suffocation S None Triexta Suffocation T None Triexta Water U None Triexta Unburned V Diesel Liquid Standard N/A W Gasoline Liquid Standard N/A X None Fiber Blank N/A

All total ion chromatograms (TICs) in this section have the retention time range of 5 min-20 min, as there were no peaks of interest found before five minutes for any of the samples. Figures 8 and 9 below are the TICs of diesel fuel and gasoline, respectively.

In these Figures, each colored line indicates the average TIC of a single sample run.

Looking at the non-normalized TICs is useful because we can see that some samples have very small overall intensities, which indicates that some sample had little- to-no ignitable liquid left to detect after burning. The most prominent examples of this can be seen in the TICs of diesel fuel (Figure 8), as the peak resolution is much poorer for diesel than for gasoline. The data collected for diesel fuel is not optimal. However, it does allow us to see the samples with less ignitable liquid residues better. In general, chromatograms with peaks that are completely resolved are preferable to those with peaks with bases that are not. The term completely resolved indicates that the chromatogram returns to the baseline (in normalized data, this is zero) between each 35 peak. If the peaks are not totally resolved, the data can be harder to analyze, because multiple compounds are entering the mass spectrometer contemporaneously, which can cause the mass spectrum collected for a peak to contain ions from several compounds, meaning it cannot be matched to a library spectrum.

One other characteristic of note that can be seen in nearly all the TICs is that they contain three peaks between 17 min and 19 min. While the intensities of these peaks are not constant throughout all the samples, the retention times are. When the mass spectra of each of these peaks were searched against the compound library on Xcalibur®, it was determined that they were all silica-based compounds. These peaks were present in the fiber blanks as well, which indicates that these peaks are a result of column bleed, where materials present in the stationary phase of the column exit the column and are detected along with the sample.

36

Figure 8. Total Ion Chromatograms of Diesel Samples

Figure 9. Total Ion Chromatograms of Gasoline Samples

The average mass spectra diesel fuel and gasoline are shown in Figures 10 and 11, respectively. The mass-to-charge ratio in the figures ranges from m/z 50-450, as the mass range detected by the instrument starts at 50, and no ions were detected above m/z

450. As Figures 10 and 11 indicate, diesel fuel has higher concentrations of compounds with lower mass-to-charge ratios compared to gasoline. A few m/z values present in both ignitable liquids are peaks around m/z 78, m/z 92, and m/z 106, which would indicate the presence of BTEX compounds (benzene, with a molar mass of 78.11 g/mol; toluene, with a molar mass of 92.14 g/mol; ethylbenzene, with a molar mass of 106.17 g/mol; and xylene, with a molar mass of 106.16 g/mol). BTEX compounds are characteristic of both gasoline and in heavy petroleum distillates such as diesel fuel.55 37

Figure 10. Average Mass Spectra of Diesel Fuel Samples

Figure 11. Average Mass Spectra of Gasoline Samples 38

The normalized total ion chromatograms (TICs) of each class were produced in

MATLAB® and are given in Figures 12-34. A general trend that can be seen in the following Figures is that the TICs of the carpet standards, both burned and unburned, tend to be less consistent than the samples containing ignitable liquid. In the samples with ignitable liquids on the carpet, all the replicates tend to fall on top of each other, which could be explained by the fact that the carpet standards had very few large peaks, which means the baseline noise is exaggerated in the normalized plots. This trend does not hold true with the ignitable liquid standards, which seem to have more noise than the carpet samples with ignitable liquids. The reason may be that there are more components in these ignitable fluid samples, as the more volatile compounds have not evaporated, or because the equilibration time used for sampling the standards was shorter so there was not enough time for the sample to equilibrate consistently with the fiber.

Figures 12(L)-13(R). TICs of Classes A and B 39

Figures 14(L)-15(R). TICs of Classes C and D

Figures 16(L)-17(R). TICs of Classes E and F

Figures 18(L)-19(R). TICs of Classes G and H 40

Figures 20(L)-21(R). TICs of Classes I and J

Figures 22(L)-23(R). TICs of Classes K and L

Figures 24(L)-25(R). TICs of Classes M and N 41

Figures 26(L)-27(R). TICs of Classes O and P

Figures 28(L)-29(R). TICs of Classes Q and R

Figures 30(L)-31(R). TICs of Classes S and T 42

Figures 32(L)-33(R). TICs of Classes U and V

Figure 34. TIC of Class W

All the papers I read in preparation for this thesis that utilized PCA to analyze

GC/MS data used it only to analyze the chromatographic data.24,48,56,57 Aside from helping to identify different peaks in the chromatograms, the mass spectral data was largely unused. To make the most use of all the data collected in this experiment, PCA was applied to the combination of the TIC and mass spectra for each run. Although this is three-dimensional data (the three dimensions being retention time, m/z, and intensity), it can be visualized in a two-dimensional plot using a color plot in which the intensity is represented by a color scale. The two-way representations of the average diesel fuel and 43 gasoline data are given in Figures 35 and 36, respectively. In these plots, the darker blue parts of the plots indicate lower intensity values, the green parts of the plots indicate intermediate intensity values, and the bright yellow parts of the plots indicate the highest intensity values.

Figure 35. Two-Way Representation of the Average Diesel Fuel Data

44

Figure 36. Two-Way Representation of the Average Gasoline Data

PCA was applied to this data, as well as the corresponding data set of the blank carpet samples. The PCA score plot is shown below in Figure 37. In this case, the first two principal components account for 84% of the variance in the dataset. As seen in the plot, the data forms a triangle shape with respect to the first two principal components, with the diesel fuel samples clustering closer to the top, the gasoline samples clustering closer to the bottom right, and the blank carpet samples clustering closer to the bottom left. Both diesel fuel and gasoline have samples that are plotted closer on the PCA plot to the carpet blanks than to the respective ignitable liquids. These samples are the ones that had little-to-no ignitable liquid left to detect that were discussed earlier in this section (the samples that were below all the others on the non-normalized TIC plots of each ignitable 45 liquid). As a comparison of the quality of the cluster separation when just the TIC data or just the spectral data are utilized, the PCA of the TIC data on its own is given in Figure

38 and the PCA of the spectral data on its own in given in Figure 39. While the clustering in the mass spectral PCA plot has only slightly more overlap than the PCA plot for the combined data, the PCA plot for the TIC data has drastically more overlap than for the combined data.

Figure 37. PCA Score Plot of the Three-Way Chromatographic and Spectral Dataset 46

Figure 38. PCA Score Plot of TIC Data

Figure 39. PCA Score Plot of Mass Spectral Data 47

LDA was also applied to the combined TIC and mass spectral data and the plot of the data with respect to the first two linear discriminants is shown below in Figure 40.

The first linear discriminant accounts for 61% of the variation in the data while the second linear discriminant accounts for 39% of the variation in the data. The clusters in the LDA plot are much more closely-packed than those in the PCA plot, and there is no overlap between the clusters. It is close to an ideal LDA score plot. This plot can be used to classify unknown samples of burned carpet with ignitable liquid on them by plotting an ellipse about the mean of each cluster that has a selected confidence interval.

If an unknown sample falls within the ellipse, there is a likelihood equal to the percentage that the confidence interval represents that the unknown sample has the matching ignitable liquid on it. Unfortunately, because only diesel and gas samples are represented in this experiment, the plot cannot be used to identify ignitable liquids falling outside these two classes.

Figure 40. Linear Discriminant Score Plot of the Combined TIC and Mass Spectral Data 48

Conclusions and Acknowledgements

Experimental Conclusions and Future Research Possibilities

Overall, this experiment was successful in validating the proposed method for testing simulated fire debris. The lack of overlapping clusters in the LDA plot is very encouraging, and I have high hopes for the future of SPME in the field of fire debris analysis. As discussed in Chapter 2 of this thesis, if SPME proves to be a reliable extraction method for fire debris analysis, it could become the preferred method for fire debris analysis, as it has little-to-no sample preparation required, which limits the chances for sample loss or introduction of contaminants, and it does not require the use of any solvents, which can be expensive and/or toxic for examiners.

While the results obtained using this method are promising, without testing the method on a more extensive set of samples, a conclusion cannot be drawn about the validity of the method in real-life situations. Unfortunately, because I will be graduating,

I will not be able to continue this project. That being said, the next things I would suggest testing for this experiment would be: 1. ignitable liquids from the other classes of ignitable liquids as defined by ASTM, 2. dry chemical extinguishing material as an extinguishing method, 3. whether this method is reliable for distinguishing different octane ratings of gasoline, and 4. different matrices other than carpet that are common in fire debris such as wood, insulation, and carpet padding. The best test of this method would be to collaborate with a local fire department or fire marshal’s office to obtain samples from training fires or actual fire scenes. Another possible advancement for this experiment would be to test the use of a two-dimensional gas chromatography-mass spectrometry (GC×GC/MS) instrument instead of a GC/MS. In two-dimensional GC 49 instruments, two different chromatography columns are used. Each column has a different stationary phase.58 This type of instrument has been shown promise in other fields such as biomarker analysis,59 but was not utilized in any of the papers reviewed for this thesis, but could increase the amount of useable information, leading to tighter clustering in PCA and LDA.

Acknowledgments

This project has been one of the most challenging things I have ever had to do in my life, and it has changed me as a person more than I can even comprehend. When I came into this program as a freshman, I thought I had it all Figured out. I had graduated top of my high school class without truly being challenged. I was used to being good at the things I tried the first time I did them, and if I was not, I did not keep trying. I prided myself on being independent, and it was almost my downfall.

This project has changed everything. I was tempted to quit so many times, but I kept at it. I owe so much of that to my support system. I used to believe being independent meant never having to ask for help from anyone, but I have come to realize that there is a big difference between being independent and being alone. Every time I came close to giving up, I found myself surrounded by people who refused to let me, and that has made all the difference. I may have been strong on my own, but with all these amazing people around me, I feel unbreakable.

There are a few people and establishments I would like to thank specifically. The first group that I would like to thank Bascom French and Aaron Dillon for assisting me with sample acquisition, as well as Carpet One Floor & Moore, Hedges Carpet Barn, and 50

Bob’s Wholesale Carpet for their generous donation of carpet samples. Next, I would like to thank Dr. Peter Harrington, Dr. Lauren McMills, Interim Dean Cary Frith, and the entire Honors Tutorial College here at Ohio University for their support and help during the entire process of picking and carrying out this thesis project.

As I stated, I have an amazing support system that I could not have hoped to reach this point without. To Michael Haggerty, thank you for always having my back and never letting me forget that even when things are hardest, I am never alone. Thank you to

Lauren Borris, Carrie Spiaser, Addy Kruse, and Andy Oliver, and Dr. Becky Barlag for always having an open ear and an encouraging response. Thank you to Ed and Marion

McKeon for their love, time, and financial contributions, without which I could not have made it to this amazing program. To all of my wonderful tutorial advisors, thank you for taking the time to teach me and lead me down this path.

Finally, thank you from the bottom of my heart to Donna and Mike McKeon. For the midnight phone calls during stress- and caffeine-fueled panicking, for driving me six hours to and from campus for my first two years at Ohio University, for reassuring me every time I felt like I was not good enough for this program or like I could not make it through the last few months, and for your unwavering support for the past twenty-two years, I will be forever grateful to you both. References (1) Wakefield, E. A. Arson Investigation. J. Crim. Law Criminol. 1931-1951 1951, 41 (5), 680–689. https://doi.org/10.2307/1138798. (2) Bennett, G. D. Physical Evidence in Arson Cases. J. Crim. Law Criminol. Police Sci. 1954, 44 (5), 652–660. https://doi.org/10.2307/1139636. (3) Feeheley, T. J. Suggestions for Improving Arson Investigations. J. Crim. Law Criminol. Police Sci. 1956, 47 (3), 357–367. https://doi.org/10.2307/1140334. (4) Swanson, W. L.; Eichmeier, R. Problems of Arson Investigation Arising under State Fire Marshal Acts. J. Crim. Law Criminol. Police Sci. 1956, 47 (4), 457–464. https://doi.org/10.2307/1140425. (5) Savage, J. J. Investigative Techniques Applied to Arson Investigation. J. Crim. Law Criminol. Police Sci. 1957, 48 (2), 213–218. https://doi.org/10.2307/1139499. (6) Burd, D. Q. Detection of Traces of Combustible Fluids in Arson Cases. J. Crim. Law Criminol. Police Sci. 1960, 51 (2), 263–264. https://doi.org/10.2307/1141203. (7) Stauffer, E.; Dolan, J. A.; Newman, R. Fire Debris Analysis; Academic Press, 2007. (8) Ignitable Liquid Classification Scheme. ohio.gov. (9) Waddell, E. E.; Frisch-Daiello, J. L.; Williams, M. R.; Sigman, M. E. Hierarchical Cluster Analysis of Ignitable Liquids Based on the Total Ion Spectrum. J. Forensic Sci. Wiley-Blackwell 2014, 59 (5), 1198. (10) Fettig, I. ( 1 ); Piechotta, C. ( 1 ); Krüger, S. ( 2 ); Deubel, J. h. ( 2 ); Werrel, M. ( 2 ); Raspe, T. ( 2 ). Evaluation of a Headspace Solid-Phase Microextraction Method for the Analysis of Ignitable Liquids in Fire Debris. J. Forensic Sci. 2014, 59 (3), 743–749. https://doi.org/10.1111/1556-4029.12342. (11) Turner, D. A.; Goodpaster, J. V. Comparing the Effects of Weathering and Microbial Degradation on Gasoline Using Principal Components Analysis. J. FORENSIC Sci. 2012, 57 (1), 64–69. (12) Stauffer, E. Concept of Pyrolysis for Fire Debris Analysts. Sci. Justice 2003, 43 (1), 29–40. https://doi.org/10.1016/S1355-0306(03)71738-9. (13) Hutches, K. Microbial Degradation of Ignitable Liquids on Building Materials. Forensic Sci. Int. 2013, 232, e38–e41. https://doi.org/10.1016/j.forsciint.2013.08.006. (14) Li, Y.; Liang, D.; Shen, H. An Analysis of Background Interference on Fire Debris. Procedia Eng. 2013, 52, 664–670. https://doi.org/10.1016/j.proeng.2013.02.203. (15) Emergency Standards | Portable Fire Extinguishers - Extinguisher Basics | Occupational Safety and Health Administration https://www.osha.gov/SLTC/etools/evacuation/portable_about.html (accessed Mar 23, 2019). (16) Baerncopf, J.; Hutches, K. A Review of Modern Challenges in Fire Debris Analysis. Forensic Sci. Int. 2014, 244, e12–e20. https://doi.org/10.1016/j.forsciint.2014.08.006. (17) Elmore, J. S.; Erbahadir, M. A.; Mottram, D. S. Comparison of Dynamic Headspace Concentration on Tenax with Solid Phase Microextraction for the Analysis of Aroma Volatiles. J. Agric. Food Chem. 1997, 45 (7), 2638–2641. https://doi.org/10.1021/jf960835m. 2

(18) Steffen, A.; Pawliszyn, J. Determination of Liquid Accelerants in Arson Suspected Fire Debris Using Headspace Solid-Phase Microextraction. Anal. Commun. 1996, 33 (4), 129–131. https://doi.org/10.1039/AC9963300129. (19) Material Safety Data Sheet: Carbon Disulfide. ScienceLab.com May 21, 2013. (20) Pawliszyn, J. Solid Phase Microextraction: Theory and Practice; Wiley-VCH: New York, 1997. (21) Mester, Z.; Sturgeon, R.; Pawliszyn, J. Solid Phase Microextraction as a Tool for Trace Element Speciation. Spectrochim. Acta Part B At. Spectrosc. 2001, 56 (3), 233–260. https://doi.org/10.1016/S0584-8547(00)00304-9. (22) Furton, K. G.; Almirall, J. R.; Bi, M.; Wang, J.; Wu, L. Application of Solid-Phase Microextraction to the Recovery of Explosives and Ignitable Liquid Residues from Forensic Specimens. J. Chromatogr. A 2000, 885 (1), 419–432. https://doi.org/10.1016/S0021-9673(00)00368-X. (23) Almirall, J. R.; Bruna, J.; Furton, K. G. The Recovery of Accelerants in Aqueous Samples from Fire Debris Using Solid-Phase Microextraction (SPME). Sci. Justice 1996, 36 (4), 283–287. https://doi.org/10.1016/S1355-0306(96)72615-1. (24) Monfreda, M.; Gregori, A. Differentiation of Unevaporated Gasoline Samples According to Their Brands, by SPME-GC-MS and Multivariate Statistical Analysis. J. Forensic Sci. Wiley-Blackwell 2011, 56 (2), 372–380. https://doi.org/10.1111/j.1556-4029.2010.01644.x. (25) Dhabbah, A.; Al-Jaber, S.; Al-Ghamdi, A.; Aqel, A. Determination of Gasoline Residues on Carpets by SPME-GC-MS Technique. Arab. J. Sci. Eng. Springer Sci. Bus. Media BV 2014, 39 (9), 6749–6756. https://doi.org/10.1007/s13369-014-1233-1. (26) Sigman, M. E.; Williams, M. R.; Castelbuono, J. A.; Colca, J. G.; Clark, C. D. Ignitable Liquid Classification and Identification Using the Summed-Ion Mass Spectrum. Instrum. Sci. Technol. 2008, 36 (4), 375–393. https://doi.org/10.1080/10739140802151440. (27) Turner, D. A.; Goodpaster, J. V. The Effects of Microbial Degradation on Ignitable Liquids. Anal. Bioanal. Chem. 2009, 394 (1), 363–371. https://doi.org/10.1007/s00216-009-2617-z. (28) Baerncopf, J. M.; McGuffin, V. L.; Smith, R. W. Effect of Gas Chromatography Temperature Program on the Association and Discrimination of Diesel Samples. J. Forensic Sci. Wiley-Blackwell 2010, 55 (1), 185–192. https://doi.org/10.1111/j.1556-4029.2009.01234.x. (29) Fernandes, M. S.; Lau, C. M.; Wong, W. C. The Effect of Volatile Residues in Burnt Household Items on the Detection of Fire Accelerants. Sci. Justice 2002, 42 (1), 7– 15. https://doi.org/10.1016/S1355-0306(02)71791-7. (30) González-Rodríguez, J.; Sissons, N.; Robinson, S. Fire Debris Analysis by Raman Spectroscopy and Chemometrics. J. Anal. Appl. Pyrolysis 2011, 91 (1), 210–218. https://doi.org/10.1016/j.jaap.2011.02.012. (31) Engewald, W.; Dettmer-Wilde, K. Introduction. In Practical Gas Chromatography: A Comprehensive Reference; pp 3–20. 3

(32) Ismail, B.; Nielsen, S. S. Basic Principles of Chromatography. In Food Analysis; Nielsen, S. S., Ed.; Food Analysis; Springer US: Boston, MA, 2010; pp 473–498. https://doi.org/10.1007/978-1-4419-1478-1_27. (33) Snyder, L. R.; Kirkland, J. J.; Dolan, J. W. Chapter One Introduction. In Introduction to Modern Liquid Chromatography; Wiley, 2010. (34) Cosme, J.; Tchernyshyov, I.; Gramolini, A. Basic Concepts in Mass Spectrometry and Protein Quantitation. In Manual of Cardiovascular Proteomics; Agnetti, G., Lindsey, M. L., Foster, D. B., Eds.; Springer International Publishing: Cham, 2016; pp 15–32. https://doi.org/10.1007/978-3-319-31828-8_2. (35) Ashcroft, A. Chapter 1: Introduction. In Ionization Methods in Organic Mass Spectrometry; The Royal Society of Chemistry, 1997; pp 1–26. (36) Illustrated Glossary of Organic Chemistry - Gas chromatography-mass spectrometry (GC-MS) http://www.chem.ucla.edu/~harding/IGOC/G/gc_ms.html (accessed Apr 2, 2019). (37) Hansen, S.; Pedersen-Bjergaard, S.; Rasmussen, K. Chapter 14: Gas Chromatography. In Introduction to Pharmaceutical Chemical Analysis; Wiley, 2012; pp 191–210. (38) Bumbrah, G. S.; Sharma, R. M. Raman Spectroscopy – Basic Principle, Instrumentation and Selected Applications for the Characterization of Drugs of Abuse. Egypt. J. Forensic Sci. 2016, 6 (3), 209–215. https://doi.org/10.1016/j.ejfs.2015.06.001. (39) Contreras, J. A.; Murray, J. A.; Tolley, S. E.; Oliphant, J. L.; Tolley, H. D.; Lammert, S. A.; Lee, E. D.; Later, D. W.; Lee, M. L. Hand-Portable Gas Chromatograph-Toroidal Ion Trap Mass Spectrometer (GC-TMS) for Detection of Hazardous Compounds. J. Am. Soc. Mass Spectrom. 2008, 19 (10), 1425–1434. https://doi.org/10.1016/j.jasms.2008.06.022. (40) Hargreaves, M. D.; Page, K.; Munshi, T.; Tomsett, R.; Lynch, G.; Edwards, H. G. M. Analysis of Seized Drugs Using Portable Raman Spectroscopy in an Airport Environment-a Proof of Principle Study. J. Raman Spectrosc. 2008, 39 (7), 873–880. https://doi.org/10.1002/jrs.1926. (41) Quang, L. X.; Lim, C.; Seong, G. H.; Choo, J.; Do, K. J.; Yoo, S.-K. A Portable Surface- Enhanced Raman Scattering Sensor Integrated with a Lab-on-a-Chip for Field Analysis. Lab. Chip 2008, 8 (12), 2214–2219. https://doi.org/10.1039/B808835G. (42) Vítek, P.; Ali, E. M. A.; Edwards, H. G. M.; Jehlička, J.; Cox, R.; Page, K. Evaluation of Portable Raman Spectrometer with 1064nm Excitation for Geological and Forensic Applications. Spectrochim. Acta. A. Mol. Biomol. Spectrosc. 2012, 86, 320–327. https://doi.org/10.1016/j.saa.2011.10.043. (43) Levy, D.; Diken, E. G. Field Identification of Unknown Gases and Vapors Via IR Spectroscopy for Homeland Security and Defense. IEEE Sens. J. 2010, 10 (3), 564– 571. https://doi.org/10.1109/JSEN.2009.2038540. (44) Lu, C.-J.; Whiting, J.; Sacks, R. D.; Zellers, E. T. Portable Gas Chromatograph with Tunable Retention and Sensor Array Detection for Determination of Complex Vapor Mixtures. Anal. Chem. 2003, 75 (6), 1400–1409. https://doi.org/10.1021/ac026092n. 4

(45) Aloglu, A. K.; Harrington, P. de B.; Sahin, S.; Demir, C.; Gunes, M. E. Chemical Profiling of Floral and Chestnut Honey Using High-Performance Liquid Chromatography-Ultraviolet Detection. J. Food Compos. Anal. 2017, 62, 205–210. https://doi.org/10.1016/j.jfca.2017.06.002. (46) Aloglu, A. K.; Harrington, P. de B.; Sahin, S.; Demir, C. Prediction of Total Antioxidant Activity of Prunella L. Species by Automatic Partial Least Square Regression Applied to 2-Way Liquid Chromatographic UV Spectral Images. Talanta 2016, 161, 503–510. https://doi.org/10.1016/j.talanta.2016.09.014. (47) Everitt, B. S.; Landau, S.; Leese, M.; Stahl, D. Cluster Analysis; John Wiley & Sons, 2011. (48) Baerncopf, J. M.; McGuffin, V. L.; Smith, R. W. Association of Ignitable Liquid Residues to Neat Ignitable Liquids in the Presence of Matrix Interferences Using Chemometric Procedures. J. Forensic Sci. Wiley-Blackwell 2011, 56 (1), 70. (49) Vidal, R.; Ma, Y.; Sastry, S. Generalized Principal Component Analysis; Springer, 2016. (50) Spruyt, V. What Are Eigenvectors and Eigenvalues? Computer vision for dummies, 2014. (51) Pearson Product-Moment Correlation - When you should run this test, the range of values the coefficient can take and how to measure strength of association. https://statistics.laerd.com/statistical-guides/pearson-correlation-coefficient- statistical-guide.php (accessed Dec 13, 2017). (52) Linear Discriminant Analysis - Introduction http://www.statistics4u.com/fundstat_eng/cc_lda_intro.html (accessed Apr 14, 2019). (53) Torion T-9 Portable GC/MS-NTSST090500|PerkinElmer //www.perkinelmer.com/product/torion-t-9-portable-gc-ms-instrument- ntsst090500 (accessed Apr 24, 2019). (54) Lu, Y.; Harrington, P. B. Forensic Application of Gas Chromatography–Differential Mobility Spectrometry with Two-Way Classification of Ignitable Liquids from Fire Debris. Anal. Chem. 2007, 79, 6752–6759. https://doi.org/10.1021/ac0707028. (55) Yang, C.; Brown, C. E.; Hollebone, B.; Yang, Z.; Lambert, P.; Fieldhouse, B.; Landriault, M.; Wang, Z. Chemical Components of Petroleum. In Oil Spoll Science and Technology; Gulf Professional Publishing, 2017. (56) Doble, P.; Sandercock, M.; Du Pasquier, E.; Petocz, P.; Roux, C.; Dawson, M. Classification of Premium and Regular Gasoline by Gas Chromatography/Mass Spectrometry, Principal Component Analysis and Artificial Neural Networks. Forensic Sci. Int. 2003, 132 (1), 26–39. https://doi.org/10.1016/S0379- 0738(03)00002-1. (57) Petraco, N. D. K.; Gil, M.; Pizzola, P. A.; Kubic, T. A. Statistical Discrimination of Liquid Gasoline Samples from Casework. J. Forensic Sci. Wiley-Blackwell 2008, 53 (5), 1092. (58) Liu, Z.; Phillips, J. B. Comprehensive Two-Dimensional Gas Chromatography Using an On-Column Thermal Modulator Interface. J. Chromatogr. Sci. 1991, 29 (6), 227– 231. https://doi.org/10.1093/chromsci/29.6.227. 5

(59) Winnike, J. H.; Wei, X.; Knagge, K. J.; Colman, S. D.; Gregory, S. G.; Zhang, X. Comparison of GC-MS and GC×GC-MS in the Analysis of Human Serum Samples for Biomarker Discovery. J. Proteome Res. 2015, 14 (4), 1810–1817. https://doi.org/10.1021/pr5011923.