Determining The Presence Of An Ignitable Liquid Residue In Fire Debris Samples Utilizing Target Factor Analysis

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1 University of Central Florida Electronic Theses and Dissertations Masters Thesis (Open Access) Determining The Presence Of An Ignitable Liquid Residue In Fire Debris Samples Utilizing Target Factor Analysis 2010 Kelly McHugh University of Central Florida Find similar works at: University of Central Florida Libraries Part of the Chemistry Commons, and the Forensic Science and Technology Commons STARS Citation McHugh, Kelly, "Determining The Presence Of An Ignitable Liquid Residue In Fire Debris Samples Utilizing Target Factor Analysis" (2010). Electronic Theses and Dissertations. Paper This Masters Thesis (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of STARS. For more information, please contact

2 DETERMINING THE PRESENCE OF AN IGNITABLE LIQUID RESIDUE IN FIRE DEBRIS SAMPLES UTILIZING TARGET FACTOR ANALYSIS by KELLY M. MCHUGH Bachelors of Science, Florida State University, 2006 A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in the Department of Forensic Science in the College of Sciences at the University of Central Florida Orlando, Florida Spring Term 2010 Major Professor: Michael E. Sigman

3 2010 Kelly McHugh ii

4 ABSTRACT Current fire debris analysis procedure involves using the chromatographic patterns of total ion chromatograms, extracted ion chromatograms, and target compound analysis to identify an ignitable liquid according to the American Society for Testing and Materials (ASTM) E 1618 standard method. Classifying the ignitable liquid is accomplished by a visual comparison of chromatographic data obtained from any extracted ignitable liquid residue in the debris to the chromatograms of ignitable liquids in a database, i.e. by visual pattern recognition. Pattern recognition proves time consuming and introduces potential for human error. One particularly difficult aspect of fire debris analysis is recognizing an ignitable liquid residue when the intensity of its chromatographic pattern is extremely low or masked by pyrolysis products. In this research, a unique approach to fire debris analysis was applied by utilizing the samples total ion spectrum (TIS) to identify an ignitable liquid, if present. The TIS, created by summing the intensity of each ion across all elution times in a gas chromatography-mass spectrometry (GC-MS) dataset retains sufficient information content for the identification of complex mixtures [1]. Computer assisted spectral comparison was then performed on the samples TIS by target factor analysis (TFA). This approach allowed rapid automated searching against a library of ignitable liquid summed ion spectra. Receiver operating characteristic (ROC) curves measured how well TFA identified ignitable liquids in the database that were of the same ASTM classification as the ignitable liquid in fire debris samples, as depicted in their corresponding area under the ROC curve. iii

5 This study incorporated statistical analysis to aid in classification of an ignitable liquid, therefore alleviating interpretive error inherent in visual pattern recognition. This method could allow an analyst to declare an ignitable liquid present when utilization of visual pattern recognition alone is not sufficient. iv

6 This work is dedicated to my family; past and future. v

7 ACKNOWLEDGMENTS Dr. Michael Sigman Dr. Andres Campiglia Dr. Christian Clausen Mary Williams Angela Fischer Charles Kazaros Joseph Castelbuono Chief Carl Chasteen Ryan Bennett Chief Barry Baker The Florida State Fire College Dr. Sigmans group at NCFS vi

8 TABLE OF CONTENTS LIST OF FIGURES...ix LIST OF TABLES... xii CHAPTER ONE: INTRODUCTION...1 Gas Chromatography...2 Mass Spectrometry...3 Headspace Vapors...3 Passive Headspace Concentration...4 Dynamic Headspace...5 Solid Phase Microextraction (SPME)...5 ASTM E The Summed Ion Mass Spectra...11 Principle Components Analysis (PCA)...13 Target Factor Analysis...19 Receiver Operating Characteristics...23 Cluster Analysis...23 CHAPTER TWO: EXPERIMENTAL...25 Sample Preparation...25 Test Set #1 (Single Substrate)...28 Test Set #2 (Multiple Substrates)...29 Test Set #3 (Approximate Limit of Detection)...31 Large Scale Burns...33 Cluster Analysis...41 Instrument Parameters...41 vii

9 CHAPTER THREE: RESULTS...42 Test Set #1 (Single Substrate)...42 Test Set #2 (Multiple Substrates)...52 Test Set #3 (Approximate Limit of Detection)...63 Large Scale Burns...72 Cluster Analysis...77 Quality Assurance...88 CHAPTER FOUR: FUTURE WORKS...89 Conclusions...89 Future Works of this Research Project...91 Future Works of Fire Debris Analysis...92 APPENDIX: ADDITIONAL TABLES AND FIGURES...94 REFERENCES viii

10 LIST OF FIGURES Figure 1: Current approach to fire debris analysis... 2 Figure 2: Weathering example of 90% weathered gasoline vs. unweathered gasoline Figure 3: Current and proposed approaches to fire debris analysis Figure 4: Skree plot obtained from PCA, prior to TFA Figure 6: Screen Shot of Knightfire Software Figure 7: Cluster analysis of select ignitable liquids TIS vs. burned substrate TIS Figure 8: Experimental set-up Figure 9: 'Window' cut out of the back of the storage container Figure 10: Layout of the four storage containers used for large scale burns Figure 11: Storage container outfitted with furnishings Figure 12: Preliminary burn Figure 13: TIS of ILs used in test set Figure 14: TIS of ILs used in test set Figure 15: TIS of ILs used in test set Figure 16: TIS of substrates used in test set Figure 17: TIS of substrates used in test set Figure 18: ROC curve of Test A Figure 19: TIS of aromatic product used in test set Figure 20: TIS of pyrolyzed substrates used in Test sets 2 and Figure 21: TIS of pyrolyzed substrates used in Test sets 2 and Figure 22: TIS of substrate control used in test set ix

11 Figure 23: SPLOM of HPD 206 data produced by TFA for test set Figure 24: 2-D plots of normal alkane SRN 241, used in test set Figure 25: 2-D plots naphthenic paraffinic liquid 243, used in test set Figure 26: 2-D plots of aromatic product 72, used in test set Figure 27: TIS of unweatherd gasoline 116 and TIS of test AC (with 0 ml and 5 ml gasoline 116) from test set Figure 28: TIS test AC (with 25 ml gasoline 116) from test set Figure 29: 2-D plots of container 1, burned with gasoline Figure 30: 2-D plots of container 2, burned with gasoline Figure 31: 2-D plots of container 3, burned with an MPD Figure 32: 2-D plots of container 4, burned with an oxygenated solvent Figure 33: 2-D plots of container 2, burned a second time with an MPD Figure 34: Cluster analysis of gasolines in the ILRC Figure 35: Cluster analysis of LPDs in the ILRC Figure 36: Cluster analysis of MPDs in the ILRC Figure 37: Cluster analysis of HPDs in the ILRC Figure 38: Cluster analysis of isoparaffinic products in the ILRC Figure 39: Cluster analysis of aromatic products in the ILRC Figure 40: Cluster analysis of naphthanic paraffinic products in the ILRC Figure 41: Cluster analysis of normal alkane products in the ILRC Figure 42: Cluster analysis of oxygenated solvents in the ILRC Figure 43: TICs of IL used in laboratory burns x

12 Figure 44: TICs of ILs used in laboratory burns Figure 45: TICs of liquids used in experimental burns Figure 46: TICs of substrates used in test set Figure 47: TICs of substrates used in laboratory burns Figure 48: TICs of substrates in laboratory burns Figure 49: TIC of burned combination of substrates used in test set Figure 50: TICs of gasoline 116 and Test AC xi

13 LIST OF TABLES Table 1: Ignitable liquid classifications set forth by ASTM E Table 2: Major ions present in mass spectra of common ignitable liquids... 6 Table 3: Divisions within ignitable liquid classifications set forth by ASTM E Table 4: Experiments of test set Table 5: Experiments of test set Table 6: Experiments of test set Table 7: Furnishings in the storage containers Table 8: Large scale burns Table 9: Time of burn and maximum temperature reached for container 1 burned as a control (no ignitable liquid added) Table 10: Time of burn and maximum temperature reached for container 1 burned with gasoline Table 11: Time of burn and maximum temperature reached for container 2 burned with gasoline Table 12: Time of burn and maximum temperature reached for container 3 burned with an MPD Table 13: Time of burn and maximum temperature reached for container 4 burned with an oxygenated solvent Table 14: Time of burn and maximum temperature reached for container 2 reburned with an MPD xii

14 Table 15: Correlation results from test set 1, (carpet and padding as substrate) Table 16: Area under the curve per test set 1, part I Table 17: Correlation results from test set 1, part II (Douglas fir wood as substrate) Table 18: Area under the curve per test set 1, part II Table 19: Correlation results from TFA applied to test set Table 20: Areas under the curve acquired from ROC analysis of test set Table 21: Areas under the curve from ROC analysis of all parameters calculated per TFA for test set Table 22: Correlation results per test set 3, varying number of PCs retained Table 23: Correlation results of test set 3, using substrate database, varying the number of PCs retained Table 24: Similarity results obtained from Knightfire software to correct IL Table 25: Similarity from Knightfire software of test set 3, control of combined burned substrates used as matrix component Table 26: Areas under the curve for aromatic product classification from test set 2 and when subclassification was applied Table 27: PCs retained for all tests for TFA xiii

15 CHAPTER ONE: INTRODUCTION In fire debris analysis, the initial task is to determine if an ignitable liquid is present in fire debris. Ascertaining whether the ignitable liquid was used as an accelerant is beyond the scope of the analyst, and should be left to the investigative team. The ASTM Standard Test Method for Ignitable Liquid Residues in Extracts from Fire Debris Samples by Gas Chromatography-Mass Spectrometry, also known as ASTM E 1618, states that fire debris samples are extracted from fire debris and subsequently analyzed with a gas chromatograph (GC) interfaced to a mass spectrometer (MS), producing a gas chromatograph and mass spectrum, respectively [2]. Data produced are then evaluated by visual pattern recognition, comparing the sample data to that of known ignitable liquids. ASTM E 1618 states that computer assisted pattern recognition techniques may be employed, as long as a visual confirmation is subsequently applied. The current approach to fire debris is depicted in Figure 1. 1

16 Figure 1: Current approach to fire debris analysis Gas Chromatography A GC is an analytical instrument utilized for separation of complex mixtures. To accomplish this, the sample must be brought into the gas phase under the chromatographic conditions. If the sample is a liquid, it is converted to the vapor state by injection into a heated port, or in response to heating in the chromatographic oven. The eluent, which in this case is helium, is then used as a carrier gas. Separation occurs as the vapor constituents equilibrate between carrier gas and the stationary phase [2]. 2

17 The sample is detected as it emerges from the column using a detector to produce a gas chromatogram. The chromatogram displays retention times and intensities of the detector response to analytes present in the sample on its x and y axes, respectively. The retention time, measured in minutes, is the time when a chromatographic peak corresponding to a component in the sample is recorded, from the time injected [2]. The area under the peak is proportional to the concentration, and so the amount of substance can be qualitatively determined [2]. Mass Spectrometry The MS is interfaced directly from a gas chromatograph so that the analytes can be further analyzed after separation by the GC. This instrument performs three vital functions when analyzing a sample. The source in the linear quadrupole mass spectrometer bombards molecules in the sample by a stream of high-energy electrons from a heated filament. This is known as electron ionization (EI) where the molecules are then accelerated at 70 electron volts (ev). This ionization technique converts the molecules in the sample to their respective molecular ion, some of which are then fragmented and accelerated in an electric field. These accelerated ions are then separated by their mass to charge (m/z) ratio and distinguished by a detector which measures the intensity of each peak. The output of this analytical technique is termed a mass spectrum, which is a graph of the intensity detected as a function of m/z [3]. Headspace Vapors A common sampling technique used in fire debris analysis is extracting ignitable liquid residues contained in the headspace vapors of a fire debris sample, as stated in ASTM E This is achieved by heating the sample to volatize any ignitable liquid residues in the debris. The 3

18 headspace vapors are then extracted and analyzed by GC-MS. Sampling the headspace vapors above a fire debris sample leaves the sample in approximately the same condition as prior to sampling, allowing further data analysis [4]. Passive Headspace Concentration Passive headspace concentration as described in ASTM E 1412 demonstrates the procedure for sampling the headspace above a fire debris sample to recover ignitable liquid residues from fire debris samples using activated charcoal, which adsorbs the residue vapor. The sample is then placed in an oven between 50 and 80 C for 8 to 24 hours. The temperature of the oven and adsorption times may be varied depending on the sample and adsorbent martial. The charcoal strips are then desorbed with carbon disulfide (CS 2 ) [5]. The passive headspace concentration method is suitable for extracting ignitable liquid residues over a wide range of concentrations. Detecting ignitable liquid residues in the headspace of a fire debris sample is especially useful when high sensitivity is required; detection of quantities less than μl have been reported in an unspecified volume, as stated in ASTM E 1412 [5]. This level of sensitivity allows the detection of very low concentrations of ignitable liquid residues in a sample. In addition, the passive headspace concentration method is essentially nondestructive. The charcoal strips from the sample can be sealed within a glass vial with a cap containing a Teflon septa, and will theoretically remain stable indefinitely, should the need for reevaluation of the evidence arise [5]. 4

19 Dynamic Headspace Dynamic headspace concentration is achieved by heating the sample to volatize components much like in passive headspace concentration. However, in the dynamic headspace concentration technique, the vapor in the headspace above the sample is then pulled or forced through a glass tube containing activated charcoal, as stated by ASTM E The charcoal is then desorbed with a solvent and the sample is analyzed through GC-MS or GC followed by infrared spectroscopy (IR). This technique is as sensitive as passive headspace concentration, but can destroy the sample therefore preventing reanalysis of the sample, unless precautions (i.e. using a different absorbent material) are taken to preserve the sample [6]. Solid Phase Microextraction (SPME) The headspace above a fire debris sample can also be extracted with a polydimethylsiloxane SPME fiber which is normally contained in a syringe configuration. The fiber is then directly introduced into an injection port of a GC instrument. This method is extremely sensitive, and is essentially nondestructive, so the sample remains in approximately the same condition prior to analysis. Problems arise due to the limited adsorption capacity of a SPME fiber. However, unlike passive headspace concentration with activated charcoal, the extract is consumed by this analysis so that further analysis with this fiber is not possible and archiving is unattainable. 5

20 ASTM E 1618 Currently, there are seven ASTM classes of ignitable liquids with an eighth category covering miscellaneous liquids which do not fall into any other classification [7]. Ignitable liquids may be grouped into one of these classifications presented in Table 1, as follows: Table 1: Ignitable liquid classifications set forth by ASTM E 1618 Gasoline Petroleum Distillates Isoparaffinic Products Aromatic Products Napthenic-Paraffinic Products Normal-Alkane Products Oxygenated Solvents Miscellaneous The compounds that comprise ignitable liquids consist of six major types: alkane (normal and branched), alkene, cycloalkane, aromatic, polynuclear aromatic, and oxygenates [7]. Compounds of each type produce characteristic major ion fragments as listed in Table 2. All classes except gasoline can be sub-classified by carbon range. The divisions are displayed in Table 3, page 8. Table 2: Major ions present in mass spectra of common ignitable liquids Compound Type m/z Examples Alkane 43, 57, 71, 85, 99 CH 3 CH 3 CH 3 CH 3 Cycloalkane and alkene 55, 69 CH 3 CH 3 CH 3 H 2 C 6

21 Compound Type m/z Examples n-alkylcyclohexanes 82, 83 CH 3 Aromatic alkylbenzenes 91, 105, 119; 92, 106, 120 CH 2 CH 2 CH 2 CH 3 Indanes 117, 118; 131, 132 Alkylnapthalenes (Condensed Ring Aromatics) 128, 142, 156, 170 CH 2 CH 3 Alkylstyrenes 104, 117, 118, 132, 146 CH 3 Alkylanthracenes 178, 192, 206 CH 3 7

22 Compound Type m/z Examples Alkylbiphenyls/ 154, 168, 182, CH 3 acenaphthenes 196 Monoterpenes 93, 136 Ketones 43, 58, 72, 86 O H 3 C Alcohols 31, 45 H 3 C OH CH 2 CH 3 Table 3: Divisions within ignitable liquid classifications set forth by ASTM E 1618 Sub-Classification Carbon Range Light C 4 -C 9 Medium C 8 -C 13 Heavy C 9 -C 20+ 8

23 Presently, total ion chromatograms and extracted ion chromatograms acquired by GC-MS are visually compared to those of reference samples by a fire debris analyst. Target compound analysis may be performed by reviewing retention times and mass spectral data to determine if certain target compounds are present in a fire debris sample. For example, 1,2,4- trimethylbenzene, 1,2,4,5-tetramethylbenzene, and 1,3-dimethylnaphthalene are commonly used as a target compounds in the identification of gasoline. The ignitable liquid is then classified by visual pattern recognition into one of the eight ASTM E 1618 classifications. Complete recovery of an ignitable liquid residue may prove difficult in some circumstances. For example, the preferential loss of the lighter components of the ignitable liquid will occur in the presence of heat. This process is referred to as weathering. Figure 2 illustrates an example of how a gasoline sample weathers in a fire. The top chromatogram depicts a 90% weathered gasoline while the lower chromatogram represents an unweathered gasoline sample. The lighter components of the 90% weathered sample have been lost or burned off due to the heat of the fire. The weathering process can alter the chromatogram so that the pattern of peaks is nearly unrecognizable. 9

24 90% Weathered Gasoline Unweathered Gasoline Figure 2: Weathering example of 90% weathered gasoline vs. unweathered gasoline Alternatively, if the temperature of the sample in the oven during the adsorption time is not sufficient to recover the heavier molecules, a pattern may be produced that is skewed toward the lighter end of the chromatogram, relative to a liquid injection standard [7]. Distortion may also occur when the heavier volatile components present in a fire debris sample displace the lighter components the charcoal strip, or other adsorptive material. This may cause a sample to appear significantly weathered, potentially misleading an analyst [8]. In addition to the loss of volatile compounds, extraneous components can be added to a sample chromatogram due to substrate interference. Fire debris samples usually contain some components inherent to the substrate(s) on which the fire occurred. Combustion products are the result of a chemical reaction, usually oxidization that produces energy in the form of heat and/or light. Pyrolysis products are produced as a result of the extreme heat of the fire and in the 10

25 absence of oxygen. Pyrolysis products arise from the breakdown of background substrates inherent to fire debris, therefore affecting the chromatographic pattern and spectral data. These processes can affect the matrix background of the sample and interfere with the identification of any ignitable liquid residue recovered. The Summed Ion Mass Spectra Though visual pattern recognition of chromatograms is the common approach to fire debris analysis, the method is not infallible. TICs are time dependent and can differ greatly from laboratory to laboratory depending on column type and instrument parameters. An ignitable liquid TIC may also differ from an expected pattern due to previously mentioned distortion or weathering. A novel approach to overcome these issues as well as alleviate human error associated with visual pattern recognition is to analyze the information extracted from fire debris samples differently. Covariance matrices calculated from GC-MS datasets have been evaluated and compared to determine feasibility in distinguishing ignitable liquid samples from one another and from their respective sources [9]. Though the utility of covariance mapping extends to grouping similar ignitable liquids by ASTM classification protocols, it is not practically employed to search large libraries, as it is computationally demanding [10]. Alternatively, the summed ion mass spectra method can be employed to calculate the TIS of a sample after analysis via GC-MS and is much more computationally efficient for searching against large libraries. The summed ion mass spectra method consists of summing the intensities of each nominal mass over all chromatographic times in a gas chromatogram [10]. These mass spectra were calculated for 440 ignitable liquids in the Ignitable Liquids Reference Collection (ILRC) at the National Center for Forensic Science (NCFS). The summed ion spectra were shown to retain 11

26 sufficient information content to allow the rapid and accurate identification of a specific ignitable liquid in a library of such spectra [1]. In order to perform automated searching, the TIS were normalized so that the intensities sum to one over all mass to charge (m/z) ratios in the mass spectrum. Variations such as concentration and size between sample properties are thus diminished by normalization. [11]. The normalized data were saved as a comma separated value (csv) file and used to perform subsequent computer analysis. The summed ion spectra method has also been applied to the analysis of pyrolysis products [10]. Pyrolysis products may have similar ions in the TIS as particular ignitable liquids, but the overall pattern of ion intensities will generally be different. Figure 3 pictorially defines the summed ion mass spectra method, in addition to current techniques applied in fire debris analysis. 12

27 Total Summed Ion Spectrum Figure 3: Current and proposed approaches to fire debris analysis Principle Components Analysis (PCA) PCA is performed following GC-MS analysis and compilation of normalization data of the TIS from a series of samples in a single data file. PCA reduces the dimensionality of a dataset and allows reproduction of the data without also reproducing the associated error from the sample. PCA can only be performed when multiple samples are available for analysis from each sample set. A sample set is comprised of multiple samples collected from a single fire. PCA 13

28 measures the variance between samples, thus requiring multiple samples. When analyzing a set of samples from a single fire, individual attributes of each fire debris sample must be compared with the range of possible patterns produced by the various components present in the fire [11], i.e. pyrolysis products from a burned substrate or ignitable liquid residues. To perform PCA, each data point in a dataset must conform to Equation 1 [12]. n di, k = ri, jc j, k + error j =1 [1] Where r and c are physically significant parameters corresponding to mass spectral intensity and sample concentration, respectively. The spectral dataset is represented by d, i indexes a specific mass/charge ratio, and k indexes concentrations or amount in the dataset. Eigenvectors and associated scores or spectra and associated concentrations are indexed by j. This equation also takes any inherent error into account as a separate contributing term. PCA is conveniently represented in matrix math notation by Equation 2. [ D ] = [ R][ C] [ D'] = [ R'][ C'] [2] Where [D] is the dataset, [R] is an abstract row matrix and [C] is an abstract column matrix. PCA measures the variance between samples, determining the minimum number of eigenvectors required to reproduce the data containing the desired level of variance. [D ] represents the data reproduced as a product of [R ] and [C ] containing the desired number of PCs. At this point in the analysis, [R ] and [C ] do not correspond to physical meaningful solutions. 14

29 Eigenvectors produced from PCA are orthogonal, meaning their dot products equal zero, represented mathematically by Equation 3. k c = 1 jk c jk The eigenvectors are also normalized, depicted mathematically by Equation 4. c jk c j The vectors constitute an orthonormal dataset. k k = 0 [3] [4] Previously, PCA has been applied to fire debris sample sets as in Equation 5, where [D] is the data matrix and [R] and [C] are abstract matrices. The values in the row matrix are the scores and the values of the column matrix are the loadings, respectively. The rows of the column matrix are the associated eigenvectors. time n time s [ D] = s[ R] n [ C] [5] Traditionally, when PCA is applied to fire debris analysis, it is arranged so that the rows in [D] correspond to individual samples and the columns consist of the individual m/z ratios. However, the problem can be inverted by taking the transpose of [D] by Equation 6. [D*] = [D] T [6] The dataset must be transposed for subsequent TFA. TFA will test resulting data from PCA against a library of test spectra to try to determine an acceptable solution. If [D] is not transposed, TFA would have to search against a library of concentrations of samples. Concentrations are not consistent for every sample in the same ignitable liquid classification. Spectral data are much more consistent between ignitable liquids within the same ASTM categories. Therefore, the transposed data matrix is used in PCA as follows in Equation 7. 15

30 m / z = m/ z n s [ D] n[ C] [7] Each eigenvalue associated with a PC is proportional to the amount of the total variance of the sample which is accounted for by the corresponding eigenvector. When retaining multiple PCs, each eigenvector collectively adds to the total variance. For example, the first PC may contain 90% of the total variance in the data set. If the second PC contains 5% of the total variance, retaining both the first and second PCs would retain 95% of the total variance of the sample. Retaining all PCs would account for 100% of the variance of the sample. Significant PCs are defined as eigenvectors containing the maximum variance. The remaining PCs are associated with noise inherent to the sample. By retaining only significant PCs, the dimensionality of the data is reduced while still containing the maximum variance of the dataset.[11]. Utilizing a skree plot is one technique used to determine how many PCs should be retained for data reproduction, while reproducing minimal noise. This plot shows the variance accounted for by the total number of PCs. A sample skree plot is depicted in Figure 4. As shown, the first PC makes up 1-3 or 7 (67%) of the total variance of the sample. The second PC, collectively with the first, accounts for 1-9 or 1 (91%) of the total variance in the sample. The third PC accounts for the remaining significant variance. The fourth and fifth PCs make up little of the total variance in the sample. As the third PC is the last component to contribute significant variance, three PCs would be retained for this particular dataset. Any PCs retained 16

31 after three are likely to reproduce the noise in the sample, rather than duplicating the data. 5 Variance Remaining Principle Components Figure 4: Skree plot obtained from PCA, prior to TFA The calculated eigenvectors are tested against physically significant vectors, known as test vectors, by target transformations. If the sample s vector that results is a close match to the test vector, that predicted vector may represent a physically meaningful solution. Possible solution vectors are tested individually without the need to identify all solution vectors. The application of PCA to aid in the classification of ignitable liquids has been studied previously. Tan et al. applied petroleum products to different matrices to investigate the effects of these matrix contributions on the ignitable liquid residue utilizing PCA [13]. Data produced from the application of PCA was followed by a soft independent model classification analogy (SIMCA) to successfully classify, by ASTM E 1618 classification, ignitable liquid residues in fire debris samples. SIMCA utilizes PCA and then determines how many PCs are needed to produce a confidence envelope to contain the data points. In this case, the confidence envelope 17

32 represented different classifications of ignitable liquids. By projecting the data onto each class, determination was made as to which classification the sample belonged [13]. Doble et al. also applied PCA to distinguish between premium and regular gasolines. By keeping four PCs, they were able to reduce the dimensionality of their dataset from 44, yet retained approximately 75% of the variance. By a pairwise comparison of scatter plots associated with PCs 1-4, they were able to visually inspect the corresponding cluster diagrams and determine if a gasoline was regular or premium grade [14]. Sandercock et al. applied PCA to 35 randomly collected samples of unevaporated gasoline. By graphing the scores that make up PC1 versus the scores of PC2, 32 clusterings were evident. They reported nearly all of the samples taken on the same day were differentiated into separate clusters by their PC scores plots [15]. The author then weathered the previously analyzed 35 gasoline samples to 25, 50, 75, and 90% evaporated gasoline by weight, respectively. The naphthalene profiles from 175 chromatograms at each level of evaporation were extracted and analyzed by PCA. Naphthalene profiles are not affected significantly by the weathering process, due to their weight. Linear discriminate analysis (LDA) was employed to examine the score plots corresponding to PCs one and two. This technique allowed them to reach the conclusion that good separation between the 35 samples at any given evaporation level is maintained because the between sample variation is greater than then within sample variation [16]. Hupp et al. applied PCA to associate or disassociate diesel fuels based on clustering. They further tested PCAs ability to identify variables that contribute to most of the variance in the data set. They determined the compounds predominantly contributing to the variance could 18

33 be identified using the first PC. The absolute values of the variance at each retention time were sorted by magnitude. Those retention times were then used to determine which individual components of the sample contributed the variance [17]. Something of note though, is that each of these authors used the data where m/z ratios were the columns in [D]. Therefore, the data used for any subsequent analysis, i.e. TFA is made up of chromatographic data. As stated, chromatographic data is inconsistent between samples and depends heavily on retention time locking and other variable circumstances. Target Factor Analysis Once a data matrix is acquired from PCA it is the purpose of TFA to transform abstract answers into physical solutions. This is where [R ], which represents intensity in the summed ion mass spectra, is tested against test vectors to potentially identify physically significant solutions. This is achieved by Equation 8 where [T] is the transformation matrix with dimensions n x n, which is composed of test vectors in the ILRC library. The matrix [R ] is the row matrix of the sample (predicted vector) produced by PCA. + [ R] [ R ][ T ] = [8] In this case, the test vectors are the TIS of each liquid in the ILRC. [T] is used in Equation 8 to obtain the predicted vector [ R ] that most closely matches a test vector. R = [ R' ] l T l 19

34 where Tl represents one column of the transformation matrix [T]. This method enables the user to search predicted vectors against one physically meaningful solutions (test vectors) individually rather than using the entire [T] which proves time consuming and computationally demanding. ' 1 T = [ λ ] [ R' ] l T R l Once Tl is determined, R (double bar) can be found. R (double bar) represents an ignitable liquid from the ILRC. To compare the test vector and predicted vector, a least squares method is utilized to measure the degree of association between vectors. This degree of association can be measured and reported as a Pearson product moment correlation. Ideally, the result vector would closely correspond with a test vector, producing a high degree of association, as for these analytes that comprise the samples. The Pearson correlation is calculated between the sample (predicted vector) and the ignitable liquid TIS in the ILRC (test vectors) as shown in Equation 9. r = ( xi ( x i x) x)( y 2 i y) ( x i x) 2 [9] The measured angle between a test vector and the predicted vector is θ. As θ approaches zero, sin 2 θ also approaches zero. The dot product of each test and predicted vector enables us to find θ, as shown by the following equation: 20

35 a b = a b cosθ The real error in target () value is calculated by Equation 10 [12], = r j = 1 = * ( r = 2 r i) i r 1 / 2 [10] Figure 5 demonstrates the relationship between the test vector and predicted vector obtained by TFA. = R (test vector) AET R (pure vector) * R REP (predicted vector) Figure 5: Diagram representing the relationship between vectors following TFA [12]. Linear Combination Calculation A linear combination calculation, expressed in Equation 11, is performed by Knightfire software written in-house at UCF. The significance of the linear combination calculation is that it can be performed when only one sample is present, unlike PCA. 21

36 Distance: D = i Z 1 N i 2 Z i 2 N Similarity: D S = 1 S =1 D D max Z ( Sample) = a * Z ( Pyrolysis) b * Z ( IL) [11] N N + a : 0 1 b : 0 1 a + b = 1 Where Z represents the TIS of each sample, ignitable liquid, or matrix component. Figure 6 shows a screen shot of the Knightfire software. The software allows the input of a mass spectral dataset of a sample, ignitable liquid, and matrix component. The similarity is then calculated between sample and ignitable liquid and/or matrix component, depicted in Equation 11. The contribution from the matrix component can be adjusted to account for more or less matrix present in the fire debris sample. N Figure 6: Screen Shot of Knightfire Software The software also allows the user to calculate the distance between a sample and an ignitable liquid TIS from a library or between a sample and a burned substrate TIS from a 22

37 library. If both libraries are available, a distance can be calculated between the sample TIS and a linear combination of ignitable liquid and matrix TIS. Receiver Operating Characteristics Evaluation of ROC curves facilitate visualization, organization and selection of classifiers based on their performance [18]. A discriminate value (correct or incorrect class identification) was assigned to each test vectors respective correlation to each predicted vector, produced by TFA, and a ROC curve was produced. The discriminate value is either a correct response for an ignitable liquid in the same ASTM classification as the test ignitable liquid, or an incorrect response if the test liquid and library entry are not in the same classification [19]. The classifications of the ignitable liquids in the library were determined by the ILRC Committee, which consists of members of the Technical Working Group for Fire and Explosives (TWGFEX). ROC curves were generated through Sigmaplot software. An area of 5 under the ROC curve indicates that a spectrum chosen from the correct response group (liquids falling within the same classification as the ignitable liquid used) has a probability of 5 of scoring higher than a spectrum chosen from the incorrect response group (an ignitable liquid not falling within the same classification as the ignitable liquid used). Cluster Analysis A pairwise comparison of the TIS of 62 ignitable liquids were compared in a batch analysis against a database of 50 burned substrate s TIS from previous work in the laboratory [20]. This batch analysis, or cluster analysis, is simply a comparison of the TIS representing ignitable liquids to the TIS of substrates, which are then reported as similarity. The hierarchiacal 23

38 cluster diagram used a single linkage, which measured the Euclidian distance between the two most similar points of two clusters. The single linkage algorithm is defined as the distance between the closest objects belonging to different clusters [21]. Figure 7 demonstrates that the pyrolysis products from many common construction and household items bear little similarity to the selected 62 ignitable liquids. A notable exception is the high similarity between the pyrolysis products from olefin carpet and ignitable liquids in the ASTM classification of MPD. Figure 7: Cluster analysis of select ignitable liquids TIS vs. burned substrate TIS 24

39 CHAPTER TWO: EXPERIMENTAL Sample Preparation Analyses performed on samples prepared in the laboratory were based on a method developed by the State of Florida Bureau of Forensic Fire and Explosive Analysis (BFFEA) [20]. The method involved placing approximately 4.0 g of substrate material, i.e. carpet and padding, wood, vinyl flooring, etc..., inside an unlined metal can and subsequently adding ignitable liquid(s) in controlled amounts. Emphasis was placed on positioning the substrates so that the top of the substrate oriented toward the bottom of the can, as the can was heated from underneath. This was to replicate, as accurately as possible, how a burn would affect the substrate materials in an actual fire scene. A vented lid consisting of seven holes approximately mm in diameter was placed on top of the can. The metal can was then placed on top of a ring stand and an iron ring that allowed the can to sit approximately 4.0 cm above a propane torch, as shown in Figure 8, page 27. The torch was ignited, which heated the underside of the can. The sample would undergo heat-induced decomposition, sometimes igniting, simulating pyrolysis occurring in a fire. The timing of the burn process began when smoke appeared through the holes in the lid. The sample was heated for two minutes after the initial appearance of smoke. The torch was then extinguished, the can removed from the apparatus, and the vented lid replaced with a lid containing no holes. This allowed the accumulation of condensed vapors within the can. Passive headspace concentration was then applied, as follows ASTM E-1412 [5]. Once the sample cooled, a charcoal strip attached to a paper clip tied with un-waxed dental floss was suspended in the headspace of the can above the post burn residue. The charcoal 25

40 strip measured cm x 3.0 cm [20]. After the charcoal was inserted, the lid was replaced and sealed. The can was placed inside an oven held at a constant temperature of 66ºC for 16 to 18 hours. The can was then taken from the oven and the charcoal strip was removed, cut in half, and one half of the strip was placed into a 2.0 ml autosampler glass vial with ml of carbon disulfide (CS 2 ). CS 2 was used to desorb any volatile compounds absorbed onto the charcoal strip. The vial was then sealed for GC-MS analysis with a Teflon septum autosampler crimp cap. The other half of the charcoal strip was placed into a 2.0 ml glass autosampler vial, sealed, and archived. This step was performed to preserve the strip, in case the need for reanalysis arises. A visual representation of the experimental setup is displayed in Figure 8. 26

41 Figure 8: Experimental set-up 27

42 Test Set #1 (Single Substrate) Table 4: Experiments of test set 1 Ignitable Liquid (SRN in ILRC) Classification Test Volume Substrate,,,,, Polyester carpet and 116 Gasoline A,, 1.5, 2.0 padding 8 LPD B,,,,,,, 1.5, 2.0 Polyester carpet and padding 30 MPD C,,,,,,, 1.5, 2.0 Polyester carpet and padding Oxygenated,,,,, Olefin carpet and 174 Solvent D,, 1.5, 2.0 padding,,,,, Olefin carpet and 59 Aromatic E,, 1.5, 2.0 padding 87 Isoparaffinic F,,,,,,, 1.5, 2.0 Olefin carpet and padding 206 HPD G,,,,,,, 1.5, 2.0 Poylester/Nylon blend carpet and padding Normal,,,,, Poylester/Nylon blend 241 Alkane H,, 1.5, 2.0 carpet and padding 243 Naphthenic Paraffinic I,,,,,,, 1.5, 2.0 Poylester/Nylon blend carpet and padding,,,,, 30 MPD J,, 1.5, 2.0 Douglas fir wood 87 Isoparaffinic K,,,,,,, 1.5, 2.0 Douglas fir wood The experiments performed comprising test set 1 are listed in Table 4. As depicted in the aforementioned table, nine sets of test burns were conducted with an ignitable liquid from each of the seven classifications set forth in ASTM 1618, with the exception of the miscellaneous class. Petroleum distillates were divided into light, medium and heavy and a sample from each sub-classification was used in the test burn set. In addition to the other six major classifications, displayed in Table 1, page 6, this totaled nine burns. Nine samples of varying amounts of 28

43 ignitable liquids were burned on a carpet and padding matrix for each classification. These samples were analyzed by GC-MS. The amounts of ignitable liquid used were ml, ml, ml, ml, ml ml ml, 1.5 ml, and 2.0 mls. The TIS was calculated and then normalized for each mass to charge (m/z) 30 to 284. The m/z of 284 was the upper limit due to the limited capacity of Excel when the data was transposed, which was used to process the data files. An upper limit of m/z 284 is not seen as a significant limitation, as most ions fall below this cutoff. Once the data were normalized so that the total ion intensity summed to one, a csv file was created with the normalized data for each of the amounts of ignitable liquids used in the burns, giving a nine column dataset, with each column representing a different amount of ignitable liquid and each row representing a nominal m/z The csv file was then imported into Mathematica software for TFA. Test Set #2 (Multiple Substrates) 29

44 Table 5: Experiments of test set 2 IL SRN in ILRC Classification Test Volume Substrate 8 LPD L 30 MPD M 206 HPD N 241 Normal Alkane O 116 Gasoline P Naphthenic Paraffinic Oxygenated Solvent Q R 87 Isoparaffinic S 72 Aromatic T,,, 1.5, 2.0,,, 1.5, 2.0,,, 1.5, 2.0,,, 1.5, 2.0,,, 1.5, 2.0,,, 1.5, 2.0,,, 1.5, 2.0,,, 1.5, 2.0,,, 1.5, 2.0 Fiberglass insulation, P.E.T. carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, P.E.T. carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, P.E.T. carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, P.E.T. carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, P.E.T. carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, P.E.T. carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, P.E.T. carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, P.E.T. carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, P.E.T. carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine The experiments performed in test set 2 are displayed in Table 5. Test set 2 involved complicating the substrate to determine if an ignitable liquid could still be identified from several contributions of background interference, rather than from a single carpet and padding or wooden substrate used in test set 1. The substrates were selected based on their similarity with ignitable liquids, as shown in Figure 7, page 34. The less similar the substrates and ignitable liquids are to one another, the 30

45 more distinguishable the ignitable liquid residues should be relative to the substrate s pyrolysis products in a fire debris sample. The selected substrates are listed in Table 5. Burns were repeated as with the first sample set with multiple amounts of ignitable liquid, as depicted in Table 5. In this test set, five samples each with a different amount of ignitable liquid ( ml. ml, ml, 1.5 ml, and 2.0 ml) were prepared, burned and analyzed identically to the procedure used in test set 1. Test Set #3 (Approximate Limit of Detection) The amount of ignitable liquid recoverable after a fire depends on several factors. If the ignitable liquid is very light and significant components were burned off during the fire, the amount of residue recovered would be small. If the ignitable liquid did not contribute significantly to the overall chromatographic pattern of the fire debris sample, identification of this liquid would prove problematic. If the ignitable liquid is extremely weathered by the evaporative process during the fire, the amount of residue recovered and probability of subsequent identification would decrease significantly. The ability to classify or even identify an ignitable liquid depends significantly on the recoverability of the ignitable liquid residue present in the sample. 31

46 Table 6: Experiments of test set 3 IL SRN in ILRC Classification Test Volume 116 Gasoline AA 116 Gasoline AB 25, 5, 116 Gasoline AC 116 Gasoline AD 5 25, 5, 8 LPD BB 25, 5, 30 MPD CC 25, Aromatic 5, 72 Product DD Oxygenated Solvent Normal Alkane Naphthenic Paraffinic EE FF GG 206 HPD HH 87 Isoparaffinic II 25, 5, 25, 5, 25, 5, 25, 5, 25, 5, Substrate Fiberglass insulation, polyester/nylon blend carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, polyester/nylon blend carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, polyester/nylon blend carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, polyester/nylon blend carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, polyester/nylon blend carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, polyester/nylon blend carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, polyester/nylon blend carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, polyester/nylon blend carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, polyester/nylon blend carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, polyester/nylon blend carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, polyester/nylon blend carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine Fiberglass insulation, polyester/nylon blend carpet and padding, polyurethane foam mattress, vinyl flooring, yellow pine 32

47 Experiments performed in test set 3 are listed in Table 6. Tests were conducted in the laboratory to determine a lower limit of the amount of ignitable liquid residue that could be present in a fire debris sample (in proportion to sample container size) where the ignitable liquid could still be recovered, analyzed, and subsequently classified. Amounts of ignitable liquid tested in this test set were ml, 5 ml, and 25 ml. Large Scale Burns Four 8 x 20 x 8 Konex storage containers were purchased for the execution of burns on a larger scale than the previously completed laboratory burns. Each of the containers had two 8 x 4 outward swinging doors used for access to the unit and a window which was cut into each container on the opposite side from the doors, as depicted in Figure 9. This window was merely a three-sided cut of the container wall, leaving the fourth side to act as a hinge. The addition of a window to the storage container allowed control of air flow to attempt to replicate 33

48 an actual structure fire. Figure 9: 'Window' cut out of the back of the storage container 34

49 A B C D E F G H I J K L M N O P Q R S T Figure 10: Layout of the four storage containers used for large scale burns Additionally, each container was built with a wooden floor, sheetrock walls, and a dividing wall separating the living area from the bedroom, as depicted in Figure 10. The storage containers were then outfitted with furnishings listed in Table 7, and shown in Figure 11. Table 7: Furnishings in the storage containers Furnishing Nylon carpet and padding Product Information suede carpet nylon 6lb pad Manufacturer 12 Full Throttle Plushstep deluxe Mainstays Distributor Home Depot Futon Futon frame Micro-fiber (suede) metal Walmart Office chair* Walmart Coffee table Wood Parsons Walmart Newspapers/ magazines Three-drawer chest Particleboard Mainstays Walmart Lamp Textured finish Linen shade Twin mattress Steel coil springs Slumber 1 Foam Twin mattress frame Steel Slumber 1 Smart Base Bedding (flat and fitted Cotton Mainstays sheets, comforter, Polyester blend bedskirt, pillow case, 35 Walmart Walmart Walmart Walmart

50 pillow sham) Two pillows Cotton Mainstays Walmart *Information not available Figure 11: Storage container outfitted with furnishings Preliminary burns were conducted using nylon carpet and padding, cardboard boxes, and wooden pallets as substrates. A volume of 500 ml of gasoline was poured directly onto the carpet and trailed toward the doors of the container used. The trail was lit and the fire continued up to the pour position, as shown in Figure 12. Examination of the results from the preliminary burns led personnel from the Florida State Fire College and BFFEA to the determination that 500 ml of each ignitable liquid per container would be a sufficient amount for the large scale burns. 36

51 Figure 12: Preliminary burn Thermocouple wires were placed at five different points in each container, at varying heights. Digital outputs, provided by BFFEA, enabled monitoring of the temperature inside the storage container throughout the burn process. The containers were burned as outlined in Table 8. Table 8: Large scale burns Container 1 Control (no ignitable liquid) Container 1 Gasoline Container 2 Gasoline Container 3 MPD Container 4 Oxygenated Solvent Container 2 MPD The first container to be burned was the designated control, meaning there was no ignitable liquid added. Newspaper and magazines were crumpled together and trailed from the 37

52 door of the container onto the futon in the living area. The newspapers were lit and the flame self-extinguished almost immediately. The newspapers were lit a second time and the fire continued to follow the trail of paper; however, the furnishings in the unit did not catch fire and the flame self-extinguished a second time. Two samples were taken from the burned area to be analyzed in the laboratory as controls. Information regarding container 1 burned as a control is depicted in Table 9. Table 9: Time of burn and maximum temperature reached for container 1 burned as a control (no ignitable liquid added) Conainer 1 Control Maximum temperature at each thermocouple location ( F) Time of burn (minutes) :50 ~ Realizing the container would unlikely burn without an accelerant, approximately 100 ml of gasoline was poured on the bed/bedding in the bedroom and 400 ml was trailed out into the living area, ending on the futon. The fire was lit on the mattress and subsequently followed the trail of gasoline to the living area. Twelve samples were taken from container 1, with gasoline as an accelerant. The burn time and maximum temperature reached at each thermocouple are shown in Table 10. Table 10: Time of burn and maximum temperature reached for container 1 burned with gasoline Conainer 1 Gasoline Maximum temperature at each thermocouple location ( F) Time of burn (minutes) :23 Not available The second container also employed 500 ml of gasoline as an accelerant. Approximately 100 ml of gasoline was poured on the bed/bedding in the bedroom and 400 ml of the gasoline 38

53 was trailed to the living area, ending on the futon. The bedding caught fire quickly once the flame was set, but the trail died out near the barrier wall separating the two rooms. Time of burn before extinguishment and maximum temperature reached are displayed in Table 11. Six samples were collected for later analysis in the laboratory. Table 11: Time of burn and maximum temperature reached for container 2 burned with gasoline Conainer 2 Gasoline Maximum temperature at each thermocouple location ( F) Time of burn (minutes) : The third container, outfitted identically to the first two was burned with 500 ml of an MPD, specifically Klean Strip paint thinner, acquired from Home Depot. Approximately 100 ml was poured on the futon and the remaining 400 ml trailed into the bedroom, ending on the bed/bedding. The fire was lit on the futon but did not follow the trail into the bedroom, therefore failing to ignite the bed/bedding. The maximum temperature reached inside the container and burn time prior to extinguishment with water are shown in Table 12 Twelve samples were taken from container 3 to be analyzed in the laboratory. Table 12: Time of burn and maximum temperature reached for container 3 burned with an MPD Conainer 3 MPD Maximum temperature at each thermocouple location ( F) Time of burn (minutes) : Container four was burned in the same fashion as the other three containers, using an oxygenated solvent, (Lowe s Deglosser) as an accelerant. Approximately 100 ml of the liquid was poured on the bed and the remaining 400 ml were used as a trail to the living area, 39

54 concluding on the futon. The fire was started on the futon and followed the trail into the bedroom. The fourth containers time of burn and maximum temperature reached before extinguishment are depicted in Table 13. After the fire was extinguished with water. Eleven samples were taken for laboratory analysis. Table 13: Time of burn and maximum temperature reached for container 4 burned with an oxygenated solvent Conainer 4 Oxygenated Solvent Maximum temperature at each thermocouple location ( F) Time of burn (minutes) : Container two was burned a second time, as the living area was not significantly damaged from the previous burn with gasoline. Approximately 450mL of the same MPD used previously were poured, commencing on the futon, and trailed into the bedroom. The fire burned the futon and carpet along the pour, but did not follow the trail into the bedroom. The bedroom had little surface area untouched by the previous fire, and therefore did not relight when the MPD was used. The burn time and maximum temperature reached at each thermocouple is displayed in Table 14. Six samples were taken from this burn to be later analyzed in the laboratory. Table 14: Time of burn and maximum temperature reached for container 2 reburned with an MPD Conainer 2 MPD Maximum temperature at each thermocouple location ( F) Time of burn (minutes) :

55 Cluster Analysis Using a pairwise comparison of samples, batch analyses were performed on each of the ignitable liquid classifications by SYSTAT software. As previously mentioned, all of the ignitable liquids in the ILRC are assigned a classification designated in ASTM 1618 by the ILRC committee. All of the ignitable liquids of a particular class were compared against themselves to determine the similarities within classes, with the exception of the miscellaneous classification. Instrument Parameters An Agilent 6890 gas chromatograph with autosampler was interfaced to an Agilent 5973 mass spectrometer, utilizing a split/splitless injector with 1µL of sample split 50:1 at a temperature of 250 C. The chromatographic column used was a HP-1 (methyl siloxane) column of mm internal diameter, 25 m length and µm film thickness. Helium carrier gas was kept at a constant flow of 34 cm/minute on the column. The initial oven temperature was 50 C for 3 minutes. Subsequently, a ramp rate of 10 C/minute was performed to a final temperature of 280 C and held for 4 minutes for a total run time of 30 minutes. The mass analyzer scanned from m/z with scan rate 2-3 scans/ second (equivalent to 6-10 scans per peak); following a 2 minute solvent delay. The mass spectrometer line was maintained at 280 C with source temperature of 230 C [20]. 41

56 CHAPTER THREE: RESULTS Test Set #1 (Single Substrate) The TIS from the multiple burns (differed IL volumes) for each sample were combined and subjected to TFA. The number of PCs retained for TFA for each experimental burn is listed in Table 24, p. 95 in the appendix. The Pearson correlation between each spectrum in the library (test vector) and the resulting (predicted) vector was determined. Correlations between the IL used in the experimental burn and the resulting vector for test set #1 (carpet and padding substrate) are listed in Table 15. Poor correlations are noted for the LPD due to minimal IL residue, which does not contribute much to the chromatographic pattern of the fire debris sample (Test B). Poor correlations were also noted for the oxygenated solvent (Test D). Results depicted in Table 15 are the Pearson correlations between the sample TIS and the TIS of that specific liquid in the ILRC. These results do not reflect the methods utility in classifying an ignitable liquid into the correct ASTM classification. 42

57 Table 15: Correlation results from test set 1, (carpet and padding as substrate) ILRC ASTM Classification Test Number of PCs retained % Variance retained with retained PCs 8 LPD A MPD B Aromatic C Isoparaffinic D Gasoline E Oxygenated solvent F HPD G Normal alkane H Naphthenicparaffinic I Correlation to known ignitable liquid from TFA 43

58 Gasoline 116 Normalized intensity Mass to charge (m/z) Light Petroleum Distillate Normalized intensity Mass to charge (m/z) Medium Petroleum Distillate 30 2 Normalized intensity Mass to charge (m/z) Figure 13: TIS of ILs used in test set 1 44

59 Oxygenated Solvent Normalized intensity Mass to charge (m/z) Aromatic Product 59 5 Normalized intensity Mass to charge (m/z) Isoparaffinic Product Normalized intensity Mass to charge (m/z) Figure 14: TIS of ILs used in test set 1 45

60 Heavy Petroleum Distillate Normalized intensity Mass to charge (m/z) Normal Alkane Normalized intensity Mass to charge (m/z) Naphthenic Paraffinic Product Normalized intensity Mass to charge (m/z) Figure 15: TIS of ILs used in test set 1 46

61 TIS of the ignitable liquids used in test set 1 are displayed in Figures 13, 14 and 15. The TIS demonstrate the variety between ignitable liquids used in experimental burns. Though certain ILs share common ions, the overall pattern of each liquid is distinguishable. The liquids corresponding TICs are displayed in the appendix in Figures 43, 44 and 45, pages TIS of the substrates used in test set 1 are depicted in Figures 16 and 17. These figures demonstrate the diversity between substrates. These spectra are also unlike the liquid spectra shown in Figures 13, 14 and 15 (pages 44-46). The substrates corresponding TICs are depicted in the appendix in Figure 46, page

62 Polyester carpet and padding Normalized intensity Mass to charge (m/z) Olefin carpet and padding Normalized intensity Mass to charge (m/z) Polyester/nylon carpet and padding 6 5 Normalized intensity Mass to charge (m/z) Figure 16: TIS of substrates used in test set 1 48

63 Douglas Fir 7 6 Normalized intensity Mass to charge (m/z) ratio Figure 17: TIS of substrates used in test set 1 Correlation results from TFA of test set 1 were utilized to produce ROC curves to determine how well the model predicted the correct classification as the ignitable liquid used in the laboratory burn. As the area under the curve approaches one, the higher the overall placements of correct qualifiers lie above the placement of the incorrect responses based on the respective correlation values. These areas under the curve from test set 1 are displayed in Table 16. The area under the curve for the ROC plots corresponding to the LPD and oxygenated solvent classifications are significantly low, though these factors do not influence the utility of TFA to identify the correct ignitable liquid, as depicted in Table 15, page 43. A sample ROC curve (Test A) is depicted in Figure

64 Table 16: Area under the curve per test set 1, part I Ignitable Liquid (SRN in ILRC) IL classification Test Area Under the Curve from ROC Analysis 116 Gasoline A 7 8 LPD B 6 30 MPD C Oxygenated Solvent D 8 59 Aromatic E 5 87 Isoparaffinic F HPD G Normal Alkane H Naphthenic Paraffinic I 8 Figure 18: ROC curve of Test A The low area under the curve associated with the oxygenated solvent and aromatic liquid in test set 1 are explained in detail later in this section. The reduced area for the LPD (Test B) is attributed to the high volatility of the specific ignitable liquid, resulting in minimal recovered residue for identification. Table 16 indicated low correlation with the isoparaffinic liquid used in the burn process (Test F). Hence, a second burn was completed to determine if a higher correlation could be achieved for this liquid on a different substrate than the carpet and padding used in Test F. 50

65 Douglas fir wood was selected for Test K because the liquid and burned substrates respective TIS were more dissimilar than those in Test F. As reported in Table 17, the correlation was raised considerably, to 75 and the area under the curve increased to 5. The selected MPD was also reanalyzed with the Douglas fir to ensure reproducibility of the burn method (Test J), with the results shown in Table 17. Table 17: Correlation results from test set 1, part II (Douglas fir wood as substrate) ILRC ASTM Classification Test Number of PCs retained % Variance retained with retained PCs 30 MPD J Isoparaffinic K Correlation to known ignitable liquid from TFA ROC analysis performed on the correlations produced by TFA of Tests J (carpet and padding substrate) and K (Douglas fir wood) are displayed in Table 18. Significantly improved correlations are noted in Test K in comparison to Test F. It is believed that the earlier determined low correlation for the isoparaffinic liquid in test set 2 was human error in that particular test. The results displayed in Tables 17 and 18 indicate this particular isoparaffinic liquid does have the capability of achieving a higher correlation when burned on a different matrix. Table 18: Area under the curve per test set 1, part II Ignitable Liquid (SRN in ILRC) IL classification Test Area Under the Curve from ROC Analysis 30 MPD J 4 87 Isoparaffinic K 5 51

66 Test Set #2 (Multiple Substrates) Test set 2 utilized the same ignitable liquids from test set 1 with the exception of the aromatic product, which was amended due to the light carbon range and high volatility of the initially selected aromatic liquid used in test set 1. The TIS of aromatic product SRN 72 is displayed in Figure 19. The TIC for aromatic product 72 is depicted in the appendix in Figure 45, page 99. The TIS of the selected substrates for test set 2 are displayed in Figures 20 and 21. Their respective TICs are depicted in the appendix in Figures 47 and 48, pages 100 and 101, respectively. Aromatic Product 72 5 Normalized intensity Mass to charge (m/z) Figure 19: TIS of aromatic product used in test set 2 52

67 Vinyl Flooring 5 Normalized intensity Mass to charge (m/z) Yellow Pine 4 2 Normalized intensity Mass to charge (m/z) P.E.T. Polyester Carpet and Padding 7 6 Normalized Intensity Mass to charge (m/z) Figure 20: TIS of pyrolyzed substrates used in Test sets 2 and 3 53

68 Fiberglass Insulation Normalized Intensity Mass to charge (m/z) Polyurethane Foam Mattress 7 6 Normalized intensity Mass to charge (m/z) Figure 21: TIS of pyrolyzed substrates used in Test sets 2 and 3 The substrates were burned and analyzed identically to the laboratory samples. Certain background matrices have more complicated chromatographic patterns than others. If any substrate produces a peak in their chromatographic pattern with a similar retention time as a peak from a compound in an ignitable liquid, the peak may be mistakenly thought to have originated from an ignitable liquid. A control sample, a sample burned in similar fashion to the sample of evidentiary value can be compared to the chromatographic pattern from the sample thought to 54

69 contain an ignitable liquid residue. This can allow a visual subtraction of peaks inherent to the substrate. Prior to commencement of experiments comprising test set 2, a control burn was conducted on the combination of substrates listed in Table 5, p.30. This was executed to establish an expected pattern for the substrates used in test set 2. The TIS of the control burn is shown in Figure 22. The TIC of the burned combination of substrates used in test set 2 is displayed in the appendix in Figure 49, page 101. Control of Combined Substrates used in Test Set Normalized intensity Mass to charge (m/z) Figure 22: TIS of substrate control used in test set 2 Table 19: Correlation results from TFA applied to test set 2 ILRC ASTM Classification Test Number of PCs retained % Variance retained with retained PCs Correlation from TFA with Correct IL 8 LPD L MPD M HPD N Normal Alkane O Gasoline P Naphthenic paraffinic Q

70 174 Oxygenated solvent R Isoparaffinic S Aromatic T Data from the burns comprising test set 2 were treated as described for the data of test set 1. The Pearson correlation results from TFA applied to test set 2 are depicted in Table 19. The correlation for the specific LPD used is lower that the others in test set 2. This is again due to the low molecular weight and high volatility commonly associated with LPDs, resulting in a minimal amount of recoverable ignitable liquid residue. All tests, with the exception of Test L, reflect minimal influence of multiple substrates contributions on the correct identification of the specific ignitable liquid used in the test when compared to the correlation results of test set 1, shown in Table 15, p. 42. Table 20: Areas under the curve acquired from ROC analysis of test set 2 IL SRN in ILRC Classification Test Area Under the Curve from ROC Analysis 8 LPD L 3 30 MPD M HPD N Normal Alkane O Gasoline P Naphthenic paraffinic Q Oxygenated solvent R 8 87 Isoparaffinic S 3 72 Aromatic T 1 The areas under the curve from ROC analysis applied to test set 2 are displayed in Table 20. The area under the curve for the oxygenated solvent is low, due to the same circumstances as test set 1. The area under the curve is also low for the aromatic liquid which is attributed to significant variations within the ignitable liquid classification. This will be addressed in more 56

71 detail later in this chapter. The ROC analyses for all other tests reflect a high probability of correct identification of the ASTM classification by this method. Multiple parameters were calculated to assess the similarity between test (liquids in the ILRC) vectors and predicted (sample) vectors by TFA. Each parameter was evaluated for correct ASTM class determination by the corresponding ROC areas displayed in Table 21. Table 21: Areas under the curve from ROC analysis of all parameters calculated per TFA for test set 2 IL SRN Classification Test r Θ AET REP Spoil sin 2 θ 8 LPD L MPD M HPD N Normal Alkane O Naphthenic Paraffinic P 116 Gasoline Q Oxygenated Solvent R 87 Isoparaffinic S Aromatic T All parameters gave ROC areas greater than for at least one test burn, with the exception of REP. The LPD, oxygenated solvent, and aromatic categories gave lower areas under the curve for all calculated parameters. Use of more than one parameter allows convenient visual evaluation of the TFA results. However, use of uncorrelated parameters is preferred, since this maximizes the information content. To determine which two parameters would be used for further analysis, a scatterplot matrix (SPLOM) plot was produced utilizing the data shown in 57

72 Table 21. A SPLOM plot displays the relationships between variables, in this case, the parameters from Table 21 [22]. This SPLOM plot is shown in Figure 23. SPOIL REP AET THETA R R THETA AET REP SPOIL Figure 23: SPLOM of HPD 206 data produced by TFA for test set 2 As noted in Figure 23, the correlation (r), θ, and sin 2 θ are correlated variables, so only one of these will be used for two dimensional (2-D) plots. Essentially, these parameters contain 58

73 the same information. Similarly, the and AET variables are also correlated per the linearity of the produced shape in Figure 23. However, as shown in Table 20, page 56, REP does not perform as well as AET or in correctly idendifying the ASTM class. Sin 2 θ paired with AET, REP, or would appear to reflect higher information content. Based on these observations, sin 2 θ and were selected to provide visual 2-D assessment of the classification of any ignitable liquid recovered in a fire debris sample. Results produced by TFA from test set 2 were used to produced 2-D plots where the and sin 2 θ are the parameters of the x and y axes, respectively. In these plots, the origin (0,0) represents a perfect match between the test vector (ignitable liquids in the ILRC) and the resulting predicted (sample) vector from TFA. The ellipse drawn for each ignitable liquid class represents a constant one standard deviation from the center of the distribution. Representative distributions were shown to be bivariate normal by quantile-quantile plots. The closer an ellipse is to the origin of the plot, the greater the overall similarity of that ignitable liquid class to the residue in the sample. All classes of ignitable liquids, with the exception of the miscellaneous class, were further analyzed utilizing the 2-D plots to enable visualization of where each class of ignitable liquid falls in relation to the origin. 59

74 Aromatic Gasoline HPD Isoparaffinic LPD MPD Miscellaneous Naphthenic Paraffinic Normal Alkane Oxygenated Solvent Figure 24: 2-D plots of normal alkane SRN 241, used in test set 2 2-D plots of the the normal alkane liquid used in Test O of test set 2 are shown in Figure 24. The sin 2 θ and values calculated for library entries corresponding to the normal alkane classification fell to the corner and are distinguished from the other ignitable liquid categories. 60

75 Aromatic Gasoline HPD Isoparaffinic LPD MPD Miscellaneous Naphthenic Paraffinic Normal Alkane Oxygenated Solvent Figure 25: 2-D plots naphthenic paraffinic liquid 243, used in test set 2 2-D results corresponding to Test Q of test set 2, which used the naphthenic paraffinic liquid SRN 243, were graphed in the same manner, as shown in Figure 25. The naphthenic paraffinic liquid classification has the smallest combined sin 2 θ and values, shown in the graph as the naphthenic paraffinic cluster is near the origin of the graph. However, the distillate classifications (specifically medium and heavy) also produced low sin 2 θ and values lying near the origin, potentially misleading an analyst in identifying the correct classification. 61

76 Aromatic Gasoline HPD Isoparaffinic LPD MPD Miscellaneous Naphthenic Paraffinic Normal Alkane Oxygenated Solvent Figure 26: 2-D plots of aromatic product 72, used in test set 2 The aromatic product, SRN 72 was further analyzed following TFA and produced 2-D graphs, per Figure 26. The aromatic class is extremely broad, as shown by the confidence interval of this ignitable liquid classification, depicted in the figure. Though specific ignitable liquids belonging to the aromatic class produced small sin 2 θ and values, resulting in these liquids lying near the origin, the category as a whole did not. This is due to the broad nature of the classification, and the chemical dissimilarity of members of the class based on carbon range and/or number of aromatic rings which comprise the liquid. 62

77 Test Set #3 (Approximate Limit of Detection) Test set 3 was designed to determine how little ignitable liquid, proportional to container size, could be recovered following the burn process. The results of test set 3 are displayed in Table 22. Increasing the number of PCs retained for TFA improves the correlation to the known ignitable liquid used in the sample, though only slightly. Retaining a larger number of PCs retains a large percent of the variance in the sample in addition to replicating the data. Table 22: Correlation results per test set 3, varying number of PCs retained ILRC Test 116 Classification Number of PCs retained % Variance retained with retained PCs Amount of IL Used Correlation to specific IL in the ILRC AA Gasoline ml 3976 AA II Gasoline ml 9244 AA III Gasoline ml 9524 AB Gasoline ml 5466 AB II Gasoline ml 8802 AC Gasoline ml 6050 AD Gasoline ml 8110 AD II Gasoline ml BB LPD 25 ml, 5 ml, ml 30 CC MPD 72 DD Aromatic Product 174 EE Oxygenated Solvent 241 FF Normal Alkane Product GG Naphthenic Paraffinic Product ml, 5 ml, 0 ml 25 ml, 5 ml, 0 ml 25 ml, 5 ml, 0 ml 25 ml, 5 ml, 0 ml 25 ml, 5 ml, 0 ml HH HPD ml,

78 87 II Isoparaffinic ml, 0 ml 25 ml, 5 ml, 0 ml 96 Tests AA-AD provided a high correlation with the specific gasoline used in the experiment. Correlations proved high for all ASTM classifications with the exception of the LPD and oxygenated solvent classes. Low molecular weight and high volatility are again attributed to the cause of such low correlations. Following TFA, where liquids comprising an ignitable liquids database were used as test vectors, the TIS of matrix components in the substrate database were used as test vectors to determine the correlations. Correlation results to selected substrates are displayed in Table 23. The five substrates used in test set 3 are yellow pine, P.E.T. polyester carpet and padding, polyurethane foam mattress, vinyl flooring, and fiberglass insulation. Table 23: Correlation results of test set 3, using substrate database, varying the number of PCs retained IL (SRN in ILRC) Class 116 Gasoline Test Yellow Pine P.E.T. Polyester Carpet and Padding Polyurethane Foam Mattress Vinyl Fiberglass Insulation AA AA II AA III AB AB II AC

79 IL (SRN in ILRC) Class Test Yellow Pine P.E.T. Polyester Carpet and Padding Polyurethane Foam Mattress Vinyl Fiberglass Insulation AD AD II LPD BB MPD CC Aromatic 72 Product DD Oxygenated Solvent EE Normal Alkane FF Naphthenic Paraffinic GG HPD HH Results, reported as similarity obtained from a linear combination, were performed by Knightfire software, of the experiments of test set 3 are listed in Table 24. The distance is measured, as depicted by Equation 11, between the TIS of the laboratory sample and the TIS of the ignitable liquids in the ILRC database. The samples were also searched against the substrate database and the software determined the percentage of matrix present. The table lists the percentage of matrix present and the corresponding substrate. As noted by Table 24, yellow pine produced high similarity in all of the tests of test set 3. This is because yellow pine contributes significantly to the overall pattern of the sample, and can therefore be recognized by the 65

80 software. P.E.T. polyester carpet and padding generally produced the second highest similarity as shown in Table 24. This substrate is not as distinguishable as yellow pine or does not contribute enough to the overall sample pattern so that the software can recognize it. The polyurethane foam mattress, vinyl and fiberglass insulation substrates do not contribute significantly to the overall pattern of the sample, making their identification difficult. Though their similarities are high in certain circumstances, these substrates similarities are significantly lower than the yellow pine and P.E.T. polyester carpet and padding matrices. Table 24: Similarity results obtained from Knightfire software to correct IL Test Predominate Substrate AA AB Fiberglass Insulation Polyurethane Foam Mattress Distance % Substrate 81 5 Vinyl Not identified in top ten Software Identified Substrate Vinyl Flooring 76 6 Sketchers Casual Shoes P.E.T. Polyester 66 4 Polyester Quilt Batting Carpet and Padding Yellow Pine 07 6 Sketchers Casual Shoe None 88 6 Yellow Pine None 02 8 Polyester Quilt Batting None Not identified in top ten None Not identified in top ten Fiberglass 01 3 Clear Hard Maple Laminate Insulation Polyurethane 69 3 Clear Hard Maple Laminate Foam Mattress Vinyl Flooring 58 3 Clear Hard Maple Laminate P.E.T. Polyester Carpet and Padding Not identified in top ten 66

81 Test Predominate Substrate Distance % Substrate Software Identified Substrate Yellow Pine 68 2 Street Smart Boots None 7 1 Roofing Tiles AC None 49 5 Street Smart Boots None 47 4 Yellow Pine None 92 7 Industrial Vinyl AD None 64 5 Vinyl None 42 Yellow Pine None 75 8 Yellow Pine None 23 3 Aspen None 47 2 Yellow Pine None 5 Yellow Pine BB None Not identified in top ten CC None Not identified in top ten DD None 96 4 Black Leather Swatch None 51 8 Yellow Pine None 91 3 Yellow Pine EE None Not identified in top ten FF None 96 4 Black Leather Swatch None 01 5 Yellow Pine None 27 8 Yellow Pine GG None 05 5 Industrial Vinyl None 14 Polyester HH None Not identified in top ten Knightfire performed poorly identifying the predominant substrate in certain cases. This is because certain substrates, such as yellow pine, contribute significantly to the overall pattern of the sample while others, such as vinyl, polyurethane foam mattress, and fiberglass insulation, do not. Substrates not present in the burned sample were identified by the software in some cases. This is because certain matrices, for example Street Smart Boots produce very 67

82 complex patterns when burned alone with no ignitable liquid contribution. When a complicated burned sample of multiple substrates and ignitable liquid is loaded into the software, peaks belonging to the ignitable liquid may be mistaken as peaks belonging to the substrate. Therefore, as the complexity of the matrix component in a burned sample increases, so will the difficulty of the software to correctly identify a single substrate. Thus, Knightfire was utilized again to calculate the linear combination on the same tests from test set 3 utilizing a control sample consisting of the five substrates burned with no ignitable liquid. The control sample was then input into Knightfire as the matrix component. The software searched the sample TIS versus the ignitable liquid library and determined how much of the substrate had to be mixed with each library entry to best reproduce the sample. Though the distance measured between the sample and the control substrate is not higher than the distances reported in Table 24, page 66, the percent contributed by the substrate increased significantly in certain tests. Though a control matrix sample is important in the collection of fire debris, it did not prove significantly useful in this experiment. Table 25: Similarity from Knightfire software of test set 3, control of combined burned substrates used as matrix component Test Distance % Substrate AC Not identified in top ten AD Not identified in top ten

83 Test Distance % Substrate 50 0 BB Not identified in top ten CC 02 DD EE Not identified in top ten FF GG HH 05 5 Not identified in top ten 8 Test AC was further evaluated. Figures 27 and 28 depict four TIS. The top TIS is that of unweathered gasoline SRN 116 in the ILRC. The second, third and fourth TIS are the 0 ml, 5 ml, and 25 ml applied to the five substrates used in test set 3, respectively. Utilizing the TIS rather an than the TICs of samples from test AC allows us to visualize the sample independent of the weathering process where the lighter end of the chromatographic pattern is nearly absent or missing entirely. This is due to the weathering process, which affects the expected chromatographic pattern gasoline as the sample is heated. This loss of the lighter, earlier eluting compounds makes a visual identification based on chromatographic patterns of gasoline difficult. When visualizing TICs, the lower the amount of ignitable liquid, the less the resulting chromatogram resembles a gasoline. This shows that a cutoff exists where simple pattern recognition may no longer be sufficient to identify an ignitable liquid. The corresponding TICs are displayed in Figure 50, page

84 Gasoline 116 Normalized intensity Mass to charge (m/z) ratio 0mL Gasoline Normalized intensity Mass to charge (m/z) ratio 5mL Gasoline Normalized intensity Mass to charge (m/z) ratio Figure 27: TIS of unweatherd gasoline 116 and TIS of test AC (with 0 ml and 5 ml gasoline 116) from test set 3 70

85 25mL Gasoline Normalized intensity Mass to charge (m/z) ratio Figure 28: TIS test AC (with 25 ml gasoline 116) from test set 3 71

86 Large Scale Burns Aromatic Gasoline HPD Isoparaffinic LPD MPD Miscellaneous Naphthenic Paraffinic Normal Alkane Oxygenated Solvent Figure 29: 2-D plots of container 1, burned with gasoline Samples taken from container 1 when burned with gasoline produced Figure 29 where the and sin 2 θ are the degrees of association on the x and y axes, respectively. As shown in Figure 29, the classification of ignitable liquid having the smallest overall and sin 2 θ values class is the gasoline category. The fire in container 1, with gasoline used as an accelerant, reached flashover. Flashover is caused by the storage and build up of heat within the structure containing the fire, specifically enclosed in the matrices present. The build up of heat inside the room raises the temperature of the combustible gases until they reach their kindling point, where 72

87 they will ignite without an ignition source (spontaneously). When the auto ignition point is reached, the combustible gases and remaining fuels erupt into flaming combustion, incinerating the entire room [23]. Aromatic Gasoline HPD Isoparaffinic LPD MPD Miscellaneous Naphthenic Paraffinic Normal Alkane Oxygenated Solvent Figure 30: 2-D plots of container 2, burned with gasoline The second container, when burned with gasoline as an accelerant produced Figure 30 after TFA. Again, the ignitable liquid classification which produces the smallest sin 2 θ and values is the gasoline category. 73

88 Aromatic Gasoline HPD Isoparaffinic VAR_2 VAR_2 VAR_2 VAR_ VAR_ VAR_ VAR_ VAR_1 LPD MPD Miscellaneous Naphthenic Paraffinic VAR_2 VAR_2 VAR_2 VAR_ VAR_ VAR_ VAR_ VAR_1 Normal Alkane Oxygenated Solvent VAR_2 VAR_ VAR_ VAR_1 Figure 31: 2-D plots of container 3, burned with an MPD The third container, containing an MPD as an accelerant produced 2-D plots as shown in Figure 31, following TFA. The 2-D plots produced following TFA of data from container 3 indicated that a distillate may be present in the fire scene based on the sin 2 θ and values of the distillate classifications. However, the gasoline and naphthenic paraffinic classifications also produced small sin 2 θ and values. Interestingly, a sealant was used on the roof of container 3 to plug small rust holes, which was heated enough by the fire within the container to ignite. A sample was not obtained of the 74

89 sealant, so it s impact on the results is unknown. Additionally, paint peeled and blistered on the exterior walls of the container, though this observation is not significant to data analysis. Aromatic Gasoline HPD Isoparaffinic LPD MPD Miscellaneous Naphthenic Paraffinic Normal Alkane Oxygenated Solvent Figure 32: 2-D plots of container 4, burned with an oxygenated solvent An oxygenated solvent was used as an accelerant in container 4 which produced 2-D plots from TFA, as depicted in Figure 32. These 2-D plots do not indicate that an oxygenated solvent was present at the fire scene. However, the broadness of the oxygenated solvent classification complicates the identification of ignitable liquids belonging to this category. Interestingly, petroleum distillates (both medium and heavy) produced small sin 2 θ and values as shown in Figure 32, though a distillate was not used as an accelerant in the container 75

90 burn. It is possible that certain substrates inside the container may have broken down into pyrolysis products to produce characteristics common to petroleum distillates. Though samples of the substrates were obtained prior to burning the storage containers, pyrolysis experiments have not yet been performed on these substrates. Aromatic Gasoline HPD Isoparaffinic VAR_2 VAR_2 VAR_2 VAR_ VAR_ VAR_ VAR_ VAR_1 LPD MPD Miscellaneous Naphthenic Paraffinic VAR_2 VAR_2 VAR_2 VAR_ VAR_ VAR_ VAR_ VAR_1 Normal Alkane Oxygenated Solvent VAR_2 VAR_ VAR_ VAR_1 Figure 33: 2-D plots of container 2, burned a second time with an MPD Container 2 was burned a second time with the MPD used as an accelerant. The data from the analysis of those samples produced 2-D plots shown in Figure 33 following TFA. Figure 33 shows the distillates (specifically medium and heavy), naphthenic paraffinic and gasoline classes falling into the corner. This leads to the conclusion that multiple ignitable liquid 76

91 classifications were potentially present in the fire. This may not be a significant conclusion as container 3 burned only with an MDP also produced 2-D plots indicating different classifications of ignitable liquids may be present, as shown in Figure 31, page 74. Cluster Analysis Cluster analyses were performed on each ignitable liquid ASTM classification of the ILRC at NCFS, with the exception of the miscellaneous class, by calculating the Manhattan distance between two TIS. The Manhattan distance is the absolute difference between coordinates of two summed ion spectra where the results were reported as similarities. Each of the numbers on the y-axis of the cluster corresponds to an ignitable liquid that has been classified into a certain ASTM category, as do the numbers on the x-axis. Each ignitable liquid corresponds 100%, or with a similarity of, to itself. The data is a mirror image with this perfect similarity line along the diagonal. 77

92 Gasoline 99% evaporated % evaporated 75% evaporated 50% evaporated 25% evaporated A100 A304 A99 A303 A98 A116 A260 A259 A258 A302 A97 A96 A115 Figure 34: Cluster analysis of gasolines in the ILRC A105 A385 A383 A301 A444 A375 A384 The cluster analysis results for the gasoline classification are shown in Figure 34. The ignitable liquid with a noticeably significantly lower correlation than the others is a 99% weathered gasoline. The loss of the lighter components that comprise gasoline greatly affects this particular gasoline s resemblance to less weathered and unweathered gasolines. As shown in the diagram, 90%, 75%, and 50% gasolines clustered with one another. f 78

93 LPD A198 A8 A68 A126 A327 A356 A214 A199 A2 A326 A357 A27 A224 A273 A295 A35 A198 A357 Figure 35: Cluster analysis of LPDs in the ILRC The cluster analysis for the LPD classification is shown in Figure 35. The LPD classification is already broken down into only light (C 4 -C 9 ) carbon range petroleum distillates. An explanation of additional clusters within the category depicted in Figure 35 is not readily apparent. A8 A356 A126 A326 A327 A214 A199 f A35 A295 A2 A33 A92 A273 A224 A272 A68 A27 A347 79

94 MPD A91 A267 A230 A221 A275 A123 A21 A227 A393 A81 A64 A26 A130 A373 A215 A435 A438 A358 A46 A95 A213 A163 A353 A293 A47 A445 A162 A161 A160 A387 A268 A265 A11 A225 A421 A349 A440 A432 A437 A368 A94 A355 A42 _91 _267 _230 _22 1 _275 _123 _21 _227 _393 _81 _64 _26 _130 _373 _215 _435 _438 _358 _46_95 _213 _163 _353 _293 _47 _45 _162 _161 _160 _387 _268 _265 _11 _225 _421 _349 _440 _432 _437 _368 _94 _355 _42 Figure 36: Cluster analysis of MPDs in the ILRC Clustering the ignitable liquids belonging to the MPD classification in the ILRC shows us the similarity between MPDs liquids. This method can point out liquids that are significantly dissimilar from others within the classification. Examination of the results of the medium petroleum distillate class, shown in Figure 36, show a distinct difference between ignitable liquid 42 and the rest of the classification. Ignitable liquid 42, is not a petroleum distillate but labeled as a miscellaneous liquid, though it does fall into the medium carbon range. This was apparent 80

95 from the cluster diagram of the liquids comprising MPD classification. The reasons for further clustering within the MPD classification are unknown. HPD Kerosene Diesel Fuel Figure 37: Cluster analysis of HPDs in the ILRC The results of the heavy petroleum distillates classification cluster diagram is displayed in Figure 37. The HPD class allows little variance between individual liquids within the classification, as they all fall within the heavy carbon (C 9 -C 20+ ) range. The cluster in the bottom right corner of the diagram corresponds to diesel fuel HPDs. Though other individual HPDs, such as kerosenes, are shown to cluster together, the main reason for the central cluster is also not readily apparent. 81

96 Isoparaffinic Light Medium Heavy Figure 38: Cluster analysis of isoparaffinic products in the ILRC The results of the cluster analysis for the isoparaffinic products class is displayed in Figure 38. The clusters within the isoparaffinic classification are grouped by carbon range, as depicted the figure. The results of the ROC curves in oxygenated solvents (test R) and aromatic liquids (test T) indicated difficulty correctly identifying oxygenated solvents and aromatic ignitable liquids respectively, as shown in Table 15, page 43 and Table 19, page 55. The difficulty in correctly identifying liquids in these classes does not lie within the method of the experiment, but rather the broad definition of the class itself. Aromatic ignitable liquids are either single ring or 82

97 multiple rings and vary significantly by their respective carbon range. These within-class distinctions complicate classification and therefore ROC analysis of aromatic ignitable liquids. Aromatics Heavy Medium Light Figure 39: Cluster analysis of aromatic products in the ILRC The cluster analysis of the aromatic products classification is presented in Figure 39. Noting the scale associated with this figure, clustering within the aromatic products classification is evident. Certain aromatic ignitable liquids cluster together well, but poorly with the remainder of the classification. When looking at how the data clusters, sub-classifications could potentially be established as light (C 7 -C 11 ), light to medium (single ring, C 7 -C 11 ), medium (single ring, C 8 - C 12 ), medium (combination of single and multiple rings, C 9 -C 13 ) and heavy (multiple rings, C 10-83

98 C 18 ), from the bottom to the top of the cluster analysis, respectively. The data resulting from TFA provided further evidence that identification of a single aromatic ignitable liquid is achievable as shown in Table 15, page 43, and Table 19, page 55. This sub-classification concept, by number of rings and carbon range (from C 7 -C 18 ) improved the ROC analysis results when the criteria for a positive response was altered to be a liquid only falling within the same sub-classification as the ignitable liquid in the sample, as shown in Table 26. Without this consideration, the ROC curve analysis seems to generate poor results that are not an accurate evaluation of the method. Table 26 reiterates the area under the curve when a ROC analysis was performed on the aromatic classification of ignitable liquids from test sets 1 and 2, and demonstrates the need for improvement. This is labeled in Table 26 as Test I. Aromatic liquid 59 was used for the laboratory burns where only a carpet and padding sample were used as the substrate (test set 1) while aromatic liquid 72 was used on the combination of multiple substrates (test set 2). Test I indicates the area under the curve when a positive response was designated as any aromatic liquid, and a negative response was designated as any liquid not falling into the aromatic classification. The area under the curve increased in Test II, when the idea of sub-classification by carbon range (light, medium, and heavy) is applied. In this case, only aromatic liquids falling within the same sub-cluster by carbon range are designated correct responses with positive qualifiers. The area under the curve increased significantly when this idea of sub-classification is applied. However, Test III was performed where only the ignitable liquids falling into the same specific cluster as the aromatic liquid used in the burn. Therefore, not only was the class divided by carbon range but were then sub0classified again by their respective number of 84

99 aromatic rings, which was apparent by the cluster analysis of Figure 39.The ROC area under the curve produced a perfect area under the curve when the idea of sub classification by carbon range and the sub-divided once again by the presence of single or multiple aromatic rings in the liquid. Table 26: Areas under the curve for aromatic product classification from test set 2 and when sub-classification was applied Ignitable Liquid Classification Test I Test II Test III 59 Aromatic Product Aromatic Product Naphthenic Paraffinic Heavy Figure 40: Cluster analysis of naphthanic paraffinic products in the ILRC The cluster diagram for the naphthenic paraffinic products classification is displayed in Figure 40. Every naphthenic paraffinic liquid in the ILRC is defined as heavy, by their respective 85

100 carbon ranges. The two most dissimilar liquids within the classification have a similarity of. The only cluster of note in the diagram is that liquids are all torch fuels. Other than this distinction, the reason for clustering within Figure 40 is not readily apparent. Normal Alkanes Heavy Figure 41: Cluster analysis of normal alkane products in the ILRC The cluster analysis for the normal alkane products classification is shown in Figure 41. All similarities among the members of the class are in the range of 5 -. Each normal alkane falls into the heavy carbon range. Each liquid within the classification greatly resembles the remaining liquids also assigned to the normal alkane class. 86