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Document Title: Smokeless Powder Reference Collection 

and SWGFEX Smokeless Powders Database 

Expansion 

Author(s): David B. Wilson, Ph.D., Ajima Olaghere, 

Ph.D., Catherine S. Kimbrell, M.A. 

Document Number: 250933

Date Received: July 2017 

Award Number: 2013-R2-CX-K008

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Justice. This resource is being made publically available through the 

Office of Justice Programs’ National Criminal Justice Reference 

Service. 

Opinions or points of view expressed are those of the author(s) and 

do not necessarily reflect the official position or policies of the U.S. 

Department of Justice. 
i 

Smokeless Powder Reference Collection and SWGFEX Smokeless Powders 

Database Expansion 

Award No. 2013-R2-CX-K008 

Author/ Principal 

Investigator

Michael E. Sigman, Ph.D. 

Department of Chemistry and National Center for 

Forensic Science

University of Central Florida

PO Box 162367

Orlando, FL 32816-2367

407-823-6469

Co-Authors Mary Williams, M.S.

Dana-Marie K. Dennis, Ph.D.

National Center for Forensic Science

University of Central Florida

PO Box 162367

Orlando, FL 32816-236

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ii

Abstract

In 1998, the National Research Council issued a report “Black and Smokeless 

Powders: Technologies for Finding Bombs and the Bomb Makers”. [1] The NRC report 

recommended that a comprehensive national powder database be developed, containing 

information about the physical characteristics and chemical composition of commercially 

available black and smokeless powders. An agency-independent effort to develop a 

smokeless powders database did not emerge until 2009, when the National Center for 

Forensic Science (NCFS) in collaboration with the Scientific Working Group for Fire and 

Explosions (SWGFEX) began work on an internet-accessible database of analytical 

information on smokeless powders (http://www.ilrc.ucf.edu/powders/). The database 

opened in early 2011 with 100 entries of legacy powders, provided by Mr. Ronald Kelly (FBIretired). Under this research grant: (1) records for an additional 600 powders were entered 

into the database, (2) 100 new powders were analyzed and their records entered into the 

database, (3) reference collections of the 100 smokeless powders were provided, free of 

charge, to 50 forensic laboratories that conduct smokeless powder exams and, (4) the data 

corresponding to the legacy and new records were utilized in research to establish 

evidentiary and investigative value associated with matching records returned from a search 

of the Smokeless Powders Database. 

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iii

Contents 

Executive Summary.............................................................................................................................................. 6

The Problem ....................................................................................................................................................... 6

Purpose of the Research ................................................................................................................................ 7

Research Design ................................................................................................................................................ 9

Findings and Conclusions .......................................................................................................................... 10

Implications for Policy and Practice ...................................................................................................... 11

I. Introduction..................................................................................................................................................... 12

Statement of the problem .......................................................................................................................... 12

Literature review .......................................................................................................................................... 15

Hypothesis or Rationale for the Research ........................................................................................... 20

II. Methods ............................................................................................................................................................ 20

Chemical Analysis ......................................................................................................................................... 20

Likelihood Ratio Approach to Smokeless Powder Comparisons ................................................ 21

General Methodology ............................................................................................................................... 21

Limiting smokeless powder comparisons ....................................................................................... 26

Discrete chemical and physical data .................................................................................................. 26

Continuous chemical composition data ............................................................................................ 27

Score-based Likelihood Ratio Calculations ..................................................................................... 28

Agglomerative Hierarchical Cluster Analysis ................................................................................. 28

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iv

Cross Validation ......................................................................................................................................... 29

Probabilistic Predictions of Manufacturers using Bayesian Networks .................................... 29

General Methodology ............................................................................................................................... 29

Discrete chemical and physical data .................................................................................................. 31

Bayes’ Theorem.......................................................................................................................................... 33

Bayesian Network ..................................................................................................................................... 34

III. Results ............................................................................................................................................................. 36

Historical Records ......................................................................................................................................... 36

New Reference Materials ........................................................................................................................... 36

Evidentiary Value of Smokeless Powder Comparisons .................................................................. 36

Discrete chemical composition ............................................................................................................ 36

Continuous chemical composition ...................................................................................................... 39

Agglomerative hierarchical cluster (AHC) analysis ..................................................................... 41

Score-based likelihood ratios (same color, shape, cluster) ...................................................... 42

Probability of a Smokeless Powder Manufacturer for Investigative Purposes ..................... 44

Conditional Independence Testing of the Smokeless Powder Random Variables .......... 44

Predicting Smokeless Powder Manufacturers using High Posterior Probabilities ......... 45

IV. Conclusions .................................................................................................................................................... 51

Discussion of Findings ................................................................................................................................. 51

Database Expansion and Reference Collection .............................................................................. 51

Evidentiary/Investigative Value of Database Searches .............................................................. 52

Probabilistic Assessment of Manufacturer ..................................................................................... 52

Implications for Policy and Practice ...................................................................................................... 53

Implications for Further Research ......................................................................................................... 53

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v 

V. References .................................................................................................................................................... 54

VI. Dissemination of Research Findings ............................................................................................ 58

Publications and Thesis .............................................................................................................................. 58

Databases ......................................................................................................................................................... 59

Patents............................................................................................................................................................... 59

Presentations .................................................................................................................................................. 59

Preliminary Presentations ..................................................................................................................... 59

Grant-funded Presentations .................................................................................................................. 59

Appendices ........................................................................................................................................................... 61

Appendix A: NCFS Smokeless Powders Analysis Method Year 2010 ....................................... 61

Appendix B: NCFS Smokeless Powders Analysis Method Year 2014 ....................................... 65

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6 

Executive Summary 

The Problem 

In 1998, the National Research Council (NRC) issued a report “Black and Smokeless 

Powders: Technologies for Finding Bombs and the Bomb Makers” [1]. The Committee on 

Smokeless and Black Powder, which prepared the report, was appointed in response to a 

mandate in the Antiterrorism and Effective Death Penalty Act of 1996 to address the key 

issues of: (1) feasibility of adding tracers to smokeless and black powder to aid in detection 

and, (2) feasibility of adding tracers for sourcing black and smokeless powders. Key findings 

in that report included the fact that 90% of the deaths and 80% of the injuries caused by pipe 

bombs that contain black and smokeless powders occurred away from security screening 

points. This finding places a heavy emphasis on the forensic investigation to help identify the 

explosive and bomb maker. 

The NRC report also pointed out that the materials recovered at bombing scenes (e.g. 

unburned powder, etc.) can aid in the identification of the product type and manufacturer, 

provided the appropriate resources (databases, spectral libraries, etc.) are available to 

forensic scientists. The report also pointed to the fact that existing databases on black and 

smokeless powders were used extensively, at that time, in bombing investigations, yet they 

were incomplete and it was difficult to match powders from a scene to database entries. 

Large federal organizations, such as the Federal Bureau of Investigation, the Bureau of 

Alcohol, Tobacco, Firearms and Explosives and the U.S. Postal Service had the resources to 

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7 

develop databases; however, smaller state and local laboratories, which also need access to 

these resources, often did not have the time and manpower to meet this need. 

In addition to the need for a comprehensive database on smokeless powders, 

members of the SWGFEX Explosives Database Committee expressed the need for a set of 

reference materials for laboratory comparisons and training. 

A more research oriented aspect of the problem was the need to assess the 

evidentiary or investigative value of a match between a questioned sample and a sample in 

the database. Implementation of probabilistic methods is heavily reliant on defining a 

relevant population. Defining a relevant population is aided by a survey of casework samples 

and the further expansion of the smokeless powders database to allow construction of a 

relevant population data source on which to base likelihood ratio methods. Estimation of the 

relevant population in this and related forensic problems remains a research problem. The 

results from this research are based on the population of the Smokeless Powders Database. 

Nonetheless, these results are important in demonstrating the methodologies under this 

constraint. Evaluation of the methodology and publication under peer-review was an 

important part of addressing this industry need. 

Purpose of the Research 

 This research directly addresses the need for an extensive and searchable database 

of smokeless powders that was identified in the 1998 NRC report. The NRC’s 

recommendation read, in part: “A comprehensive national powder database containing 

information about the physical characteristics and chemical composition of commercially 

available black and smokeless powders should be developed and maintained.”[1] While 

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necessarily reflect the official position or policies of the U.S. Department of Justice 
8 

various agencies, or individuals within agencies, created databases for their own use, a 

nationwide agency-independent effort did not emerge until 2009, when the National Center 

for Forensic Science (NCFS) in collaboration with the Technical and Scientific Working 

Groups for Fire and Explosions (TWGFEX/SWGFEX) began work on an internet-accessible 

database of analytical information on smokeless reloading powders 

(http://www.ilrc.ucf.edu/powders/). One purpose of this work was to expand the NCFS 

Smokeless Powders Database to contain over 800 records covering both legacy and newly 

purchased smokeless powders. As a result of meeting this goal, the Smokeless Powders 

Database would serve as a resource for local, state and federal law enforcement. The 

information would be freely available on the internet and therefore the database would also 

be a criminal justice tool for defense experts and the court. 

 Another purpose of this research was to address the need for reference materials by 

providing a set of 100 smokeless powder samples to each of 50 ASCLD/LAB accredited 

laboratories. Forty two (42) of the 50 laboratories accepted the samples. Data from the NCFS 

analysis of each reference material was entered into the Smokeless Powders Database and 

the references were provided along with the sample reference number for the corresponding 

database record. 

 Finally, a purpose of this research was to utilize the Smokeless Powders Database to 

provide a statistical assessment of the value of a match between a database record and the 

physical and chemical properties of a questioned sample, as the result of a database query. 

The need for this aspect of the research is clear, based on reports of matches to an individual 

product as the result of a search on other databases. It is easy to assume that the short list 

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9 

returned by a database query contains one or more powders made by the same 

manufacturer. However, over the years, several mergers and acquisitions of one smokeless 

powder manufacturer by another have occurred. The process of relabeling a powder prior 

to distribution further complicates the issue. While the impact of these industry practices 

are not directly addressed, the study presented here is based on a knowledge of the groundtruth manufacturer and provides an upper limit for the evidentiary or investigative value of 

a query-match. 

Research Design 

 The Smokeless Powders Database opened in early 2011 with 100 entries. An 

additional 538 data sets from the FBI were available for uploading. In addition, at the time 

of the original proposal, NCFS had undertaken the task of maintaining the database, and 

performing the chemical analysis and data entry for new samples of smokeless powders 

following a standard operating procedure (SOP), collaboratively developed by SWGFEX and 

NCFS. Eighty-seven powders had been analyzed by NCFS and the associated data entered 

into the database. Under this proposal, FBI data from the remaining 538 legacy powders 

were uploaded into the database. An additional 100 powders, purchased during the 

performance period, were analyzed and the data was entered into the Smokeless Powders 

Database. 

 After each of the 100 new samples was analyzed, the remaining material was 

dispersed into 50 sample vials (5 dram each). The sample vials from all 100 samples were 

assembled into 42 sets of reference materials for distribution to accredited laboratories. The 

total number of vials prepared for distribution was 4200. 

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10

 Following the entry of both new and legacy data records into the Smokeless Powders 

Database, cross validation was used to test the accuracy and evidentiary value of matches 

between a test sample and records returned by query. Two approaches were taken, one that 

relied entirely on discrete data (i.e., matching kernel shape, color, presence/absence of a 

perforation, measurement overlap, and matching subsets of 13 chemical analytes). The 

second method utilized Fisher transform of the correlation between the total ion spectra 

(average mass spectra across the chromatographic profiles) as a continuous score for 

comparison of powders, conditioned on the requirement of same shape, color, state of 

perforation and size. The continuous score based method was enhanced by further 

conditioning comparisons on membership of the two powders in the same clusters defined 

by the major ions in the total ion spectra. 

 A second study was conducted for the purpose of examining the accuracy of 

predicting the manufacturer of a smokeless powder based on the chemical and physical 

properties of the material. A Bayesian network was utilized in this work to calculate the 

posterior probability of each of 6 different manufacturers represented in the powder set 

analyzed by NCFS. The class conditional probabilities were calculated based on frequencies 

in the dataset and the sample was assigned to the manufacturer with the largest calculated 

posterior probability. 

Findings and Conclusions 

 All of the records for the legacy powders were entered into the database and are 

available online. Similarly, all of the analyses were completed for the 100 new powders and 

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11

the records were made available in the database for use by the forensic community. The 

reference material collections were sent to 42 laboratories. 

 Analysis of the evidentiary or investigative value of a query match between a 

questioned material and database entry was shown to be fairly low by either a discrete 

match of properties (likelihood ratio of 2.81), or a match based on a continuous score derived 

from the Fisher transform of the Pearson correlation coefficient between the total ion 

spectra from the questioned and query-returned samples (likelihood ratios below 5 for score 

values below 5). Both of these likelihood ratios provide weak evidence for the observation 

of matching criteria given the same manufacturer as opposed to different manufacturers. 

Implications for Policy and Practice 

 The expansion of the Smokeless Powders Database has provided additional data on 

smokeless powders that assists the analyst in assessing casework on a daily basis. The 

reference collections provide a physical link to the database. These advances in data 

accessibility eliminate the need for individual databases in each laboratory and provides 

ASCLD/LAB accredited laboratories with smokeless powder references that they would not 

otherwise be able to attain. The results from probabilistic assessment of the evidentiary 

value of matching physical characteristics and chemical composition informs the community 

of the value of such comparisons for the purpose of investigative and probative applications. 

The generally low evidentiary value of these “matches” should influence practice and future 

policy regarding the introduction of smokeless powder evidence. The use of Bayesian 

networks to predict the manufacturer of a smokeless powder sample based on chemical and 

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12

physical characteristics was shown to provide a nearly 80% correct prediction rate, which 

exceeds the 17% random probability based on a flat prior, or 52% probability of correctly 

assessing the manufacturer if the analyst always guesses Hodgdon Powder Company. These 

results provide an incentive for using these statistical methods in the investigative process. 

I. Introduction 

Statement of the problem 

The National Research Council (NRC) issued a report “Black and Smokeless Powders: 

Technologies for Finding Bombs and the Bomb Makers” in 1998 [1]. Key findings in that 

report included the fact that 90% of the deaths and 80% of the injuries caused by pipe bombs 

that contain black and smokeless powders occurred away from security screening points. 

This finding places a heavy emphasis on the forensic investigation to help identify the 

explosive and bomb maker. The report also pointed out that the materials recovered at 

bombing scenes (e.g. unburned powder, etc.) can aid in the identification of the product type 

and manufacturer, provided the appropriate resources (databases, spectral libraries, etc.) 

are available to forensic scientists. The report also pointed to the fact that existing databases 

on black and smokeless powders were used extensively, at that time, in bombing 

investigations, yet they were incomplete and it was difficult to match powders from a scene 

to database entries. Large federal organizations, such as the Federal Bureau of Investigation, 

the Bureau of Alcohol, Tobacco, Firearms and Explosives and the U.S. Postal Service had the 

resources to develop databases; however, smaller state and local laboratories, which also 

need access to these resources, often did not have the time and manpower to meet this need. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
13

In 1998, the NRC stopped short of recommending the implementation of a tagging system 

for black and smokeless powders due to the lack of information regarding safety issues 

associated with the addition of tags to these products. However, the NRC’s recommendation 

read, in part: “A comprehensive national powder database containing information about the 

physical characteristics and chemical composition of commercially available black and 

smokeless powders should be developed and maintained.”[1] While various agencies, or 

individuals within agencies, created databases for their own use, a nationwide agencyindependent effort did not emerge until 2009, when the National Center for Forensic Science 

(NCFS) in collaboration with the Technical and Scientific Working Groups for Fire and 

Explosions (TWGFEX/SWGFEX) began work on an internet-accessible database of analytical 

information on smokeless powders (http://www.ilrc.ucf.edu/powders/). The database 

opened in early 2011 with 100 entries based on data provided by Mr. Ronald Kelly (FBIretired). An additional 538 datasets were available for uploading into the system from the 

FBI. In addition, at the time of the original proposal, NCFS had undertaken the task of 

maintaining the database, performing the chemical analysis and data entry for new samples 

of smokeless powders following a standard operating procedure (SOP) collaboratively 

developed by SWGFEX and NCFS. The SOP is given in Appendix A. Eighty-seven new powders 

had been analyzed by NCFS and the associated data entered into the database. Entry of the 

remainder of the legacy data needed to be entered into the database. 

NCFS also maintains the Ignitable Liquids Reference Collection and Database (ILRC) 

in collaboration with the SWGFEX Committee on ignitable liquids. Samples of each ILRC 

database entry are retained by NCFS and can be obtained by fire debris analysts upon 

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14

request, which effectively makes the ILRC a “reference collection”. However, due to limits 

placed on the storage of smokeless powders by ATF and the Florida Fire Marshal’s Office, 

NCFS could not retain samples of all of the powders in the database to serve as a reference 

collection for forensic laboratories. Rather than dispose of newly purchased and analyzed 

samples under this proposal, we proposed to provide a “reference collection” of analyzed 

powders to ASCLD/LAB-accredited laboratories that perform smokeless powders analysis. 

Reference collections carry no requirement for traceability; however, the value of a 

reference collection is enhanced when the samples are cataloged in a larger database and 

when forensic laboratories can locate database samples with properties similar to the 

properties of the reference collection. Individualization of smokeless powder samples is not 

currently possible, and in the event that individualization should ever become a possibility, 

the continually changing pool of available powders would prevent accurate estimates of the 

probability of randomly encountering a specific powder. Knowledge of the amount produced 

and sold for a specific powder does not directly address this issue since use (detonation) 

consumes the powders and therefore the distribution of powders in the pool of available 

powders is continually in flux. Nonetheless, certain combinations of chemical and physical 

properties may prove to be relatively rare in the manufacture of smokeless powders in 

particular. Determination of the evidentiary or investigative value of matching chemical and 

physical properties of smokeless powders can be accomplished using the Smokeless 

Powders Database. In achieving this goal, it will be necessary to complete the database entry 

of records for 600 legacy samples and records from the analysis of 100 newly purchased 

samples. Regardless of the success in establishing a statistical basis for evaluating the value 

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15

of casework samples as a function of chemical and physical properties, the proposed project 

provided the forensic community with a fully online and highly valuable smokeless powders 

database and a smokeless powders reference collection.

Literature review 

 In 2011 the Smokeless Powders Database, http://www.ilrc.ucf.edu/powders, 

became available online as a collaborative effort between the National Center for Forensic 

Science (NCFS) and the Scientific Working Group on Fire and Explosions (SWGFEX) 

Explosives Database Committee. The database contains information on the manufacturer, 

physical characteristics (size, shape and color) and chemical characteristics (major 

components). The database contains gas chromatography-mass spectrometry (GC-MS) 

datasets that can be downloaded by users. Information on the Fourier transform infrared 

(FTIR) spectra of new data entries are also included in the database. This information 

represents the data most commonly used in the laboratories by members of the SWGFEX 

Explosives Database Committee. 

Other data types have been shown to be useful in the analysis of smokeless powders, 

although the analytical methodologies may not be common among all of the SWGFEX 

laboratories represented on the Explosives Database Committee. Capillary zone 

electrophoresis (CZE) has been used to measure inorganic ion profiles and characterize 

burned and unburned smokeless powders[2]. CZE has also been used to simultaneously 

detect both cations and anions from smokeless powders [3]. Micellar electrokinetic capillary 

chromatography (MEKC) has been successfully applied to the separation of organic 

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components of smokeless powders and compared with separations performed by high 

performance liquid chromatography (HPLC) [4]. HPLC and MEKC gave comparable precision 

of elution and migration times; however, the highest reproducibility of peak area was 

exhibited by MEKC. The sensitivity and identification power of MEKC was improved by 

interfacing the instrument to a diode array UV detector. In another study, ultrasonic solvent 

extraction was used to recover nitroglycerin (NG), diphenylamine (DPA), and ethyl centralite 

(EC) for capillary electrophoresis analysis in less than one hour [5]. 

 There have been several reports on the use of HPLC coupled with MS detection for 

the analysis of smokeless powders. In addition to the comparison of HPLC with MEKC [5], 

HPLC with electrospray ionization mass spectrometry detection (HPLC-ESI-MS) has been 

characterized for the analysis of selected smokeless powder stabilizers to optimize 

chromatographic analysis of these compounds [6]. 

Vibrational spectroscopies can be useful in the analysis of smokeless powders; 

however, Raman analysis can lead to ignition of smokeless and black powders [7]. 

 The presence of smokeless powders have been shown to be identifiable by ion 

mobility with the aid of a planar solid phase microextraction air sampling device [8, 9]. The 

field detection of smokeless powders can be facilitated by standards developed by the 

National Institute of Standards and Technology [10]. 

There have been several reported studies that focused on reasonable and reliable 

analysis of smokeless powders by identification and quantitation of the propellant and 

stabilizers. In one study, quantitative measurements of nitroglycerin (NG), ethyl centralite 

(EC), diphenylamine (DPA) and N-nitrosodiphenylamine (NnDPA) were reported by five 

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17

participating laboratories [11]. In this international measurement comparison exercise, NG 

was found to be the most unstable analyte. Participants self-reported a 1 – 5% measurement 

uncertainty, whereas a 5 – 50% uncertainty was reported among four concordant 

participants. In a follow-up study, the propellant to stabilizer ratio (P/S) was tested as a 

method of establishing a positive association between organic gunshot residue and 38 

caliber ammunition powders. It was determined that the average and dispersity of the P/S 

values provided information for the association or exclusion of powder samples. Comparison 

of measurements from residue particles with those of residue samples collected from test 

firing of the same weapon and ammunition was found to be a useful technique to account for 

changes in composition that occurred during the firing of the weapon [12]. 

 It is important to note that all of the comparative studies reported here were done on 

relatively small sample sets and the results of those studies are not representative of what 

can be expected when searching for a “match” or set of lead powders contained in a database 

of several hundreds or thousands of records. The NCFS/SWGFEX database currently uses a 

logical AND search routine that utilizes identification of smokeless powder shape, average 

measurement or maximum and minimum measurement, organic propellants and stabilizers 

present. Combining several properties (i.e., shape and phenylamine, etc.) limits the number 

of returned records; however, the independence of the frequency of property occurrence 

within the database and the value of the combined frequencies have not previously been 

established. 

 Smokeless powders are well-characterized materials comprised of the energetic 

component nitrocellulose (single base powder), along with nitroglycerin (double base 

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18

powder) or nitroglycerin and nitroquanidine (triple base powder) [13]. Smokeless powders 

also contain a number of other organic and inorganic components that modify the 

performance characteristics of the materials [13-15]. It is the single and double base 

powders that are commercially available in the U.S. and these materials are often used in 

pipe bombs. 

As previously described, the Smokeless Powders Database[16], contains data from 

analytical methods commonly used in the forensic analysis of these materials, including 

stereomicroscopy,[17] Fourier-transform infrared (FT-IR) spectroscopy,[18, 19] and gas 

chromatography-mass spectrometry (GC-MS).[20] The chemical and physical properties of 

intact powders, utilized in a database search, have been reported to facilitate the 

identification of an individual product or a short list of potential products.[17, 21] The 

certainty with which product identifications can be made has not been determined 

statistically and previous assertions are not grounded in peer-reviewed research. One aspect 

of the research conducted under this grant was to investigate the evidentiary and/or 

investigative value of smokeless powder identifications based on a search of the 726 samples 

in the Smokeless Powders Database.[22] 

 There have been several peer-reviewed reports on the differentiation of limited sets 

of smokeless powders. In 1989, an analysis of 12 smokeless powders from three different 

manufacturers was attempted using pyrolysis gas chromatography[23]. Each powder 

represented different lots of a single product. The distributions of the correlations between 

pyrograms resulting from between-manufacturer and within-manufacturer comparisons 

exhibited overlap, resulting in limited discrimination between manufacturers. A 1992 study 

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19

of the correlation between the chromatographic profiles of pre-firing and post-firing 

powders from the same manufacturer and product suggested a possible route to linking 

shooting victims when the gun and/or projectile were not available[24]. The author of the 

study suggested, but did not demonstrate, the use of the chromatographic data to determine 

the manufacturer of a smokeless powder. A 2009 peer-reviewed study of a set of seven 

smokeless powders by nESI-MS (nanoelectrospray ionization mass spectrometry) and 

stereomicroscopy was unable to visually discriminate some powders[6]; however, 

combining the visual inspection, nESI-MS data and extraction efficiency resulted in the 

ability to discriminate all but two of the powders. In 2013, a set of smokeless powders from 

five manufacturers were correctly classified by manufacturer based on ESI-MS coupled with 

chemometric methods[25]. The chemometric methods employed in the study involved first 

using principal components analysis (PCA) for dimension reduction, followed by k-nearest 

neighbors classification. In this process, data from a new sample is projected into the PCA 

space and the probability of class membership is based on the number of (k) nearest 

neighbors from the known samples. These studies suggest the possibility of discriminating 

between smokeless powders, or identification of the product and/or manufacturer, based on 

studies of relatively small data sets. These studies do not provide a probabilistic estimate of

the evidentiary or investigative value of the results from a smokeless powder database 

search. The evidentiary/investigative value question was addressed in this research and the 

results were published under peer-review [26]. In this report, we provide a synopsis of the 

methods used in that research and the results obtained. For full details, the reader is referred 

to our published work.

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20 

Hypothesis or Rationale for the Research 

 The rationale for this research was to meet the needs of the forensic community, as 

identified by the National Research Council, by providing a publically accessible database of 

smokeless powders for use in casework and research. An additional need of the community 

was met by providing reference samples that were indexed in the database. The hypotheses 

of the research were that the database would assist in casework by providing data to aid in 

the identification of samples, and establish the statistical significance of comparisons of 

physical and chemical characteristics among powders. Based on the literature review and 

statement of the problem, the goals of this research were: 

(1) Complete entry of data from 600 legacy samples into the database. 

(2) Purchase 100 new smokeless powders, which were analyzed by NCFS and the data 

entered into the database. The 100 new powders were distributed as a reference 

collection to ASCLD/LAB accredited laboratories that perform smokeless powders 

analysis. 

(3) Utilize the properties of the new 100 samples and the legacy samples to establish the 

value of property-based relationships in estimating the uniqueness of a smokeless 

powder. 

II. Methods 

Chemical Analysis 

 The Smokeless Powders Database was developed in 2009 – 2010 by NCFS and 

T\SWGFEX Explosives committee. Part of the development included creating analytical 

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21

methods for the measurement of the kernels, gas chromatography – mass spectrometry (GCMS) analysis, and Fourier Transform – infrared (FTIR) analysis for samples analyzed by the 

NCFS. The original SOP can be found in Appendix A. Since 2010, NCFS has modified the SOP 

because both the GC-MS and FTIR spectrometer were replaced with newer instruments in 

2013 and 2014, respectively. The internal reflectance element in the new Bruker FTIR is 

germanium, the wavenumber range is 4000 – 600 cm-1, and the number of scans was 

increased to 64. During the transition to the new GC-MS, the chromatographic column was 

replaced; however, the stationary phase and dimensions were the same as the original 

column. The new SOP can be found in Appendix B. 

 The powder images for all of smokeless powders in the database were 

reviewed by the Explosives committee and consensus agreement of the shape for each 

powder was determined. In the course of assigning powder shape, additional shapes were 

added to the “shape” search field in the database. 

The data used in the statistical aspects of this research were taken from the Smokeless 

Powders Database. Samples analyzed at NCFS followed the methods and protocols described 

in Appendices A and B. Legacy samples had been analyzed by source-specific protocols listed 

in the database. 

Likelihood Ratio Approach to Smokeless Powder Comparisons 

General Methodology 

The Smokeless Powders Database contains nominal discrete data relating to several 

chemical and physical characteristics of smokeless powders. Nominal discrete data is limited 

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to specified values (i.e., there are a limited number of kernel shape descriptors; disk, ball, 

flattened ball, etc.). If two powders do not share the same nominal values for a set of 

properties, the powders differ (a “non-match”). If two powders share the same nominal 

values for a set of properties, they are not discriminated on the basis of these properties and 

we designate the powders as a “match”. In this research, we used comparisons of nominal 

properties shape (s), color (c), presence/absence of a perforation (p), overlapping 

measurement range (m), and the presence/absence of identical subsets of 13 chemical 

components or analytes, designated A. Other properties recorded in the Smokeless Powders 

Database, such as distinguishing features and marker colors, are so infrequent or subjective 

that they were not used to condition record comparisons in this work. In a second 

comparison method, rather than examining the correspondence of subsets of 13 chemical 

components, the chemical comparison was based on a similarity score measure. The Fisher 

transform of the Pearson correlation of the total ion spectra, designated as z, was used as the 

similarity score. Pairwise comparisons were made between 726 records contained in the 

Smokeless Powders Database. The evidentiary/investigative value of a comparison was 

assessed as a likelihood ratio value. The likelihood ratio discussed here addresses 

propositions at the source level [27, 28], where the source is defined under the prosecution’s 

proposition, HP, as the same product (P), with additional conditioning requirements of same 

kernel shape, color, etc., as described above. Under the defense’s proposition, HD, the 

respective statement is that the two powders are not the same product (P̅), again with the 

additional conditioning requirements. Designating two powders as the same product 

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implicitly requires that they correspond to the same manufacturer, but does not speak to the 

same or different production lot. The likelihood ratio is designated in equations 1 and 2. 

LR = 

Pr(A|P,s,c,p,m)

Pr(A|P̅,s,c,p,m) (1) 

LR = 

f(z|P,s,c,p,m)

f(z|P̅,s,c,p,m) (2) 

 The numerator of equation 1, is the probability of observing the same subset of the 

13 specified chemicals, A, given the proposition of the same product, P, and matching s, c, p 

and m nominal discrete properties. The denominator of equation 1 is the probability of 

observing the same subset of 13 specified chemicals given the proposition of different 

product, P̅, and matching s, c, p and m nominal discrete properties. Equation 2 expresses a 

similar ratio of probability density estimates, f, of the distributions of z, given the 

propositions and matching nominal discrete values as described above. The significance of 

equation 2 is shown graphically in Figure 1. In this figure, the curve on the left represents 

the probability density estimate of z values that come from different products that have the 

same shape, color, perforation and measurements, i.e., the dashed curve represents the 

function f(z|P̅, s, c, p, m) . This function of z reflects the probability of observing different 

values of z for comparison of different products. The solid curve on the right represents the 

probability density estimate of z values that come from same product comparisons, i.e., the 

solid curve represents the function (z|P, s, c, p, m) . This function of z reflects the probability 

of observing various values of z for comparison of samples of the same product. The vertical 

red line represents a value, z’, that was determined for two powders that had matching s, c, 

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24

p and m nominal discrete properties; however we do not know if these two powders are the 

same product. The likelihood ratio is determined by the ratio of the height where the vertical 

line intersects the solid curve, divided by the height where the vertical line intersects the 

dashed curve. In this example, the likelihood ratio will be greater than one. The meaning of 

this ratio is that “it is more likely to observe a score of z’ if the two powders are the same 

product, than if they are different products”. Similarly, if z’ were smaller (i.e., the vertical red 

line were further to the left), the height where the vertical red line intersects the dashed 

curve may be larger than the height where it intersects the solid curve. In that case the 

likelihood ratio would be less than one and the meaning would be that “it is more likely to 

observe a score of z’ if the two powders are different products”. These two results do not 

mean that it is either more or less likely that the powders are the same product or different 

products! To answer the question of the relative probabilities that the powders are the same 

product or different products, you must know the prior probability of two powders being 

the same product or different products. You need equation 3, the odds form of Bayes’ 

equation. 

Fig. 1. Shows the probability density estimates of z values for different product (dashed curve) and same 

product (solid curve) comparisons 

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Pr(P|z,s,c,m,p)

Pr(P̅|z,s,c,m,p) = 

f(z|P,s,c,p,m)

f(z|P̅,s,c,p,m)

×

Pr(P|s,c,p,m)

Pr(P̅|s,c,p,m) (3) 

In equation 3, the first term on the left is the posterior odds, which is the odds of two 

samples being the same product versus different products given the value of z and the same 

s, c, p and m nominal discrete values. The second term is the likelihood ratio, discussed 

above. The third term is the prior odds, which is the odds of two powders being the same 

product versus different products given that they have the same s, c, p and m nominal 

discrete values. Note the prior odds does not take into account the value of z. For example, if 

it were 100 times more likely for same product powders (rather than different products) to 

share matching s, c, p and m, the prior odds would be 100. If you measure z and determine 

your likelihood ratio to be 10, then the posterior odds are 1,000 (10 x 100). The odds that 

the two powders were the same product would be 1,000. On the other hand, if you measure 

a value of z corresponding to a likelihood ratio of 1 x 10-4 , the odds that the two powders 

were the same product would be 0.01 (100 x 1 x 10-4). 

The prior and posterior odds are generally accepted to be within the purview of the 

court, and should not be addressed by the forensic scientist. The likelihood ratio is generally 

accepted to be within the purview of the forensic scientist and this is the only term in 

equation 3 that should be addressed by the forensic scientist. 

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26

Limiting smokeless powder comparisons 

 The values for the nominal discrete variables for each record in the Smokeless 

Powders Database were tabulated in a comma-separated values file, along with the 

manufacturer and product designations. The values were read into R – statistical software 

and unique pairwise comparisons were performed [29]. The number of “match” and “nonmatch” comparisons were counted and tabulated for same-product and different product 

comparisons. The comparisons are given in tabular form in the results section. 

Discrete chemical and physical data 

 The presence of 13 organic compounds (2,4-dinitrotoluene, 2-nitrodiphenylamine, 2-

nitrosodiphenylamine, 4-nitrodiphenylamine, 4-nitrosodiphenylamine, dibutyl phthalate, 

diethyl phthalate, dioctyl phthalate, diphenylamine, ethyl centralite, methyl centralite, 

nitroglycerin, dipentyl phthalate), commonly observed in smokeless powders, were 

determined for each of 726 smokeless powders based on the database records. When two 

powders share the same subset of components (i.e., both samples contain only 

diphenylamine, ethyl centralite and nitroglycerin), they are designated as a “match”, so long 

as they have the same values for the nominal discrete characteristics. If two samples have 

the same values for the nominal discrete characteristics, but contain different subsets of the 

13 organic compounds, they are designated a “non-match”. The comparison is limited to the 

13 organic compounds that are tabulated in the database. The output of each comparison is 

discrete (“match” or “non-match”) and the number of each discrete outcome is counted for 

each of the two ground-truth cases, same-product and different-product. The values are 

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27

tabulated in a 2x2 confusion matrix, as shown in Table 1. The values in a 2x2 confusion 

matrix can be interpreted to determine a true positive rate (TPR = TP/P) and false positive 

rate (FPR = FP/N).[30] The slope of a line passing from the intercept (0,0) in ROC space 

through the single point (FPR, TPR) is equivalent to the likelihood ratio[31, 32], and is a 

measure of the evidentiary/investigative value of a returned “match” from a database search.

Table 1. Shows the number of discrete outcomes for each of two ground-truth states, same-product and 

different product 

Continuous chemical composition data 

 The total ion spectra from 43 – 400 m/z were calculated for data from GC-MS analysis 

of 726 smokeless powders (87 analyzed at NCFS and 639 analyzed by FBI) [33]. The TIS is 

equivalent to the average MS across the chromatographic profile with normalization to the 

base peak, and has been useful for the analysis and modeling of ignitable liquids and fire 

debris [33-38]. Undecane has been added before GC-MS analysis of the smokeless powders 

in the database which originated from NCFS or FBI. While the concentration of undecane is 

known in the NCFS samples, it is not known for the FBI samples. To remove influence of 

undecane on the score calculations performed in this research, intensities of ions 

corresponding to undecane (m/z 43, 55, 56, 57, 69, 70, 71, 84, 85, 98, 99, 113 and 156) were 

removed from the TIS. The remaining ions were used for calculating the score for pairwise 

comparisons between same and different products, conditioned on the requirement that the 

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compared samples match in the shape, color, perforation and measurement nominal discrete 

values.

Score-based Likelihood Ratio Calculations 

 The Fisher transform, z, of the Pearson correlation coefficients, r, was calculated by 

equation 4 as a similarity score of each pairwise comparison of TIS. The distributions of 

same-product and different-product scores were modeled using a kernel density estimate 

based on a Gaussian kernel. All calculations were performed in the R Statistical Software 

using code written in house [16]. 

z = 

1

2

ln (

1+r

1−r) (4) 

Agglomerative Hierarchical Cluster Analysis 

The kernel density estimates of the same-product and different-product scores, 

calculated as described in the previous section, were found to be bimodal. In an effort to 

identify the source of the bimodal nature of the distributions, we investigated the possibility 

of natural groupings of the smokeless powders, beyond the single base and double base 

designations. Following previous work in the determination of natural groupings in chemical 

data [39, 40], agglomerative hierarchical clustering, AHC, was used to identify additional 

groupings of the smokeless powders. AHC calculations were also performed in R [16], using 

the amap [41] package for distance calculations and the cba [42] package for optimal leaf 

ordering in the dendrograms that can be produced from AHC. Optimal leaf ordering was 

utilized to “maximize the sum of the similarities of adjacent elements” [43] in the dendrogram 

so that highly similar samples were arranged in the center of the overall cluster. The final 

ordering based on distances between samples is given in a permutation vector. The resulting 

data can be viewed by plotting the m/z intensities for an ordering along the sample axis 

determined by AHC [44-52]. The AHC calculations performed in this research are explained 

in greater detail elsewhere [26]. 

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Cross Validation 

The calculated set of z values for same product and different product comparisons 

were divided into model and test sets by withdrawing 10% of the z values for same product 

comparisons and 10% of the different product comparison z values to comprise the cross 

validation test set. The remaining values constituted the model (training) data set. The cross 

validation draw of test data was performed using a random uniform distribution. The data 

in the model set was used to construct kernel density functions for the same product and 

different product z values. Likelihood ratios were calculated for the cross validation test data 

using the modeled kernel density estimates. Results from the cross validation calculations 

were compared against the known ground truth (same product or different product) for each 

sample. 

Probabilistic Predictions of Manufacturers using Bayesian Networks 

General Methodology 

Bayesian networks are probabilistic graphical structures comprised of nodes and 

arrows. Within the network structure, the nodes which are depicted by circles, represent 

random variables or events. The causal relationships between the nodes are denoted by 

arrows to form a directed acyclic graph (DAG); where a node located at the arrow’s tail is 

referred to as a parent node, a node at the arrow’s head is referred to as a child node, and a 

node without an arrow pointing into it is known as a root node. Figure 2 illustrates a simple 

Bayesian network structure comprising four nodes: A, B, C, and D. Nodes A and C are parent 

nodes; A is the parent of B and C, and C is the parent of D. Node A is also a root node, since 

there is no arrow directed into the node. Within the network each node, representing either 

a discrete or continuous random variable, contains an exhaustive list of mutually exclusive 

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states which can be either categorical, discrete, or continuous [53, 54]. States represent 

possible outcomes for the random variable, and each state has a probability value ascribed 

to it. A table associated with each node encodes the probability distribution across all states, 

or combination of states, within the node. Accordingly, a root node table encodes 

unconditional probabilities for the states within that node, and the table associated with a 

child node encodes conditional probabilities for all states within the child node, where each 

state is conditioned on the states of the parent node(s) [55]. 

Fig. 2. Shows a simple Bayesian Network structure comprising four nodes, A – D. Nodes A and C are 

parent nodes, and nodes B, C and D are child nodes. 

 Referring to Figure 2, the table associated with node A encodes unconditional 

probabilities, while the tables associated with nodes B, C, and D encode conditional 

probabilities. The probabilities for each state in nodes B and C are conditioned on each state 

in node A, and the probabilities for each state in node D are conditioned on each state in node 

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C. The probabilities across all states of a child node, conditioned on each state in its parent 

node, sum to one. A requirement which must be satisfied within the structure of a Bayesian 

network is that of conditional independence, where two child nodes must be conditionally 

independent given their parent(s). In Figure 2, nodes B and C must be conditionally 

independent given node A. Consequently, a Bayesian network describes the probabilistic and 

independence relationships between a set of random variables by conditioning child nodes 

upon their parent nodes. Additionally, the joint probability distribution for a set of random 

variables, X1, ... . , Xn, is decomposed to the product of their probabilities conditioned on 

their parents [53, 54]. The expression describing this relationship, known as the chain rule, 

is given in Equation 5. 

Pr(X1, ... . , Xn

) = ∏ Pr(Xi

|parents(Xi

))

n

i=1 ( 5 ) 

 Bayesian networks are instantiated to update probabilities of all variable states based 

on the observation of new evidence. Within the network, a random variable is instantiated 

by setting one state in the variable’s node to certainty; that is, the probability of the state is 

set equal to 1. 

Discrete chemical and physical data 

GC-MS data for 726 smokeless powder samples from the NCFS database have been 

utilized in a previous study [26]. In this study, the smokeless powders GC-MS data were 

extracted to common data format (CDF) files which were used to generate total ion spectra 

(TIS) for the samples [26, 56]. Each powder was assigned to a class, or cluster, C1 – C6, 

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resulting from agglomerative hierarchical cluster analysis (AHC). The AHC method is 

described above and the results leading to the generation of six clusters (based on the most 

intense ions of m/z 46, 120, 134, 149, 165 and 169 in the TIS) are described in the results 

section of this report. A discrete representation of each sample was generated by first 

assigning the cluster designation C1 – C6, and then breaking the relative intensities of the 

observed AHC ions into one-third increments of the base peak intensity. Consequently, if an 

ion was not observed it was assigned as zero (z); an ion having an intensity on the interval 

(0, 0.33] was assigned as weak (wk); an ion with a relative intensity on the interval (0.33, 

0.67] was assigned as medium (med); and an ion of relative intensity (0.67, 1] was assigned 

as strong (strg). For example, the TIS of SRN 121 (assigned to C1) had relative intensities of 

1, 0.127, 0.005, 0.037, 0.007, and 0.199 for ions m/z 46, 120, 134, 149, 165 and 169, 

respectively. The discrete representation of SRN 121 was C1, strg, wk, wk, wk, wk, wk. 

Stereomicroscopic examination of intact smokeless powders enables the determination 

of the powder’s physical attributes such as kernel shape and dimensions. For each Smokeless 

Powder Database record, kernel dimensions are reported as minimum and maximum 

measurements of the diameter, and length or thickness, in millimeters. Average and standard 

deviations of the measurements are also reported for smokeless powders which were 

analyzed by NCFS. In this work, the average measurements (AM) were converted to discrete 

data by assigning alphanumeric variables to define specific ranges of measurement. To 

define the complete range of measurement, the minimum value was set to 0 for kernel 

shapes for which length/thickness measurements were not recorded, such as ball powders; 

the maximum value was set to the maximum length observed in the data for cylindrical 

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powders. The specific ranges of measurement were broken into 0.4 mm increments of the 

maximum length observed. For example, kernel dimensions within the range 0 mm ≤ AM < 

0.4 mm were assigned “d1” and “l1” for average diameter and average length, respectively. 

Table 2 lists the ranges of measurement, and their corresponding alphanumeric 

assignments. For example, SRN 121 had a diameter (1.6 mm) within the range designated d5 

and a length (0 mm – disk shape) within the range designated l1. 

Table 2. Shows alphanumeric assignments for average diameter and average length of the smokeless 

powder kernels 

Kernel Dimension (mm) Average Diameter Average Length

0 ≤ AM < 0.4 d1 l1

0.4 ≤ AM < 0.8 d2 l2

0.8 ≤ AM < 1.2 d3 l3

1.2 ≤ AM < 1.6 d4 l4

1.6 ≤ AM < 2.0 d5 l5

2.0 ≤ AM ≤ 2.4 d6 l6

Bayes’ Theorem

The computational capacity of Bayesian networks relies on the concept of Bayes’ 

theorem. Within the network, dependence relationships exist between child and parent 

nodes since the state(s) of the child node depend on the state(s) of the parent node(s). These 

dependencies, which are expressed as conditional probabilities, are updated as new 

information is observed in the network. New information is propagated through the network 

to update the probabilities in other nodes. Bayes’ theorem, shown in Equation 6, provides 

the mathematical framework for calculating these probabilities [55, 57, 58]. 

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Pr(Θ|X) = 

Pr(X|Θ)Pr(Θ)

Pr(X)

(6) 

Bayes’ theorem, which is derived from the third law of probability for dependent events, 

relates unconditional and conditional probabilities. In Equation 6, the conditioning bar, | , is 

read “given”, the symbol Θ represents the hypothesis, and X represents the data [57, 58]. 

Equation 6 describes the posterior probability that the hypothesis is true given the data, 

Pr(Θ|X), as the product of the likelihood, Pr(X|Θ), and the prior probability, Pr(Θ), divided 

by the marginal likelihood, Pr(X) [58]. Note that the posterior probability and the likelihood 

are conditional probabilities; whereas, the prior probability and the marginal likelihood are 

unconditional probabilities. In this work, Θ is the smokeless powder manufacturer, and X

represents the chemical and physical characteristics of the smokeless powder sample. 

Bayesian Network 

As previously discussed, the goal of smokeless powder analysis is to characterize 

smokeless powders through determination of their physical and chemical features for the 

purpose of identification. The records in the NCFS Smokeless Powders Database contain 

chemical and physical information about the powders which is typically used toward 

smokeless powder characterization. The chemical and physical characteristics described 

above were utilized in this work to predict smokeless powders manufacturers using 

Bayesian networks. To determine the network structure, the causal relationships between 

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manufacturers and the chemical and physical characteristics of their smokeless powder 

products were considered and assigned as random variables. Additionally, the conditional 

independence relationships between the variables within the proposed network structure 

were determined using the asymptotic chi-squared test. Conditional independence 

calculations were performed in R software [29] using the bnlearn package [59] which was 

accessed from the Comprehensive R Archive Network (CRAN) repository. 

The Bayesian network structure that satisfied the causal relationships and conditional 

independence requirements was generated using R code written in-house. The DAG was 

specified using the gRbase [60] package, and the probability tables were extracted and 

compiled within the network using the gRain [61] and gRim [62] packages which were 

accessed from the CRAN repository. Within the code, 100 iterations were performed for 

cross validation of the network. For each repetition, 10% of the data was withheld as the 

cross validation test set, and the remaining 90% was used to develop and compile the 

network model comprising the nodes and their associated probability tables. The 

probabilities for all states within the nodes were calculated based on frequencies within the 

model data. The network was instantiated by entering the chemical and physical 

characteristics of each smokeless powder within the test set. Manufacturer posterior 

probabilities were calculated for each test sample, and predictions were made based on the 

highest posterior probability. Percent correct rates were determined for each smokeless 

powder manufacturer by calculating the number of correct predictions; that is, where the 

ground truth and predicted manufacturer were the same. 

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III. Results 

Historical Records 

Ron Kelly from the Federal Bureau of Investigation provided 638 records of 

smokeless powders they had analyzed over the last 30 - 40 years. In 2010, one hundred of 

these records were chosen to be the first records in the database (SRN 1 – 100). The 

remaining 538 records were uploaded to the Smokeless Powders Database during this 

project. 

New Reference Materials 

One hundred new smokeless powders were purchased and analyzed following the 

SOP in Appendix B. The data and product information for these samples were uploaded to 

the database. These smokeless powders were used to create a reference collection which 

was distributed to ASCLD/LAB accredited laboratories performing explosives analysis. 

Evidentiary Value of Smokeless Powder Comparisons 

Discrete chemical composition 

 Searches of the Smokeless Powders Database requires the user to select discrete 

values of the various properties that are indexed in the database. The search returns all of 

the records that match the specified criteria and have additional properties not specified in 

the search. For example, a search for records having the same shape, color, perforation, 

measurement, and containing nitroglycerin and diphenylamine, will return all records 

matching these criteria, as well as, those that match the criteria but may also contain 2,4-

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dinitrotoluene, which was not specified in the search. The research conducted under this 

grant considered only exact matches to the search criteria. This approach corresponds to 

specifying the entire subset of the 13 organic compounds that are present in a sample. 

Returned records that contain non-matching subsets of organic compounds are therefore 

designated as a non-match. When the possible 263,175 pairwise combinations were 

restricted to comparisons between records having the same physical properties (s, c, p and 

m), a total of 4,367 comparisons were identified. The resulting confusion matrix of 

“matching” and “non-matching” comparisons are given in Table 3. 

Table 3. Shows comparisons of “matching” (identical subset of organic compounds) and “non-matching” 

(subset of organic compounds did not match) records for same-product and different-product pairwise 

combinations. 

Following the formulations given in an earlier section for the calculation of true 

positive rate, TPR, and false positive rate, FPR, a TPR of 0.31 and an FPR of 0.11 can be 

calculated. The TPR is a measure of the sensitivity of the database search to return records 

with characteristics matching the search criteria which correspond to the same product. The 

Comparison of Organics

“match” “nonmatch”Total

GroundTruth

Same Product 52 116 168

Different 

Product

462 3737 4199

Total 514 3853 4367

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FPR is equal to the quantity of one minus the specificity (1-specificity) of the search. These 

two criteria (TPR and FPR) define the receiver operating characteristics (ROC) space and the 

discrete results from Table 3 represent a single point in ROC space [30]. The slope of a line 

passing from the intercept through the single point in ROC space is equivalent to the 

likelihood ratio of 2.81 [31, 32]. The likelihood ratio from this single point represents the 

probability of observing a “match” in composition, given that the search sample and database 

record are the same product (with the same s, c, p, and m properties), divided by the 

probability of observing a “match” in composition given that the search sample and database 

record correspond to different products (also having the same s, c, p and m). The likelihood 

ratio offers only limited support for the same product proposition as opposed to the different 

product proposition support, when using Evett’s verbal scale [28]. The accuracy of the 

method is 0.87 (87%) [30]. 

The database search evaluation presented here does not take into account the 

presence of compounds other than the 13 organic analytes considered. The likelihood ratio 

would change if the cut-off criteria for detection of the organic analytes were changed. In 

order for the likelihood ratio provided here to be useful, the presence or absence of the 13 

organics in the sample being searched against the database would have to be determined 

with the same criteria used to establish the database. Positive identification of an analyte for 

samples in the database that were analyzed by NCFS was determined by a 3:1 signal-to-noise 

ratio in the total ion chromatograph, the presence of at least three major ions (for all analytes 

except nitroglycerin which only has two major ions in the EI-MS) , and a retention time 

within ± 0.1 min of the corresponding standard. 

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Continuous chemical composition 

 The score, z, was calculated for all pairwise comparisons of same product and 

different product pairs that share the same s, c, p and m. The kernel distributions were 

calculated for the z scores from same product and different product distributions and 

examined graphically. The different sample distribution was bimodal and the same sample 

distribution exhibited negative kurtosis. The two distributions were significantly 

overlapped. The area under the ROC curve (AUC) was 0.814 and represents the accuracy of 

the method. The accuracy of the continuous score method was slightly less than the for the 

discrete comparison method (0.87), as described above. The likelihood ratios calculated 

from Equation 1, have values of 0.10, 0.29, 1.32, 2.20 and 10.61 at z values of 0.3, 1, 2, 3, 4 

and 5, respectively. These values provide only limited support for either HD or HP using 

Evett’s verbal scale [63], with the exception of the likelihood ratio calculated at z = 5, which 

provides moderate support for HP and corresponds to a correlation coefficient of r = 0.99991 

between TIS. A large correlation between TIS will only result from virtually identical total 

ion chromatograms. Cross validation with a 10% hold-out repeated 20 times was used to 

further test the method. The discriminating power of the method was estimated from 

detection error tradeoff (DET) plots to be approximately 30% [64.]. Tippett and empirical 

cross entropy (ECE) plots revealed approximately 30% misleading evidence; however, the 

method appeared to be well calibrated. Tippett, DET and ECE plots will be discussed in 

greater detail in the text that follows. The following observations were made regarding the 

kernel density estimate overlap at low z values: 

“The contributions to the same product distribution at low z values result from 

comparisons between products that have undergone changes in formulation. 

For example, comparison of two samples of Hodgdon TRAP 100 (SRN 400 and 

670), yields a z of 0.618 (r = 0.549). Both samples contain nitroglycerin, 2,4-

dinitrotoluene, diphenylamine, ethyl centralite, dibutyl phthalate and 2-

nitrodiphenylamine; however the SRN 670 sample also contains 4-

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nitrodiphenylamine as a major constituent. A similar example is a comparison 

of two samples of Hercules 2400 (SRN 219 and 680), which have a z score of 

1.39 (r = 0.883). Both samples contain nitroglycerin, diphenylamine and 2-

nitrodiphenylamine in similar ratios; however, SRN 219 also contains a 

significant amount of ethyl centralite.” [26] 

An overlap in kernel density estimates for same product and different product scores was 

also observed at higher score values (i.e., at higher similarities). The overlap at higher scores 

reflects the fact that some powders representing different products have highly similar 

physical and chemical characteristics. Some examples of these were provided in our 

publication: 

“An example is the comparison of DuPont 700x and Alliant Bullseye (SRN 405 

and 800). This comparison has a z of 4.72 (r = 0.9998), and both samples 

contain nitroglycerin as well as ethyl centralite as major components. The total 

ion chromatograms of the two samples are highly similar. Another example is 

the comparison of Accurate 3100 and Accurate Magnum Rifle (SRN 609 and 

618). This comparison has a z of 4.84 (r = 0.9999), both samples contain 2,4-

dinitrotoluene, diphenylamine, 2-nitrodiphenylamine and dioctyl phthalate as 

major components, and their total ion chromatograms are highly similar. A 

third example is the comparison of Hodgdon 870 and Accurate MR-8700 (SRN

472 and 668). In this case z for the comparison is 3.58 (r = 0.998), and both 

samples contain nitroglycerin, diphenylamine, dibutyl phthalate and 2-

nitrodiphenylamine; however, the Hodgdon product also contains a small 

amount of 2,4-dinitrotoluene.” [26]

Implementing a method with a continuous similarity score, while conditioning comparisons 

on matching s, c, p and m discrete values, did not have a superior performance to the discrete 

comparisons of matching subsets of the 13 specified organic analytes. In an attempt to 

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improve on the performance of the continuous model, classification of smokeless powders 

beyond single and double base was addressed. 

 

Agglomerative hierarchical cluster (AHC) analysis 

The results of AHC analysis of the smokeless powders is shown in Figure 3. The 

optimally ordered dendrogram is shown along the top of Figure 3, with boxes drawn to 

demonstrate the six clusters of samples. The intensities of the ions in the TIS are shown 

below the dendrogram. The ions are ordered by increasing m/z, starting from the bottom of 

the graph. From the graph, it can be seen that each of the six groups has a corresponding 

most intense ion that defines the group. This observation allows the analyst to classify 

smokeless powders into one of the six clusters based on observation of the most intense ion 

in the TIS. The chemical explanation of cluster membership is summarized in Table 4. While 

the grouping is not perfect, as observed in Figure 3, 98% of all smokeless powders were 

grouped by AHC into clusters corresponding to a common most intense ion in their TIS. 

Table 4. Shows the six clusters observed from AHC analysis along with the dominant ions that define the 

clusters, and their associated analytes. 

Cluster Most intense ion 

(m/z)

Associated Analyte

C1 46 nitroglycerin

C2 149 phthalates

C3 120 ethyl centralite

C4 165 dinitrotoluenes

C5 169 diphenylamines

C6 134 methyl centralite

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Fig. 3. Shows the optimally ordered dendrogram and heat map obtained from AHC analysis of 726 

Smokeless Powders Database samples. 

Score-based likelihood ratios (same color, shape, cluster) 

The score-based likelihood ratio discrimination method discussed above was 

extended by limiting smokeless powder comparisons to those powders that had same 

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discrete values for shape (s), color (c), perforation (p), measure (m) and cluster (cl). The 

likelihood ratio equation then becomes the following: 

LR = 

f(z|P,s,c,p,m,cl)

f(z|P̅,s,c,p,m,cl)

 (7) 

Limiting the comparisons by including cluster membership reduced the number of 

comparisons to 2,154, which included 157 same product and 1,996 different product 

comparisons. The histograms of the distributions of the score, z, for same product (dark 

shading) and different product (light shading) groups are shown in Figure 4a. These 

distributions are closer to normal and are modeled by normal distributions, N (mean, 

standard deviation). The solid curve, N (3.17, 0.93), shows the same product model and the 

dashed curve, N (2.52, 0.95), shows the different product distribution. The graph on the right, 

Figure 4b, shows the ROC curve for the data and exhibits an AUC of 0.6832. The square 

symbol in Figure 4b represents the single point in ROC space for the discrete comparisons 

based on subsets of the thirteen organic components. The likelihood ratios corresponding to 

Equation 7, have values of 0.25, 0.54, 1.13, 2.33 and 4.65 at z values of 1, 2, 3, 4 and 5, 

respectively. These values provide only limited support for either HD or HP using Evett’s 

verbal scale [63]. The range of values once again indicates that the evidentiary and 

investigative value of a database search match, while helpful, is nowhere near a unique 

powder identification. The importance of these results is that the significance attached to 

any individual item returned from a database search is not high; however, the investigative 

value of a list of powders matching the physical and chemical characteristics of a questioned 

item may prove useful. 

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Fig. 4. (a) Shows the distribution for the Fisher transform of Pearson correlation coefficients for same 

product (dark bars) and different product (light bars) comparisons after imposing the same AHC cluster 

membership constraint. The solid line and dashed line show the normal density distributions for the 

same product and different product comparisons, respectively. (b) Shows the ROC curve (dashed) 

derived from the two normal distributions shown in (a). The square symbol represents the single point 

in ROC space for the discrete comparisons based on subsets of the thirteen organic components 

Probability of a Smokeless Powder Manufacturer for Investigative Purposes 

Conditional Independence Testing of the Smokeless Powder Random Variables 

The smokeless powder physical features which were tested for conditional 

independence include kernel shape; color; luster; average diameter; average length; the 

presence/absence of a perforation; and the presence/absence of a bias cut. Additional 

physical characteristics which may be observed in smokeless powders, such as marker color; 

and distinguishing features such as teardrops, oblongs, etc., were not included in this study 

since they were either observed infrequently, or their identification was subjective. The 

smokeless powder chemical characteristics which were tested for conditional independence 

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include cluster, as previously determined from AHC analysis, and the cluster associated 

dominant ions – m/z 46, 120, 134, 149, 165, and 169. The causal relationships between the 

manufacturer and the smokeless powder features described above were used to propose the 

structure of the Bayesian network, and to determine the combination of features to be tested 

for conditional independence. Consequently, the physical features and cluster were tested 

against each other given the manufacturer; while the AHC dominant ions were tested against 

each other given the cluster. Of those characteristics tested, perforation was found to be 

conditionally dependent on other smokeless powder physical features given the 

manufacturer; therefore, it was removed from the network structure. 

Predicting Smokeless Powder Manufacturers using High Posterior Probabilities 

The smokeless powder Bayesian network structure, comprising 14 nodes, which 

satisfied the causal relationships and conditional independence requirements is shown in 

Figure 5. Two parent nodes, Man and Clstr, representing smokeless powder manufacturers 

and AHC cluster identifiers, respectively, are present in the network’s structure. The Man

node is the only root node within the network structure; that is, this node is not a child of 

any other node. Conversely, the Clstr node is both a child of the Man node, and the parent of 

the dominant ion nodes: m/z 46, 120, 134, 149, 165, and 169. Additional smokeless powder 

features represented in the network as children of the Man node are: average diameter, Dia; 

average length, Len; color, Clr; kernel shape, Shpe; luster, Lstr; and the absence/presence 

of a bias cut, Bias. 

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Fig. 5. Shows the Smokeless Powder Bayesian Network which satisfied the causal relationships and 

conditional independence requirements. The Man and Clstr nodes are parent nodes; the Clstr node is 

also a child of the Man node. 

Within the network, probability tables associated with root parent and child nodes encode 

unconditional and conditional probabilities, respectively. For example, the table associated 

with the Man node encodes unconditional probabilities for six states representing the 

smokeless powder manufacturers: Alliant Powder Company, APC; Hodgdon Powder 

Company, HPC; Norma Precision AB, NPAB; Nobel Sport, NS; Vihta Vuori, ViVu; and Western 

Powders, WP; whereas, the table associated with the Clstr node encodes conditional 

probabilities for multiple states describing the unique AHC cluster identifiers, C1 to C6, each 

of which are contingent on each smokeless powder manufacturer within the Man node. 

Similarly, each node corresponding to an AHC dominant ion, such as m/z 46, contains 

multiple states describing the strength of the ion as previously described, conditioned on 

each state in the Clstr node. The unconditional probabilities in the Man node represent the 

prior probabilities for each smokeless powder manufacturer; whereas, conditional 

probabilities in the child nodes represent the likelihoods of observing the states within those 

nodes, conditioned on the states within their parent nodes. 

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 The probabilities ascribed to the states in each node were calculated based on 

frequencies in the model data, comprising 90% of the smokeless powder dataset. Smokeless 

powders manufactured by HPC comprise approximately 50% of the model data; while 

powders manufactured NPAB and ViVu occur with the lowest prior probabilities, 5% and 

8%, respectively. Each smokeless powder sample in the test set, comprising 10% of the 

smokeless powders data, was used to instantiate the network. The network was instantiated 

by setting one state in each child node to certainty; that is, the probability was set equal to 1 

when the state was observed. For example, using SRN 121, the probability of the state “C1” 

was set equal to 1 in the Clstr node; the probability of state “strg” was set equal to 1 in the 

m/z 46 node; the probability of state “wk” was set equal to 1 in the m/z 120 node; the 

probability of state “wk” was set equal to 1 in the m/z 134 node, etc. These probabilities 

were propagated in the network, and the prior probabilities in the Man node were updated 

to posterior probabilities. Within the code, the smokeless powder manufacturer for each test 

sample was predicted based on the highest posterior probability. Correct predictions were 

determined when the ground truth and predicted manufacturer were the same. Incorrect 

predictions resulted when ground truth and predicted manufacturers were not the same. As 

an example, APC was the known manufacturer of SRN 121 and the network calculated a 

posterior probability of 0.99 for the same manufacturer; this resulted in a correct prediction. 

Conversely, the known manufacturer of SRN 151 was HPC; however, the highest posterior 

probability (0.97) was observed for WP which resulted in an incorrect prediction. 

Percent correct rates were calculated for each smokeless powder manufacturer, and 

for the overall test set. The confusion matrix shown in Table 5 summarizes the percent 

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correct rates for predicting manufacturers based on the physical and chemical 

characteristics of the smokeless powders. Smokeless powders manufactured by HPC and 

ViVu exhibited the highest percent correct rates at 82% for each manufacturer; low percent 

correct rates of 39% and 65% were observed for powders manufactured by Nobel Sport (NS) 

and Norma Precision AB (NPAB). The low correct rate for NS and NPAB powders is 

attributed to the high similarity between their physical and chemical profiles and those of 

Hodgdon, Alliant Powder Company and Vihta Vuori powders. Table 5 also summarizes 

incorrect manufacturer predictions by the network (off-diagonal values). Smokeless 

powders manufactured by APC which were incorrectly predicted to have been manufactured 

by HPC, NPAB, and WP. Similarly, smokeless powders manufactured by HPC which were 

incorrectly predicted were assigned as APC and WP products. NPAB powders which were 

incorrectly predicted were assigned as APC and HPC products, and incorrect predictions of 

NS powders were assigned as HPC and ViVu products. Incorrect predictions of smokeless 

powders manufactured by ViVu and WP were assigned exclusively to NS and HPC, 

respectively. An explanation toward incorrect predictions by the network was provided by 

comparing the physical and chemical profiles of smokeless powder samples in the test set, 

to profiles of samples in the model data. Specifically, the profiles of the test samples were 

compared to profiles of samples in the model data which corresponded to the manufacturer 

predicted by the network. A number of samples in the test data which were incorrectly 

predicted exhibited similar or identical profiles to samples in the model data. Table 6 lists 

some examples of test samples where the network predicted different manufacturers than 

the ground truth along with corresponding samples from the predicted manufacturers, 

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which have identical physical and chemical profiles. For example the ground truth 

manufacturer for SRN 119 was APC; however, the network predicted HPC as the 

manufacturer of that sample. SRN 368, manufactured by HPC, exhibited an identical physical 

and chemical profile as SRN 119. Other examples are given in Table 6. Though there were a 

number of incorrect predictions for smokeless powder manufacturers, the overall percent 

correct rate was 76%, which is significantly higher than the approximate 17% random 

chance of correct assignment when considering six manufacturers. 

Table 5. Confusion Matrix summarizing predictions of Smokeless Powder Manufacturers based on high 

posterior probabilities using the Bayesian network. The highest percent correct rates were obtained for 

smokeless powders manufactured by HPC and ViVu, and the lowest percent correct rate was obtained 

for smokeless powders manufactured by NS. The overall percent correct rate for predictions by the 

network was 76%. 

Ground 

Truth

Predictions based on the Highest Posterior Probability Percent 

APC HPC NPAB NS ViVu WP SUM Correct

APC 173 36 9 0 0 12 230 75

HPC 66 665 0 0 0 76 807 82

NPAB 20 5 46 0 0 0 71 65

NS 0 32 0 50 47 0 129 39

ViVu 0 0 0 20 93 0 113 82

WP 0 53 0 0 0 163 216 75

SUM 259 791 55 70 140 251 1566 76

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Table 6. Discrete profiles of some smokeless powder test samples incorrectly predicted by the network (first line) and model data having similar or identical 

physical and chemical profiles (second line). 

SRN Ground 

Truth Prediction Shpe Bias Clr Lstr Dia Len Clstr m/z46

m/z

120

m/z

134

m/z

149

m/z 

165

m/z

169

119 APC HPC Disk No Gray Dull d5 l1 C1 strg wk wk wk wk wk

368 HPC Disk No Gray Dull d5 l1 C1 strg wk wk wk wk wk

151 HPC WP Ball No Black Dull d2 l1 C2 strg wk wk strg wk med

186 WP Ball No Black Dull d2 l1 C2 strg wk wk strg wk med

377 ViVu NS Cylinder No Brown Dull d2 l2 C5 wk wk wk wk wk strg

395 NS Cylinder No Brown Dull d2 l2 C5 wk wk wk wk wk strg

179 WP HPC Flattened 

Ball No Gray Dull d2 l1 C2 strg wk wk strg wk med

148 HPC Flattened 

Ball No Gray Dull d2 l1 C2 strg wk wk strg wk med

. 

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IV. Conclusions 

Discussion of Findings 

Database Expansion and Reference Collection 

 The database expansion included the addition of 538 historical records spanning 30 

- 40 years and the addition of 100 new smokeless powders currently on the reloading 

market. It is important to continue populating the database with powders that are being sold 

on the market. For example, the French manufacturer Nobel Sport began exporting their 

Vectan smokeless powders to the United States market in 2015. Some of these powders have 

shapes and colors not seen elsewhere in the database. The database provides a means to 

search though large numbers of smokeless powders by their physical characteristics and 

chemical components. 

 The 100 new smokeless powders were used to create smokeless powder reference 

collections. These were distributed to ASCLD/Lab accredited laboratories to incorporate 

into their in-house collections. Smokeless powders are classified as flammable solids, which 

have regulatory limitations for the quantity stored in a facility. The smallest containers sold 

on the market weigh 12 – 16 ounces. This means a reference collection would be limited in 

the number of powders it contained, or the excess powder would need to be disposed in 

order to expand the collection. Disposal of excess amounts of smokeless powder is difficult 

since it is a hazardous material. It was advantageous for NCFS as well as the accredited 

laboratories to share the smokeless powder reference materials. The reference collections 

are also valuable for comparing analytical results between laboratories and the database. 

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Evidentiary/Investigative Value of Database Searches 

The evidentiary and investigative value of a same product assertion based on a 

Smokeless Powders Database search has been examined. The probability of observing the 

evidence given the same product proposition, as opposed to the different product 

proposition will generally be less than 10. The probabilities that constitute the likelihood 

ratio are conditioned on the smokeless powders having the same kernel shape, color, 

presence/absence perforation and measurement. The evidence corresponds to an exact 

match in the subset of organic analytes present in the sample (discrete data) or the Fisher 

transformation of the Pearson correlation between the TIS of two samples. When the 

pairwise comparisons are further conditioned by restricting comparisons to samples 

belonging to the same AHC cluster, the range of evidentiary and investigative value is slightly 

smaller. The results demonstrate the limits on the strength of probabilistic assertions that 

can be made regarding smokeless powder comparisons using the properties examined in 

this work.

Probabilistic Assessment of Manufacturer 

A Bayesian network for predicting smokeless powder manufacturers based on high 

posterior probabilities has been examined. The physical and chemical characteristics which 

are typically utilized in a database search were used to instantiate the network and to 

calculate posterior probabilities for each manufacturer using these smokeless powder 

features. Higher percent correct rates were observed when predicting six smokeless powder 

manufacturers than the approximate 17% rate which would be observed for random choice. 

The results demonstrate the utility of a Smokeless Powders Bayesian network as an 

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investigative tool for making probabilistic predictions of the manufacturer based on the 

physical and chemical properties of a smokeless powder sample. 

Implications for Policy and Practice 

 The expansion of the Smokeless Powders Database has provided additional data on 

smokeless powders that assists the analyst in assessing casework on a daily basis. The 

reference collections provide a physical link to the database. These advances in data 

accessibility eliminate the need for individual databases in each laboratory and provides 

ASCLD/LAB accredited laboratories with smokeless powder references that they would not 

otherwise be able to attain. The results from probabilistic assessment of the evidentiary 

value of matching physical characteristics and chemical composition informs the community 

of the value of such comparisons for the purpose of investigative and probative applications. 

The generally low evidentiary value of these “matches” should influence practice and future 

policy regarding the introduction of smokeless powder evidence. The use of Bayesian 

networks to predict the manufacturer of a smokeless powder sample based on chemical and 

physical characteristics was shown to provide a nearly 80% correct prediction rate, which 

exceeds the 17% random probability based on a flat prior, or 52% probability of correctly 

assessing the manufacturer if the analyst always guesses Hodgdon Powder Company. These 

results provide an incentive for using these statistical methods in the investigative process.

Implications for Further Research 

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The Smokeless Powders Database has been expanded, as detailed in the proposed 

work, and reference collections have been prepared and disseminated to forensic 

laboratories. The composition of the marketplace (i.e., manufacturers, types of powders, 

powder composition, etc.) continues to change and the database is only a snapshot in time 

of the marketplace composition. There is a need to continually update and expand the 

database, especially given the widespread availability of smokeless powders in the U.S. and 

their frequent use in bombings. 

While it can be argued that individualization of smokeless powders is not possible, 

the need still exists for improved evidentiary and investigative value from comparisons of 

the physical characteristics and chemical composition of smokeless powders. The path 

forward may require the application of additional analytical methods to the analysis of 

smokeless powders, or improved data analysis. Implementation of probabilistic methods is 

heavily reliant on defining a relevant population. Defining a relevant population is aided by 

a survey of casework samples and the further expansion of the smokeless powders database 

to allow construction of a relevant population data source on which to base likelihood ratio 

methods. 

Improved characterization of smokeless powders, especially the inorganic 

components, may offer better characterization of the manufacturer and improve the 

evidentiary and investigative value of smokeless powders. 

V. References 

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[1] Council NR. Black and Smokeless Powders: Technologies for Finding Bombs and the 

Bomb Makers. Washington, DC: The National Academies Press; 1998. 

[2] Hopper KG, McCord BR. A comparison of smokeless powders and mixtures by capillary 

zone electrophoresis. J Forensic Sci. 2005;50:307-15. 

[3] Hopper KG, Leclair H, McCord BR. A novel method for analysis of explosives residue by 

simultaneous detection of anions and cations via capillary zone electrophoresis. Talanta. 

2005;67:304-12.

[4] Cascio O, Trettene M, Bortolotti F, Milana G, Tagliaro F. Analysis of organic components 

of smokeless gunpowders: high-performance liquid chromatogaphy vs. micellar 

electrokinetic capillary chromatography. Electrophoresis. 2004;25:1543-7.

[5] Reardon MR, MacCrehan WA. Developing a quantitative extraction technique for 

determining the organic additives in smokeless handgun powder. J Forensic Sci. 

2001;46:802-7.

[6] Scherperel G, Reid GE, Smith RW. Characterization of smokeless powders using 

nanoelectrospray ionization mass spectrometry (nESI-MS). Analytical & Bioanalytical 

Chemistry. 2009;394:2019-28.

[7] Harvey SD, Peters TJ, Wright BW. Safety considerations for sample analysis using a 

near-infrared (785 nm) Raman laser source. Appl Spectrosc. 2003;57:580-7.

[8] Guerra-Diaz P, Gura S, Almirall JR. Dynamic planar solid phase microextraction-ion 

mobility spectrometry for rapid field air sampling and analysis of illicit drugs and 

explosives. Anal Chem. 2010;82:2826-35.

[9] Joshi M, Delgado Y, Guerra P, Lai H, Almirall JR. Detection of odor signatures of 

smokeless powders using solid phase microextraction coupled to an ion mobility 

spectrometer. Forensic Sci Int. 2009;188:112-8.

[10] MacCrehan WA, Bedner M. Development of a smokeless powder reference material for 

propellant and explosives analysis. Forensic Science International.163:119-24.

[11] MacCrehan WA, Reardon MR, Duewer DL. A quantitative comparison of smokeless 

measurements. J Forensic Sci. 2002;47:1283-7.

[12] MacCrehan WA, Reardon MR, Duewer DL. Associating gunpowder and residues from 

commercial ammunition using compositional analysis. J Forensic Sci. 2002;47:260-6.

[13] de Perre C, Corbin I, Blas M, McCord BR. Separation and identification of smokeless 

gunpowder additives by capillary electrochromatography. Journal of Chromatography A. 

2012;1267:259-65. 

[14] Heramb RM, McCord BR. The Manufacture of Smokeless Powders and their Forensic 

Analysis: A Brief Review. Forensic Science Communications. 2002;4. 

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VI. Dissemination of Research Findings 

Publications and Thesis 

Dana-Marie K. Dennis, Mary R. Williams, Michael E. Sigman “Assessing the evidentiary value 

of smokeless powder comparisons”, Forensic Sci. International (2016) Vol. 259, p179–187. 

DANA-MARIE KARINE DENNIS “CHEMICAL ANALYSIS, DATABASING, AND STATISTICAL 

ANALYSIS OF SMOKELESS POWDERS FOR FORENSIC APPLICATION”, A dissertation 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
59

submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in 

the Department of Chemistry in the College of Sciences at the University of Central Florida, 

Orlando, Florida, Summer Term, 2015. 

Dana-Marie K. Dennis; Mary R. Williams; Michael E. Sigman, “Investigative Probabilistic 

Predictions of Smokeless Powder Manufacturers utilizing a Bayesian Network”, Forensic 

Chemistry, manuscript submitted, 2016. 

Databases 

Smokeless Powders Database: http://www.ilrc.ucf.edu/powders/

Patents 

None 

Presentations 

Preliminary Presentations 

Dana-Marie K. Dennis, Michael E. Sigman, Mary R. Williams “Classification of Smokeless 

Powders by Cluster Analysis,” American Academy of Forensic Sciences 65th Anniversary 

Meeting, Washington, DC, February 18 – 23, 2013. 

Dana-Marie K. Dennis, Erin E. Waddell, Mary R. Williams, Michael E. Sigman, “Classification 

of Smokeless Powders by Cluster Analysis”, 89th Annual Florida American Chemical Society 

Meeting and Exposition (FAME), May 9 – 11, 2013, Palm Harbor, FL. 

Grant-funded Presentations 

Dana-Marie Dennis, Michael E. Sigman, Mary R. Williams “Cluster Analysis of Smokeless 

Powders and Classification by Discriminant Analysis”, 90th Annual Florida American 

Chemical Society Meeting and Exposition (FAME), May 8 – 10, 2014, Palm Harbor, FL. 

Quashanna Price; Mary R. Williams, MS; Michael E. Sigman, PhD, “Fourier-Transform 

Infrared Spectroscopy Investigations of Smokeless Powders”, American Academy of 

Forensic Sciences 67th Annual Scientific Meeting, Orlando, FL, February 16-21, 2015. 

Dana-Marie K. Dennis, BS; Jessica L. Frisch-Daiello, PhD; Mary R. Williams, MS; Michael E. 

Sigman, PhD, “Smokeless Powders Database at the National Center for Forensic Science”, 

American Academy of Forensic Sciences 67th Annual Scientific Meeting, Orlando, FL, 

February 16-21, 2015. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
60

Mary R. Williams MS* and Michael E. Sigman, “How Forensic Databases Aid Forensic 

Scientists”, 7th European Academy of Forensic Science Conference, Prague, Czech Republic, 

September 6 – 11, 2015. 

Dana-Marie Dennis, Mary R. Williams and Michael E. Sigman, “Investigative Predictions of 

Smokeless Powder Manufacturers”, American Academy of Forensic Sciences 68th Annual 

Scientific Meeting, Las Vegas, NV, February 22 – 27, 2016. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
61

Appendices 

Appendix A: NCFS Smokeless Powders Analysis Method Year 2010 

o Micrometry 

 A stage micrometer is used to calibrate the Leica Stereomicroscope 

micron bar in order to make measurements of the smokeless powder 

kernels. Measurements are made in millimeters rounded to one 

decimal place. 

 The smokeless powder kernels are placed on a white background, and 

a total of fifty kernels are photographed. The micrographs are taken 

at X10.1 magnification. 

 Double-sided tape is used to stand kernels, such as short tubed 

powders upright so that micrographs may be taken for measurement 

of thickness. Thickness measurements are not recorded for discs and 

flattened ball samples. 

 ImageTool software is used to calibrate the micron bar and make 

measurements of each of the kernels in the micrographs. Calibration 

of the micron bar is done when the micrograph image is at a zoom of 

1:1 and measurements are taken when the micrograph image is at a 

zoom of 1:2. (The software may be downloaded from 

http://ddsdx.uthscsa.edu/dig/itdesc.html.) 

 Length and thickness measurements are taken from the images of the 

kernels in the micrographs. Thickness measurements of the short 

tube kernels are obtained from the micrographs taken using the 

double-sided tape. 

 The averages and standard deviations of the measurements are 

calculated and the maximum and minimum measurements noted of 

the 50 kernels measured. 

o GC-MS

 Extraction 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
62

 The smokeless powder kernels are cut, and approximately 10 mg of 

the cut kernels are placed into a previously weighed small vial. 

Research indicates that cutting the kernels is necessary for the 

optimal extraction of the analytes. 

 300 μL of methylene chloride with 10 ppm undecane are added to the

contents of the tube to facilitate extraction (undecane functions as an 

internal standard). The tube is allowed to sit for three hours. 

 Remove the supernatant from the vial and place into GC vial (with 200 

μL vial insert) for analysis. A 1:10 dilution is also analyzed 

 Analysis 

 Analyses of the samples are conducted using a Hewlett-Packard 6890 

gas chromatograph interfaced to a 5973 mass spectrometer. 

 One micro liter injections of the sample are introduced using an 

Agilent ALS autosampler G2614A splitless injector. 

 The capillary column is an HP-5MS 5% phenyl methyl siloxane with a 

nominal diameter of 250.00 μm, a film thickness of 0.25 μm and a 

length of 30 meters. 

 The injection port temperature was maintained at 170°C. 

 Helium carrier gas is maintained at a flow rate of 1.2 mL/min on the 

column with an average velocity of 40 cm/sec. 

 The initial oven temperature of 40°C is held for 1 minute, followed by 

a temperature ramp of 25°C/min to a final temperature of 280°C, 

which is held for three minutes. 

 The mass spectrometer transfer line is maintained at 250°C, with a 

source temperature of 230°C and a quadrupole temperature of 150°C. 

 Mass spectra are scanned between 43.0 and 400.0 m/z. The solvent 

delay is 4.00 minutes. 

 Positive identification of an analyte is determined by a 3:1 signal-tonoise ratio, the presence of at least three major ions, and its retention 

time as compared to a standard. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
63

o ATR-FTIR 

 Extraction 

 Approximately 2.5 mg of smokeless powder kernels are added to a 

small vial with 80 μL of acetone and allowed to sit for thirty minutes. 

 The supernatant is spotted on a microscope slide, the solvent allowed 

to evaporate, followed by another application of the supernatant 

(added as a second layer to the first spot) and subsequent solvent 

evaporation. The film is analyzed using ATR-FTIR. 

 Analysis 

 Analyses of the samples are conducted on an ATI Mattson Infinity 

Series FTIR and Spectra Tech IR Plan Advantage IR microscope. 

 The internal reflectance element used to analyze the sample is silicon. 

 Four spectra from four different areas of the film are collected in 

absorbance mode using OMNIC software with the number of scans 

totaling 32 and a resolution of 4. An ATR correction was applied using 

the OMNIC software which corrects for the variance in the 

penetration depth. The data image is reported in percent 

transmittance. The spectrum with the flattest baseline and best 

resolution is chosen and presented in transmittance. 

Table 1. List of Analytes, CAS Numbers, and Three Major Ions (m/z) with 

Relative Intensities of the Target Analyte

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
64

* % Abundance determined using standards and in-house GC-MS method 

 **denotes internal standard 

Analyte CAS Number Mass-to-Charge % Abundance*

Nitroglycerine 55-63-0 46 100

76 15

43 13

Camphor 21368-68-3 95 100

81 64

108 41

Dibutyl Phthalate 84-74-2 149 100

223 6

205 5

Diethyl Phthalate 84-66-2 149 100

177 26

105 7

Dipentyl Phthalate 131-18-0 149 100

150 9

237 8

Diphenylamine 122-39-4 169 100

84 10

51 7

Dioctyl Phthalate 117-81-7 149 100

167 35

57 17

Ethyl Centralite 85-98-3 120 100

148 81

268 46

Methyl Centralite 611-92-7 134 100

240 62

106 45

2,4-Dinitrotoluene 121-14-2 165 100

89 50

63 27

4-nitrodiphenylamine 836-30-6 214 100

167 83

184 26

2-nitrodiphenylamine 119-75-5 214 100

167 82

180 37

4-nitrosodiphenylamine 156-10-5 167 100

198 72

184 12

Undecane ** 1120-21-4 57 100

43 72

71 55

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
65

Appendix B: NCFS Smokeless Powders Analysis Method Year 2014 

o Micrometry 

 A stage micrometer is used to calibrate the Leica Stereomicroscope 

micron bar in order to make measurements of the smokeless powder 

kernels. Measurements are made in millimeters rounded to one 

decimal place. 

 The smokeless powder kernels are placed on a white background, and 

a total of fifty kernels are photographed. The micrographs are taken 

at X10.1 magnification. 

 Double-sided tape is used to stand kernels, such as short tubed 

powders upright so that micrographs may be taken for measurement 

of thickness. Thickness measurements are not recorded for discs and 

flattened ball samples. 

 ImageTool software is used to calibrate the micron bar and make 

measurements of each of the kernels in the micrographs. Calibration 

of the micron bar is done when the micrograph image is at a zoom of 

1:1 and measurements are taken when the micrograph image is at a 

zoom of 1:2. (The software may be downloaded from 

http://ddsdx.uthscsa.edu/dig/itdesc.html.) 

 Length and thickness measurements are taken from the images of the 

kernels in the micrographs. Thickness measurements of the short 

tube kernels are obtained from the micrographs taken using the 

double-sided tape. 

 The averages and standard deviations of the measurements are 

calculated and the maximum and minimum measurements noted of 

the 50 kernels measured. 

o GC-MS

 Extraction 

 The smokeless powder kernels are cut, and approximately 10 mg of 

the cut kernels are placed into a previously weighed small vial. 

Research indicates that cutting the kernels is necessary for the 

optimal extraction of the analytes. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
66

 300 μL of methylene chloride with 10 ppm undecane are added to the 

contents of the tube to facilitate extraction (undecane functions as an 

internal standard). The tube is allowed to sit for three hours. 

 Remove the supernatant from the vial and place into GC vial (with 200 

μL vial insert) for analysis. A 1:10 dilution is also analyzed 

 Analysis 

 As of July 2013, analyses of the samples are conducted using an 

Agilent 7890A gas chromatograph interfaced to a 5975C inert XL 

EI/CI triple-axis mass spectrometer. Analyses are performed in EI. 

 As of July 2013, one micro liter injections of the sample are introduced 

using an Agilent ALS autosampler G4513A splitless injector.

 As of July 2013, the capillary column is a Restek Rtx-5MS, 5% phenyl 

methyl siloxane with a nominal diameter of 250.00 μm, a film 

thickness of 0.25 μm and a length of 30 meters. 

 The injection port temperature was maintained at 170°C. 

 Helium carrier gas is maintained at a flow rate of 1.2 mL/min on the 

column with an average velocity of 40 cm/sec. 

 The initial oven temperature of 40°C is held for 1 minute, followed by 

a temperature ramp of 25°C/min to a final temperature of 280°C, 

which is held for three minutes. 

 The mass spectrometer transfer line is maintained at 250°C, with a 

source temperature of 230°C and a quadrupole temperature of 150°C. 

 Mass spectra are scanned between 43.0 and 400.0 m/z. The solvent 

delay is 4.00 minutes. 

 Positive identification of an analyte is determined by a 3:1 signal-tonoise ratio, the presence of at least three major ions, and its retention 

time as compared to a standard. 

o ATR-FTIR 

 Extraction 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
67

 Approximately 2.5 mg of smokeless powder kernels are added to a 

small vial with 80 μL of acetone and allowed to sit for thirty minutes. 

 The supernatant is spotted on a microscope slide, the solvent allowed 

to evaporate, followed by another application of the supernatant 

(added as a second layer to the first spot) and subsequent solvent 

evaporation. The film is analyzed using ATR-FTIR. 

 Analysis 

 As of September 2014, analyses of the samples are conducted using a 

Bruker LUMOS (© Bruker Optik GmbH2012) FT-IR microscope. 

 The internal reflectance element used to analyze the sample is 

germanium. 

 Four spectra from four different areas of the film are collected in 

absorbance mode using OPUS software (version 7.2 Build: 7, 2, 139, 

1294) with the number of scans totaling 64 at a resolution of 4 cm1. 

An ATR correction was applied within the software which corrects for 

the variance in the penetration depth. The data image is reported in 

percent transmittance. The data is collected within the 4000 – 600 

cm1 range. The spectrum with the flattest baseline and best 

resolution is chosen and presented in transmittance.

Table 1. List of Analytes, CAS Numbers, and Three Major Ions (m/z) with Relative 

Intensities of the Target Analyte

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
68

* % Abundance determined using standards and in-house GC-MS method 

 **denotes internal standard 

Analyte CAS Number Mass-to-Charge % Abundance*

Nitroglycerine 55-63-0 46 100

76 15

43 13

Camphor 21368-68-3 95 100

81 64

108 41

Dibutyl Phthalate 84-74-2 149 100

223 6

205 5

Diethyl Phthalate 84-66-2 149 100

177 26

105 7

Dipentyl Phthalate 131-18-0 149 100

150 9

237 8

Diphenylamine 122-39-4 169 100

84 10

51 7

Dioctyl Phthalate 117-81-7 149 100

167 35

57 17

Ethyl Centralite 85-98-3 120 100

148 81

268 46

Methyl Centralite 611-92-7 134 100

240 62

106 45

2,4-Dinitrotoluene 121-14-2 165 100

89 50

63 27

4-nitrodiphenylamine 836-30-6 214 100

167 83

184 26

2-nitrodiphenylamine 119-75-5 214 100

167 82

180 37

4-nitrosodiphenylamine 156-10-5 167 100

198 72

184 12

Undecane ** 1120-21-4 57 100

43 72

71 55

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
69

Table 1. List of Analytes, CAS Numbers, and Three Major Ions (m/z) with Relative 

Intensities of the Target Analyte

* % Abundance determined using standards and in-house GC-MS method 

Analyte CAS Number Mass-to-Charge % Abundance*

Nitroglycerine 55-63-0 46 100

76 15

43 13

Camphor 21368-68-3 95 100

81 64

108 41

Dibutyl Phthalate 84-74-2 149 100

223 6

205 5

Diethyl Phthalate 84-66-2 149 100

177 26

105 7

Dipentyl Phthalate 131-18-0 149 100

150 9

237 8

Diphenylamine 122-39-4 169 100

84 10

51 7

Dioctyl Phthalate 117-81-7 149 100

167 35

57 17

Ethyl Centralite 85-98-3 120 100

148 81

268 46

Methyl Centralite 611-92-7 134 100

240 62

106 45

2,4-Dinitrotoluene 121-14-2 165 100

89 50

63 27

4-nitrodiphenylamine 836-30-6 214 100

167 83

184 26

2-nitrodiphenylamine 119-75-5 214 100

167 82

180 37

4-nitrosodiphenylamine 156-10-5 167 100

198 72

184 12

Undecane ** 1120-21-4 57 100

43 72

71 55

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice 
70

 **denotes internal standard 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice