1. Physical and Chemical
Characterization of Matchsticks
for Forensic Classification and
Commercial Brand
Determination
Ciaran F. A. Phillip

2. Matchsticks
 Three functional classes
i. Safety – purpose built striking surface
ii. Strike anywhere – no purpose built striking surface
iii. Waterproof – water resistant
 Many different commercial brand manufacturers
2

3. Matchstick Forensics
Stereomicroscopy SEM-EDS X-ray Diffraction
Visually
different?
Y/N
Chemically
different?
Y/N
Structurally
different?
Y/N
Qualitative similarity
assessment on Q vs. K
3

4. Matchstick Forensics
Stereomicroscopy SEM-EDS X-ray Diffraction
Visually
different?
Y/N
Chemically
different?
Y/N
Structurally
different?
Y/N
Qualitative similarity
assessment on Q vs. K
How can we improve
trace evidence analysis?
4

5. Objective
Single multivariate forensic signature
Stereomicroscopy FTIR ICP-MS
5

6. Objective
Single multivariate forensic signature
Stereomicroscopy FTIR ICP-MS
Investigative leads Strengthens associations
Functional Class and
Commercial Brand of Q
6

7. Samples
 Four brands of safety match
 Four brands of waterproof match
 Two brands of strike anywhere match
7

8. Samples
8

9. Samples
9

10. Objective
Single multivariate forensic signature
Stereomicroscopy FTIR ICP-MS
10

11. Stereomicroscopy
Safety
Diamond SB
Diamond MB
UCO & Fancy Fish
Waterproof
Coleman & Coghlan
REI
Proforce
Strike Anywhere
Redbird
Diamond SA
11

12. Stereomicroscopy
12

13. Objective
Single multivariate forensic signature
Stereomicroscopy FTIR ICP-MS
13

14. FTIR: Safety & Strike Anywhere
Diamond Deluxe Match Books
No polymer binder
1448.02 cm−1: CO3
2−
950.92 cm−1: PO4
3−
931.93 cm−1: ClO3
−
14

15. FTIR: Waterproof
Shellac binder
1697.2cm−1: aromatic carbonyl
1454.2cm−1: azo
1375.2cm−1: aromatic C―N
1241.5cm−1: C―O
15

16. FTIR: Waterproof
Shellac binder
1697.2cm−1: aromatic carbonyl
1454.2cm−1: azo
1375.2cm−1: aromatic C―N
1241.5cm−1: C―O
REI
Alkyd binder
1715.4cm−1: carbonyl
1253.7cm−1: C―H
737.2cm−1: phenyl
16

17. FTIR: Waterproof
Shellac binder
1697.2cm−1: aromatic carbonyl
1454.2cm−1: azo
1375.2cm−1: aromatic C―N
1241.5cm−1: C―O
REI
Alkyd binder
1715.4cm−1: carbonyl
1253.7cm−1: C―H
737.2cm−1: phenyl
Coghlan
&
Coleman
Nitrocellulose binder
1635.5cm−1: NO2
1268.6cm−1: C―N
17

18. Objective
Single multivariate forensic signature
Stereomicroscopy FTIR ICP-MS
18

19. ICP-MS Data
ICP-MS Metal Element Concentration Profiling Results [ppb·mg−1]
Brand B11 Mg25 Fe57 Zn (all) Zn70
Coleman 59.0843 138.6581 31.7377 3.4087 0.6359
Coleman 30.8953 90.0515 18.1351 2.0764 0.3827
Coleman 37.9094 98.9309 23.1432 2.3294 0.3956
Coleman 32.7572 95.0507 19.4906 2.3343 0.3967
ICP-MS Metal Element Concentration Profiling Results
All Values Standardized by Maximum Value in each Variable
Brand B11 Mg25 Fe57 Zn (all) Zn70
Coleman 100.0000100.0000 66.5726 1.2271 2.0766
Coleman 52.2902 64.9450 38.0400 0.7475 1.2497
Coleman 64.1615 71.3488 48.5449 0.8386 1.2918
Coleman 55.4415 68.5504 40.8833 0.8403 1.2954
ICP-MS Metal Element Concentration Profiling Results
All Values Transformed by LN(X+1)
Brand B11 Mg25 Fe57 Zn (all) Zn70
Coleman 4.6151 4.6151 4.2132 0.8007 1.1238
Coleman 3.9758 4.1888 3.6646 0.5582 0.8108
Coleman 4.1769 4.2815 3.9029 0.6090 0.8294
Coleman 4.0332 4.2421 3.7349 0.6100 0.8309
How do we display this?
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20. PCA
 Linear combinations of all variables for each axis
Variable 1 = ln(11Bstd+1)
Variable 2 = ln(25Mgstd+1)
Variable 3 = ln(57Festd+1)
Variable 4 = ln(Znstd+1)
Variable 5 = ln(70Znstd+1)
Principal component 1 = cV1PC1(Variable 1)+cV2PC1(Variable 2)……
Principal component 2 = cV1PC2(Variable 1)+cV2PC2(Variable 2)……
Principal component 3 = cV1PC3(Variable 1)+cV2PC3(Variable 2)……
Principal component 4 = cV1PC4(Variable 1)+cV2PC4(Variable 2)……
Principal component 5 = cV1PC5(Variable 1)+cV2PC5(Variable 2)……
20

21. PCA
 Account for maximum variation between samples
PC
Percent
Variation
Cumulative Percent
Variation
1 55.2 55.2
2 36.6 91.8
3 5.7 97.5
4 2.2 99.8
5 0.2 100.0
21

22. ICP-MS PCA
22

23. ICP-MS PCA
23

24. ANOSIM
 PCA emphasises differences between sample groups
 Are the emphasised differences statistically significant?
𝑅 =
𝑟𝐵 − 𝑟 𝑊
𝑛(𝑛 − 1)/4
Test Statistic R
−1 ≤ R ≤ 1
R→1 Differences between
groups become more significant
24

25. ANOSIM
ANOSIM R values
W vs. S 0.163
W vs. SA 0.59
S vs. SA 0.171
25

26. Objective
Single multivariate forensic signature
Stereomicroscopy FTIR ICP-MS
26

27. Data Pretreatment
Matchstick Multivariate Data Set Incorporating Physical and Chemical
Data
Brand
B
11
Mg
25
Fe
57
Zn
(all)
Zn
70
Binder
P/A
NC
Binder
Alkyd
Binder
Shellac
Binder
Head
Structure
Paper
Splint
REI 0.1 4.0 0.0 1.4 0.2 1 0 1 0 2 0
REI 0.1 3.9 0.0 1.3 0.1 1 0 1 0 2 0
REI 0.1 4.6 0.0 1.3 0.2 1 0 1 0 2 0
REI 0.0 4.2 0.0 1.2 0.1 1 0 1 0 2 0
Matchstick Multivariate Data Set Incorporating Physical and Chemical
Data
All Variable Values Standardized by Maximum Value in each Variable
Brand
B
11
Mg
25
Fe
57
Zn
(all)
Zn
70
Binder
P/A
NC
Binder
Alkyd
Binder
Shellac
Binder
Head
Structure
Paper
Splint
REI 0.1 2.9 0.0 0.5 0.5 100 0 100 0 100 0
REI 0.1 2.8 0.0 0.5 0.5 100 0 100 0 100 0
REI 0.1 3.3 0.0 0.5 0.5 100 0 100 0 100 0
REI 0.1 3.1 0.0 0.4 0.5 100 0 100 0 100 0
Matchstick Multivariate Data Set Incorporating Physical and Chemical
Data
All Variable Values Transformed by LN(X+1)
Brand
B
11
Mg
25
Fe
57
Zn
(all)
Zn
70
Binder
P/A
NC
Binder
Alkyd
Binder
Shellac
Binder
Head
Structure
Paper
Splint
REI 0.1 1.3 0.0 0.4 0.4 4.6 0.0 4.6 0.0 4.6 0
REI 0.1 1.3 0.0 0.4 0.4 4.6 0.0 4.6 0.0 4.6 0
REI 0.1 1.5 0.0 0.4 0.4 4.6 0.0 4.6 0.0 4.6 0
REI 0.1 1.4 0.0 0.4 0.4 4.6 0.0 4.6 0.0 4.6 0
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28. Gower dissimilarity measure
Dissimilarity Analysis
|𝑦1𝑗 − 𝑦2𝑗 |
(𝑚𝑎𝑥𝑗 − 𝑚𝑖𝑛𝑗 )
𝑝
𝑗=1
Assesses both discrete and
continuous variable
contributions to similarity
between samples
𝑤12𝑗 𝑠12𝑗
𝑝
𝑗=1
𝑤12𝑗
𝑝
𝑗=1
28

29. NMDS
 Displays sample relationships in multivariate space
 Based on calculated dissimilarities
 MDS for metric dissimilarity measure
 NMDS for non-metric dissimilarity measure
A B
C
D(A, B) ≥ 0
D(A, B) = 0 ⟷ A=B
D(A, B) = D(B, A)
D(B, C) ≥ [D(A, B) + D(C, A)]
29

30. Forensic Signature
ANOSIM R values
W vs. S 0.609
W vs. SA 0.981
S vs. SA 0.431
30

31. Forensic Signature
ANOSIM R values
W vs. S 0.609
W vs. SA 0.981
S vs. SA 0.431
ANOSIM R values
R=1 for all
Commercial Brand
pairwise comparisons
31

32. Forensic Signature
𝑅 =
𝑟𝐵 − 𝑟 𝑊
𝑛(𝑛 − 1)/4
As n increases the robustness of R also increases
32

33. Summary
 Combination of analyses excellent for classification and
brand determination
 Multivariate statistical analysis confirms hypothesis
 Very visual and intuitive final result
33

34. Discussion
 How do we improve Trace Evidence Analysis?
 Matchsticks are a good model
 Branching out: fibers, paint, glass
 Sufficient sample sizes
 Automated statistical methods
34

35. Acknowledgements
Mr. Chad Schennum – Forensic Scientist1
Mr. Tomson Huynh – FTIR Assistant2
Mr. Spencer Nwogoku – Laboratory Assistant2
Dr. Joseph Turner – Director of Instrumentation2
Mr. Thomas Pugh – Introducing Presenter2
1VA Department of Forensic Science
2Virginia Commonwealth University
35

36. Questions/Comments
36